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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease

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Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis.

Ming Bo Cai¹, Michael Shvartsman², Anqi Wu³

¹International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Japan; Princeton Neuroscience Institute, Princeton University, United States.

Neuropsychologia

|May 21, 2020

Summary

This summary is machine-generated.

New computational tools for functional magnetic resonance imaging (fMRI) data analysis leverage probabilistic graphical models. These advanced methods improve insights from complex brain imaging data by incorporating assumptions and domain knowledge.

Keywords:

Bayesian Big data Cognitive neuroscience Factor model Matrix normal Probabilistic graphical model fMRI

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Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

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Area of Science:

Cognitive Neuroscience
Neuroimaging Analysis

Background:

Functional magnetic resonance imaging (fMRI) generates large, high-dimensional, and noisy datasets.
Aggregating fMRI data for scientific insights is challenging due to data heterogeneity and complexity.

Purpose of the Study:

To review recently developed computational algorithms for fMRI data analysis.
To advocate for the adoption of explicit model construction in cognitive neuroscience.

Main Methods:

Review of algorithms in naturalistic tasks, functional connectivity, pattern classification, representational similarity, and structured residuals.
Algorithms incorporate assumptions about neural data and domain knowledge into probabilistic graphical models.

Main Results:

Probabilistic graphical models effectively address challenges in fMRI data, including noise and dimensionality.
These methods enhance the accuracy of estimations and the aggregation of data across subjects.

Conclusions:

Explicit model construction using probabilistic graphical models is a powerful approach for cognitive neuroscience research.
The principles discussed are applicable beyond fMRI to other neuroimaging modalities.