http://www.newbi4fmri.com/home daily 1.0 2021-02-12 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592265585273-WUQO9SJKEU90B2YZ2TIY/Brainscan+logo+purple.PNG What is NeWBI? https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602260222573-BFL34NW0BA3D4TT5YZYH/BV-EDU.png What is NeWBI? http://www.newbi4fmri.com/experiment-details daily 0.75 2025-11-21 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106439117-CCBJWSXSYPYZ7T3F7S80/Screen+Shot+2020-02-07+at+3.10.57+PM.png NeWBI Experiment Figure 2: Example stimuli for localizer runs https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106524179-4DGAQL30EZLHEE08Z4K9/Screen+Shot+2020-02-07+at+3.11.20+PM.png NeWBI Experiment Figure 5: Example of main experiment stimuli order https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578766576566-0YRT0DVKY9NTTQEN4W0V/Screen+Shot+2020-01-11+at+1.15.44+PM.png NeWBI Experiment Figure 1: Example stimuli for main experiment https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106512030-LSZGBQMJCFAMWWJK2Y86/Screen+Shot+2020-02-07+at+3.11.14+PM.png NeWBI Experiment Figure 4: Localizer 1 block order https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106464649-MOHIVCVR3UPV5LQU645S/Screen+Shot+2020-02-07+at+3.11.05+PM.png NeWBI Experiment Figure 3: Stimuli for face, hand, body and scrambled blocks http://www.newbi4fmri.com/tutorial-1-v1 daily 0.75 2020-07-27 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770628164-9N5NV48RK64EV5H6VKFE/Screen+Shot+2020-01-11+at+2.15.58+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770306058-7HDFSV42JQCU5IUZDTUY/Screen+Shot+2020-01-11+at+2.15.25+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770778215-5FGNVJUXNVF4NJKWHQUQ/image-asset.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770879353-GORVZZLJCOX6UJBYKRSA/Screen+Shot+2020-01-11+at+2.16.29+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578771060605-PCY57S7DJ4WW419YWNLK/image-asset.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770264157-H7O8E1H6D77FKT5R0EP6/Screen+Shot+2020-01-11+at+2.15.18+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770418893-BHGPV3V38UV7EYR9NIVL/image-asset.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578766971008-TW4EDPXVSOGSC6VYFL3V/Screen+Shot+2020-01-11+at+1.21.10+PM.png Tutorial 1: Data - v1 Figure 1. Single 2D anatomical image https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770355635-LAKWKWDNBRX0LJVIH3VE/Screen+Shot+2020-01-11+at+2.15.32+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770927476-45W5XFVHOQUJT1RRSJFQ/Screen+Shot+2020-01-11+at+2.16.38+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578771031568-MMOLTC5BRX2LXTLE3TDY/Screen+Shot+2020-01-11+at+2.16.58+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770973697-7GJ4ZB8M6R6565Z9P7BB/Screen+Shot+2020-01-11+at+2.16.45+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578767003206-R853G198L0TTKWY1MER9/Screen+Shot+2020-01-11+at+1.21.19+PM.png Tutorial 1: Data - v1 Figure 2. Single functional DICOM file https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770673164-M4YYJ7B352N8PPRM8TA5/Screen+Shot+2020-01-11+at+2.16.05+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770451809-PTHKWOCJYY0WWTJ01K06/image-asset.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578770551888-SFWIOVGWA13LWU1Y5LGT/Screen+Shot+2020-01-11+at+2.15.51+PM.png Tutorial 1: Data - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578771000603-NYRRX0QYQDVW5HKQYI4I/Screen+Shot+2020-01-11+at+2.16.52+PM.png Tutorial 1: Data - v1 http://www.newbi4fmri.com/tutorial-2-v1 daily 0.75 2020-08-04 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774456532-TMBCS6UF5ABYVWF5KVE4/image-asset.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773173638-QL2PPWXQWFEWDUUJBBRR/Screen+Shot+2020-01-11+at+3.01.34+PM.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773282669-BUSS5B716HXEPM05B2LF/image-asset.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773081218-33R4IL6S0Y2NGFA319GX/image-asset.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773134933-4I1X5WIPO7MUQ1VTV0UT/image-asset.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1596557685599-5CNCJE5BNSJBXROE1L57/Convolution.gif Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774190948-JHGU0TT4U4P4FP84IQO7/Screen+Shot+2020-01-11+at+3.20.43+PM.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773237195-46Y10BB6SKYTKZENCY3W/image-asset.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973001682-ZA6FEL2T9I24O2B5I06A/sine1.gif Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973017615-DBZVW7RROH7VFXFQGARX/sine2.gif Tutorial 2: GLM - v1 Animation 2 : Fitting a noisy sine wave https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578772976904-LLXIB7VRHNVW6NZR1K32/Screen+Shot+2020-01-11+at+3.01.10+PM.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774294145-3R9X3CFC40H54WHDZPJW/Screen+Shot+2020-01-11+at+3.20.50+PM.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774351784-QLMW1Q3J7AHE6NJ6O260/Screen+Shot+2020-01-11+at+3.20.56+PM.png Tutorial 2: GLM - v1 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578773008698-LQ81BM20ZWLFF9VJFPIL/image-asset.png Tutorial 2: GLM - v1 http://www.newbi4fmri.com/tutorial-5-motion daily 0.75 2025-10-29 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602640134403-L9QGBL1C75W6FCYBRHYW/GLM-improve.png Tutorial 5: Motion Figure 5-2. Recall that GLM stats can be improved by increasing the fit of the POIs, reducing residuals, and shifting known sources of noise into the GLM by adding PONIs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/7013150f-1358-4484-988e-88c81549163d/Screenshot+2025-10-29+140842.png Tutorial 5: Motion Figure 5-6. The “Load” button allows you to view motion parameters https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578776010077-TFUTJQKRM6VNA7VZRPC2/Screen+Shot+2020-01-11+at+3.43.56+PM.png Tutorial 5: Motion Figure 5-8. You can select an entire slice to see the average time course of all voxels (above a certain intensity that corresponds to the voxels inside the brain) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578776116758-AREQ73ZLS9LJ95WHMILS/image-asset.png Tutorial 5: Motion Figure 5-10. Left = A typical contrast using only POIs. Right = a contrast using PONIs. Although PONIs, by definition, are generally not of particular interest, nevertheless, performing such contrasts can give you an idea of how problematic head motion is and which brain regions might be particularly vulnerable. You can also inspect the time courses of the artifact regions to see how bad the effects are. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775704328-TTTD1Z7H2UYLOPVRP2DR/Screen+Shot+2020-01-11+at+3.43.33+PM.png Tutorial 5: Motion Figure 5-3. To play a movie for an .fmr file, go to Options/Time course movie https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/caa48a40-bfca-4b1b-b5fe-51b8c86accb4/T2_Slice_movie.png Tutorial 5: Motion - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775741059-CYE9JVF85P5E1GWDH34A/Screen+Shot+2020-01-11+at+3.43.36+PM.png Tutorial 5: Motion Figure 5-4. Time course movie controls https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775495125-EX8933APGQOYP7P0DYDW/Screen+Shot+2020-01-11+at+3.43.01+PM.png Tutorial 5: Motion Figure 5-1. Motion of a rigid body (like the head) in 3D space can be quantified and corrected using 6 motion parameters (3 translations and 3 rotations) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/8c94f7fc-cd19-41de-bc2c-d876b5cedc35/Screenshot+2025-10-29+140938.png Tutorial 5: Motion Figure 5-5. Loading predictors for a Single-Study GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775835662-29DB5XTDRMV7JJHZLEQT/image-asset.png Tutorial 5: Motion Figure 5-7. Motion parameters concatenated across all runs within a session http://www.newbi4fmri.com/tutorial-6-filtering daily 0.75 2025-11-13 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/bdc549b4-4b74-4bab-aa9d-53e28cd671f7/Screenshot+2025-11-12+at+8.48.06%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-8. Fourier spectrum of the time course shown in Figure 6-7 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/99350322-cbd0-4602-8449-a21121b99f62/Screenshot+2025-11-12+at+8.56.47%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-11. Fourier spectrum of the time course shown in Figure 6-10. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/84a4a232-e64c-47c0-867b-7cc36ac6952f/Screenshot+2025-11-12+at+7.52.03%E2%80%AFAM.png Tutorial 6: Filtering Figure 6-18. Example protocol with three conditions, Faces (in pink), Hands (in purple) and baseline (in black). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/0ab28fed-b59e-4a93-848e-7e6489f3536e/Screenshot+2025-11-12+at+8.01.03%E2%80%AFAM.png Tutorial 6: Filtering Figure 6-9. For more complex, multi-condition protocols, the expected stimulation frequencies may be more complicated. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/51a89862-82e1-4de4-8d29-b76a9f447ea6/Screenshot+2025-11-12+at+8.56.05%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-10. The expected activation for a region with an equivalent visual response across all four stimulus conditions compared to baseline. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602973072430-UDM9QBKJ7DB25WXG35LY/VMP.jpg Tutorial 6: Filtering Figure 6-4. A file with multiple maps of the same contrast (Faces - Hands) after different combinations of spatial smoothing and temporal filtering. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/412109f5-3286-4ffd-989f-3cb3e337a9d2/Screenshot+2025-11-12+at+9.26.02%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-17. Fourier spectrum of the time course shown in Figure 6-16. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/4ce08620-fe6f-4dd9-9030-dbe266486338/Screenshot+2025-11-12+at+8.46.50%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-7. Expected activation after HRF convolution. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/e1561763-4ef7-4905-869a-26ce7182084b/Screenshot+2025-11-12+at+9.10.10%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-14. Time course from a voxel in LOTChand. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/cc9ef515-3ace-4237-8c72-a5d56831e942/Screenshot+2025-11-12+at+8.05.57%E2%80%AFAM.png Tutorial 6: Filtering Figure 6-6. For simple two-condition protocol, it is easy to predict the predominant frequency of activation. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602968916768-FGSMN1F9XA2FI74LWDPQ/TemporalFiltering.gif Tutorial 6: Filtering Widget 6-2. How temporal filtering affects the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602733532242-H76A9QD1BH7S0BRN5PHJ/LinearDrift.gif Tutorial 6: Filtering Widget 6-1. How linear trend removal affects the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602698889028-QBCK3FNDFTOT0IRY9IHV/how-to-fix-grainy-photos-hero-image.jpg Tutorial 6: Filtering Figure 6-2. The BEFORE photo shows a grainy image that is due to noise related to taking a photo in dim light. The AFTER photo shows how spatial smoothing can make the picture look better. Adjacent pixels share differences in intensity (e.g., the cloud pixels are darker than sky pixels) but noise also leads to speckles (more fine-scale differences in intensity). When intensity levels for each pixel are smoothed through a weighted combination with neighboring pixels, differences in signal reinforce one another (e.g., dark cloud pixels are combined with other dark cloud pixels; light with light) while differences due to noise cancel one another out (a noisily bright pixel will be next to other pixels with lower intensities). Photo credit. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603303271469-5B2BKE9GUHJL7LH7KL04/VOI2.jpg Tutorial 6: Filtering Figure 6-5. How to load an ROI/VOI file. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602700123956-PZ7O40J8OJTMOW5YDRVI/spatsmooth.png Tutorial 6: Filtering Figure 6-3. One functional data slice with different smoothing options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578781260936-5558UGOLPTIRE1XA7QH8/image-asset.png Tutorial 6: Filtering Figure 6-19. I hate this figure. Need to replace it. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578781074230-0Z9XKDJ160X0UNZ5DNFV/image-asset.png Tutorial 6: Filtering Figure 6-1. A Gaussian filter is characterized but the full-width at half maximum (FWHM). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/f1482c72-33a2-4418-bbc1-bf61c8715b01/Screenshot+2025-11-12+at+9.00.50%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-13. Fourier spectrum of the time course shown in Figure 6-12. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/6ba39791-0995-4ce8-b3e6-1f61cd7b71b3/Screenshot+2025-11-12+at+8.59.46%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-12. The expected activation for a region with a response to faces. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/14cc60e5-478c-4d1a-99cc-345196953ef5/Screenshot+2025-11-12+at+9.25.03%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-16. Time course from a voxel with low-frequency drift https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/2347edc1-be6f-4630-bd80-88215e37e812/Screenshot+2025-11-12+at+9.13.20%E2%80%AFAM.png Tutorial 6: Filtering - Make it stand out Figure 6-15. Fourier spectrum of the time course shown in Figure 6-14. http://www.newbi4fmri.com/tutorial-7-er-decon daily 0.75 2025-11-20 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/96fe27df-51b7-48fd-9ba9-163f1c36cca0/Tutorial_7_new_colours_image_17.png Tutorial 7: ER&Decon Figure 7-18. Running a deconvolution analysis for the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/07350073-8672-4b13-959e-d704c4ee7e9a/Tutorial_7_new_colours_image_15.png Tutorial 7: ER&Decon Figure 7-16. Selecting the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772346607-OMS2702LDJZDJTOSSXAW/Neurosynth_LOTChand.png Tutorial 7: ER&Decon Figure 7-9. A 7-mm diameter spherical region interest centred on the hotspot of activation for “hands” in Neurosynth. Note the excellent correspondence with the experimental activation in Figure 7-8. