Srihari, V. H. | Search bioRxiv

bioRxiv2025

Transdiagnostic connectome-based predictive modeling of many behavioral phenotypes reveals brain network mediators of clinical-cognitive relationships

A key assumption of the NIMHs RDoC framework is that disordered circuits in the brain should manifest in observable behaviors, including psychiatric symptomatology and cognitive deficits. However, how disordered circuitry impacts multiple behaviors remains poorly understood. Connectome-based predictive modeling (CPM) applied to functional MRI connectivity data can identify networks associated with specific behavioral measures across individuals. Prediction strength reflects how closely a measure relates to network connectivity, while derived networks provide evidence of where an individuals disordered circuits are located. Using CPM, we predicted a broad range of self-reported clinical and objective cognitive measures in a large, transdiagnostic sample with extensive fMRI data (n = 317). Prediction performance varied substantially across instruments, with objective cognitive tests yielding stronger models than self-reported clinical measures (p < 0.001). To test whether circuits underlying cognitive deficits related to symptomatology reside in regions where networks overlap, we examined the prediction strength of these sparsely shared circuits. Their connectivity strongly predicted cognitive performance and were primarily localized within the frontoparietal network and between the frontoparietal and default mode networks. These findings reveal how much various behavioral measures reflect brain networks and how circuits within the shared network space contribute to cognitive deficits associated with symptomatology.

neuroscience↗

bioRxiv2024

Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms Are Reflecting

BackgroundSubstantial strides have been made in developing connectome-based predictive models that establish connections between external measures of cognition and/or symptoms obtained through testing performed in a clinical setting, and the human functional connectome. Often referred to as brain-behavior modeling such models offer insights into the functional brain organization supporting the test scores of these external measures1-7. Here, we depart from the conventional feed-forward approach and introduce a feed-back approach that provides new insight into the systems the external measures are reflecting and provides a framework for developing new test instruments that better target specific brain systems. MethodsIn fMRI data from 227 demographically and clinically diverse subjects (healthy participants and patients), we a priori define connectivity networks for the six cognitive constructs and employ kernel ridge regression in a predictive modeling framework to quantify each networks contribution to performance across a spectrum of standardized tests. ResultsThis approach provides a ranking of test scores according to the predictive power of each cognitive network, allowing one to choose the best test to probe a specific brain network. It yields a brain-driven process for forming new tests through selection of combinations of measures that probe the same brain systems. These new composite tests yield better external measures, as reflected by higher predictive power in brain-behavior modeling. We also evaluate the inclusion of specific subtests within a composite score, revealing instances where composite scores are reinforced or weakened by subtest inclusion regarding the specificity of the brain network they interrogate. ConclusionsThe brain-behavior modeling problem can be reconfigured to provide a biologically driven approach to the selection of external measures directed at specific brain systems. It opens new avenues of research by providing a framework for the development of measures, both cognitive and clinical, guided by quantitative brain metrics.

neuroscience↗