updating papers

Author: manishsaggar1

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+---
+layout: paper
+title: The control costs of human brain dynamics
+image: /images/papers/eric-cost.png
+authors: Ceballos E.G., Luppi A.I., Castrillon G., Saggar M., Misic B., Riedl V.
+year: 2024
+ref: Ceballos E.G., Luppi A.I., Castrillon G., Saggar M., Misic B., Riedl V. (2024) BioRxiv
+journal: "BioRxiv"
+doi: 10.1101/2024.01.24.577068
+github:
+pdf: /pdfs/papers/eric-cost.pdf
+---
+
+# Abstract
+The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

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+layout: paper
+title: A Data-Driven Latent Variable Approach to Validating the Research Domain Criteria (RDoC) Framework
+image: /images/papers/shaun-rdoc.png
+authors: Quah S.K.L., Jo B., Geniesse C., Uddin L.Q., Mumford J.A., Barch D.M., Fair D.A., Gotlib I.H., Poldrack, R.A., Saggar M.
+year: 2024
+ref: Quah S.K.L., Jo B., Geniesse C., Uddin L.Q., Mumford J.A., Barch D.M., Fair D.A., Gotlib I.H., Poldrack, R.A., Saggar M. (2024) BioRxiv
+journal: "BioRxiv"
+doi: 10.1101/2024.01.31.577486
+github:
+pdf: /pdfs/papers/shain-rdoc.pdf
+---
+
+# Abstract
+Despite the widespread use of the Research Domain Criteria (RDoC) framework in psychiatry and neuroscience, recent studies suggest that the RDoC is insufficiently specific, or excessively broad, relative to the underlying brain circuitry it seeks to elucidate, leading to potential misrepresentation of circuit-function relations. We used a latent variable approach to address this issue, specifically utilizing bifactor analysis. We examined a total of 84 whole-brain task-based fMRI (tfMRI) activation maps from 19 studies with a total of 6,192 participants. Within this set of 84 maps, a curated subset of 37 maps with a balanced representation of RDoC domains constituted the training set of our analysis, and the remaining held-out maps formed the internal validation set. Furthermore, we externally validated the factor solutions from our curated training dataset using an independent set of 36 coordinate maps sourced through Neurosynth. We used RDoC constructs as seed terms for Neurosynth topic meta-analysis. We hypothesized that if boundaries of RDoC domains warrant refinement, this would be indicated by the presence of overlapping domains or domains lacking specificity. Our findings suggest that a bifactor data-driven structure fits better with the current corpus of tfMRI data, with a general domain representing task-general patterns of brain activation. The data-driven model also proposes a different group of major domains, particularly splitting the RDoC cognitive systems domain into distinct domains. Data-driven models are useful for revising the posited circuit-function relations outlined in the current RDoC framework.