Biology subjects

Theis, N.

Publications and source records attributed to Theis, N..

6 recordsLinked to original sources

bioRxiv2026

Dynamic network reconfigurations during task engagement following resting state in adolescent onset schizophrenia

Understanding how functional brain networks in resting state configurations reorganize to perform cognitive tasks is critical for uncovering the nature and mechanisms underlying network dysconnectivity in psychiatric disorders. We applied energy landscape analysis (ELA), a statistical physics-based computational approach, to functional MRI data from 23 adolescent-onset schizophrenia (AOS) and 44 healthy control (HC) subjects, acquired during rest followed by executive function task. ELA maps brain activity into distinct network states and quantifies how the brain transitions among them, capturing differences in stability of network states and transition complexity across conditions. AOS and HC showed markedly different condition-dependent patterns of brain state organization and dynamics. At rest, AOS exhibited reduced dynamical complexity compared to HC (7 vs. 14 stable states) that reversed during the task with more than 2-fold increase in accessible but rarely occupied brain states, while HC showed an opposite pattern. These results suggest that cognitive demands unmask latent fragmentation of the energy landscape, comprising a proliferation of accessible but rarely occupied states not apparent at rest, in AOS. State occupancy analysis revealed a small number of dominant states accounting for the majority of brain activity time, with AOS showing greater persistence in the fully-active DMN state during task performance compared to HC. These findings suggest that the rest-to-task transition features fundamentally different neural dynamics in AOS compared to HC. Combined analysis of resting fMRI and task-induced brain dynamics revealed neural factors that may contribute to cognitive dysfunction and psychiatric symptoms in schizophrenia, with important implications for development of biomarkers and treatment targets.

neuroscience↗

bioRxiv2026

Maximum entropy model reveals frequent brain state switching in psychotic disorders in a multiversal brain function analysis

To design network-based treatments, it is critical to understand dynamic changes in brain networks over time. We comprehensively examined network structure using regional activation-only, pairwise-coactivation-only, and systems-level maximum entropy models (MEM) applied to the Human Connectome Project-Early Psychosis (HCP-EP) resting fMRI data (patients=109, controls=56). The MEM integrates regional activation levels and pairwise correlation-based functional connectivity (FC), providing the potential for better characterization of clinically meaningful brain states and how they change over time. Using the HCP/Glasser atlas to define brain regions within the default mode (DMN) and dorsal attention (DAN) networks, group differences in regional activation, graph measures of FC, and MEM features such as transition rates between minima in an energy landscape along with correlations with cognitive and psychopathological measures were examined. Psychosis was associated with reduced activation in several regions and multiple FC graph metric alterations. MEM demonstrated a wider variety of DMN and DAN activation/deactivation configurations, with more frequent switching between them, more pronounced network configuration differences and higher basin transition rates with reduced basin dwell times suggesting temporally unstable network configurations, meaning the brain cannot rely on any particular configuration for cognitive processing. Activation of only three regions correlated with working memory, but none of the FC metrics did. MEM features, in particular basin transitions and total energy, correlated negatively with working memory. Our findings suggest that MEM provides unique information, specifically attenuated inter-network connectivity with reduced stability of DMN and DAN brain states, to characterize networks as targets and their features as markers for novel treatment development.

neuroscience↗

bioRxiv2025

Independence-based causal discovery analysis reveals statistically non-significant regions to be functionally significant

Background and HypothesisTraditional fMRI analyses often ignore regions that fail to reach statistical significance, assuming they are biologically unimportant. We tested the accuracy of this assumption using causal discovery based-analysis that go beyond associations/correlations to test the causality of one regions influence over the other. We hypothesized that the network of statistically significant (active network, AN) and non-significant regions (silent network, SN) causally interact and their features will causally influence psychopathology severity and working memory performance. Study DesignWe examined AN and SN during N-BACK task on 25 FHR and 37 controls. Clusters with significantly different activations were juxtaposed to 360 Glasser atlas parcellations. The PC algorithm for causal discovery was implemented. Connectivity of regions with the highest alpha-centrality (HAC) were examined. ResultsSeventy-seven Glasser regions were in the AN and the rest were silent nodes. Two regions showed HAC for FHR and HC. Among controls, one HAC region was silent (auditory association cortex) and the other one was active (insula). Among FHR, both were silent nodes (early auditory cortex). These HAC regions in both groups had bidirectional directed edges between each other forming a reciprocal circuit whose edge-weights causally "increased" magical ideation severity. ConclusionCausal connectivity between SN and AN suggests that the statistically non-significant and significant regions influence each other. Our findings question the merit of ignoring statistically non-significant regions and exclusively including statistically significant regions in the pathophysiological models. Our study suggests that causality analysis should receive greater attention.

