Biology subjects

Piras, F.

Publications and source records attributed to Piras, F..

10 recordsLinked to original sources

bioRxiv2026

CALM-VLM: CALIBRATION AND SELECTIVE PREDICTION IN VISION-LANGUAGE MODELS FOR RELIABLE BRAIN MRI CLASSIFICATION

AO_SCPLOWBSTRACTC_SCPLOWRecent advances in vision-language models (VLMs) have demonstrated strong multimodal capabilities for medical image analysis. However, their confidence in diagnostic predictions is often unclear, limiting adoption in clinical settings. We introduce CALM-VLM (CALibration Mechanism for Vision-Language Models), which integrates confidence calibration and selective prediction into a generative 3D VLM. To create CALM-VLM, we fine-tuned the Med3DVLM architecture for Alzheimers disease (AD) and stroke classification as initial test cases. To improve reliability, we incorporated temperature scaling on the VLMs generative outputs. The calibrated model then selectively abstained from predictions when uncertain; this also improved its diagnostic accuracy. Experiments across multi-site MRI datasets, from 10 countries worldwide, show that CALM-VLM improved confidence relative to uncalibrated VLMs. Coverage-adjusted test receiver-operator characteristic curve-area under the curve (ROC-AUC) increased by 5% to 13% for both diagnostic tasks across independent test sets. Our calibrated VLM achieved a test ROC-AUC of 0.951 for AD classification and 0.905 for stroke classification. These findings highlight the importance of calibrated, uncertainty-aware VLMs for trustworthy neuroimaging AI.

neuroscience↗

bioRxiv2025

Neuroanatomical Correlates of Negative Symptoms in Schizophrenia

BackgroundSchizophrenia is characterized by widespread structural brain abnormalities, but associations between structural abnormalities and negative symptom severity are not well understood. Negative symptoms have been conceptualized in a hierarchical structure of two second-order dimensions--motivation and pleasure (MAP) and expression (EXP)--and five first-order domains: anhedonia, avolition, and asociality (MAP), and blunted affect and alogia (EXP). A better understanding of the neural circuitry underlying negative symptom dimensions and domains is important given their reported association with poor functional outcome and lack of available treatments. Study DesignThe meta-analysis included 1,591 individuals with schizophrenia across 16 samples with structural imaging and Scale for Assessment of Negative Symptoms data. The study generated correlations of cortical thickness and subcortical volumes with the negative symptom dimensions and domains. Study resultsNegative symptoms showed mainly negative associations with cortical thickness and subcortical volumes. The effect sizes were small but there was a pattern of associations in predominantly frontal lobe cortical thickness and limbic subcortical volumes. The regional correlation patterns of cortical thickness and subcortical volumes with symptom domains support the conceptualized hierarchical structure of negative symptoms: correlations of MAP domains were stronger with the MAP than EXP dimension, and vice versa. Exploratory analyses with receptor densities further supported the hierarchy. ConclusionOur findings reveal small but consistent associations between negative symptom dimensions and predominantly prefrontal region cortical thickness, and limbic region volumes. These findings advance our understanding of the network of anatomical regions that may contribute to the severity of negative symptoms in schizophrenia.

neuroscience↗

bioRxiv2024

Sex differences in deep brain shape and asymmetry persist across schizophrenia and healthy individuals: A meta-analysis from the ENIGMA-Schizophrenia Working Group

