Biology subjects

Manso-Ortega, L.

Publications and source records attributed to Manso-Ortega, L..

4 recordsLinked to original sources

bioRxiv2026

Resting-state Compensatory Remapping in Patients with Brain Tumour Before and After Surgery

Brain tumours invade neural tissue, disrupting the functional organisation of neural networks. This disruption can trigger compensatory neuroplastic mechanisms that help preserve cognitive function despite pathological burden. Resting-state functional connectivity (rs-FC) provides a valuable approach for examining these alterations, yet the scope and trajectory of network-level changes remain unclear. In this study, we used rs-FC to characterise compensatory responses induced by left-hemisphere brain tumours affecting the integrity of the language network. The comparison of connectivity patterns at diagnosis and after surgery allowed us to track the temporal progression of these patterns. Relative to healthy participants, patients showed significant deviations in pre-surgical rs-FC, with widespread changes involving the dorsal attention, sensorimotor, frontoparietal, default mode, and cerebellar networks. These differences persisted post-surgically, with no significant longitudinal modifications. Network-level alterations also influenced topological properties: patients showed increased segregation while preserving global integration relative to controls. These properties changed significantly from pre- to postoperative period, with a postoperative increase in segregation and integration towards a pattern more closely resembling that of healthy controls. Furthermore, preoperative network segregation was identified as a predictor of postoperative cognitive recovery. These findings suggest that brain tumours induce network remapping, affecting functional connectivity far beyond the regions strictly related to the affected region. The observed changes in network topological organisation likely reflect the adaptive role of domain-general networks in facilitating homeostatic processes. Surgical strategies aimed at preserving these changes seem to promote sustained cognitive function, regardless of malignancy grade or lesion extent. Key pointsO_LIPresurgical patients show widespread inter- and intrahemispheric rs-FC alterations, reflecting tumour-driven network remapping. C_LIO_LINetwork integration and segregation show longitudinal recovery, approaching neurotypical topology four months post-surgery. C_LIO_LIPreoperative network segregation predicts postoperative cognitive recovery. C_LI Importance of the StudyBrain tumours disrupt large-scale brain networks, yet the extent, temporal dynamics, and cognitive relevance of these alterations remain poorly understood. Using resting-state functional connectivity, this study provides a longitudinal, network-level characterisation of tumour-induced neuroplasticity in patients with left-hemisphere lesions affecting language-related regions. Compared with prior work focused on local effects or single networks, our results demonstrate widespread inter- and intrahemispheric connectivity changes involving multiple domain-general resting-state networks, highlighting extensive network remapping beyond lesion boundaries. Importantly, we show that postoperative recovery is accompanied by a rebalancing of network integration and segregation toward a neurotypical configuration. Critically, preoperative network segregation emerges as a predictor of postoperative cognitive recovery, identifying a potential biomarker of resilience. These findings have direct translational relevance, suggesting that preserving and supporting large-scale network organisation during surgical planning may promote cognitive outcomes independently of tumour grade or lesion extent.

neuroscience↗

bioRxiv2025

Contralesional grey matter volume as an index of macrostructural plasticity in patients with brain tumors

This research challenges the traditional localizationist view that brain tumors affect only regions directly associated with the lesion, by examining whether they also induce macrostructural alterations in the contralesional hemisphere. We applied Voxel-Based Morphometry, linear regression, and Principal Component Analysis (PCA) to a cohort of 107 adults, including patients with gliomas in the language-dominant left hemisphere and healthy participants. Unlike previous studies, a subset of the clinical population was followed longitudinally for up to four months after oncological treatment, allowing us to describe the temporal progression of structural grey matter changes. Interestingly, a principal component model based on anomaly detection enabled robust differentiation between patients and controls. Patients exhibited significantly greater grey matter volume in the contralesional hemisphere compared to healthy participants, and these structural differences evolved over time, improving the models AUC-ROC metrics. Although exploratory, a correlation analysis revealed that these structural changes were negatively associated with postsurgical cognitive performance. Together with the PCA findings, these results suggest that brain tumors induce extensive and dynamic adaptive mechanisms in the contralateral, unaffected hemisphere, likely reflecting altered patterns of structural covariance rather than simple regional volume increases. Understanding whether these changes could represent potential predictors of postoperative cognitive recovery is crucial for developing comprehensive clinical strategies. Key PointsO_LILeft-hemisphere tumors induce contralesional grey matter increases pre- and post-sugery C_LIO_LIPCA detects altered structural covariance and distinguishes patients from healthy participants C_LIO_LIContralesional grey matter volume changes correlate with postoperative cognitive performance C_LI Importance of the StudyThis study challenges the traditional view that brain tumors cause only localized effects by demonstrating widespread macrostructural alterations in the contralesional hemisphere. We reveal increased grey matter volume and altered patterns of structural covariance outside the tumor region. Longitudinal follow-up after surgery shows these changes are dynamic and evolve over time. Further, we identify moderate associations between contralesional grey matter alterations and cognitive performance, suggesting a link between large-scale neuroplastic responses and functional outcomes. These findings offer new insights into tumor-related neuroplasticity and position structural covariance as a promising marker for tracking brain-wide adaptation in this population. The study has translational relevance for developing predictive tools to monitor recovery and guide personalized rehabilitation.

neuroscience↗

bioRxiv2025

NEUROLINGUA: A Neuroimaging Database Tailored to Unravel the Complexity of Multilingual Comprehension

The neural mechanisms underlying language processing involve a well-defined brain network, including mainly left perisylvian areas. Yet, the extent of individual variability remains largely unexplored, particularly in bilingual and multilingual contexts. Differences in linguistic profiles (e.g., age of acquisition, exposure, proficiency) provide an opportunity to assess how network topology is shaped by sociolinguistic factors. To address this, we developed NEUROLINGUA, a comprehensive database of functional and structural MRI data, enriched with sociodemographic, sociolinguistic, and behavioral information. It includes 725 healthy individuals aged 18-82 immersed in a Basque-Spanish multilingual environment, ranging from near-monolinguals to highly proficient multilinguals. Participants completed a functional MRI language localizer with both auditory and visual comprehension tasks, enabling cross-modal comparisons, as well as sentences involving arithmetic problem-solving. Exploratory analyses confirmed associations between structural MRI, sociodemographic, and cognitive measures. We demonstrate that NEUROLINGUAs functional MRI data localize the language comprehension network and thus capture linguistic profile effects. This integrative dataset offers an unparalleled resource to investigate factors influencing language network adaptability and variability in diverse sociolinguistic contexts.

neuroscience↗

bioRxiv2023

Grey matter reshaping of language-related regions depends on tumor lateralization

A brain tumor in the left hemisphere can decrease language laterality as assessed with fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of language hubs exclusively affects macrostructural properties of contralateral homologues (as suggested by previous research), or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients left-dominant for language. Eighteen patients had brain tumors in the left hemisphere, and 13 had tumors in the right hemisphere. A cohort of 71 healthy individuals matched on age and sex was used as a baseline. We defined 10 ROIs per hemisphere known to subserve language function. Two separate repeated-measures ANOVAs were conducted with the volume per region as the dependent variables. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates a global volumetric change affecting left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that compensatory functional mechanisms are supported by the rearrangement of the grey matter, although future longitudinal research should determine the temporal course of such changes.

neuroscience↗