Biology subjects

Spaniel, F.

Publications and source records attributed to Spaniel, F..

5 recordsLinked to original sources

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↗