BackgroundSchizophrenia (SCZ) is characterized by a disconnect from reality that manifests as various clinical and cognitive symptoms, and persistent neurobiological abnormalities. Sex-related differences in clinical presentation imply separate brain substrates. The present study characterized deep brain morphology using shape features to understand the independent effects of diagnosis and sex on the brain, and to determine whether the neurobiology of schizophrenia varies as a function of sex. MethodsThis study analyzed multi-site archival data from 1,871 male (M) and 955 female (F) participants with SCZ, and 2,158 male and 1,877 female healthy controls (CON) from twenty-three cross-sectional samples from the ENIGMA Schizophrenia Workgroup. Harmonized shape analysis protocols were applied to each sites data for seven deep brain regions obtained from T1-weighted structural MRI scans. Effect sizes were calculated for the following main contrasts: 1) Sex effects;2) Diagnosis-by-Sex interaction; 3) within sex tests of diagnosis; 4) within diagnosis tests of sex differences. Meta-regression models between brain structure and clinical variables were also computed separately in men and women with schizophrenia. ResultsMass univariate meta-analyses revealed more concave-than-convex shape differences in all regions for women relative to men, across diagnostic groups (d = -0.35 to 0.20, SE = 0.02 to 0.07); there were no significant diagnosis-by-sex interaction effects. Within men and women separately, we identified more-concave-than-convex shape differences for the hippocampus, amygdala, accumbens, and thalamus, with more-convex-than-concave differences in the putamen and pallidum in SCZ (d = -0.30 to 0.30, SE = 0.03 to 0.10). Within CON and SZ separately, we found more-concave-than-convex shape differences in the thalamus, pallidum, putamen, and amygdala among females compared to males, with mixed findings in the hippocampus and caudate (d = -0.30 to 0.20, SE = 0.03 to 0.09). Meta-regression models revealed similarly small, but significant relationships, with medication and positive symptoms in both SCZ-M and SCZ-F. ConclusionsSex-specific variation is an overriding feature of deep brain shape regardless of disease status, underscoring persistent patterns of sex differences observed both within and across diagnostic categories, and highlighting the importance of including it as a critical variable in studies of neurobiology. Future work should continue to explore these dimensions independently to determine whether these patterns of brain morphology extend to other aspects of neurobiology in schizophrenia, potentially uncovering broader implications for diagnosis and treatment. Key PointsO_LIStatistical analyses revealed significant main effects for diagnosis and sex in deep brain shape morphology. Among patients with schizophrenia, there was a pattern of thinning and surface contraction in the bilateral hippocampus, amygdala, accumbens, and thalamus, and a pattern of significant thickening and surface expansion in the bilateral putamen and pallidum compared to healthy control participants. Between males and females, there was a pattern of significant thinning and surface contraction in the bilateral thalamus, pallidum, putamen, and amygdala in females compared to males. C_LIO_LIThere was no significant interaction between diagnosis and biological sex, suggesting that sex differences in deep brain shape and asymmetry among patients with schizophrenia reflect those observed in healthy individuals. C_LIO_LISmall but statistically significant relationships exist between brain structure and clinical correlates of schizophrenia were similar for both men and women with the disease, such that higher CPZ was associated with shape-derived thinning and surface contraction in the caudate, accumbens, hippocampus, amygdala, and thalamus, and elevated positive symptoms were associated with shape-derived thinning and surface contraction in the bilateral caudate, right hippocampus, and right amygdala. C_LI

neuroscience↗

bioRxiv2024

A generalizable normative deep autoencoder for brain morphological anomaly detection: application to the multi-site StratiBip dataset on bipolar disorder in an external validation framework.

The heterogeneity of psychiatric disorders makes researching disorder-specific neurobiological markers an ill-posed problem. Here, we face the need for disease stratification models by presenting a generalizable multivariate normative modelling framework for characterizing brain morphology, applied to bipolar disorder (BD). We employed deep autoencoders in an anomaly detection framework, combined with a confounder removal step integrating training and external validation. The model was trained with healthy control (HC) data from the human connectome project and applied to multi-site external data of HC and BD individuals. We found that brain deviating scores were greater, more heterogeneous, and with increased extreme values in the BD group, with volumes prominently from the basal ganglia, hippocampus and adjacent regions emerging as significantly deviating. Similarly, individual brain deviating maps based on modified z scores expressed higher abnormalities occurrences, but their overall spatial overlap was lower compared to HCs. Our generalizable framework enabled the identification of subject- and group-level brain normative-deviating patterns, a step forward towards the development of more effective and personalized clinical decision support systems and patient stratification in psychiatry.

neuroscience↗

bioRxiv2023

Estimating multimodal brain variability in schizophreniaspectrum disorders: A worldwide ENIGMA study

ObjectiveSchizophrenia is a multifaceted disorder associated with structural brain heterogeneity. Despite its relevance for identifying illness subtypes and informative biomarkers, structural brain heterogeneity in schizophrenia remains incompletely understood. Therefore, the objective of this study was to provide a comprehensive insight into the structural brain heterogeneity associated with schizophrenia. MethodsThis meta- and mega-analysis investigated the variability of multimodal structural brain measures of white and gray matter in individuals with schizophrenia versus healthy controls. Using the ENIGMA dataset of MRI-based brain measures from 22 international sites with up to 6139 individuals for a given brain measure, we examined variability in cortical thickness, surface area, folding index, subcortical volume and fractional anisotropy. ResultsWe found that individuals with schizophrenia are distinguished by higher heterogeneity in the frontotemporal network with regard to multimodal structural measures. Moreover, individuals with schizophrenia showed higher homogeneity of the folding index, especially in the left parahippocampal region. ConclusionsHigher multimodal heterogeneity in frontotemporal regions potentially implies different subtypes of schizophrenia that converge on impaired frontotemporal interaction as a core feature of the disorder. Conversely, more homogeneous folding patterns in the left parahippocampal region might signify a consistent characteristic of schizophrenia shared across subtypes. These findings underscore the importance of structural brain variability in advancing our neurobiological understanding of schizophrenia, and aid in identifying illness subtypes as well as informative biomarkers.

neuroscience↗

bioRxiv2023

Data-driven biomarkers outperform theory-based biomarkers in predicting stroke motor outcomes