neuroscience↗

bioRxiv2024

Subject-specific maximum entropy model of resting state fMRI shows diagnostically distinct patterns of energy state distributions

ObjectiveExisting neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters. MethodMEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions. ResultsThe model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN. ConclusionsDiagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.

neuroscience↗

bioRxiv2023

Energy in functional brain states correlates with cognition in adolescent schizophrenia and healthy persons

Adolescent-onset schizophrenia (AOS) is rare, under-studied, and associated with more severe cognitive impairments and poorer outcomes than adult-onset schizophrenia. Neuroimaging has shown altered regional activations (first-order effects) and functional connectivity (second-order effects) in AOS compared to controls. The pairwise maximum entropy model (MEM) integrates first- and second-order factors into a single quantity called energy, which is inversely related to probability of occurrence of brain activity patterns. We take a combinatorial approach to study multiple brain-wide MEMs of task-associated components; hundreds of independent MEMs for various sub-systems are fit to 7 Tesla functional MRI scans. Acquisitions were collected from 23 AOS individuals and 53 healthy controls while performing the Penn Conditional Exclusion Test (PCET) for executive function, which is known to be impaired in AOS. Accuracy of PCET performance was significantly reduced among AOS compared to controls. A majority of the models showed significant negative correlation between PCET scores and the total energy attained over the fMRI. Across all instantiations, the AOS group was associated with significantly more frequent occurrence of states of higher energy, assessed with a mixed effects model. An example MEM instance was investigated further using energy landscapes, which visualize high and low energy states on a low-dimensional plane, and trajectory analysis, which quantify the evolution of brain states throughout this landscape. Both supported patient-control differences in the energy profiles. Severity of psychopathology was correlated positively with energy. The MEMs integrated representation of energy in task-associated systems can help characterize pathophysiology of AOS, cognitive impairments, and psychopathology.

neuroscience↗

bioRxiv2021

Evaluating Network Threshold Selection for Structural and Functional Brain Connectomes

IntroductionStructural and functional brain connectomes built using macroscale data collected through magnetic resonance imaging (MRI) may contain noise that contributes to false-positive edges, which can obscure structure-function relationships with implications for data interpretation. Thresholding procedures are routinely applied in practice to optimize network density by removing low-signal edges, but there is limited consensus regarding the appropriate selection of thresholds. We compare existing methods and propose a novel alternative objective function thresholding (OFT) method. MethodsThe performance of thresholding approaches, including a percolation-based approach and an objective function-based approach, is assessed by (a) computing the normalized mutual information (NMI) of community structure between a known network and a simulated, perturbed networks to which various forms of thresholding have been applied, and (b) comparing the density and the clustering coefficient (CC) between the baseline and thresholded networks. ResultsIn our analysis, the proposed objective function-based threshold exhibits the best performance in terms of high similarity between the underlying networks and their perturbed, thresholded counterparts, as quantified by NMI and CC analysis on the simulated functional networks. DiscussionExisting network thresholding methods yield widely different results when graph metrics are computed. Thresholding based on the objective function appears to maintain a set of edges such that the resulting network shares the community structure and clustering features present in the original network. This outcome provides proof-of-principle evidence that thresholding based on the objective function could offer a useful approach to reducing the network density of functional connectivity data. Impact StatementNetwork thresholding refers to removing edges between node pairs in a functional network that have weak edge-weights that may arise from unwanted variability or noise. Since edge-weight cutoffs used to generate a binary network can be sensitive to thresholding, we introduce a novel thresholding algorithm. We find that when applied to networks derived via perturbations, namely through simulated functional connectivity of a known network, this approach yields a binary network that is more similar to the known network compared to using existing thresholding approaches. Thus, our algorithm is a competitive candidate for use in thresholding of brain connectome.

neuroscience↗