Biology subjects

Mueller, V. I.

Publications and source records attributed to Mueller, V. I..

4 recordsLinked to original sources

bioRxiv2025

Disentangling Brain-Psychopathology Associations: A Systematic Evaluation of Transdiagnostic Bifactor Models

Identifying robust brain-psychopathology associations with neuroimaging remains difficult, in part due to substantial heterogeneity within and comorbidity between diagnostic categories. Transdiagnostic latent factor models aim to address this structure by separating shared and unique symptom variance. However, it remains unclear whether latent factor modeling translates into stronger and more interpretable brain-psychopathology associations within contemporary whole-brain prediction frameworks. Using two large developmental cohorts, we systematically compared transdiagnostic bifactor models, correlated factor models, and typical summary scores derived from the Child Behaviour Checklist (CBCL) in their reliability and multivariate associations with whole-brain structure (MRI) and function (resting-state fMRI). General psychopathology, internalising, externalising, and attention dimensions could be significantly predicted from resting-state connectivity but not cortical thickness. We found no consistent evidence that latent factors (bifactor or correlated factor models) strengthened reliability or brain-psychopathology associations, relative to summary scores. Neural signatures were also highly consistent across all scoring methods, with triple network (DMN, FPN, CO) involvement in general, internalizing, and externalising psychopathology. Bifactor scores did, however, display more distinct neural signatures between general, internalising, and externalising dimensions than did summary or correlated factor scores. Collectively, results suggest that additional phenotypic modelling of psychopathology alone does not systematically strengthen the predictive utility of neuroimaging, possibly reflecting fundamental limits on the amount of explainable symptom variance by brain features. While latent factor models may aid in distinguishing neural correlates between constructs, improving phenotypic assessment may be necessary for improvements to brain-psychopathology association strength.

neuroscience↗

bioRxiv2025

Unveiling the Multifaceted Networks of the Left DLPFC for Precision TMS Targeting

The left dorsolateral prefrontal cortex (lDLPFC) is the standard transcranial magnetic stimulation (TMS) target for treatment-resistant depression (TRD), yet non-response rates remain high. TMS efficacy has been linked to the stimulation sites functional connectivity, particularly its anti-correlation with the subgenual cingulate cortex (SGC). While this pragmatic strategy has demonstrated clinical utility, it offers limited insight into how the lDLPFCs network interactions contribute to site-dependent variability in treatment response. Here, we used connectivity-based parcellation within a region of interest encompassing common TMS targets in the left prefrontal cortex (TMS-PFC) to delineate functionally distinct subregions and characterize their large-scale network connectivity and behavioral associations. Our results revealed a hierarchical organization: a coarse two-pole antagonism between anterior-central and superior-posterior subregions and a finer nine-cluster architecture exposing the heterogeneity along anterior-posterior and ventral-dorsal axes within TMS-PFC. Anterior-central areas were strongly anti-correlated with SGC and default-mode network, positively connected with salience, dorsal attention, and control networks, and associated with cognitive control. In contrast, superior-posterior subregions displayed the inverse pattern, while ventral clusters engaged somatomotor and visual networks, and language-related processes. Central and superior-anterior clusters showed differentiated profiles, including associations with inhibition, social cognition, and perceptual functions. To aid clinical translation, we derived a likelihood map integrating granularities, highlighting the anterior-central subregion as the strongest TMS candidate given its connectivity and behavioral relevance in depression, while indicating that neighboring subregions have distinct functions. These findings underscore the hierarchical and heterogeneous organization of TMS-PFC and provide a network-informed reference for developing individualized, symptom-specific TMS interventions. HighlightsO_LITMS targets within the left PFC encompass nine functionally distinct subregions C_LIO_LISubregions differ in SGC, DMN, salience, attention, and control network connectivity C_LIO_LIAnterior-central and superior-posterior subregions show antagonistic profiles C_LIO_LIA likelihood map identifies the anterior-central subregion as optimal TMS target C_LIO_LIFindings provide a network-informed reference for symptom-specific TMS targeting C_LI

neuroscience↗

bioRxiv2023

The Burden of Reliability: How Measurement Noise Limits Brain-Behaviour Predictions

Major efforts in human neuroimaging strive to understand individual differences and find biomarkers for clinical applications by predicting behavioural phenotypes from brain imaging data. An essential prerequisite for identifying generalizable and replicable brain-behaviour prediction models is sufficient measurement reliability. However, the selection of prediction targets is predominantly guided by scientific interest or data availability rather than reliability considerations. Here we demonstrate the impact of low phenotypic reliability on out-of-sample prediction performance. Using simulated and empirical data from the Human Connectome Projects, we found that reliability levels common across many phenotypes can markedly limit the ability to link brain and behaviour. Next, using 5000 subjects from the UK Biobank, we show that only highly reliable data can fully benefit from increasing sample sizes from hundreds to thousands of participants. Overall, our findings highlight the importance of measurement reliability for identifying brain-behaviour associations from individual differences.

neuroscience↗

bioRxiv2020

Connectivity patterns of task-specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to molecular architecture

BackgroundDespite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. MethodsWe investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior molecular imaging in healthy populations. ResultsTen-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. ConclusionsWe revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the molecular architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.

neuroscience↗