Decoding task specific and task general functional architectures of the brain

The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain-based fingerprints of individual subjects and cognitive states based on cognitively-demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously-unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity-based brain fingerprinting and state decoding. Additionally, 16 different pre-processing pipelines are compared in order to characterize the effects of different aspects of the production of functional connectomes (FCs) on the accuracy of subject and task classification, and to identify possible confounds.

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“…In other related work, some authors have looked at cognitive state decoding with goals different from our own [10, 13].…”

Section: Introductionmentioning

confidence: 99%

High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding

Hannum

Lopez

Blanco

et al. 2023

Preprint

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show abstract

“…In other related work, some authors have looked at cognitive state decoding with goals different from our own (Gao et al, 2020 ; Gupta et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

Hannum

Lopez

Blanco

et al. 2023

Human Brain Mapping

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The human brain is a complex network comprised of functionally and anatomically interconnected brain regions. A growing number of studies have suggested that empirical estimates of brain networks may be useful for discovery of biomarkers of disease and cognitive state. A prerequisite for realizing this aim, however, is that brain networks also serve as reliable markers of an individual. Here, using Human Connectome Project data, we build upon recent studies examining brain‐based fingerprints of individual subjects and cognitive states based on cognitively demanding tasks that assess, for example, working memory, theory of mind, and motor function. Our approach achieves accuracy of up to 99% for both identification of the subject of an fMRI scan, and for classification of the cognitive state of a previously unseen subject in a scan. More broadly, we explore the accuracy and reliability of five different machine learning techniques on subject fingerprinting and cognitive state decoding objectives, using functional connectivity data from fMRI scans of a high number of subjects (865) across a number of cognitive states (8). These results represent an advance on existing techniques for functional connectivity‐based brain fingerprinting and state decoding. Additionally, 16 different functional connectome (FC) matrix construction pipelines are compared in order to characterize the effects of different aspects of the production of FCs on the accuracy of subject and task classification, and to identify possible confounds.

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“…These task-related changes in brain network activity provide valuable prior knowledge for understanding the mechanisms underlying brain disorders (Briend et al, 2019;Xia et al, 2019;Kofler et al, 2020;Riedel et al, 2022). However, previous research on ND classification often overlooked this valuable prior information (Gupta et al, 2022;Jiang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

Lang,

Yang,

2023

Front. Neurosci.

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Neuropsychiatric disorder (ND) is often accompanied by abnormal functional connectivity (FC) patterns in specific task contexts. The distinctive task-specific FC patterns can provide valuable features for ND classification models using deep learning. However, most previous studies rely solely on the whole-brain FC matrix without considering the prior knowledge of task-specific FC patterns. Insight by the decoding studies on brain-behavior relationship, we develop TSP-GNN, which extracts task-specific prior (TSP) connectome patterns and employs graph neural network (GNN) for disease classification. TSP-GNN was validated using publicly available datasets. Our results demonstrate that different ND types show distinct task-specific connectivity patterns. Compared with the whole-brain node characteristics, utilizing task-specific nodes enhances the accuracy of ND classification. TSP-GNN comprises the first attempt to incorporate prior task-specific connectome patterns and the power of deep learning. This study elucidates the association between brain dysfunction and specific cognitive processes, offering valuable insights into the cognitive mechanism of neuropsychiatric disease.

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Decoding task specific and task general functional architectures of the brain

Cited by 4 publications

References 61 publications

High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding

High-Accuracy Machine Learning Techniques for Functional Connectome Fingerprinting and Cognitive State Decoding

High‐accuracy machine learning techniques for functional connectome fingerprinting and cognitive state decoding

TSP-GNN: a novel neuropsychiatric disorder classification framework based on task-specific prior knowledge and graph neural network

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