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/575fb52a-1760-41d9-87bd-824fcd6a4f15/Tutorial_7_new_colours_image_4-5.png Tutorial 7: ER&Decon Figure 7-5. All 6 predictors of interest + 6 predictors of no interest (motion parameters) superimposed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/ba3b26ba-90d5-4cef-8fa7-4b4fc46f6b64/Tutorial_7_new_colours_image_16.png Tutorial 7: ER&Decon Figure 7-17. Loading the deconvolution design matrix. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/8a53bbd4-c74f-49bf-baa1-4debae631f1e/Tutorial_7_new_colours_image_3.png Tutorial 7: ER&Decon Figure 7-3. The convolved predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/986e863c-fcb2-4ae2-b2cc-e6f7c2f00990/Tutorial_7_new_colours_image_77.png Tutorial 7: ER&Decon Figure 7-7. A contrast of all three face conditions vs. all three hand conditions in the experimental runs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/ea512b47-90e0-4463-836a-aee224e775a3/Tutorial_7_new_colours_image_2.png Tutorial 7: ER&Decon Figure 7-2. The box car predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/72a5f29e-9b7e-4453-aa90-9ae21d927cce/Tutorial_7_new_colours_image_14..png Tutorial 7: ER&Decon Figure 7-15. The GLM output for the deconvolution analysis has 120 predictors of interest. For complex contrasts, a .ctr file can be loaded. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/e7c91b9c-63f2-456e-94c1-baed4f8bfaf2/Tutorial_7_new_colours_image_5.png Tutorial 7: ER&Decon Figure 7-4. All 6 predictors of interest superimposed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772441686-YHUSDYDYQXETJQVPWK4I/GLM_LOTChand.png Tutorial 7: ER&Decon Figure 7-8: Activation for the contrast of Faces - Hands. Crosshairs are centred on hand-selective activation in the expected location of LOTChand https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/0f82e0ce-ad37-4c60-9365-02d57ff53d68/Tutorial_7_new_colours_image_13.png Tutorial 7: ER&Decon Figure 7-14. The options tab for a deconvolution analysis. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973123172-W4TOD5KOQ6JHOBKSB5B3/deconvolution.gif Tutorial 7: ER&Decon Widget 7-1. Fitting a deconvolution model https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/997928d8-41f9-4411-bf6c-3a08dd1ba264/Tutorial_7_new_colours_image_6.png Tutorial 7: ER&Decon Figure 7-6. The multi-study design matrix for the 7 runs of the experiment for Subject 15. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/56065b44-a8b1-4c56-b2bf-4be2626b91aa/Tutorial_7_new_colours_image_18.png Tutorial 7: ER&Decon Figure 7-19. Options to select for Fitting the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/a31116ce-ee0a-43b2-b9c4-ec5a995cc89d/Tutorial_7_new_colours_image_12.png Tutorial 7: ER&Decon Figure 7-13. To access the menu for a deconvolution design, go into Options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/efafae68-53b5-483e-8a30-92b649b33dde/Tutorial_7_new_colours_image_10_red.png Tutorial 7: ER&Decon Figure 7-10. Selecting Left LOTChand as a region of interest. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/867fd4fe-f955-42be-bb42-ca9e5c66a48e/Tutorial_7_new_colours_image_11.png Tutorial 7: ER&Decon Figure 7-11. Loading the file to generate an event-related average for the region used to extract the time course. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/d8685f82-7e29-4bf9-825c-2aa518fdbed1/Tutorial_7_new_colours_image_1.png Tutorial 7: ER&Decon Figure 7-1. Protocol for one order of the main experiment, showing a jittered rapid event-related design in which trials were spaced every 4 or 8 s in an optimized order that balanced the n-1 trial history. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740290034-VQJO0L70B9EQGTWYBM42/ER+average+results.JPG Tutorial 7: ER&Decon Figure 7-12. The event-related average from Left LOTChand. http://www.newbi4fmri.com/tutorial-8-group-data daily 0.75 2025-10-02 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605055777656-9EVWMLZFY1FL4T7ZT8GJ/SPSB+GLM.png Tutorial 8: Group Data Figure 8-6. FFX GLM Output (with Separate Subject Predictors, SPSB) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605055613144-2CBY2HS2KJP9EWKD06XE/FFX+GLM.png Tutorial 8: Group Data Figure 8-5. FFX GLM Output (without Separate Subject Predictors) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604954858214-WA8495NGOFS1JB4T098K/18T1s.png Tutorial 8: Group Data Figure 8-1. Screenshot of the average T1 anatomical from 18 participants with crosshairs near the hand knob. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604955838015-NF83O6M478UFW8FEHJYU/MDM_SPSB.png Tutorial 8: Group Data Figure 8-3. This is the same multi-subject design matrix as in Figure 2 but now with separate subject predictors. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605055851732-6V00PB9WPDNZDXEDFETL/RFX+GLM Tutorial 8: Group Data Figure 8-7. RFX GLM Output https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605056688585-L4AXVSTDFLASTWD0SQDR/3+maps.png Tutorial 8: Group Data Figure 8-7. We have created maps for the contrast of Faces > Hands for each of the three model types. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604956310285-NAYEIFT3QMDDV6FX7TT8/MDM_RFX.png Tutorial 8: Group Data Figure 8-4. This is the same multi-subject design matrix as in Figure 2 but now with RFX GLM enabled. http://www.newbi4fmri.com/tutorial-9-mvpa-rsa daily 0.75 2025-12-01 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849743771-673GJ8M8MUZS8B2KPN29/Screen+Shot+2020-01-12+at+12.21.20+PM.png Tutorial 9: MVPA-RSA Figure 9-8. Metacorrelations between the data RSMs and model RSM for the faces vs. hands model across multiple brain regions (FFA = Fusiform Face Area; OFA = Occipital Face Area; STS = face-selective Superior Temporal Sulcus; aIPS = Anterior Intraparietal Sulcus; FEF = Frontal Eye Fields. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605714411884-78S75S4M76M0PKX0423F/diag4b.png Tutorial 9: MVPA-RSA Figure 9-13. Excluding the main diagonal and off-diagonals provides one solution for analyzing factorial designs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849468183-GNBEPT964H680CFEPRXP/image-asset.png Tutorial 9: MVPA-RSA Figure 9-5. This RSMmodel tests whether there is higher similarities across the exact same condition than all other pairs of conditions. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605653945409-KE0JHID3G3WUXEFKEIIC/brain-is-fried.jpg Tutorial 9: MVPA-RSA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849956931-JB0G14153EAOLWOZVH1I/Screen+Shot+2020-01-12+at+12.25.04+PM.png Tutorial 9: MVPA-RSA Figure 9-9. Metacorrelations between the Data RSM for Left M1 hand and various model RSMs. No models do much better than chance (especially considering the number of comparisons) but that is unsurprising considering that the noise ceiling is very low. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605714541841-G9IYN988YVFHB6Y64SAI/diag1b.png Tutorial 9: MVPA-RSA Figure 9-10. In theory, a data RSM with null effects should have a correlation of zero with any model. However, because unsplit data has r = 1 along the diagonal, this leads to erroneously high correlations. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849305428-ZRRQUJ4PF4H371SVXTIQ/image-asset.png Tutorial 9: MVPA-RSA Figure 9-3. The representational similarity matrix for the left fusiform face area using split data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605652791675-H7IUGOBXOVVJS8793LZN/split.png Tutorial 9: MVPA-RSA Figure 9-14. Split data RSMs do not force the diagonal to have r = 1. This enables meaningful metacorrelations with the Diagonal Model and allows contrasts in factorial designs to remain balanced. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849582672-AFS564JE7FVHHLTOY2N3/Screen+Shot+2020-01-12+at+12.17.06+PM.png Tutorial 9: MVPA-RSA Figure 9-6. This RSMmodel tests whether similarities are higher within categories than between categories. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849191605-FJ21FFOOL7RRXEPVVTRK/image-asset.png Tutorial 9: MVPA-RSA Figure 9-2. The representational similarity matrix for the left motor cortex (hand knob) using unsplit data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849703709-4XWSJ06J5OSS97PV5CMQ/Screen+Shot+2020-01-12+at+12.21.15+PM.png Tutorial 9: MVPA-RSA Figure 9-7. Metacorrelations between the data RSMs and model RSM for the diagonal model across multiple brain regions (FFA = Fusiform Face Area; OFA = Occipital Face Area; STS = face-selective Superior Temporal Sulcus; aIPS = Anterior Intraparietal Sulcus; FEF = Frontal Eye Fields. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605714381370-TRPLA6QBYL5BBZXSFS4E/diag3b.png Tutorial 9: MVPA-RSA Figure 9-12. Excluding the data in the model is NOT a good choice for unsplit data on factorial designs like ours. Here, the data RSM shows no main effect of category (faces vs. hands) but does show an effect of orientation (left/centre/right). Excluding the main diagonal in the model also excludes all the green cells with the same orientation from the model. Because the conditions with the same orientation were not excluded from the red cells in the model, the contrast is unbalanced and the correlation becomes negative. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605714492904-BVOG4370N6SBUCB6XVVK/diag2b.png Tutorial 9: MVPA-RSA Figure 9-11. Excluding the data in the model corrects for the problem shown in Figure 8-10. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849095526-5IAKE2Y4NUA63DCTAW5V/image-asset.png Tutorial 9: MVPA-RSA Figure 9-1. The representational similarity matrix for the left fusiform face area using unsplit data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849388836-PHHOUZONTKWPALBVEKC8/image-asset.png Tutorial 9: MVPA-RSA Figure 9-4. Multidimensional scaling output for Left FFA and Left M1 Hand Area. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578849876982-X44OHAJ7UB97RWX5EKVK/Screen+Shot+2020-01-12+at+12.23.34+PM.png Tutorial 9: MVPA-RSA Figure 9-9. Metacorrelations between the Data RSM for Left FFA and various model RSMs, including the Diagonal model (leftmost) and Hand vs. Face model (second from Left). Note that the noise ceiling in FFA is relatively high (~.8) and the Hand vs. Face model falls within the range of the noise ceiling. This indicates that this model does as well as any model could be expected to do. Error bars represent 95% confidence limits. Many models, including models based on gaze/hand orientation, do no better than zero (chance). http://www.newbi4fmri.com/tutorial-10-ica-old daily 0.75 2020-11-24 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850383386-2US4K3VB1SV7NVJ6SEI0/Screen+Shot+2020-01-12+at+12.32.27+PM.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850372372-C61FNOPYX0UA68LHVIBH/image-asset.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850433255-MXSU7ZJIMSHW1VMFSFIB/image-asset.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850722765-0R8YGRP57D3D3TNZKW89/Screen+Shot+2020-01-12+at+12.36.25+PM.png Tutorial 10: ICA (Old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850537479-IM86YECEIZRB0AYHUM7M/image-asset.png Tutorial 10: ICA (Old) http://www.newbi4fmri.com/newbi-data daily 0.75 2020-12-28 http://www.newbi4fmri.com/tutorials-index daily 0.75 2021-09-01 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412216028-FUHMMUWSYJ7H32PKVJMZ/Tutorial+1+image.PNG Tutorials index Tutorial 1: Data https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412219085-8OQ0JIZQ2R0LOJE02535/Tutorial+2+image.PNG Tutorials index Tutorial 2: Statistics & Maps https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/5eae4518-109b-43f1-a178-03c9fbbcbd04/Tutorial+3+image.PNG Tutorials index Tutorial 3: GLM - part 2 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630457335423-U9UGWKJGPAFDD5C2NA5Y/Mini%2BTutorial%2BSingal%2Bto%2Bnoise.jpg Tutorials index Signal-to-noise Ratio https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603687155991-JOG8Q3ANQSTWU2WNXQUO/Tutorial+3.JPG Tutorials index Tutorial 4: Statistical corrections and contrasts https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412215928-DRO8KGJMZOS713DOS0EH/Tutorial+3+image.PNG Tutorials index Tutorial 5: fMRI preprocessing and quality assurance (Motion artifacts) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412215662-JXF4810DO6C1W7684NVT/Tutorial+4+image.PNG Tutorials index Tutorial 6: Spatiotemporal smoothing https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412216183-OOJRQR9FIAVN2L71NRWF/Tutorial+5+image.PNG Tutorials index Tutorial 7: Event-related data analyses and deconvolution https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412216861-53B8B71CC8LOSZQDAHHD/Tutorial+6+image.PNG Tutorials index Tutorial 8: Spatial normalization and group GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412216351-9BCPJUF221JQA3RE9ZLR/Tutorial+7+image.PNG Tutorials index Tutorial 9: Multivariate Analyses Methods https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1592412216717-J5Y1W5Z7U4AHDAY06JGO/Tutorial+8+image.PNG Tutorials index Tutorial 10: Independent Component Analysis http://www.newbi4fmri.com/troubleshooting daily 0.75 2021-08-23 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595201417030-TPKS3J5YKCAUDGTDGWGT/image-asset.png Troubleshooting https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595201107717-GNPYO4JE981ZYTWE0ZLG/IP+refused+to+connect.PNG Troubleshooting http://www.newbi4fmri.com/faqs daily 0.75 2021-07-22 http://www.newbi4fmri.com/bv-file-names daily 0.75 2021-05-10 http://www.newbi4fmri.com/tutorial-1u-data daily 0.75 2022-02-03 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578766971008-TW4EDPXVSOGSC6VYFL3V/Screen+Shot+2020-01-11+at+1.21.10+PM.png Tutorial 1U: Data Figure 1-1. Single 2D anatomical image https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578767003206-R853G198L0TTKWY1MER9/Screen+Shot+2020-01-11+at+1.21.19+PM.png Tutorial 1U: Data Figure 1-2. Single functional DICOM file https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671028106-I8VQ1DS9TG7DQRF8VGXU/FMR+screen.PNG Tutorial 1U: Data Figure 1-7. Functional slices shown in a 2D matrix (.fmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673295090-TU7Y9BTO52RW2I64UI4N/Visual+VOI.PNG Tutorial 1U: Data Figure 1-21. A time course superimposed on a protocol. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599689133255-3XCW8AZRFOEKKTRKOUV1/BIDS%2Bexample.jpg Tutorial 1U: Data Figure 1-3. Example of the BIDS organization for one participant (sub-10). The folders shown contain raw BIDS data. The larger files (.gz) contain data. The smaller files (JSON and .tsv) contain header information about how the data were collected. The derivatives folder contains non-BIDS formatted data like BrainVoyager files. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671633424-U5OXZTBCF77J5SIES280/Link+VTC+menu.PNG Tutorial 1U: Data Figure 1-12. To use or visualize functional data, after opening a .vmr file, link it to a .vtc file. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673102163-NOQPBJ3QMJPPKYF8SGYU/Protocol+menu.PNG Tutorial 1U: Data Figure 1-20. A protocol file shows the order of conditions for a given scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629670821196-VQIQ2TAKUZCVB4BWD7ND/AMR+boxes.PNG Tutorial 1U: Data Figure 1-5. Contols to adjust the slice matrix dimensions https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672906379-VJE51GS9506GI5QD1KDW/ROI+time+course.PNG Tutorial 1U: Data https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672888377-FRH27V1QPDNVNRVYBSI2/Show+ROI+time+course.PNG Tutorial 1U: Data Figure 1-19. Viewing time courses. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671840103-51337FM2PU0D1JQ5JI2E/Blue+box.PNG Tutorial 1U: Data Figure 1-14. The reduced window for blue box mode shows only the 3D coordinates. To expand the view, click the “Full Dialog >” button. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597779262902-37H6MEPZJ4EXAGVVEHGV/IntroFileType2.PNG Tutorial 1U: Data Figure 1-4. fMRI data typically include anatomical and functional scans. Data is initially stored as a matrix of 2D slices. In later processing steps, it can be converted to 3D volumes to make visualization, navigation, and analysis easier. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629670931542-VH6NZBVY7UCVTVSNRE81/AMR+screen.PNG Tutorial 1U: Data Figure 1-6. Anatomical slices shown in a 2D matrix (.amr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672117482-BMNA5YQ6GMIG2GW4Y0OM/Blue+box+full.PNG Tutorial 1U: Data Figure 1-15. The full window for blue box mode gives many more options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671394407-2FR3CWT0YBQF46AKL5EO/Time+course+movie.PNG Tutorial 1U: Data Figure 1-9. Time course movies allow you to visualize functional volumes over time to check for head motion and other artifacts. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671526913-G4AFSE97F6YYTE2QRX6K/VMR+properties+menu.PNG Tutorial 1U: Data Figure 1-11. Showing VMR propeties. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671095379-473NH9I2E6J4CF239419/FMR+properties.PNG Tutorial 1U: Data Figure 1-8. The fMR properties show the key details about the spatial and temporal resolution of the scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672197609-B1UHO2TCBYCL0YWNOQVV/Blue+box+spat+transf.PNG Tutorial 1U: Data Figure 1-16. To visualize one volume of the 3D functional data, click “Show VTC Vol” for the volume number you want to see. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671466077-0NNDGR7TPK4D0B0W5F9I/VMR+screen.PNG Tutorial 1U: Data Figure 1-10. Anatomical data shown in a 3D volumetric view (.vmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672054066-EG035C90CRS8WOB4KVI8/Blue+box+button.PNG Tutorial 1U: Data Figure 1-13. You can visualize and control many features of 3D volumetric data by opening “blue box mode”. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673131089-R5C8B3LN4NM29SERMYX5/Protocol.PNG Tutorial 1U: Data http://www.newbi4fmri.com/tutorial-3-glm daily 0.75 2025-10-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629675560960-FP7ADCU6WPE6SSDQ6ZE9/GLM+options.PNG Tutorial 3: GLM Figure 3-3. This tab on the Single Study GLM dialog allows you to exclude the baseline condition if it is the first or last condition specified. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547693981-YIGOU20JCIWRQYVYT4EJ/T3-Four-predictor+model.gif Tutorial 3: GLM Widget 3-1. Fitting a 4-POI GLM to the localizer data from 3 voxels https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547717282-74NYCU5GDK8FHV4VPU6J/T3-Baseline+predictor.gif Tutorial 3: GLM Widget 3-2. The effect of erroneously including a redundant baseline predictor. To make the two predictors completely redundant, we used the unconvolved (box car) versions of the predictors. The redundancy would be slightly less with convolved predictors; nevertheless, redundancy would be suboptimal. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/3e8c0817-d5eb-4575-a0c6-d3c810ad2c95/Tutorial_3_new_colours_image2.png Tutorial 3: GLM Figure 3-6. The voxel beta plot shows beta weights for a given voxel when you place the cursor over it. Beta weights are relative to the baseline = 0. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599585679379-ZQHDAH6KRK3KFU8MUG0U/GLMEquation6.PNG Tutorial 3: GLM Figure 3-1. Here the GLM is used for the simplest possible situation, a correlation. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/b7cf871e-2b41-4213-a783-b797b065bc67/tutorial_3_new_colours_image1.png Tutorial 3: GLM Figure 3-2. Defining predictors. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/b12efc5f-f017-4255-9ac7-bdf3f62670b2/Screenshot+2025-10-09+at+10.50.48%E2%80%AFAM.png Tutorial 3: GLM Figure 3-4. GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629678143661-099EJKTOVGL6457K564K/Face+over+hand+contrast.PNG Tutorial 3: GLM Figure 3-5. An example contrast for Faces vs. Hands. http://www.newbi4fmri.com/newbi-experiment daily 0.75 2020-09-08 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106704023-SF28ET1U3BCD711RAWZ7/Screen+Shot+2020-02-07+at+3.17.25+PM.png NeWBI Variants - Order 2 Alternating 16-s blocks of faces and hands https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106817519-S8TSYAIQ0RUZOH36NY2F/Screen+Shot+2020-02-07+at+3.17.38+PM.png NeWBI Variants - Order 4 Alternating 4-s blocks of hands and faces with starting and ending hand baselines https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106829235-ZSLUUKXSGOCD1UYSQE08/Screen+Shot+2020-02-07+at+3.17.31+PM.png NeWBI Variants - Order 3 Alternating 4-s blocks of hands and faces https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106686646-E05779FYP4ROAH9EV232/Screen+Shot+2020-02-07+at+3.17.18+PM.png NeWBI Variants - Order 1 Alternating 16-s blocks of hands and faces https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617109807-OKUYNZMTZG3BXZLVR8BG/Order+2.PNG NeWBI Variants - Order 2 alternating blocks of baseline (16 s) and stimulus (16 s), where stimulus alternates between hands and faces, ending with additional baseline (16 s). Duration is 336 s. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1593379070049-YH3Y0TFZXW8E171XN568/Screen+Shot+2020-02-07+at+3.17.18+PM.png NeWBI Variants - Order 7 Alternating blocks of hands and faces with long block duration (64 s) with ending hand baseline. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617064715-7XKFQJB97KP4CTH50EUI/Order+1.PNG NeWBI Variants - Order 1 alternating blocks of baseline (16 s) and stimulus (16 s), where stimulus alternates between faces and hands, ending with additional baseline (16 s). Duration is 336 s. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617501445-0VQLFXK39E2RDINDKFRA/Order+5.PNG NeWBI Variants - Order 7 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617593388-WKQV434GQ4VIMWCPD39A/Order+7.PNG NeWBI Variants - Order 6 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106809998-UGI9KIR1CAF9ENRSO6BR/Screen+Shot+2020-02-07+at+3.17.43+PM.png NeWBI Variants - Order 5 Alternating blocks of hands and faces with different block durations (8 s for hands and 4 s for faces) with starting and ending hand baselines (16 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1593379043313-OLZ5S0HVAD9E0WPJ2YA1/Order+1.6.png NeWBI Variants - Order 6 Alternating blocks of hands and faces with different block durations: hands (4 s) and faces (16 s) with starting and ending hand baselines (16 s) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1581106574138-CN7NU9ZKT457BXYU2P7F/Screen+Shot+2020-02-07+at+3.11.29+PM.png NeWBI Variants https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617480607-J51X9ESEVO5PPW8EMFQQ/Order+4.PNG NeWBI Variants - Order 4 Repeating blocks of baseline (16 s), hands (16 s), and faces (16 s), ending with additional baseline (16 s). Order duration is 304 s. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617157924-LVRCMM02JEIJJSSHXAIL/Order+3.PNG NeWBI Variants - Order 3 Repeating blocks of baseline (16 s), faces (16 s), and hands (16 s), ending with additional baseline (16 s). Order duration is 304 s. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1590617531709-6RZSAEFA6ZQQABQGZ1ZU/Order+6.PNG NeWBI Variants - Order 5 http://www.newbi4fmri.com/the-newbi-strategy daily 0.75 2020-09-08 http://www.newbi4fmri.com/tutorial-4-stat-corrections daily 0.75 2025-10-22 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774294145-3R9X3CFC40H54WHDZPJW/Screen+Shot+2020-01-11+at+3.20.50+PM.png Tutorial 4: Corrections + Contrasts Figure 4-9. Example output from the cluster correction routine. UNDER CONSTRUCTION: Jody needs to correct this. Cluster correction doesn’t work in Native space. Confirm in MNI space and specify CDT in description. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599780405836-AJF508AW0VTGHPLNBODI/Random_residuals.png Tutorial 4: Corrections + Contrasts Figure 4-14. A simulated residuals plot in which data points are truly random. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454435977-T3B6V5SXZYA18OE32CAZ/Faces-hands+contrast.PNG Tutorial 4: Corrections + Contrasts Figure 4-3. Overlay General Linear model window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454395835-96IC8RLHO0T2S6O85UHA/Multi+run+MDM.PNG Tutorial 4: Corrections + Contrasts Figure 4-1. Multi-Study, Multi-Subject window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599778372217-C6UJA16X70COVVN2KVHJ/image-asset.png Tutorial 4: Corrections + Contrasts Figure 4-15. Use this spreadsheet to plot the Autocorrelation function. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630456088280-J8GNLFJGC5OQ2XBWDI4P/Various+maps.PNG Tutorial 4: Corrections + Contrasts Figure 4-22. The overlap maps function can be used to store multiple voxelwise maps. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599790355583-XYVQ0NN6GFHURPRXTE1Q/AutoTemporalCorrWidget.JPG Tutorial 4: Corrections + Contrasts Widget 4-1. Allows you to calculate the correlation between the original residuals function and a shifted version. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454534781-59T1RTHUYO0QHFVSQWDS/Turn+off+cluster+threshold.PNG Tutorial 4: Corrections + Contrasts Figure 4-6. How to turn off the cluster threshold. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454420311-00W18N6HCCUJBXHEF5HW/Multi+run+options.PNG Tutorial 4: Corrections + Contrasts Figure 4-2. Mask restriction with this mask will only perform statistical tests on within the brain in the functional scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454457967-6BPSU2N6QRYVIJJO4PBM/VOI+analysis.PNG Tutorial 4: Corrections + Contrasts Figure 4-4. Region of interest window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602035082152-DSUAZQPF0HU2IBIHMF9V/Bracci.png Tutorial 4: Corrections + Contrasts Figure 4-24. Bracci et al., 2010 first discovered LOTChand as an area distinct from the EBA. The crucial contrasts, areas, and beta weights are indicated in the red-outlined section https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454552526-P0C6ST4FZ84KEPZTLPU6/No+correction.PNG Tutorial 4: Corrections + Contrasts Figure 4-7. How to set a map threshold to a specific p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454625711-LP6Q21W2C7U2REQ1RG2F/Cluster+correction.PNG Tutorial 4: Corrections + Contrasts Figure 4-10. How to apply a cluster threshold. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602032791220-KQ953SKASJW829R0PGYY/VoxelA_Residuals.jpg Tutorial 4: Corrections + Contrasts Figure 4-13. The same plot as Figure 12, showing only the residuals. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455103830-7ZZX4EFPU5TXE5PWF7A3/Link+VMR.PNG Tutorial 4: Corrections + Contrasts Figure 4-19. If you have tiled multiple windows, you can link the VMRs so that when you move the crosshairs in one VMR, the crosshairs in the VMRs will be moved to the same 3D coordinates. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454638446-VFSGEXLW0GAKB7XI8RLZ/FDR+correction.PNG Tutorial 4: Corrections + Contrasts Figure 4-11. How to apply a False-Discovery-Rate Correction https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599775409635-AMA0V072NP8W8ARSQGVB/VoxelA_ROIGLM.png Tutorial 4: Corrections + Contrasts Figure 4-12. Output of the ROI-GLM from Voxel A showing the data (blue), best-fit model (green) and residuals. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454581863-CNUT6G8PT83CGLLLFWIF/Bonferroni+correction.PNG Tutorial 4: Corrections + Contrasts - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455475863-T55LSIT1HQD8LXGDQZH1/Comparing+maps+serial+correlation.PNG Tutorial 4: Corrections + Contrasts Figure 4-21. Comparison of maps without and with correction for serial correlations. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630456102150-JSR7BZ417HX8KNZW4M9Z/Various+maps+options.PNG Tutorial 4: Corrections + Contrasts Figure 4-23. To superimpose multiple maps, you must enable Multiple selections. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455361238-LXITEZQ5UGPVT97MH4DX/Common+statistical+threshold.PNG Tutorial 4: Corrections + Contrasts Figure 4-20. When comparing multiple maps, for a fair comparison, set the threshold to the same value in each. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/53b7e20f-70c1-47ed-9c49-7e9dce865a91/Tutorial_4_new_colours_image_4-5.png Tutorial 4: Corrections + Contrasts Figure 4-5. Visualizing the time course for run 1 and 2 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454891811-PCHH40KPU2ZECYHPCZGF/GLM+faces-hands.PNG Tutorial 4: Corrections + Contrasts Figure 4-17. Contrast of Faces vs. Hands https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454720145-RE4EA4OYCG0I3HWTYK6G/Serial+correlation.PNG Tutorial 4: Corrections + Contrasts Figure 4-16. How to apply a correction for serial correlations to a voxelwise map. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454568385-5MPK2I19SKXPAB7WUW99/Nb+of+voxels.PNG Tutorial 4: Corrections + Contrasts Figure 4-8. How to see how many voxelwise tests were performed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454992165-X2M21RD4OCQ2SKQ9R72E/TIle+view.PNG Tutorial 4: Corrections + Contrasts Figure 4-18. The Tile function is a very useful way to compare multiple data sets (works best for an even number of data sets). http://www.newbi4fmri.com/tutorial-2-glm-1 daily 0.75 2020-09-23 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598036879557-K4O1LNUZ9D3Q7RMXVBVM/OpenComputeCorrelations.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1600749444758-96HGK7FM2YCAHVDU5NKI/GLMmodel2.JPG Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597070481941-SNA7E8472PXBNU7K3GN9/sine2.gif Tutorial 2: GLM (Copy) Animation 2 : Fitting a noisy sine wave https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599623610876-24LUKFKQBBRTZ3G6UYOC/Convolution1Pred.gif Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599586545098-SN33EED1ZIXD16AYYAO2/image-asset.jpeg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599616020650-ECQFEYOLS4HB1MLO0MZN/Contrasts-Face-Hands2.JPG Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598128049066-02QRR2E5PY472C7WOLLX/ModellingVisPred2.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598041243258-01C0V8QPFCHOZHEZ3D4Q/ThreshldIcons.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598037523734-EOMA9UFIME2HF9CGWRO9/image-asset.jpeg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598200472697-ZAB1LQBK3GFPH4FKU06Z/VolumeMapsOptions2.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599623636090-NP54HAKPJ7J4Z9QX06B1/Baseline.gif Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1600749141250-KU988ZYYT0D34NYUHM2Y/GLMmodelOptions2.JPG Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599617779459-LU0KGPET5POIFUESVO8J/Hands-FacesHeatMap.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597070468095-A6LUKI9JU2EW46CH4WZT/sine1.gif Tutorial 2: GLM (Copy) Widget 1. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597163635369-5487RGP34Y1NJ0T35F9P/Signal-to-noise.PNG Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599585679379-ZQHDAH6KRK3KFU8MUG0U/GLMEquation6.PNG Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598037317385-H4R3CL3PVARM0WFCP5AG/image-asset.jpeg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598197049987-XJ2XOUMTPMZPM1QPX58B/Convolution1Pred.gif Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598202031627-XY8KZFGMAE8YVGE02LG6/VolumeMapsStatistics.jpg Tutorial 2: GLM (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598038140587-904W8SCMVMB0YLNBAGPM/HoverStats.jpg Tutorial 2: GLM (Copy) http://www.newbi4fmri.com/widgets daily 0.75 2024-07-11 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1626933509511-TCZ6YISL24IUKFIULUOH/deconvolution.gif Course Widgets - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602715068180-CQVMCQVYRMB2ONSU9MX7/Baseline.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602733591502-FBNJXLC376ULNOYWONSM/image-asset.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602714994218-GJU6G7LUFT1GJV3QWA6V/Localizer4Pred.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602733600763-8J8I7CFEAC1VRQGFETPZ/image-asset.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602714918809-WAD2HSJ7CE8JLRUVRDDX/image-asset.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602968973606-X7MCH1WCECBCU2ZPLBRW/TemporalFiltering.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602714945849-IVUJLZU22XP1UERML1K9/Convolution1Pred.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602714846639-7YLGXRV18GCG93U5D2NY/SineCorr.gif Course Widgets https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602715235771-UL2TFSQV659J90HD9Y0M/SerialCorrelation.gif Course Widgets http://www.newbi4fmri.com/tutorial-7-erdecon daily 0.75 2020-10-22 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799276020-LSNEJ9SRHLPBS1C8162C/Screen+Shot+2020-01-11+at+9.59.36+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798794530-F7E6NO31ERG5JPIOTRKU/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799528384-Q44AXXCGKIY5MNVC27A7/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798917872-39D3EAS1TX95Z4WPPKUZ/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798736636-K25H3UQ7OYPOK8W6SIU6/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798856772-EBKKSBA3GN6T9IWPCTFC/Screen+Shot+2020-01-11+at+9.59.03+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799042228-8KHVMTZGC8T6PGFL8EA6/Screen+Shot+2020-01-11+at+9.59.29+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799414162-AZ6OSJ9SXCXN5ZWSFTNL/Screen+Shot+2020-01-11+at+9.59.50+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799012192-IOWB8KKD0E330P9FHZN6/Screen+Shot+2020-01-11+at+9.59.21+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799318153-XBSV7KE7GZBGAJXDW0KS/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798808528-J0HPMP5AIRJA58VGF13U/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798780767-9I5YWZJZPP9X6CL9KEDD/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798904312-JW69U3DFF8SUK94NMBAR/Screen+Shot+2020-01-11+at+9.59.08+PM.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973123172-W4TOD5KOQ6JHOBKSB5B3/deconvolution.gif Tutorial 7: ER&Decon (Copy) Animation 1: Temporal filtering https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799492736-7N7HYESSVFG2WXK42TMM/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799027464-KVN080K06LSTLOSQBFYA/image-asset.png Tutorial 7: ER&Decon (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799678019-A444PSMPUIS3BU1VE2PX/image-asset.png Tutorial 7: ER&Decon (Copy) http://www.newbi4fmri.com/bv-edu-access-file daily 0.75 2020-12-02 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605062002991-67MQOSVW8IQHSC6TNNG3/adddialog.png BV EDU Access File https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606936611923-0COD0ORN4ZFBORTAGN6D/Mac-path.png BV EDU Access File Here is an example of the path for the access file on a Mac https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605061887835-CE7MJDQ4HH7LBGWUNPLP/edudata.png BV EDU Access File https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605061941383-OM7VIX65PPOGUIS7ADQP/adddatesets.png BV EDU Access File https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606936296821-6354259SPPQDR5QI29RR/hiddenfiles.png BV EDU Access File On a Mac, you can show hidden files (grayed out on left) by typing command-shift-period http://www.newbi4fmri.com/tutorial-10-ica daily 0.75 2025-12-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606261755770-7DTU8SWVHVNMU6STO4JS/ica_screen.png Tutorial 10: ICA (old) Figure 10-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 10: ICA (old) Figure 10-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310852774-247H70SIVXD8EC8UP7XU/image-asset.jpeg Tutorial 10: ICA (old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310865884-GI076WEJ9BZMWTWNK6C2/Sorting+Pred+index2.jpg Tutorial 10: ICA (old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310410812-XABJUAKRS7H3JCUC6B6J/SortingRMS2.jpg Tutorial 10: ICA (old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310685469-47HT1W4XS9OWIMFEID9D/image-asset.jpeg Tutorial 10: ICA (old) REBEKKA REDO AND FLAG FINGERPRINT BUTTON TOO https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 10: ICA (old) Figure 10-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA (old) Figure 10-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA (old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310392194-1B61MV6G3KLWR9LUDPZE/image-asset.jpeg Tutorial 10: ICA (old) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 10: ICA (old) Figure 10-6. Neurosynth.org map for “default mode” http://www.newbi4fmri.com/data-1 daily 0.75 2020-12-28 http://www.newbi4fmri.com/tutorial-2-glm-part-1-2 daily 0.75 2021-07-22 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598197049987-XJ2XOUMTPMZPM1QPX58B/Convolution1Pred.gif Tutorial 2: GLM part 1 & 2 Widget 3. Comparison of correlations with and without convolution of the predictor https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599616020650-ECQFEYOLS4HB1MLO0MZN/Contrasts-Face-Hands2.JPG Tutorial 2: GLM part 1 & 2 Figure 2-14. An example contrast for Faces vs. Hands. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597070468095-A6LUKI9JU2EW46CH4WZT/sine1.gif Tutorial 2: GLM part 1 & 2 Widget 1. A predictor function can be scaled by a beta weight to fit simulated data for two voxels and a Pearson correlation coefficient (r) can be determined. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598038140587-904W8SCMVMB0YLNBAGPM/HoverStats.jpg Tutorial 2: GLM part 1 & 2 Figure 2-6. Hovering the mouse over a voxel will show its coordinates, intensity, statistical parameter value and p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597070481941-SNA7E8472PXBNU7K3GN9/sine2.gif Tutorial 2: GLM part 1 & 2 Widget 2. A model with one predictor (visual stimulation vs. baseline) can be used to fit different voxels from one run of one participant for the course localizer data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599623636090-NP54HAKPJ7J4Z9QX06B1/Baseline.gif Tutorial 2: GLM part 1 & 2 Widget 5. The effect of erroneously including a redundant baseline predictor. To make the two predictors completely redundant, we used the unconvolved (box car) versions of the predictors. The redundancy would be slightly less with convolved predictors; nevertheless, redundancy would be suboptimal. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601523840274-ANR4K73DV9SZLX4YW8TF/ModellingVisPred2.jpg Tutorial 2: GLM part 1 & 2 Figure 2-4. Shows how the convolved predictor should appear. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601526013948-S8U5DZUXQHYKTR26BRRK/GLMdecomposition3.JPG Tutorial 2: GLM part 1 & 2 Figure 2-13. GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598200472697-ZAB1LQBK3GFPH4FKU06Z/VolumeMapsOptions2.jpg Tutorial 2: GLM part 1 & 2 Figure 2-9. A specific p value can be specified in Map Options tab of the Volume Maps panel. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601524265649-R7720PGRL8N3TF7D4K1M/ModellingVisPred2.jpg Tutorial 2: GLM part 1 & 2 Figure 2-3. Explanation of how to manually create a box car predictor and convolve it. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599623610876-24LUKFKQBBRTZ3G6UYOC/Convolution1Pred.gif Tutorial 2: GLM part 1 & 2 Widget 4. Fitting a 4-POI GLM to the localizer data from 3 voxels https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597163635369-5487RGP34Y1NJ0T35F9P/Signal-to-noise.PNG Tutorial 2: GLM part 1 & 2 Figure 2-1. Any data (such as a time series) can be decomposed into explained and unexplained variance. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599585679379-ZQHDAH6KRK3KFU8MUG0U/GLMEquation6.PNG Tutorial 2: GLM part 1 & 2 Figure 2-10. Here the GLM is used for the simplest possible situation, a correlation. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601416832495-ZVE1PRGULALG62WA2KIF/threshicons.png Tutorial 2: GLM part 1 & 2 Figure 2-7. The minimum p value (threshold) can be quickly adjusted using these buttons to increase the threshold (bigger blob button) or decrease the threshold (smaller blob button). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601524014455-F1DZY3EJDZQCVWE8MVW2/GLMmodel2.JPG Tutorial 2: GLM part 1 & 2 Figure 2-11. Defining predictors. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601526344000-QHTUS6JZA5LRWPG70PWZ/Hands-FacesHeatMap.jpg Tutorial 2: GLM part 1 & 2 Figure 2-15. The voxel beta plot shows beta weights for a given voxel when you place the cursor over it. Beta weights are relative to the baseline = 0. