Biology subjects

Spaniel, F.

Publications and source records attributed to Spaniel, F..

8 recordsLinked to original sources

bioRxiv2026

Sex-stratified fixel-based analysis reveals reduced frontal fibre density in males with first-episode of psychosis

Background: Schizophrenia has been conceptualised as a disorder of brain disconnectivity, with white matter abnormalities contributing to altered neural communication. However, fibre-specific white matter alterations in early illness and their potential sex-related differences remain insufficiently characterised. We applied fixel-based analysis to investigate sex-stratified white matter microstructural alterations in first-episode schizophrenia (FES). Methods: Diffusion MRI data from individuals with FES and healthy controls were stratified by biological sex. The male cohort included 79 patients and 29 controls, and the female cohort included 48 patients and 46 controls. Fixel-based analysis was performed separately in males and females to assess fibre density, fibre-bundle cross-section, and their combined measure. Head motion was included as a covariate in the fixel-based analyses. A descriptive follow-up voxel-scale analysis extracted mean fibre density from a binary mask derived from significant male fixels and applied the same region to the female cohort. Results: Males with first-episode schizophrenia showed significantly reduced fibre density compared with male controls, primarily in bilateral frontal white matter and a thin segment of the anterior corpus callosum. The voxel-scale follow-up confirmed lower mean fibre density in males within the significant fixel-derived mask. In contrast, females showed no significant group differences in any fixel-based metric or within the male-derived region. Conclusions: Early white matter microstructural alterations were detected in males with FES but not in females. These findings highlight a differential pattern emerging from sex-stratified analyses and support further investigation of sex-related differences in white matter alterations during the early stages of schizophrenia.

neuroscience↗

bioRxiv2026

Individualized surface parcellation enhances characterization of resting-state brain dynamics and their alterations in schizophrenia

Resting-state functional MRI (rs-fMRI) studies in schizophrenia commonly rely on normalization to volumetric templates and fixed atlas parcellations derived from neurotypical populations. While these approaches enable group-level comparisons, they may obscure individual variation in cortical organization and intrinsic brain dynamics. In this study, we compared four preprocessing and parcellation strategies across two independent schizophrenia cohorts (MRI site 1, n=159; MRI site 2, n=255) to evaluate how analytic choices affect static functional connectivity and dynamic quasi-periodic pattern (QPP) measures, including default mode-dorsal attention network opposition, QPP component rank, explained variance, event rate, and associations with PANSS symptom severity. Across datasets, individualized surface-based parcellation (IndiPar) consistently detected more pronounced QPP dynamics, stronger default mode / dorsal attention network opposition, and greater explained variance of QPPs relative to atlas-based pipelines. IndiPar also produced larger and more reproducible patient-control differences in functional connectivity and QPP event-rate measures, suggesting improved sensitivity through preservation of subject-specific organization. IndiPar additionally detected a significantly increased QPP event rate and more symptom associations in patients in the larger dataset. However, associations between fMRI measures and symptom severity showed limited stability across cohorts. These findings extend previous reports of altered resting-state activity in schizophrenia, and demonstrate that preprocessing and parcellation choices substantially influence both static and dynamic rs-fMRI results. Individualized surface-based parcellation appears to better preserve subject-specific variability and improves detection of intrinsic brain dynamics. At the same time, the limited cross-dataset replication of symptom associations highlights the challenges of deriving stable brain-symptom relationships from heterogeneous psychiatric cohorts.

neuroscience↗

bioRxiv2026

Domain Adaptation Enables Cross-site Classification of First-episode Schizophrenia from Multimodal Neuroimaging Data

Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalise across scanning sites. In this study, we first establish best performing within-site models, and then systematically investigate cross-site generalisation in first-episode schizophrenia (FES) classification and evaluate strategies for mitigating site-related distribution shifts. Using data from two acquisition sites (n = 389 in total), we perform train-on-site/test-on-site experiments to analyze performance degradation under domain shift and examine the effectiveness of ComBat, optimal transport, and adversarial adaptation strategies. Across functional, structural, and diffusion-based features, both traditional machine learning (TML) and neural network (NN) models achieve comparable performance in within-site classification, with resting state fMRI functional connectivity providing the most robust unimodal features. When models are transferred across sites, performance degrades substantially across all approaches, highlighting the impact of site-related variability. Distribution-alignment methods partially mitigate this degradation, with ComBat and optimal transport yielding more consistent cross-site improvements than adversarial adaptation. Increasing model complexity alone does not result in systematic performance gains, and simple models combined with effective alignment strategies often perform comparably to more complex neural architectures, while multimodal feature fusion does not consistently outperform functional connectivity alone. Overall, our findings indicate that controlling for site effects is more critical than model complexity for achieving generalisable classification in FES, underscoring the importance of rigorous evaluation designs and explicit distribution-alignment strategies for neuroimaging-based predictive models with potential clinical utility.

neuroscience↗

bioRxiv2025

Altered Sense of Agency in First-Episode Schizophrenia Patients: A Comparative Study Across Two Sites

BackgroundDisturbances in the sense of agency are a key marker of schizophrenia and are closely linked to core symptoms such as hallucinations and delusions of influence. Neuroimaging studies have implicated cortical midline structures and the defaul t mode network (DMN) associated with self-related processing, but replication across sites and larger samples is needed. MethodsWe examined neural correlates of self versus other-agency judgments in two independent cohorts of first-episode schizophrenia patients (n = 177) and controls (n = 123) recruited at separate MRI centers. During fMRI, participants performed an agency-related task. Data was analyzed using independent component analysis and statistical parametric mapping, focusing on networks associated with self-referential processing. ResultsAcross both sites, self-agency consistently engaged the anterior and posterior cingulate cortex and the precuneus, key regions of the DMN. Schizophrenia patients exhibited significantly reduced DMN activation duri ng self-referential processing compared to controls. While site-related differences were noted in the magnitude of activation, the core pattern of DMN disruption was robust across analytic approaches and datasets. ConclusionsThis study replicates and extends prior findings of altered self-agency processing in schizophrenia, demonstrating consistent DMN hypo-activation in first-episode patients across two independent cohorts. These results validate the agency paradigm as a reliable probe of self-related neural mechanisms and support models of schizophrenia as a disorder of altered self-perception.

neuroscience↗

bioRxiv2025

Impact of Connectivity Granularity: A Comparison of ROI and Network-Level Approaches for Early Schizophrenia Classification

While schizophrenia diagnosis relies on clinical interviews, there is growing interest in neuroimaging-based computational tools to aid classification. In particular, resting-state fMRI-derived functional connectivity has been explored as a potential biomarker, with applications not only in supporting clinical assessment but also in research contexts such as patient stratification and probing disease mechanisms. Here, we compare two common approaches to computing functional connectivity - region of interest (ROI)-level and brain network-level - and evaluate their predictive power for classifying first-episode schizophrenia patients, in contrast to most prior work focusing on chronic patients. We show that ROI-level features consistently outperform network-level features. Despite the simplicity of our classification models, we achieved accuracies up to 83.15% using the AAL90 atlas. We also found that non-lagged functional connectivity generally outperforms lagged variants, suggesting that added temporal complexity may introduce noise rather than improve predictive power. Overall, our findings highlight region-based connectivity from a medium-resolution atlas as a promising representation for early-stage schizophrenia classification, while emphasising the need for validation on independent datasets to confirm generalisability.

neuroscience↗

bioRxiv2025

Characterising Structural Brain Connectivity of Patients with First Episode of Psychosis