Chronic motor impairments are a leading cause of disability after stroke. Previous studies have predicted motor outcomes based on the degree of damage to predefined structures in the motor system, such as the corticospinal tract. However, such theory-based approaches may not take full advantage of the information contained in clinical imaging data. The present study uses data-driven approaches to predict chronic motor outcomes after stroke and compares the accuracy of these predictions to previously-identified theory-based biomarkers. Using a cross-validation framework, regression models were trained using lesion masks and motor outcomes data from 789 stroke patients (293 female/496 male) from the ENIGMA Stroke Recovery Working Group (age 64.9{+/-}18.0 years; time since stroke 12.2{+/-}0.2 months; normalised motor score 0.7{+/-}0.5 (range [0,1]). The out-of-sample prediction accuracy of two theory-based biomarkers was assessed: lesion load of the corticospinal tract, and lesion load of multiple descending motor tracts. These theory-based prediction accuracies were compared to the prediction accuracy from three data-driven biomarkers: lesion load of lesion-behaviour maps, lesion load of structural networks associated with lesion-behaviour maps, and measures of regional structural disconnection. In general, data-driven biomarkers had better prediction accuracy - as measured by higher explained variance in chronic motor outcomes - than theory-based biomarkers. Data-driven models of regional structural disconnection performed the best of all models tested (R2 = 0.210, p < 0.001), performing significantly better than predictions using the theory-based biomarkers of lesion load of the corticospinal tract (R2 = 0.132, p< 0.001) and of multiple descending motor tracts (R2 = 0.180, p < 0.001). They also performed slightly, but significantly, better than other data-driven biomarkers including lesion load of lesion-behaviour maps (R2 =0.200, p < 0.001) and lesion load of structural networks associated with lesion-behaviour maps (R2 =0.167, p < 0.001). Ensemble models - combining basic demographic variables like age, sex, and time since stroke - improved prediction accuracy for theory-based and data-driven biomarkers. Finally, combining both theory-based and data-driven biomarkers with demographic variables improved predictions, and the best ensemble model achieved R2 = 0.241, p < 0.001. Overall, these results demonstrate that models that predict chronic motor outcomes using data-driven features, particularly when lesion data is represented in terms of structural disconnection, perform better than models that predict chronic motor outcomes using theory-based features from the motor system. However, combining both theory-based and data-driven models provides the best predictions.

neuroscience↗

bioRxiv2023

Large language models are universal biomedical simulators

Computational simulation of biological processes can be a valuable tool in accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Recently, large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks by generating human language at a very large scale. Here we explore the potential of leveraging LLMs as simulators of biological systems. We establish proof-of-concept of a text-based simulator, SimulateGPT, that uses LLM reasoning. We demonstrate good prediction performance for various biomedical applications, without requiring explicit domain knowledge or manual tuning. LLMs thus enable a new class of versatile and broadly applicable biological simulators. This text-based simulation paradigm is well-suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulation, but for which extensive knowledge and context is available as written text.

bioinformatics↗

bioRxiv2021

Chronic stroke sensorimotor impairment is related to smaller hippocampal volumes: An ENIGMA analysis

Persistent sensorimotor impairments after stroke can negatively impact quality of life. The hippocampus is involved in sensorimotor behavior but has not been widely studied within the context of post-stroke upper limb sensorimotor impairment. The hippocampus is vulnerable to secondary degeneration after stroke, and damage to this region could further weaken sensorimotor circuits, leading to greater chronic sensorimotor impairment. The purpose of this study was to investigate the cross-sectional association between non-lesioned hippocampal volume and upper limb sensorimotor impairment in people with chronic stroke. We hypothesized that smaller ipsilesional hippocampal volumes would be associated with worse upper-limb sensorimotor impairment. Cross-sectional T1-weighted brain MRIs were pooled from 357 participants at the chronic stage after stroke (>180 days post-stroke) compiled from 18 research cohorts worldwide in the ENIGMA Stroke Recovery Working Group (age: median = 61 years, interquartile range = 18, range = 23-93; 135 women and 222 men). Sensorimotor impairment was estimated from the Fugl-Meyer Assessment of Upper Extremity scores. Robust mixed-effects linear models were used to test associations between post-stroke sensorimotor impairment and hippocampal volumes (ipsilesional and contralesional separately; Bonferroni-corrected, p-value < 0.025), controlling for age, sex, lesion volume, and lesioned hemisphere. We also performed an exploratory analysis to test whether sex differences influence the relationship between sensorimotor impairment and hippocampal volume. Upper limb sensorimotor impairment was positively associated with ipsilesional (p = 0.005; d = 0.33) but not contralesional (p = 0.96; d = 0.01) hippocampal volume, such that impairment was worse for participants with smaller ipsilesional hippocampal volume. This association remained significant independent of lesion volume or other covariates (p = 0.001; d = 0.36). Evidence indicates an interaction between sensorimotor impairment and sex for both ipsilesional (p = 0.008; d = -0.29) and contralesional (p = 0.006; d = -0.30) hippocampal volumes, whereby women showed progressively worsening sensorimotor impairment with smaller hippocampal volumes compared to men. The present study has identified a novel association between chronic post-stroke sensorimotor impairment and ipsilesional, but not contralesional, hippocampal volume. This finding was not due to lesion size and may be stronger in women. We also provide supporting evidence that smaller hippocampal volume post-stroke is likely a consequence of ipsilesional damage, which could provide a link between vascular disease and other disorders, such as dementia.