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598037523734-EOMA9UFIME2HF9CGWRO9/image-asset.jpeg Tutorial 2: GLM part 1 & 2 Figure 2-5. Activation depicted with BrainVoyager’s Classic LUT https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1600749141250-KU988ZYYT0D34NYUHM2Y/GLMmodelOptions2.JPG Tutorial 2: GLM part 1 & 2 Figure 2-12. This tab on the Single Study GLM dialog allows you to exclude the baseline condition if it is the first or last condition specified. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598036879557-K4O1LNUZ9D3Q7RMXVBVM/OpenComputeCorrelations.jpg Tutorial 2: GLM part 1 & 2 Figure 2-2. Menu to compute Linear Correlation Maps https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1598202031627-XY8KZFGMAE8YVGE02LG6/VolumeMapsStatistics.jpg Tutorial 2: GLM part 1 & 2 Figure 2-8. Many features of the statistical map, including the minimum (threshold) and maximum can be controlled precisely using the Volume Maps control panel. http://www.newbi4fmri.com/tutorial-2-stats-maps daily 0.75 2025-10-02 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674145812-4Z3NTZ6SSJ2658K50M8W/Correlation+model.PNG Tutorial 2: Stats & Maps Figure 2-3. Explanation of how to manually create a box car predictor and convolve it. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547193098-TTTCQRDA736J5N346H5Z/T2-Single+predictor+1.gif Tutorial 2: Stats & Maps Widget 1. A predictor function can be scaled by a beta weight to fit simulated data for two voxels and a Pearson correlation coefficient (r) can be determined. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547169189-S7IN16B0VKOGZWPF6CW7/T2-Single+predictor+2.gif Tutorial 2: Stats & Maps Widget 2. A model with one predictor (visual stimulation vs. baseline) can be used to fit different voxels from one run of one participant for the course localizer data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631546954603-JWHOAOKVLEAMCIOB5REJ/T3-Box+car+function.gif Tutorial 2: Stats & Maps Widget 3. Comparison of correlations with and without convolution of the predictor https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601416832495-ZVE1PRGULALG62WA2KIF/threshicons.png Tutorial 2: Stats & Maps Figure 2-7. The minimum p value (threshold) can be quickly adjusted using these buttons to increase the threshold (bigger blob button) or decrease the threshold (smaller blob button). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597163635369-5487RGP34Y1NJ0T35F9P/Signal-to-noise.PNG Tutorial 2: Stats & Maps Figure 2-1. Any data (such as a time series) can be decomposed into explained and unexplained variance. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674439090-CIO392QDEXT4A5IOV774/Voxel+wise+test.PNG Tutorial 2: Stats & Maps Figure 2-6. Hovering the mouse over a voxel will show its coordinates, intensity, statistical parameter value and p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673689476-2RV67JMRFUR6IY4KHSOU/Correlation+menu.PNG Tutorial 2: Stats & Maps Figure 2-2. Menu to compute Linear Correlation Maps https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674425648-MRUM1ERQKSZWP79LLLX5/Corr+map.PNG Tutorial 2: Stats & Maps Figure 2-5. Activation depicted with BrainVoyager’s Classic LUT https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674077688-ZHIXS3XVF79FKC8BTFMB/Convolved+corr+model.PNG Tutorial 2: Stats & Maps Figure 2-4. Shows how the convolved predictor should appear. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674705177-AAHG5VC8OFO4D6JYUUMP/Pval+threshold.PNG Tutorial 2: Stats & Maps Figure 2-9. A specific p value can be specified in Map Options tab of the Volume Maps panel. http://www.newbi4fmri.com/how-to-download daily 0.75 2023-09-29 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681489572-78MEXD6DMPWPFPWZT2VN/Step+2.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1632100551664-C1M7DO7ML8CMESW2ALCV/Import+eduexdat.bin+file.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681534049-943WHBQLB7KFG782019Y/Step+4.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1632100513535-G69IGFIEEQPJV8IOOOOL/Intro+screen+-+add+datasets.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681912048-U0W9EFL8BZ6BWI36LUOC/Step+3.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681898905-S68YL8C730FJVUIXLJ25/Step+2b.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681943948-CS1PZOKKDP2B7FOV2FMU/Step+5.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629681475563-JWHFNJMEM1REFVLWGWMA/Step+1.PNG How to download - Make it stand out Whatever it is, the way you tell your story online can make all the difference. http://www.newbi4fmri.com/mini-tutorial-signal-to-noise daily 0.75 2021-09-08 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630356920957-MWX70JFF2HEX6DQ4XI2T/Signal+to+noise.gif Mini Tutorial: Signal to noise - Make it stand out Widget SN-1 : Manipulate the signal and noise sliders to see the impact on the reliability/strength of the activation to a given stimulus https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631041266874-Q1I03K4PLEJZGDBBB2EH/Spatial+SNR+Equation.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630370231875-F0YMAY1QXSJZNNYPE726/Spatial+SNR+example.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631041318972-SOX3C9AUTNOXLGP77X2O/Temporal+SNR+Equation.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631066959356-UO03WHAZREI4P24W0SJC/tCNR+visual+example+-+Amplitude.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631062156478-N52DDDYRF4YCO9UX061H/Spatial+CNR+formula.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631062874082-D77M6ZSQ7ZUGTQ2E2FB9/Temporal+CNR+Equation+-+c1-+c2.PNG Mini Tutorial: Signal to noise - Make it stand out Whatever it is, the way you tell your story online can make all the difference. http://www.newbi4fmri.com/tutorial-1-data daily 0.75 2025-09-15 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672054066-EG035C90CRS8WOB4KVI8/Blue+box+button.PNG Tutorial 1: Data Figure 1-13. You can visualize and control many features of 3D volumetric data by opening “blue box mode”. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672441440-LTQ7KRQGAVLNR54L3K29/Without+trilin.PNG Tutorial 1: Data Figure 1-17. Sagittal slice without trilinear interpolation https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629670821196-VQIQ2TAKUZCVB4BWD7ND/AMR+boxes.PNG Tutorial 1: Data Figure 1-5. Contols to adjust the slice matrix dimensions https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672197609-B1UHO2TCBYCL0YWNOQVV/Blue+box+spat+transf.PNG Tutorial 1: Data Figure 1-16. To visualize one volume of the 3D functional data, click “Show VTC Vol” for the volume number you want to see. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671095379-473NH9I2E6J4CF239419/FMR+properties.PNG Tutorial 1: Data Figure 1-8. The fMR properties show the key details about the spatial and temporal resolution of the scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673102163-NOQPBJ3QMJPPKYF8SGYU/Protocol+menu.PNG Tutorial 1: Data Figure 1-20. A protocol file shows the order of conditions for a given scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597779262902-37H6MEPZJ4EXAGVVEHGV/IntroFileType2.PNG Tutorial 1: Data Figure 1-4. fMRI data typically include anatomical and functional scans. Data is initially stored as a matrix of 2D slices. In later processing steps, it can be converted to 3D volumes to make visualization, navigation, and analysis easier. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673519217-AL3WWEYBRKI3T4T9RLA8/ROI+analysis.PNG Tutorial 1: Data Figure 1-22. The Region-/Volume-of-Interest dialogue allows you to view time courses for a specific region (or here, voxel). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672906379-VJE51GS9506GI5QD1KDW/ROI+time+course.PNG Tutorial 1: Data https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671028106-I8VQ1DS9TG7DQRF8VGXU/FMR+screen.PNG Tutorial 1: Data Figure 1-7. Functional slices shown in a 2D matrix (.fmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671526913-G4AFSE97F6YYTE2QRX6K/VMR+properties+menu.PNG Tutorial 1: Data Figure 1-11. Showing VMR propeties. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671840103-51337FM2PU0D1JQ5JI2E/Blue+box.PNG Tutorial 1: Data Figure 1-14. The reduced window for blue box mode shows only the 3D coordinates. To expand the view, click the “Full Dialog >” button. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671633424-U5OXZTBCF77J5SIES280/Link+VTC+menu.PNG Tutorial 1: Data Figure 1-12. To use or visualize functional data, after opening a .vmr file, link it to a .vtc file. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672888377-FRH27V1QPDNVNRVYBSI2/Show+ROI+time+course.PNG Tutorial 1: Data Figure 1-19. Viewing time courses. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671394407-2FR3CWT0YBQF46AKL5EO/Time+course+movie.PNG Tutorial 1: Data Figure 1-9. Time course movies allow you to visualize functional volumes over time to check for head motion and other artifacts. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/bcf93b76-c0a1-4659-b65c-8175cdcfa89b/T2_Slice_movie.png Tutorial 1: Data - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/2b54f244-165b-4e24-a1c1-d8b340c9cdf9/LoadPRT.png Tutorial 1: Data https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629670931542-VH6NZBVY7UCVTVSNRE81/AMR+screen.PNG Tutorial 1: Data Figure 1-6. Anatomical slices shown in a 2D matrix (.amr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672117482-BMNA5YQ6GMIG2GW4Y0OM/Blue+box+full.PNG Tutorial 1: Data Figure 1-15. The full window for blue box mode gives many more options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672462531-KKM4LHQKOYGN9W29CI65/With+trilin.PNG Tutorial 1: Data Figure 1-18. Sagittal slice with trilinear interpolation https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578766971008-TW4EDPXVSOGSC6VYFL3V/Screen+Shot+2020-01-11+at+1.21.10+PM.png Tutorial 1: Data Figure 1-1. Single 2D anatomical image https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578767003206-R853G198L0TTKWY1MER9/Screen+Shot+2020-01-11+at+1.21.19+PM.png Tutorial 1: Data Figure 1-2. Single functional DICOM file https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671466077-0NNDGR7TPK4D0B0W5F9I/VMR+screen.PNG Tutorial 1: Data Figure 1-10. Anatomical data shown in a 3D volumetric view (.vmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599689133255-3XCW8AZRFOEKKTRKOUV1/BIDS%2Bexample.jpg Tutorial 1: Data Figure 1-3. Example of the BIDS organization for one participant (sub-10). The folders shown contain raw BIDS data. The larger files (.gz) contain data. The smaller files (JSON and .tsv) contain header information about how the data were collected. The derivatives folder contains non-BIDS formatted data like BrainVoyager files. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/174b2e28-0531-4511-bd54-e53ef3caa0a3/VoxelA_timecourse.png Tutorial 1: Data Figure 1-21. A time course superimposed on a protocol. http://www.newbi4fmri.com/tutorial-2u-intro-to-stats daily 0.75 2022-02-10 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630356920957-MWX70JFF2HEX6DQ4XI2T/Signal+to+noise.gif Tutorial 2U: Intro to stats - Make it stand out Widget 3. Manipulate the signal and noise sliders to see the impact on the significance of the beta weight https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547193098-TTTCQRDA736J5N346H5Z/T2-Single+predictor+1.gif Tutorial 2U: Intro to stats Widget 1. A predictor function can be scaled by a beta weight to fit simulated data for two voxels. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/f5c59b7a-6fdf-4c17-ae45-d2b4dccbbf4c/Convolved+corr+model.PNG Tutorial 2U: Intro to stats Figure 2-4. Shows how the convolved predictor should appear. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547169189-S7IN16B0VKOGZWPF6CW7/T2-Single+predictor+2.gif Tutorial 2U: Intro to stats Widget 2. A model with one predictor (visual stimulation vs. baseline) can be used to fit different voxels from one run of one participant for the course localizer data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674705177-AAHG5VC8OFO4D6JYUUMP/Pval+threshold.PNG Tutorial 2U: Intro to stats Figure 2-9. A specific p value can be specified in Map Options tab of the Volume Maps panel. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/989e45ab-dc0f-42fc-98ef-b3f90c4584ed/U_Voxel+wise+test.PNG Tutorial 2U: Intro to stats Figure 2-6. Hovering the mouse over a voxel will show its coordinates, intensity, statistical parameter value and p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/a38cc2c6-83ce-4349-8d4d-eaea0c7ca585/Visual+GLM.PNG Tutorial 2U: Intro to stats Figure 2-3. Explanation of how to manually create a box car predictor and convolve it. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/61dcbfeb-5eca-41c8-8838-3ffde62ab62b/U_GLM+menu.PNG Tutorial 2U: Intro to stats Figure 2-2. Menu to compute maps using the General Linear Model https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/721a1c8c-8310-415f-9319-98761d59d377/U_LUT+thresholds+t-val.PNG Tutorial 2U: Intro to stats Figure 2-8. Many features of the statistical map, including the minimum (threshold) and maximum can be controlled precisely using the Volume Maps control panel. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/6bc5595f-5c75-454b-9d3f-b67a4c9279fd/U_T-test_map.PNG Tutorial 2U: Intro to stats Figure 2-5. Activation depicted with BrainVoyager’s Classic LUT https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597163635369-5487RGP34Y1NJ0T35F9P/Signal-to-noise.PNG Tutorial 2U: Intro to stats Figure 2-1. Any data (such as a time series) can be decomposed into explained and unexplained variance. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/169bcbfa-bba2-474d-96b2-cb151a613d69/R-squared+formula_simple.PNG Tutorial 2U: Intro to stats - Make it stand out Equation 2-1. Equation to find the proportion of explained variance. Note that we can multiply the proportion of explained variance by 100 to obtain the percentage of explained variance. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601416832495-ZVE1PRGULALG62WA2KIF/threshicons.png Tutorial 2U: Intro to stats Figure 2-7. The minimum p value (threshold) can be quickly adjusted using these buttons to increase the threshold (bigger blob button) or decrease the threshold (smaller blob button). http://www.newbi4fmri.com/tutorial-3u-glm daily 0.75 2022-02-16 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454625711-LP6Q21W2C7U2REQ1RG2F/Cluster+correction.PNG Tutorial 3U: GLM - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454534781-59T1RTHUYO0QHFVSQWDS/Turn+off+cluster+threshold.PNG Tutorial 3U: GLM - Make it stand out How to turn off the cluster threshold. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629675503290-7YP2X4DL759NL99IUH9F/four+predictor+GLM.PNG Tutorial 3U: GLM Figure 3-1. Defining predictors. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629678143661-099EJKTOVGL6457K564K/Face+over+hand+contrast.PNG Tutorial 3U: GLM Figure 3-5. An example contrast for Faces vs. Hands. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629677560042-XRB7B3H8S7MYQ17P38VQ/Broken+down+GLM.PNG Tutorial 3U: GLM Figure 3-3. Four predictor GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454568385-5MPK2I19SKXPAB7WUW99/Nb+of+voxels.PNG Tutorial 3U: GLM - Make it stand out How to see how many voxelwise tests were performed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454581863-CNUT6G8PT83CGLLLFWIF/Bonferroni+correction.PNG Tutorial 3U: GLM - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629675560960-FP7ADCU6WPE6SSDQ6ZE9/GLM+options.PNG Tutorial 3U: GLM Figure 3-2. This tab on the Single Study GLM dialog allows you to exclude the baseline condition if it is the first or last condition specified. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629678205565-TZT9Q2CLXXUARNS44H2T/Face+over+hands+voxel.PNG Tutorial 3U: GLM Figure 3-6. The voxel beta plot shows beta weights for a given voxel when you place the cursor over it. Beta weights are relative to the baseline = 0. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454552526-P0C6ST4FZ84KEPZTLPU6/No+correction.PNG Tutorial 3U: GLM - Make it stand out How to set a map threshold to a specific p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547693981-YIGOU20JCIWRQYVYT4EJ/T3-Four-predictor+model.gif Tutorial 3U: GLM Widget 3-1. Fitting a 4-POI GLM to the localizer data from 3 voxels http://www.newbi4fmri.com/tutorial-4u-preprocessing daily 0.75 2022-03-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602733600763-8J8I7CFEAC1VRQGFETPZ/TemporalFiltering.gif Tutorial 4U: Preprocessing - Make it stand out Widget 5-2. How temporal filtering affects the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775495125-EX8933APGQOYP7P0DYDW/Screen+Shot+2020-01-11+at+3.43.01+PM.png Tutorial 4U: Preprocessing Figure 5-1. Motion of a rigid body (like the head) in 3D space can be quantified and corrected using 6 motion parameters (3 translations and 3 rotations) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602700123956-PZ7O40J8OJTMOW5YDRVI/spatsmooth.png Tutorial 4U: Preprocessing - Make it stand out Figure 5-13. One functional data slice with different smoothing options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578776116758-AREQ73ZLS9LJ95WHMILS/image-asset.png Tutorial 4U: Preprocessing Figure 5-10. Left = A typical contrast using only POIs. Right = a contrast using PONIs. Although PONIs, by definition, are generally not of particular interest, nevertheless, performing such contrasts can give you an idea of how problematic head motion is and which brain regions might be particularly vulnerable. You can also inspect the time courses of the artifact regions to see how bad the effects are. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603303271469-5B2BKE9GUHJL7LH7KL04/VOI2.jpg Tutorial 4U: Preprocessing - Make it stand out Figure 5-15. How to load an ROI/VOI file. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602698889028-QBCK3FNDFTOT0IRY9IHV/how-to-fix-grainy-photos-hero-image.jpg Tutorial 4U: Preprocessing - Make it stand out Figure 5-12. Example of spatial smoothing in image processing. Photo credit. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775772808-EMEANKO68ABHNT8SF5OR/Screen+Shot+2020-01-11+at+3.43.41+PM.png Tutorial 4U: Preprocessing Figure 5-5. Loading predictors for a Single-Study GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775808648-6A6UWX8T6D9ASWWF6S0E/Screen+Shot+2020-01-11+at+3.43.46+PM.png Tutorial 4U: Preprocessing Figure 5-6. The “Load” button allows you to view motion parameters https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775704328-TTTD1Z7H2UYLOPVRP2DR/Screen+Shot+2020-01-11+at+3.43.33+PM.png Tutorial 4U: Preprocessing Figure 5-3. To play a movie for an .fmr file, go to Options/Time course movie https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602733532242-H76A9QD1BH7S0BRN5PHJ/LinearDrift.gif Tutorial 4U: Preprocessing - Make it stand out Widget 5-1. How linear trend removal affects the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578776026823-AQ33AOWQOZDEVDJ9TD0D/Screen+Shot+2020-01-11+at+3.44.01+PM.png Tutorial 4U: Preprocessing Figure 5-9. Example time course, showing motion artifacts. Can you spot one “spike” and one “glitch”? https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602640134403-L9QGBL1C75W6FCYBRHYW/GLM-improve.png Tutorial 4U: Preprocessing Figure 5-2. Recall that GLM stats can be improved by increasing the fit of the POIs, reducing residuals, and shifting known sources of noise into the GLM by adding PONIs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578775741059-CYE9JVF85P5E1GWDH34A/Screen+Shot+2020-01-11+at+3.43.36+PM.png Tutorial 4U: Preprocessing Figure 5-4. Time course movie controls https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602706171367-5CT6FONHZSQ35FB49LTI/2%2Bcond.jpg Tutorial 4U: Preprocessing - Make it stand out Figure 5-16. For simple two-condition protocol, it is easy to predict the predominant frequency of activation. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578781074230-0Z9XKDJ160X0UNZ5DNFV/image-asset.png Tutorial 4U: Preprocessing - Make it stand out Figure 5-11. A Gaussian filter is characterized but the full-width at half maximum (FWHM). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602973072430-UDM9QBKJ7DB25WXG35LY/VMP.jpg Tutorial 4U: Preprocessing - Make it stand out Figure 5-14. A file with multiple maps of the same contrast (Faces - Hands) after different combinations of spatial smoothing and temporal filtering. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578776010077-TFUTJQKRM6VNA7VZRPC2/Screen+Shot+2020-01-11+at+3.43.56+PM.png Tutorial 4U: Preprocessing Figure 5-8. You can select an entire slice to see the average time course of all voxels (above a certain intensity that corresponds to the voxels inside the brain) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602705858652-R0L68HF8PVCZBXJ7P8KZ/locprt.png Tutorial 4U: Preprocessing - Make it stand out Figure 5-17. For more complex, multi-condition protocols, the expected stimulation frequencies may be more complicated. http://www.newbi4fmri.com/tutorial-5u-group-data daily 0.75 2022-03-16 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604956310285-NAYEIFT3QMDDV6FX7TT8/MDM_RFX.png Tutorial 5U: Group Data Figure 8-4. This is the same multi-subject design matrix as in Figure 2 but now with RFX GLM enabled. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605055777656-9EVWMLZFY1FL4T7ZT8GJ/SPSB+GLM.png Tutorial 5U: Group Data Figure 8-6. FFX GLM Output (with Separate Subject Predictors, SPSB) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605056688585-L4AXVSTDFLASTWD0SQDR/3+maps.png Tutorial 5U: Group Data Figure 8-7. We have created maps for the contrast of Faces > Hands for each model type. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604954858214-WA8495NGOFS1JB4T098K/18T1s.png Tutorial 5U: Group Data Figure 8-1. Screenshot of the average T1 anatomical from 18 participants with crosshairs near the hand knob. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1605055851732-6V00PB9WPDNZDXEDFETL/RFX+GLM Tutorial 5U: Group Data Figure 8-7. RFX GLM Output https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1604955838015-NF83O6M478UFW8FEHJYU/MDM_SPSB.png Tutorial 5U: Group Data Figure 8-3. This is the same multi-subject design matrix as in Figure 2 but now with separate subject predictors. http://www.newbi4fmri.com/tutorial-6u-connectivity daily 0.75 2022-03-30 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 6U: Connectivity Figure 6-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606261755770-7DTU8SWVHVNMU6STO4JS/ica_screen.png Tutorial 6U: Connectivity Figure 6-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 6U: Connectivity https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 6U: Connectivity Figure 6-6. Neurosynth.org map for “default mode” https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310392194-1B61MV6G3KLWR9LUDPZE/image-asset.jpeg Tutorial 6U: Connectivity https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 6U: Connectivity Figure 6-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 6U: Connectivity Figure 6-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310410812-XABJUAKRS7H3JCUC6B6J/SortingRMS2.jpg Tutorial 6U: Connectivity https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310852774-247H70SIVXD8EC8UP7XU/image-asset.jpeg Tutorial 6U: Connectivity https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310685469-47HT1W4XS9OWIMFEID9D/image-asset.jpeg Tutorial 6U: Connectivity Figure 6-1. ICA map controls https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310865884-GI076WEJ9BZMWTWNK6C2/Sorting+Pred+index2.jpg Tutorial 6U: Connectivity http://www.newbi4fmri.com/new-page daily 0.75 2024-07-11 http://www.newbi4fmri.com/widgets-under-development daily 0.75 2024-07-11 http://www.newbi4fmri.com/tutorial-1-data-1 daily 0.75 2025-02-05 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673295090-TU7Y9BTO52RW2I64UI4N/Visual+VOI.PNG Tutorial 0: Stats Refresher Figure 1-21. A time course superimposed on a protocol. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672054066-EG035C90CRS8WOB4KVI8/Blue+box+button.PNG Tutorial 0: Stats Refresher Figure 1-13. You can visualize and control many features of 3D volumetric data by opening “blue box mode”. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672197609-B1UHO2TCBYCL0YWNOQVV/Blue+box+spat+transf.PNG Tutorial 0: Stats Refresher Figure 1-16. To visualize one volume of the 3D functional data, click “Show VTC Vol” for the volume number you want to see. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672441440-LTQ7KRQGAVLNR54L3K29/Without+trilin.PNG Tutorial 0: Stats Refresher Figure 1-17. Sagittal slice without trilinear interpolation https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671633424-U5OXZTBCF77J5SIES280/Link+VTC+menu.PNG Tutorial 0: Stats Refresher Figure 1-12. To use or visualize functional data, after opening a .vmr file, link it to a .vtc file. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672117482-BMNA5YQ6GMIG2GW4Y0OM/Blue+box+full.PNG Tutorial 0: Stats Refresher Figure 1-15. The full window for blue box mode gives many more options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673519217-AL3WWEYBRKI3T4T9RLA8/ROI+analysis.PNG Tutorial 0: Stats Refresher Figure 1-22. The Region-/Volume-of-Interest dialogue allows you to view time courses for a specific region (or here, voxel). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673102163-NOQPBJ3QMJPPKYF8SGYU/Protocol+menu.PNG Tutorial 0: Stats Refresher Figure 1-20. A protocol file shows the order of conditions for a given scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/de09a764-2254-43c2-8659-abae35a20fc4/stats_intuitions.png Tutorial 0: Stats Refresher - Make it stand out Figure 0-1. Effects of hypothetical Drug X on Intelligence. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671095379-473NH9I2E6J4CF239419/FMR+properties.PNG Tutorial 0: Stats Refresher Figure 1-8. The fMR properties show the key details about the spatial and temporal resolution of the scan. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671394407-2FR3CWT0YBQF46AKL5EO/Time+course+movie.PNG Tutorial 0: Stats Refresher Figure 1-9. Time course movies allow you to visualize functional volumes over time to check for head motion and other artifacts. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/7bc388df-3c56-4663-9db3-2b1612cb8c00/Screenshot+2025-02-05+at+3.47.30%E2%80%AFPM.png Tutorial 0: Stats Refresher - Make it stand out Figure 0-2. Descriptives tab on JASP https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672462531-KKM4LHQKOYGN9W29CI65/With+trilin.PNG Tutorial 0: Stats Refresher Figure 1-18. Sagittal slice with trilinear interpolation https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671028106-I8VQ1DS9TG7DQRF8VGXU/FMR+screen.PNG Tutorial 0: Stats Refresher Figure 1-7. Functional slices shown in a 2D matrix (.fmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671526913-G4AFSE97F6YYTE2QRX6K/VMR+properties+menu.PNG Tutorial 0: Stats Refresher Figure 1-11. Showing VMR propeties. