Browsing by Author "Abbas, Kausar"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease
    (Wiley, 2021-08) Svaldi, Diana O.; Goñi, Joaquín; Abbas, Kausar; Amico, Enrico; Clark, David G.; Muralidharan, Charanya; Dzemidzic, Mario; West, John D.; Risacher, Shannon L.; Saykin, Andrew J.; Apostolova, Liana G.; Medicine, School of Medicine
    Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
  • Thumbnail Image
    Item
    A Structural Connectivity Disruption One Decade before the Typical Age for Dementia: A Study in Healthy Subjects with Family History of Alzheimer's Disease
    (Oxford University Press, 2021-08-27) Ramírez-Toraño, F.; Abbas, Kausar; Bruña, Ricardo; de Pedro, Silvia Marcos; Gómez-Ruiz, Natividad; Barabash, Ana; Pereda, Ernesto; Marcos, Alberto; López-Higes, Ramón; Maestu, Fernando; Goñi, Joaquín; Radiology and Imaging Sciences, School of Medicine
    The concept of the brain has shifted to a complex system where different subnetworks support the human cognitive functions. Neurodegenerative diseases would affect the interactions among these subnetworks and, the evolution of impairment and the subnetworks involved would be unique for each neurodegenerative disease. In this study, we seek for structural connectivity traits associated with the family history of Alzheimer's disease, that is, early signs of subnetworks impairment due to Alzheimer's disease. The sample in this study consisted of 123 first-degree Alzheimer's disease relatives and 61 nonrelatives. For each subject, structural connectomes were obtained using classical diffusion tensor imaging measures and different resolutions of cortical parcellation. For the whole sample, independent structural-connectome-traits were obtained under the framework of connICA. Finally, we tested the association of the structural-connectome-traits with different factors of relevance for Alzheimer's disease by means of a multiple linear regression. The analysis revealed a structural-connectome-trait obtained from fractional anisotropy associated with the family history of Alzheimer's disease. The structural-connectome-trait presents a reduced fractional anisotropy pattern in first-degree relatives in the tracts connecting posterior areas and temporal areas. The family history of Alzheimer's disease structural-connectome-trait presents a posterior-posterior and posterior-temporal pattern, supplying new evidences to the cascading network failure model.
  • Thumbnail Image
    Item
    Sub-concussive Hit Characteristics Predict Deviant Brain Metabolism in Football Athletes
    (Taylor and Francis, 2015) Poole, Victoria N.; Breedlove, Evan L.; Shenk, Trey E.; Abbas, Kausar; Robinson, Meghan E.; Leverenz, Larry J.; Nauman, Eric A.; Dydak, Ulrike; Talavage, Thomas M.; Department of Radiology and Imaging, IU School of Medicine
    Magnetic resonance spectroscopy and helmet telemetry were used to monitor the neural metabolic response to repetitive head collisions in 25 high school American football athletes. Specific hit characteristics were determined highly predictive of metabolic alterations, suggesting that sub-concussive blows can produce biochemical changes and potentially lead to neurological problems.
  • Thumbnail Image
    Item
    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).