Department of Neurology

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Browsing Department of Neurology by Author "Abbas, Kausar"

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    A morphospace of functional configuration to assess configural breadth based on brain functional networks
    (MIT Press, 2021-09) Duong-Tran, Duy; Abbas, Kausar; Amico, Enrico; Corominas-Murtra, Bernat; Dzemidzic, Mario; Kareken, David; Ventresca, Mario; Goñi, Joaquín; Neurology, School of Medicine
    The quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.
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    Tangent functional connectomes uncover more unique phenotypic traits
    (Elsevier, 2023-08-12) Abbas, Kausar; Liu, Mintao; Wang, Michael; Duong-Tran, Duy; Tipnis, Uttara; Amico, Enrico; Kaplan, Alan D.; Dzemidzic, Mario; Kareken, David; Ances, Beau M.; Harezlak, Jaroslaw; Goñi, Joaquín; Neurology, School of Medicine
    Functional connectomes (FCs) containing pairwise estimations of functional couplings between pairs of brain regions are commonly represented by correlation matrices. As symmetric positive definite matrices, FCs can be transformed via tangent space projections, resulting into tangent-FCs. Tangent-FCs have led to more accurate models predicting brain conditions or aging. Motivated by the fact that tangent-FCs seem to be better biomarkers than FCs, we hypothesized that tangent-FCs have also a higher fingerprint. We explored the effects of six factors: fMRI condition, scan length, parcellation granularity, reference matrix, main-diagonal regularization, and distance metric. Our results showed that identification rates are systematically higher when using tangent-FCs across the “fingerprint gradient” (here including test-retest, monozygotic and dizygotic twins). Highest identification rates were achieved when minimally (0.01) regularizing FCs while performing tangent space projection using Riemann reference matrix and using correlation distance to compare the resulting tangent-FCs. Such configuration was validated in a second dataset (resting-state).