Three Faces of Cognitive Processes

Das, J. P.

doi:10.1016/b978-0-12-410388-7.00003-8

Cited by 9 publications

(1 citation statement)

References 55 publications

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“…More detailed demographic characteristics of participants are presented in Table 1. Moreover, each participant completed the behavioral and cognitive tests administered by a trained neuropsychologist, including the Wechsler adult intelligence scale test (WAIS‐IV) (Coalson et al., 2010), stroop test (Das, 2015), and the digit symbol test (Demakis et al., 2001). Resting‐state fMRI images were collected on a 3.0 T Philips MRI scanner with a 15‐channel head coil using an axial‐gradient spin‐echo sequence.…”

Section: Methodsmentioning

confidence: 99%

Discriminating mild traumatic brain injury using sparse dictionary learning of functional network dynamics

Fan

et al. 2021

Brain and Behavior

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Mild traumatic brain injury (mTBI) is usually caused by a bump, blow, or jolt to the head or penetrating head injury, and carries the risk of inducing cognitive disorders.However, identifying the biomarkers for the diagnosis of mTBI is challenging as evident abnormalities in brain anatomy are rarely found in patients with mTBI. In this study, we tested whether the alteration of functional network dynamics could be used as potential biomarkers to better diagnose mTBI. We propose a sparse dictionary learning framework to delineate spontaneous fluctuation of functional connectivity into the subject-specific time-varying evolution of a set of overlapping group-level sparse connectivity components (SCCs) based on the resting-state functional magnetic resonance imaging (fMRI) data from 31 mTBI patients in the early acute phase (<3 days postinjury) and 31 healthy controls (HCs). The identified SCCs were consistently distributed in the cohort of subjects without significant inter-group differences in connectivity patterns. Nevertheless, subject-specific temporal expression of these SCCs could be used to discriminate patients with mTBI from HCs with a classification accuracy of 74.2% (specificity 64.5% and sensitivity 83.9%) using leave-one-out cross-validation. Taken together, our findings indicate neuroimaging biomarkers for mTBI individual diagnosis based on the temporal expression of SCCs underlying time-resolved functional connectivity.

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Section: Methodsmentioning

confidence: 99%