"Enhancing the network specific individual characteristics in rs-fMRI f" by Pratik Jain, Ankit Chakraborty et al.
 

Enhancing the network specific individual characteristics in rs-fMRI functional connectivity by dictionary learning

Document Type

Article

Publication Date

6-1-2023

Abstract

Most fMRI inferences are based on analyzing the scans of a cohort. Thus, the individual variability of a subject is often overlooked in these studies. Recently, there has been a growing interest in individual differences in brain connectivity also known as individual connectome. Various studies have demonstrated the individual specific component of functional connectivity (FC), which has enormous potential to identify participants across consecutive testing sessions. Many machine learning and dictionary learning-based approaches have been used to extract these subject-specific components either from the blood oxygen level dependent (BOLD) signal or from the FC. In addition, several studies have reported that some resting-state networks have more individual-specific information than others. This study compares four different dictionary-learning algorithms that compute the individual variability from the network-specific FC computed from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data having 10 scans per subject. The study also compares the effect of two FC normalization techniques, namely, Fisher Z normalization and degree normalization on the extracted subject-specific components. To quantitatively evaluate the extracted subject-specific component, a metric named (Formula presented.) is proposed, and it is used in combination with the existing differential identifiability (Formula presented.) metric. It is based on the hypothesis that the subject-specific FC vectors should be similar within the same subject and different across different subjects. Results indicate that Fisher Z transformed subject-specific fronto-parietal and default mode network extracted using Common Orthogonal Basis Extraction (COBE) dictionary learning have the best features to identify a participant.

Identifier

85153177620 (Scopus)

Publication Title

Human Brain Mapping

External Full Text Location

https://doi.org/10.1002/hbm.26289

e-ISSN

10970193

ISSN

10659471

First Page

3410

Last Page

3432

Issue

8

Volume

44

Grant

R01 MH131335

Fund Ref

National Institutes of Health

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