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Related Experiment Video

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

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

Published on: November 14, 2017

Penalized model-based clustering of fMRI data.

Andrew Dilernia¹, Karina Quevedo², Jazmin Camchong²

¹Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.

Biostatistics (Oxford, England)

|February 2, 2021

Summary

This study introduces a new method for grouping patients using brain functional connectivity (FC) from fMRI scans. The model simultaneously identifies patient groups and their unique FC patterns, aiding in diagnoses.

Keywords:

Brain connectivity Gaussian graphical models Machine learning Model-based clustering Neuroimaging Schizophrenia fMRI

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Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroimaging
Computational Neuroscience
Statistical Modeling

Background:

Functional magnetic resonance imaging (fMRI) provides valuable insights into brain functional connectivity (FC).
FC analysis is crucial for understanding neurodegenerative and psychiatric disorders.
Current methods lack the ability to simultaneously cluster subjects and estimate subject- and group-level FC.

Purpose of the Study:

To develop a novel unsupervised clustering model for fMRI data.
To concurrently cluster subjects based on FC and estimate subject- and group-level FC networks.
To improve diagnostic insights for physicians by identifying patient subgroups with shared connectivity features.

Main Methods:

Proposed a random covariance clustering model (RCCM).
RCCM concurrently clusters subjects and estimates individual and group FC networks.
Evaluated performance through simulations and application to a real fMRI dataset.

Main Results:

RCCM demonstrated competitive performance against existing methods in simulations.
Achieved improved subject clustering and FC network estimation.
Successfully applied to resting-state fMRI data from healthy controls and schizophrenia patients.

Conclusions:

The proposed RCCM is an effective method for clustering subjects based on brain FC.
The model provides simultaneous estimation of subject- and group-level FC networks.
This approach has significant potential for clinical applications in diagnosing brain disorders.