Alex Fornito

The Hierarchical Taxonomy of Psychopathology (HiTOP) model is an empirically based dimensional alternative to current consensus based diagnostic classification systems. It was developed through the synthesis of decades of empirical studies, but its construct and structural validity have never been comprehensively evaluated within a single sample using a confirmatory modeling approach. We assessed 108 psychopathology phenotypes, incorporating 37 novel components, in 542 monozygotic and 155 dizygotic twin pairs with diverse mental health histories to validate the hierarchical dimensional structure of psychopathology and quantify its genetic and environmental influences. Analyses largely replicated the theorized dimensional structure at lower, but not upper, tiers of the model. Heritability was lower and more variable at lower (median h2 = 43.5) than upper (median h2 = 57.1) tiers. Our findings provide partial construct and structural validation for the HiTOP model while identifying novel psychopathological phenotypes and heritability estimates to guide future biological studies.

Working memory is a fundamental process that underlies cognition. Accordingly, the neural mechanisms that support working memory performance are of great interest in cognitive neuroscience. We present a publicly accessible dataset comprising 123 healthy adults (mean age = 28.58 years, SD = 7.56; age range = 18–46 years; 75 females) to facilitate investigation of the neural mechanisms underlying working memory performance in humans. Across two days of assessment, participants underwent a battery of cognitive tasks assessing working memory and other cognitive domains (day 1), followed by 62-channel EEG recordings (day 2). EEG data were acquired at rest, during a visual working memory task, and following single-pulse transcranial magnetic stimulation (TMS-EEG) targeting key brain regions involved in working memory. This article provides a detailed overview of the study design, methodology, and data characteristics.

The cerebral cortex is organized along a dominant sensorimotor-to-association (S-A) axis, anchored by modality-specific primary sensorimotor areas at one end and transmodal association areas forming distributed networks that support abstract cognition at the other^1-11. The developmental mechanisms shaping this axis remain unclear^9-24. Here we present converging multispecies evidence supporting the multinodal induction-exclusion in network development (MIND) model, in which S-A patterning is governed by competing processes of induction and exclusion driven by two opposing transcriptomically defined programs. 'Pericentral' programs are induced around the frontotemporal poles, progress inwards toward the central regions of the undifferentiated neocortex and define higher-order association features. 'Central' programs are induced centrally through first-order sensorimotor thalamocortical inputs, establish primary areas and exclude pericentral programs. These conserved programs compete for space, resulting in compartmentalized expression of axon guidance, cell-cell adhesion, retinoic acid signalling, synaptogenesis, WNT signalling and autism-risk-associated genes. Notably, PLXNC1 and SEMA7A, a receptor-ligand pair representing pericentral and central programs, respectively, exhibit repulsive interactions between primary and higher-order association corticocortical axons. Induction and exclusion together establish an S-A organization in which primary areas emerge as focal islands within a broader ocean of distributed association networks. The MIND model provides a unifying framework for experimental, evolutionary and clinical phenomena, revealing induction and exclusion as antagonistic yet complementary principles shaping the S-A axis and processing hierarchies.

