Biology subjects

Fox, P. T.

Publications and source records attributed to Fox, P. T..

5 recordsLinked to original sources

bioRxiv2026

Hidden Markov Modeling Reveals Altered Functional Network Dynamics in Multiple Sclerosis

Purpose: Multiple sclerosis (MS) is a neurodegenerative and demyelinating disease of the central nervous system. Recent evidence suggests that localized gray matter atrophy in MS may be involved in distributed functional brain networks. However, the dynamic trajectory of functional network alterations in MS remains unclear. The current study extends prior network-mediated findings and aims to investigate the dynamic functional connectivity (dFC) and energetic landscape across an atrophy-prone network in MS. Methods: Twenty MS participants and twenty healthy controls (HC) underwent resting-state fMRI. The atrophy-based functional network (AFN) model was implemented, and time series from atrophy-prone regions were fit to a 12-state hidden Markov model. Each functional network state, a recurring pattern of activity across the AFN, was characterized by the AFN regions with the strongest activation. The states were then grouped into two metastates. Dynamic functional network metrics were compared between groups by permutation testing and correlated with disability and cognition. The energetic landscape of the network was mapped with a pairwise maximum-entropy model. Temporal irreversibility was also measured, which defines the arrow-of-time asymmetry of network dynamics. Results: People with MS showed lower occupancy of state 1, which peaked on the right middle frontal and anterior cingulate cortex. Occupancy and mean lifetime were higher in states 3 and 10 (p < 0.05). In state 3, cortical activity was below the network average, and state 10 peaked on the postcentral gyrus. At the metastate level, MS participants spent more time in metastate M1 and less in M2 (p = 0.04). Within the MS group, a longer interval between visits to state 3 was associated with greater disability (r = 0.57), and a higher switching rate in state 10 with poorer verbal fluency (r = -0.50). The energetic landscape was similar between groups. Temporal irreversibility was lower in MS, mainly across subcortical connections. Conclusion: MS participants dwelled longer in specific dFC states of the AFN, while the rate of transition between states did not differ from healthy controls. Dynamic functional network metrics were associated with disability and cognitive performance and may serve as functional correlates of clinical impairment. The states were similar between groups, whereas the directed drive between states was lower across subcortical connections. This pattern suggests altered network dynamics rather than altered network architecture in MS. Findings from this study characterize the functional network dynamics of neurodegeneration in MS, which may inform future development of network-based approaches for monitoring disease progression.

neuroscience↗

bioRxiv2025

Advancing Fair and Explainable Machine Learning for Neuroimaging Dementia Pattern Classification in Multi-Ethnic Populations

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches frequently demonstrate inconsistent precision and impartiality, particularly among diverse cultural groups. This study investigates performance discrepancies in dementia classification among White American, African American, and Hispanic populations. We reveal significant cross-group bias, particularly when models trained on one group are tested on another. To address this, we introduce a novel combination of few-shot learning and domain alignment to improve model adaptability across underrepresented populations. Our results show that these techniques substantially reduce inter-group performance gaps, especially between White American and Hispanic cohorts. This finding highlights the crucial need for fairness-aware strategies and the inclusion of diverse populations in training data to ensure accurate and equitable dementia diagnoses.

neuroscience↗

bioRxiv2024

Bias-accounting meta-analyses overcome cerebellar neglect to refine the cerebellar behavioral topography

The cerebellum plays important roles in motor, cognitive, and emotional behaviors. Previous cerebellar coordinate-based meta-analyses (CBMAs) have complemented precision-mapping and parcellation approaches by finding generalizable cerebellar activations across the largest possible set of behaviors. However, cerebellar CBMAs face challenges due to inherent methodological limitations exacerbated by historical cerebellar neglect in neuroimaging. Here, we show overrepresentation of superior activations, rendering the null hypothesis of standard activation likelihood estimation (ALE) unsuitable. Our new method, cerebellum-specific ALE (C-SALE), finds behavioral convergence beyond baseline activation rates. It does this by testing experimental activations versus null models sampled from a data-driven probability distribution of finding activations at any cerebellar location. Task-specific mappings in the BrainMap meta-analytic database illustrated improved specificity of the new method. Multiple (sub)domains reached convergence in specific cerebellar subregions, supporting dual motor representations and placing cognition in posterior-lateral regions. We show our method and findings were replicable within NeuroSynth. Across both databases, 54/138 task domains or behavioral terms, including sustained attention, somesthesis, inference, anticipation and rhythm, reached convergence in specific cerebellar subgregions. Maps largely corresponded with cerebellar atlases but also showed many complementary mappings. Repeated subsampling showed that motor behaviors, and to a lesser extent language and working memory, mapped to especially consistent cerebellar subregions. Lastly, we found that cerebellar clusters were parts of brain-wide coactivation networks with cortical and subcortical regions implied in these behaviors. Together, our method further complements and expands understanding of cerebellar involvement in human behavior, highlighting regions for future investigation in both basic and clinical applications. Highlights[bullet] Biases in reported cerebellar activations strongly favors superior regions. [bullet]A new method of meta-analysis increases cerebellar mapping specificity and accuracy. [bullet]Large-scale meta-analyses support cerebellar roles in cognitive, affective, and motor behaviors. [bullet]54 task domains/ terms converged, including sustained attention, somesthesis, inference, anticipation and rhythm. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/621398v3_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@716ea0org.highwire.dtl.DTLVardef@11601fforg.highwire.dtl.DTLVardef@1dd1208org.highwire.dtl.DTLVardef@caf535_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

neuroscience↗

bioRxiv2017

Estimating the number of missing experiments in a neuroimaging meta-analysis

Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple fMRI experiments with the goal of obtaining results that are more likely to generalise. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publications bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modelling approach that allows us to estimate the prevalence of non-significant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported.

neuroscience↗

bioRxiv2017

Beyond Consensus: Embracing Heterogeneity in Neuroimaging Meta-Analysis

Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).

neuroscience↗