neuroscience↗

bioRxiv2019

White Matter Microstructure and its Relation to Clinical Features of Obsessive-Compulsive Disorder: Findings from the ENIGMA OCD Working Group

ImportanceMicrostructural alterations in cortico-subcortical connections are thought to be present in Obsessive-Compulsive Disorder (OCD). However, prior studies have yielded inconsistent findings, perhaps because small sample sizes provided insufficient power to detect subtle abnormalities. ObjectiveTo investigate microstructural white matter alterations and their relation to clinical features in the largest dataset of adult and pediatric OCD to date. Design, Setting, and ParticipantsIn this cross-sectional case-control magnetic resonance study, we investigated diffusion tensor imaging metrics from 700 adult patients and 645 adult controls, as well as 174 pediatric patients and 144 pediatric controls across 19 sites participating in the ENIGMA-OCD Working Group. Main Outcomes and MeasuresWe extracted measures of fractional anisotropy (FA) as main outcome, and mean diffusivity, radial diffusivity, and axial diffusivity as secondary outcomes for 25 white matter regions. We meta-analyzed patient-control group differences (Cohens d) across sites, after adjusting for age and sex, and investigated associations with clinical characteristics. ResultsAdult OCD patients showed significant FA reduction in the sagittal stratum (d=-0.21, z=-3.21, p=0.001) and posterior thalamic radiation (d=-0.26, z=-4.57, p<0.0001). In the sagittal stratum only, lower FA was associated with a younger age of onset (z=2.71, p=0.006), longer duration of illness (z=-2.086, p=0.036) and a higher percentage of medicated patients in the cohorts studied (z=-1.98, p=0.047). No significant association with symptom severity was found. Pediatric OCD patients did not show any detectable microstructural abnormalities compared to matched controls. Conclusions and RelevanceMicrostructural alterations in projection and association fibers to posterior brain regions were found in adult OCD, and related to disease course and medication status. Such results are relevant to models positing deficits in connectivity as a crucial mechanism in OCD. KEY POINTSO_ST_ABSQuestionC_ST_ABSDo patients with Obsessive-Compulsive Disorder (OCD) show white matter microstructural alterations, and are these alterations related to clinical features? FindingsData from 19 sites of the ENIGMA-OCD Consortium were included, involving 700 adult patients and 645 adult controls, 174 pediatric patients and 144 pediatric controls. Diffusion tensor imaging data were meta-analyzed using a harmonized data processing and analysis protocol. Adult, but not pediatric, patients showed alterations in the sagittal stratum and posterior thalamic radiation; sagittal stratum differences were associated with clinical features. MeaningMicrostructural abnormalities found in adult but not in the pediatric cohort, are related to illness duration and medication status.

neuroscience↗

bioRxiv2018

Elevated plasma ceramide levels in post-menopausal women

Circulating ceramide levels are abnormally elevated in age-dependent pathologies such as cardiovascular diseases, obesity and Alzheimers disease. Nevertheless, the potential impact of age on plasma ceramide levels has not yet been systematically examined. In the present study, we quantified a focused panel of plasma ceramides and dihydroceramides in a cohort of 164 subjects (84 women) 19 to 80 years of age. After adjusting for potential confounders, multivariable linear regression analysis revealed a positive association between age and ceramide (d18:1/24:0) ({beta} (SE) = 5.67 (2.38); p = .0198) and ceramide (d18:1/24:1) ({beta} (SE) = 2.88 (.61); p < .001) in women, and between age and ceramide (d18:1/24:1) in men ({beta} (SE) = 1.86 (.77); p = .0179). In women of all ages, but not men, plasma ceramide (d18:1/24:1) was negatively correlated with plasma estradiol (r = -0.294; p = .007). Finally, in vitro experiments in human cancer cells expressing estrogen receptors showed that incubation with estradiol (10 nM, 24 h) significantly decreased ceramide accumulation. Together, the results suggest that aging is associated with an increase in circulating ceramide levels, which in post-menopausal women may be at least partially dependent on lower estradiol levels.

biochemistry↗