Traditional diagnostic systems (i.e., the DSM and ICD) present challenges in characterizing psychopathology following traumatic brain injury (TBI). To address this, we recently developed the Hierarchical Taxonomy of Psychopathology Following TBI (HiTOP-TBI) model, an empirical taxonomy that organized 57 psychopathology symptom/trait components into hierarchical dimensions based on latent variable modeling. However, the applied measurement of these dimensions and their relationship with external factors of potential relevance to post-TBI psychopathology remain unclear. To facilitate application of the model, we compared methods for quantifying ten HiTOP-TBI dimensions and examined their associations with sociodemographics, injury factors, and clinical outcomes. Data were analyzed from 410 adults with moderate-severe TBI (Mage=48.81, range=19.42–79.43), who completed the HiTOP-TBI assessment battery between 0.40 and 35.42 years post injury (M=12.77, SD=9.10). We assessed whether simpler scoring methods (i.e., factor score estimates, weighted, and unweighted sum-scores) adequately approximated the more complex latent-variable representations of the HiTOP-TBI dimensions. We then examined associations with sociodemographics, injury factors, and clinical outcomes, assessing comparability in results across the four scoring methods. To assess the utility of different levels within the HiTOP-TBI hierarchy, we compared the performance of models representing the general psychopathology, internalizing and externalizing spectra, and seven lower-order factors in predicting sociodemographics, injury factors, and clinical outcomes. Model performance was evaluated by balancing explanatory power against parsimony using the Bayesian Information Criterion (BIC). We found that unweighted sum-scores closely approximated more complex representations of the HiTOP-TBI dimensions (all rs ≥.89), supporting the utility of the simplest scoring method in practice. Across all three levels of the hierarchy, higher HiTOP-TBI scores, reflecting more severe psychopathology, were generally associated with younger age, male sex, lower education, unemployment, being single, and a pre-injury history of mental health treatment, while injury factors showed minimal associations. Additionally, higher scores on nearly all HiTOP-TBI dimensions were associated with greater functional disability, lower life satisfaction, and an increased likelihood of receiving mental health treatment. Standardized effect sizes varied by .02 on average across the four scoring methods (range=.00–.14). Models based on the General Problems factor (the highest-order level of the hierarchy) provided the most parsimonious account of many factors. However, second- and lower-order dimensions showed improved performance for sex, age, functional disability, and life satisfaction, indicating that greater hierarchical specificity may be informative for selected correlates.

Attention-deficit/hyperactivity disorder (ADHD) is thought to result from aberrant neurodevelopmental wiring of connectome architecture, yet the precise mechanisms underpinning this miswiring remain unclear. Here, we used a generative mathematical model to understand how physical and geometric constraints on brain network development might be altered in the disorder. Leveraging multisite diffusion magnetic resonance imaging (dMRI) datasets comprising 713 ADHD children and 304 neurotypical controls (aged 6-18 years), we fitted the recently developed geometric eigenmode model (GEM) to individual data describing connectivity between each pair of cortical regions, as quantified using probabilistic tractography. Replicating past work, the GEM was able to recover both topological and topographical properties of the empirical connectomes, but reconstruction accuracy was lower in ADHD children, suggesting a relative untethering of connectome architecture from geometric constraints. This untethering was particularly salient for long-range connections, which were disproportionately affected in the ADHD cohort. Regional variations in the degree to which connectivity was untethered from geometry were correlated with spatial maps of cortical developmental expansion, myelination, and transcriptomic organization, consistent with a neurodevelopmental basis for geometric untethering that is spatially patterned along the cortical hierarchy. Together, these findings suggest that aberrant neurodevelopment in ADHD may untether connectome development from typical geometric and physical constraints, resulting in subtle disruptions of long-range inter-regional connectivity.

Numerous methods exist for quantifying statistical dependencies, termed “functional coupling” (FC), between regional brain activity recorded with resting-state functional magnetic resonance imaging (rs-fMRI). However, their efficacy in mitigating the effects of known sources of noise, such as those induced by participant head motion, and in augmenting effect sizes for brain-wide association studies (BWAS), remains unclear. Here we compared 10 different measures of FC, including correlations, partial correlations, coherence, mutual information, and partial information decomposition, and one measure of effective connectivity (EC; regression dynamic causal modeling), across two independent datasets comprising a total of 1,797 participants (867 males). Each method was evaluated for its ability to mitigate motion-related confounds in FC/EC estimates (assessed via framewise-displacement – edgewise FC correlations) and for its utility in predicting 94 behavioral measures (assessed via cross-validated kernel ridge regression). Our analyses showed that EC was most resistant to motion artifacts but had the weakest behavioral predictions. Conversely, traditional correlation-based methods showed the highest sensitivity to motion, but offered the strongest behavioral prediction across most domains and datasets. Nonetheless, relative differences in predictive accuracies were small, indicating that the use of different FC or EC metrics in rs-fMRI does not significantly impact BWAS effect sizes.