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Machine Learning for Multimodal Neuroimaging
Published on Feb 4, 20253425 Views
The combination of multiple neuroimaging modalities has become an important field of research. While the technical challenges associated with multimodal neuroimaging have been mastered more than a dec
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Presentation
Machine Learning for Multimodal Neuroimaging00:00
Acknowledgements00:21
Motivation for Multimodal Neuroimaging02:25
Electromagnetic Field Changes - 102:53
Electromagnetic Field Changes - 204:25
Neurovascular Coupling05:21
Blood-Oxygen Level Dependent Signal - 107:45
Blood-Oxygen Level Dependent Signal - 209:27
Blood-Oxygen Level Dependent Signal - 310:33
Multimodal Neuroimaging: Benefits and Challenges11:19
Outline13:16
Multimodal Neuroimaging14:10
Analysis of Multimodal Neuroimaging Data14:27
Analysis of Multimodal Neuroimaging Data - 114:30
Analysis of Multimodal Neuroimaging Data - 215:13
Supervised Analyses - 117:35
Supervised Analyses - 217:37
Supervised Analysis: General Linear Models18:15
EEG Bandpower as Label for fMRI GLM19:14
Supervised Analyses - 319:58
Supervised Analyses - 420:21
Supervised Analyses - 520:53
Unsupervised Analyses22:17
Principal Component Analysis - 123:46
Principal Component Analysis - 224:32
Principal Component Analysis - 325:23
Principal Component Analysis - 426:05
Principal Component Analysis - 526:43
Principal Component Analysis - 627:12
Principal Component Analysis - 727:52
Principal Component Analysis - 829:02
Kernel Principal Component Analysis29:07
Principal Component Analysis - 930:16
Unimodal Unsupervised Analyses31:11
Multimodal Unsupervised Analyses32:42
Classical Approach To Multimodal Neuroimaging32:50
When Unimodal Methods Fail: CCA vs. PCA33:14
Canonical Correlation Analysis - 133:39
Canonical Correlation Analysis - 235:47
Derivation CCA36:01
CCA and Other Projection Methods37:12
Why You Should Not Interpret Filter Coefficients38:51
Transforming Filters into Patterns40:59
CCA Filters and CCA Patterns41:42
Information Theoretic Measures42:46
CCA for Multiple Modalities - 144:44
CCA for Multiple Modalities - 245:39
Unimodal and Multimodal Analyses46:38
Problems of Standard CCA48:34
Kernel CCA - 148:58
Kernel CCA - 250:24
Problems of Standard Kernel CCA51:36
Temporal Kernel CCA - 153:12
Temporal Kernel CCA - 253:49
Temporal Kernel CCA - 354:21
Non-instantaneous Coupling: CCA vs. tkCCA54:48
Multimodal Unsupervised EEG-fMRI Source Estimation - 155:42
Multimodal Unsupervised EEG-fMRI Source Estimation - 257:49
EEG-fMRI Source Estimation - 159:09
EEG-fMRI Source Estimation - 201:00:36
EEG-fMRI Source Estimation - 301:01:15
Multimodal Source Power Correlation Analysis - 101:02:03
Multimodal Source Power Correlation Analysis - 201:02:47
Multimodal Source Power Correlation Analysis - 301:03:12
Multimodal Source Power Correlation Analysis - 401:03:59
Multimodal Source Power Correlation Analysis - 501:04:15
Multimodal Source Power Correlation Analysis - 601:04:38
Applications - 101:04:53
Applications - 201:04:58
Hybrid BCIs Improve Mental State Detection - 101:05:42
Hybrid BCIs Improve Mental State Detection - 201:05:44
Hybrid BCIs Improve Mental State Detection - 301:05:48
Hybrid BCIs Improve Mental State Detection - 401:06:05
Hybrid BCIs Improve Mental State Detection - 501:06:40
Summary - 101:07:11
Hybrid BCIs Improve Mental State Detection - 601:07:31
PCA For Artifact Removal in Multimodal Recordings01:09:29
Simultaneous Neural and Hemodynamic Measurements01:09:38
Online Recording System01:10:53
Scanner Gradient Artifact Removal - 101:11:29
Scanner Gradient Artifact Removal - 201:12:13
Empirical Criteria for Artifact Removal01:14:10
Summary - 201:16:14
Decoding Neural Information from fMRI Signals01:19:19
Standard Model of Neurovascular Coupling01:19:21
When Canonical HRF Models Fail01:19:40
Is Spatiotemporal HRF Variability Neural Information?01:21:07
Decoding Neural Information: Workflow01:21:09
Predicting Neural Amplitude from fMRI01:21:24
Temporal Dynamics Extracted01:21:43
TkCCA vs Canonical HRF Models - 101:21:50
TkCCA vs Canonical HRF Models - 201:22:18
Optimal Preprocessing for fMRI Decoding01:22:33
How Much Neural Information is in fMRI signals?01:22:36
Mutual Information Estimates01:22:38
Mutual Information Estimates: EEG-fMRI01:22:45
Summary - 301:22:57
Multimodal Analyses for Hyperscanning - 101:23:01
Multimodal Analyses for Hyperscanning - 201:23:16
Hyperscanning01:25:00
Common Activation Patterns - 101:25:17
Common Activation Patterns - 201:26:51
Summary - 401:27:07
Applications01:27:45
Thank you01:28:37