BACKGROUND AND HYPOTHESISSchizophrenia is associated with widespread neuroanatomical abnormalities affecting both grey matter and white matter (WM). Early symptoms are often linked to dysfunctions in the frontal cortex and the temporal lobe. This study investigates WM disruptions and explores how structural connectivity (SC) may contribute to the underlying mechanisms of the disorder. STUDY DESIGNWe analysed SC derived from diffusion MRI in 127 patients experiencing their first episode of schizophrenia (FES group), compared with healthy controls. Focusing on the fronto-parietal-temporal network, we examined SC across three hierarchical levels: network, node, and connection. SC metrics were compared between groups using 3-factor ANCOVA, accounting for relevant covariates. We also investigated associations between SC metrics and core positive symptoms using non-parametric correlations. RESULTSThe FES group showed significantly reduced average SC strength and global efficiency within the fronto-parietal-temporal network. At the nodal level, SC strength was significantly lower in the left inferior and middle temporal gyri (L.ITG, L.MTG), and in the right inferior parietal gyrus (R.IPG) and temporal pole (R.TP). No significant group differences emerged at the connection level. Notably, SC strength in the R.IPG was negatively correlated with Conceptual Disorganisation scores. CONCLUSIONSOur findings reveal global and regional SC disruptions in early psychosis, particularly in areas supporting cognitive, language, and executive functions. The observed association between R.IPG connectivity and Conceptual Disorganisation supports the link between disrupted SC and formal thought disorder, reinforcing the role of impaired structural integration in early psychosis.

neuroscience↗

bioRxiv2024

Resting-state hyper- and hypo-connectivity in early schizophrenia: which tip of the iceberg should we focus on?

In this study, we explore the intricate landscape of brain connectivity in the early stages of schizophrenia, focusing on the patterns of hyper- and hypoconnectivity. Despite existing literatures support for altered functional connectivity (FC) in schizophrenia, inconsistencies and controversies persist regarding specific dysconnections. Leveraging a large sample of 100 first-episode schizophrenia patients (42 females/58 males) and 90 healthy controls (50 females/40 males), we compare the functional connectivity across 90 brain regions of the Automated Anatomical Labeling atlas. We inspected the effects of medication and examined the association between FC changes and duration of untreated psychosis, duration of antipsychotic treatment, as well as symptom severity of the disorder. Our approach also includes a comparative analysis of three denoising strategies for functional magnetic resonance imaging data. In patients, 15 region pairs exhibited increased FC, whereas 150 pairs showed reduced FC relative to controls. Despite this numerical asymmetry, the overall distribution of FC changes was relatively balanced: the median FC was not systematically shifted, indicating no global tendency toward either hyper- or hypoconnectivity. Notably, seveFC alterations were significantly associated with variability in symptom severity and antipsychotic medication across patients. Taken together, these results suggest a pattern of localized dysconnections embedded within an otherwise globally balanced change in connectivity profile in early schizophrenia. Importantly, this balance was substantially disrupted towards dominant observation of hypoconnectivity when less stringent denoising strategies were applied, with results increasingly dominated by hypoconnectivity, pointing to data preprocessing as a critical source of variability across studies.

neuroscience↗

bioRxiv2023

Using normative models pre-trained on cross-sectional data to evaluate longitudinal changes in neuroimaging data

Longitudinal neuroimaging studies offer valuable insight into intricate dynamics of brain development, ageing, and disease progression over time. However, prevailing analytical approaches rooted in our understanding of population variation are primarily tailored for cross-sectional studies. To fully harness the potential of longitudinal neuroimaging data, we have to develop and refine methodologies that are adapted to longitudinal designs, considering the complex interplay between population variation and individual dynamics. We build on normative modelling framework, which enables the evaluation of an individuals position compared to a population standard. We extend this framework to evaluate an individuals longitudinal change compared to the longitudinal change reflected by the (population) standard dynamics. Thus, we exploit the existing normative models pre-trained on over 58,000 individuals and adapt the framework so that they can also be used in the evaluation of longitudinal studies. Specifically, we introduce a quantitative metric termed "z-diff" score, which serves as an indicator of a temporal change of an individual compared to a population standard. Notably, our framework offers advantages such as flexibility in dataset size and ease of implementation. To illustrate our approach, we applied it to a longitudinal dataset of 98 patients diagnosed with early-stage schizophrenia who underwent MRI examinations shortly after diagnosis and one year later. Compared to cross-sectional analyses, which showed global thinning of grey matter at the first visit, our method revealed a significant normalisation of grey matter thickness in the frontal lobe over time. Furthermore, this result was not observed when using more traditional methods of longitudinal analysis, making our approach more sensitive to temporal changes. Overall, our framework presents a flexible and effective methodology for analysing longitudinal neuroimaging data, providing insights into the progression of a disease that would otherwise be missed when using more traditional approaches.

neuroscience↗