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673131089-R5C8B3LN4NM29SERMYX5/Protocol.PNG Tutorial 0: Stats Refresher https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672906379-VJE51GS9506GI5QD1KDW/ROI+time+course.PNG Tutorial 0: Stats Refresher https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671840103-51337FM2PU0D1JQ5JI2E/Blue+box.PNG Tutorial 0: Stats Refresher Figure 1-14. The reduced window for blue box mode shows only the 3D coordinates. To expand the view, click the “Full Dialog >” button. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629671466077-0NNDGR7TPK4D0B0W5F9I/VMR+screen.PNG Tutorial 0: Stats Refresher Figure 1-10. Anatomical data shown in a 3D volumetric view (.vmr file) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629672888377-FRH27V1QPDNVNRVYBSI2/Show+ROI+time+course.PNG Tutorial 0: Stats Refresher Figure 1-19. Viewing time courses. http://www.newbi4fmri.com/tutorial-2-stats-maps-1 daily 0.75 2025-10-02 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629673689476-2RV67JMRFUR6IY4KHSOU/Correlation+menu.PNG Tutorial 2: Stats & Maps Figure 2-2. Menu to compute Linear Correlation Maps https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1597163635369-5487RGP34Y1NJ0T35F9P/Signal-to-noise.PNG Tutorial 2: Stats & Maps Figure 2-1. Any data (such as a time series) can be decomposed into explained and unexplained variance. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/4e9100bb-8969-4e2c-86d3-63194e2dafbd/tutorial_2_new_colours_hrf2.png Tutorial 2: Stats & Maps Figure 2-4. Shows how the convolved predictor should appear. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547193098-TTTCQRDA736J5N346H5Z/T2-Single+predictor+1.gif Tutorial 2: Stats & Maps Widget 1. A predictor function can be scaled by a beta weight to fit simulated data for two voxels and a Pearson correlation coefficient (r) can be determined. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674439090-CIO392QDEXT4A5IOV774/Voxel+wise+test.PNG Tutorial 2: Stats & Maps Figure 2-6. Hovering the mouse over a voxel will show its coordinates, intensity, statistical parameter value and p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1601416832495-ZVE1PRGULALG62WA2KIF/threshicons.png Tutorial 2: Stats & Maps Figure 2-7. The minimum p value (threshold) can be quickly adjusted using these buttons to increase the threshold (bigger blob button) or decrease the threshold (smaller blob button). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547169189-S7IN16B0VKOGZWPF6CW7/T2-Single+predictor+2.gif Tutorial 2: Stats & Maps Widget 2. A model with one predictor (visual stimulation vs. baseline) can be used to fit different voxels from one run of one participant for the course localizer data. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631546954603-JWHOAOKVLEAMCIOB5REJ/T3-Box+car+function.gif Tutorial 2: Stats & Maps Widget 3. Comparison of correlations with and without convolution of the predictor https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674705177-AAHG5VC8OFO4D6JYUUMP/Pval+threshold.PNG Tutorial 2: Stats & Maps Figure 2-9. A specific p value can be specified in Map Options tab of the Volume Maps panel. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/5ffe15d1-ea46-4840-9700-f0382f3e92ac/tutorial_2_new_colours_box.png Tutorial 2: Stats & Maps Figure 2-3. Explanation of how to manually create a box car predictor and convolve it. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629674425648-MRUM1ERQKSZWP79LLLX5/Corr+map.PNG Tutorial 2: Stats & Maps Figure 2-5. Activation depicted with BrainVoyager’s Classic LUT http://www.newbi4fmri.com/tutorial-3-glm-1 daily 0.75 2025-10-02 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547717282-74NYCU5GDK8FHV4VPU6J/T3-Baseline+predictor.gif Tutorial 3: GLM Widget 3-2. The effect of erroneously including a redundant baseline predictor. To make the two predictors completely redundant, we used the unconvolved (box car) versions of the predictors. The redundancy would be slightly less with convolved predictors; nevertheless, redundancy would be suboptimal. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1631547693981-YIGOU20JCIWRQYVYT4EJ/T3-Four-predictor+model.gif Tutorial 3: GLM Widget 3-1. Fitting a 4-POI GLM to the localizer data from 3 voxels https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629677560042-XRB7B3H8S7MYQ17P38VQ/Broken+down+GLM.PNG Tutorial 3: GLM Figure 3-4. GLM https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599585679379-ZQHDAH6KRK3KFU8MUG0U/GLMEquation6.PNG Tutorial 3: GLM Figure 3-1. Here the GLM is used for the simplest possible situation, a correlation. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1ba9cab1-01d3-475c-9298-d8d957c921c5/tutorial_3_new_colours_image1.png Tutorial 3: GLM Figure 3-2. Defining predictors. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629678205565-TZT9Q2CLXXUARNS44H2T/Face+over+hands+voxel.PNG Tutorial 3: GLM Figure 3-6. The voxel beta plot shows beta weights for a given voxel when you place the cursor over it. Beta weights are relative to the baseline = 0. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629675560960-FP7ADCU6WPE6SSDQ6ZE9/GLM+options.PNG Tutorial 3: GLM Figure 3-3. This tab on the Single Study GLM dialog allows you to exclude the baseline condition if it is the first or last condition specified. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1629678143661-099EJKTOVGL6457K564K/Face+over+hand+contrast.PNG Tutorial 3: GLM Figure 3-5. An example contrast for Faces vs. Hands. http://www.newbi4fmri.com/tutorial-4-stat-corrections-1 daily 0.75 2025-10-21 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454435977-T3B6V5SXZYA18OE32CAZ/Faces-hands+contrast.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-3. Overlay General Linear model window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454625711-LP6Q21W2C7U2REQ1RG2F/Cluster+correction.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-10. How to apply a cluster threshold. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454395835-96IC8RLHO0T2S6O85UHA/Multi+run+MDM.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-1. Multi-Study, Multi-Subject window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602035082152-DSUAZQPF0HU2IBIHMF9V/Bracci.png Tutorial 4: Corrections + Contrasts (Copy) Figure 4-24. Bracci et al., 2010 first discovered LOTChand as an area distinct from the EBA. The crucial contrasts, areas, and beta weights are indicated in the red-outlined section https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454552526-P0C6ST4FZ84KEPZTLPU6/No+correction.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-7. How to set a map threshold to a specific p value. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578774294145-3R9X3CFC40H54WHDZPJW/Screen+Shot+2020-01-11+at+3.20.50+PM.png Tutorial 4: Corrections + Contrasts (Copy) Figure 4-9. Example output from the cluster correction routine. UNDER CONSTRUCTION: Jody needs to correct this. Cluster correction doesn’t work in Native space. Confirm in MNI space and specify CDT in description. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599780405836-AJF508AW0VTGHPLNBODI/Random_residuals.png Tutorial 4: Corrections + Contrasts (Copy) Figure 4-14. A simulated residuals plot in which data points are truly random. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455475863-T55LSIT1HQD8LXGDQZH1/Comparing+maps+serial+correlation.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-21. Comparison of maps without and with correction for serial correlations. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454638446-VFSGEXLW0GAKB7XI8RLZ/FDR+correction.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-11. How to apply a False-Discovery-Rate Correction https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599778372217-C6UJA16X70COVVN2KVHJ/image-asset.png Tutorial 4: Corrections + Contrasts (Copy) Figure 4-15. Use this spreadsheet to plot the Autocorrelation function. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630456088280-J8GNLFJGC5OQ2XBWDI4P/Various+maps.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-22. The overlap maps function can be used to store multiple voxelwise maps. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454457967-6BPSU2N6QRYVIJJO4PBM/VOI+analysis.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-4. Region of interest window https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455361238-LXITEZQ5UGPVT97MH4DX/Common+statistical+threshold.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-20. When comparing multiple maps, for a fair comparison, set the threshold to the same value in each. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1602032791220-KQ953SKASJW829R0PGYY/VoxelA_Residuals.jpg Tutorial 4: Corrections + Contrasts (Copy) Figure 4-13. The same plot as Figure 12, showing only the residuals. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454534781-59T1RTHUYO0QHFVSQWDS/Turn+off+cluster+threshold.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-6. How to turn off the cluster threshold. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454581863-CNUT6G8PT83CGLLLFWIF/Bonferroni+correction.PNG Tutorial 4: Corrections + Contrasts (Copy) - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599775409635-AMA0V072NP8W8ARSQGVB/VoxelA_ROIGLM.png Tutorial 4: Corrections + Contrasts (Copy) Figure 4-12. Output of the ROI-GLM from Voxel A showing the data (blue), best-fit model (green) and residuals. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454568385-5MPK2I19SKXPAB7WUW99/Nb+of+voxels.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-8. How to see how many voxelwise tests were performed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630456102150-JSR7BZ417HX8KNZW4M9Z/Various+maps+options.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-23. To superimpose multiple maps, you must enable Multiple selections. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454992165-X2M21RD4OCQ2SKQ9R72E/TIle+view.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-18. The Tile function is a very useful way to compare multiple data sets (works best for an even number of data sets). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630455103830-7ZZX4EFPU5TXE5PWF7A3/Link+VMR.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-19. If you have tiled multiple windows, you can link the VMRs so that when you move the crosshairs in one VMR, the crosshairs in the VMRs will be moved to the same 3D coordinates. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454480854-P2EOHPO3II0B579TGLBZ/VOI+time+course.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-5. Visualizing the time course for run 1 and 2 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1599790355583-XYVQ0NN6GFHURPRXTE1Q/AutoTemporalCorrWidget.JPG Tutorial 4: Corrections + Contrasts (Copy) Widget 4-1. Allows you to calculate the correlation between the original residuals function and a shifted version. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454720145-RE4EA4OYCG0I3HWTYK6G/Serial+correlation.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-16. How to apply a correction for serial correlations to a voxelwise map. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454891811-PCHH40KPU2ZECYHPCZGF/GLM+faces-hands.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-17. Contrast of Faces vs. Hands https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1630454420311-00W18N6HCCUJBXHEF5HW/Multi+run+options.PNG Tutorial 4: Corrections + Contrasts (Copy) Figure 4-2. Mask restriction with this mask will only perform statistical tests on within the brain in the functional scan. http://www.newbi4fmri.com/tutorial-7-er-decon-1 daily 0.75 2025-11-19 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799528384-Q44AXXCGKIY5MNVC27A7/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-18. Running a deconvolution analysis for the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973123172-W4TOD5KOQ6JHOBKSB5B3/deconvolution.gif Tutorial 7: ER&Decon (Copy) Widget 7-1. Fitting a deconvolution model https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772441686-YHUSDYDYQXETJQVPWK4I/GLM_LOTChand.png Tutorial 7: ER&Decon (Copy) Figure 7-8: Activation for the contrast of Faces - Hands. Crosshairs are centred on hand-selective activation in the expected location of LOTChand https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799276020-LSNEJ9SRHLPBS1C8162C/Screen+Shot+2020-01-11+at+9.59.36+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-15. The GLM output for the deconvolution analysis has 120 predictors of interest. For complex contrasts, a .ctr file can be loaded. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603821072435-3RDIJ34LKAN9AMLMPMBW/ER+convolution.JPG Tutorial 7: ER&Decon (Copy) Figure 7-3. The convolved predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603557122243-V12RSH74R1KO6ZCX6FH1/image-asset.jpeg Tutorial 7: ER&Decon (Copy) Figure 7-6. The multi-study design matrix for the 7 runs of the experiment for Subject 15. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799678019-A444PSMPUIS3BU1VE2PX/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-19. Options to select for Fitting the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740290034-VQJO0L70B9EQGTWYBM42/ER+average+results.JPG Tutorial 7: ER&Decon (Copy) Figure 7-12. The event-related average from Left LOTChand. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740157934-G9D37D94REC2BWDYXY1Z/POI+predictors+single.JPG Tutorial 7: ER&Decon (Copy) Figure 7-4. All 6 predictors of interest superimposed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798736636-K25H3UQ7OYPOK8W6SIU6/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-1. Protocol for one order of the main experiment, showing a jittered rapid event-related design in which trials were spaced every 4 or 8 s in an optimized order that balanced the n-1 trial history. