Transdiagnostic connectome signatures from resting-state fMRI predict individual-level intellectual capacity

Abstract

Medication and other therapies for psychiatric disorders show unsatisfying efficacy, in part due to the significant clinical/ biological heterogeneity within each disorder and our over-reliance on categorical clinical diagnoses. Alternatively, dimensional transdiagnostic studies have provided a promising pathway toward realizing personalized medicine and improved treatment outcomes. One factor that may influence response to psychiatric treatments is cognitive function, which is reflected in one’s intellectual capacity. Intellectual capacity is also reflected in the organization and structure of intrinsic brain networks. Using a large transdiagnostic cohort (n = 1721), we sought to discover neuroimaging biomarkers by developing a resting-state functional connectome-based prediction model for a key intellectual capacity measure, Full-Scale Intelligence Quotient (FSIQ), across the diagnostic spectrum. Our cross-validated model yielded an excellent prediction accuracy (r = 0.5573, p < 0.001). The robustness and generalizability of our model was further validated on three independent cohorts (n = 2641). We identified key transdiagnostic connectome signatures underlying FSIQ capacity involving the dorsal-attention, frontoparietal and default-mode networks. Meanwhile, diagnosis groups showed disorder-specific biomarker patterns. Our findings advance the neurobiological understanding of cognitive functioning across traditional diagnostic categories and provide a new avenue for neuropathological classification of psychiatric disorders.

Oxytocin enhances the triangular association among behavior, resting‐state, and task‐state functional connectivity

“…It predicts individual variability in behavior or psychiatric symptoms by extracting and summarizing the most relevant features from FC using full cross‐validation (Shen et al, 2017 ). Many prior studies have demonstrated the robustness of CPM (Dadi et al, 2019 ; Yoo et al, 2019 ) in predicting individual differences in fluid intelligence (Tong et al, 2022 ), attention (Yoo et al, 2018 ), creative ability (Beaty et al, 2018 ), cheating behavior (Pang et al, 2022 ). While the resting state still dominates BWAS, data from tasks have empirically demonstrated benefits (Finn, 2021 ).…”

Reliable and generalizable brain-based predictions of cognitive functioning across common psychiatric illness

“…Here we demonstrate that the meta-matching model generalizes not only across diagnostic categories, but also between independent datasets relying on different measures of cognition, neuroimaging protocols, and data processing strategies. Usually, models trained in one dataset lose much of their predictive capacity when applied to an independent dataset, even when the two datasets are diagnostically or demographically similar 2,38,[41][42][43] . The meta-matching approach likely achieves this high level of generalizability by exploiting correlations amongst phenotypes, relying on a common set of neurobiological features which predict a broad range of behaviors that underlie an individual's global cognitive performance, independent of diagnosis or measurement methods.…”

“…This requirement far exceeds the vast majority of samples available to psychiatric research groups, calling into question both the utility and feasibility of developing clinically focused predictive models. Moreover, even brain-cognition predictive models derived from consortia-level samples can fail to generalize or show substantially reduced accuracy when applied to different datasets 2,38,[41][42][43] , greatly diminishing the scope of their potential applications. In large population-based cohorts, the functioning of specific brain systems can be leveraged to predict a broad variety of phenotypes, ranging from demographic factors to physical and mental health-related variables [44][45][46][47] .…”

Can Intelligence Affect Alcohol-, Smoking-, and Physical Activity-Related Behaviors? A Mendelian Randomization Study

“…Rather, it reflects a broader and deeper capability for comprehending our surroundings—‘catching on’, ‘making sense’ of things, or ‘figuring out’ what to do” ( Gottfredson 1997 ). In the past decades, a number of strategies have been developed to measure intelligence ( Deary 2001 ; Deary et al 2010 ), including mental tests and some emerging imaging methods such as fMRI ( Colom and Thompson 2011 ; Saxe et al 2018 ; Tong et al 2022 ). Numerous studies and their following replications have collectively suggested the effectiveness of intelligence measurement, which enables subsequent studies on the associations between intelligence and various health outcomes.…”