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799042228-8KHVMTZGC8T6PGFL8EA6/Screen+Shot+2020-01-11+at+9.59.29+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-14. The default settings generate 120 predictors (20 predictors/condition x 6 conditions. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603734792202-MRFXRRB58I17Q6P6H63L/Faces-Hands2.jpg Tutorial 7: ER&Decon (Copy) Figure 7-7. A contrast of all three face conditions vs. all three hand conditions in the experimental runs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603821059938-MKHNHI2EXLWDJ61BQWPM/ER+square+wave.JPG Tutorial 7: ER&Decon (Copy) Figure 7-2. The box car predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799414162-AZ6OSJ9SXCXN5ZWSFTNL/Screen+Shot+2020-01-11+at+9.59.50+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-16. Selecting the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799027464-KVN080K06LSTLOSQBFYA/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-13. The options tab for a deconvolution analysis. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798856772-EBKKSBA3GN6T9IWPCTFC/Screen+Shot+2020-01-11+at+9.59.03+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-10. Selecting Left LOTChand as a region of interest. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740272156-DCGT8FH2C2B7P52R9V81/image-asset.jpeg Tutorial 7: ER&Decon (Copy) Figure 7-11. Loading the file to generate an event-related average for the region used to extract the time course. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772346607-OMS2702LDJZDJTOSSXAW/Neurosynth_LOTChand.png Tutorial 7: ER&Decon (Copy) Figure 7-9. A 7-mm diameter spherical region interest centred on the hotspot of activation for “hands” in Neurosynth. Note the excellent correspondence with the experimental activation in Figure 7-8. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799012192-IOWB8KKD0E330P9FHZN6/Screen+Shot+2020-01-11+at+9.59.21+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-12. To access the menu for a deconvolution design, go into Options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799492736-7N7HYESSVFG2WXK42TMM/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-17. Loading the deconvolution design matrix. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740232200-QE8KM6YQ3JXDZ2PT46J8/POI%2BPONI+predictors+single2.JPG Tutorial 7: ER&Decon (Copy) Figure 7-5. All 6 predictors of interest + 6 predictors of no interest (motion parameters) superimposed. http://www.newbi4fmri.com/tutorial-7-er-decon-2 daily 0.75 2025-11-19 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603734792202-MRFXRRB58I17Q6P6H63L/Faces-Hands2.jpg Tutorial 7: ER&Decon (Copy) Figure 7-7. A contrast of all three face conditions vs. all three hand conditions in the experimental runs. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798736636-K25H3UQ7OYPOK8W6SIU6/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-1. Protocol for one order of the main experiment, showing a jittered rapid event-related design in which trials were spaced every 4 or 8 s in an optimized order that balanced the n-1 trial history. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740272156-DCGT8FH2C2B7P52R9V81/image-asset.jpeg Tutorial 7: ER&Decon (Copy) Figure 7-11. Loading the file to generate an event-related average for the region used to extract the time course. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1595973123172-W4TOD5KOQ6JHOBKSB5B3/deconvolution.gif Tutorial 7: ER&Decon (Copy) Widget 7-1. Fitting a deconvolution model https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740232200-QE8KM6YQ3JXDZ2PT46J8/POI%2BPONI+predictors+single2.JPG Tutorial 7: ER&Decon (Copy) Figure 7-5. All 6 predictors of interest + 6 predictors of no interest (motion parameters) superimposed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799678019-A444PSMPUIS3BU1VE2PX/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-19. Options to select for Fitting the GLM. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772346607-OMS2702LDJZDJTOSSXAW/Neurosynth_LOTChand.png Tutorial 7: ER&Decon (Copy) Figure 7-9. A 7-mm diameter spherical region interest centred on the hotspot of activation for “hands” in Neurosynth. Note the excellent correspondence with the experimental activation in Figure 7-8. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740157934-G9D37D94REC2BWDYXY1Z/POI+predictors+single.JPG Tutorial 7: ER&Decon (Copy) Figure 7-4. All 6 predictors of interest superimposed. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799042228-8KHVMTZGC8T6PGFL8EA6/Screen+Shot+2020-01-11+at+9.59.29+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-14. The default settings generate 120 predictors (20 predictors/condition x 6 conditions. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799414162-AZ6OSJ9SXCXN5ZWSFTNL/Screen+Shot+2020-01-11+at+9.59.50+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-16. Selecting the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799027464-KVN080K06LSTLOSQBFYA/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-13. The options tab for a deconvolution analysis. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603772441686-YHUSDYDYQXETJQVPWK4I/GLM_LOTChand.png Tutorial 7: ER&Decon (Copy) Figure 7-8: Activation for the contrast of Faces - Hands. Crosshairs are centred on hand-selective activation in the expected location of LOTChand https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799012192-IOWB8KKD0E330P9FHZN6/Screen+Shot+2020-01-11+at+9.59.21+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-12. To access the menu for a deconvolution design, go into Options. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603740290034-VQJO0L70B9EQGTWYBM42/ER+average+results.JPG Tutorial 7: ER&Decon (Copy) Figure 7-12. The event-related average from Left LOTChand. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799492736-7N7HYESSVFG2WXK42TMM/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-17. Loading the deconvolution design matrix. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799276020-LSNEJ9SRHLPBS1C8162C/Screen+Shot+2020-01-11+at+9.59.36+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-15. The GLM output for the deconvolution analysis has 120 predictors of interest. For complex contrasts, a .ctr file can be loaded. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578799528384-Q44AXXCGKIY5MNVC27A7/image-asset.png Tutorial 7: ER&Decon (Copy) Figure 7-18. Running a deconvolution analysis for the LOTChand ROI. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603557122243-V12RSH74R1KO6ZCX6FH1/image-asset.jpeg Tutorial 7: ER&Decon (Copy) Figure 7-6. The multi-study design matrix for the 7 runs of the experiment for Subject 15. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603821072435-3RDIJ34LKAN9AMLMPMBW/ER+convolution.JPG Tutorial 7: ER&Decon (Copy) Figure 7-3. The convolved predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1603821059938-MKHNHI2EXLWDJ61BQWPM/ER+square+wave.JPG Tutorial 7: ER&Decon (Copy) Figure 7-2. The box car predictor for the Face_Left condition. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578798856772-EBKKSBA3GN6T9IWPCTFC/Screen+Shot+2020-01-11+at+9.59.03+PM.png Tutorial 7: ER&Decon (Copy) Figure 7-10. Selecting Left LOTChand as a region of interest. http://www.newbi4fmri.com/tutorial-10-ica-1 daily 0.75 2025-12-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 10: ICA (Copy) Figure 10-6. Neurosynth.org map for “default mode” https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310410812-XABJUAKRS7H3JCUC6B6J/SortingRMS2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310392194-1B61MV6G3KLWR9LUDPZE/image-asset.jpeg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA (Copy) Figure 10-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 10: ICA (Copy) Figure 10-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606261755770-7DTU8SWVHVNMU6STO4JS/ica_screen.png Tutorial 10: ICA (Copy) Figure 10-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310865884-GI076WEJ9BZMWTWNK6C2/Sorting+Pred+index2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 10: ICA (Copy) Figure 10-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310685469-47HT1W4XS9OWIMFEID9D/image-asset.jpeg Tutorial 10: ICA (Copy) REBEKKA REDO AND FLAG FINGERPRINT BUTTON TOO https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310852774-247H70SIVXD8EC8UP7XU/image-asset.jpeg Tutorial 10: ICA (Copy) http://www.newbi4fmri.com/tutorial-10-ica-2 daily 0.75 2025-12-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 10: ICA (Copy) Figure 10-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 10: ICA (Copy) Figure 10-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310865884-GI076WEJ9BZMWTWNK6C2/Sorting+Pred+index2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310685469-47HT1W4XS9OWIMFEID9D/image-asset.jpeg Tutorial 10: ICA (Copy) REBEKKA REDO AND FLAG FINGERPRINT BUTTON TOO https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310410812-XABJUAKRS7H3JCUC6B6J/SortingRMS2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310392194-1B61MV6G3KLWR9LUDPZE/image-asset.jpeg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 10: ICA (Copy) Figure 10-6. Neurosynth.org map for “default mode” https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606261755770-7DTU8SWVHVNMU6STO4JS/ica_screen.png Tutorial 10: ICA (Copy) Figure 10-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310852774-247H70SIVXD8EC8UP7XU/image-asset.jpeg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA (Copy) Figure 10-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. http://www.newbi4fmri.com/tutorial-10-ica-3 daily 0.75 2025-12-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310392194-1B61MV6G3KLWR9LUDPZE/image-asset.jpeg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310410812-XABJUAKRS7H3JCUC6B6J/SortingRMS2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 10: ICA (Copy) Figure 10-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606261755770-7DTU8SWVHVNMU6STO4JS/ica_screen.png Tutorial 10: ICA (Copy) Figure 10-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 10: ICA (Copy) Figure 10-6. Neurosynth.org map for “default mode” https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 10: ICA (Copy) Figure 10-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310865884-GI076WEJ9BZMWTWNK6C2/Sorting+Pred+index2.jpg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310685469-47HT1W4XS9OWIMFEID9D/image-asset.jpeg Tutorial 10: ICA (Copy) REBEKKA REDO AND FLAG FINGERPRINT BUTTON TOO https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA (Copy) Figure 10-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606310852774-247H70SIVXD8EC8UP7XU/image-asset.jpeg Tutorial 10: ICA (Copy) https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA (Copy) http://www.newbi4fmri.com/tutorial-10-ica-4 daily 0.75 2025-12-09 https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850178739-X04EOLDZR7R1HM2Q83K7/image-asset.png Tutorial 10: ICA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/4b5cb20f-0c7d-492a-9f04-ece5eb8df3e7/Image+10.2.png Tutorial 10: ICA Figure 10-2. Try to arrange the windows on your screen so you can see all these windows simultaneously. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606262870283-XUGWU9HPH59JM19IP56X/image.jpg Tutorial 10: ICA Figure 10-3. An example from BrainVoyager explaining a fingerprint . The fingerprint is a polar plot that represents 11 pieces of information . Each piece of information is represented by a different angle “around the clock”. The strength of that feature is represented by the distance from the centre of the plot. The fingerprint in panel E shows strong clustering of activation (B) and skewness in the histogram (A) (12 and 1 o’clock positions), a strong one-lag autocorrelation (4 o’clock position), and high power in the intermediate temporal frequencies of the power spectrum (D): ( 9 and 10 o’clock positions: 0.02-0.1 Hz; cycles of 10-50 s) but not low frequencies (7 o’clock position, < .02 Hz; cycles > 50 s) or high frequencies (11 o’clock position: > 0.1 Hz, cycles < 10 s). https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1578850576335-1UE54DFQM9YFP6KCWB1B/Screen+Shot+2020-01-12+at+12.35.49+PM.png Tutorial 10: ICA Figure 10-4. Different types of noise have different fingerprints. From De Martino et al., 2007, NeuroImage. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/fc24f1f9-323f-49d0-b800-1b4378c4a411/Image+10.4.png Tutorial 10: ICA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/d75e8e71-e98b-4b7c-9fa6-59a6f0a62def/Image+10.3.png Tutorial 10: ICA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/dcb520c5-9d6a-4d5d-b389-dbaef3e0f277/Image+10.5.png Tutorial 10: ICA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/cead285f-013b-46a9-b501-f7306e883016/Image+10.1.png Tutorial 10: ICA Figure 10-1. https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/9dfa86c9-9246-42e3-bad9-e2984c7dc69d/Image+10.6.png Tutorial 10: ICA https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606267952344-41OYV8JS83K23CYLSHXH/dmn+raichle.png Tutorial 10: ICA Figure 10-5. Default-Mode Network (blue-green) and Task-Positive Network (red-yellow) from Fox & Raichle, 2007, Nature Reviews Neuroscience https://images.squarespace-cdn.com/content/v1/5e18e027c727fe50ee503275/1606268055611-V281766QLDE3NHSZYWFS/dmn.png Tutorial 10: ICA Figure 10-6. Neurosynth.org map for “default mode”