Daniela Vecchio

@hsantalucia.it

Department of Clinical and Behavioural Neurology - Laboratory of Neuropsychyatry
IRCCS Santa Lucia Foundation

Daniela Vecchio


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https://researchid.co/danive

EDUCATION

MS Cognitive Neuroscience
PhD Behavioural Neuroscience

RESEARCH INTERESTS

Neuroimaging, Connectivity, Network Analysis, Machine Learning, Mental Disorder, Neurodegenerative Disorder
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Scopus Publications

Scopus Publications

  • Optimizing the Approach to Calculating CST Lesion Load in Ischemic Stroke Patients for Understanding Motor Outcomes
    Brady J. Williamson, Tyler Behymer, Anne Schwarz, Achala Vagal, Carolee J. Winstein, et al.
    Neurorehabilitation and Neural Repair, 2026
    Introduction Corticospinal tract lesion load (CST-LL) is a biomarker used for studying motor outcomes after stroke. However, the optimal method for calculating this metric is unknown. Methods This is a cross-sectional study of a large ischemic stroke cohort from the ENIGMA Stroke Recovery Consortium (n = 221; mean age = 59.8 years, 56% male) to compare 4 lesion load metrics across 3 CST templates. We then validate these findings in another large, independent stroke cohort (n = 125; mean age = 64.6 years, 54% male). Results Results indicate that variance in behavioral outcome was best explained using the maximum weighted cross-sectional overlap between lesion and the CST (Max-WLL), and when using an age-appropriate normative CST template (generated from the HCP Aging study). This was true both when the outcome was motor impairment, measured using the Fugl-Meyer Upper Extremity scale (FMUE, relative explained variance (REV) = 58.9%), and when it was global function, measured using the Barthel Index (BI, REV = 60%). In the validation cohort, FMUE results were replicated (REV = 47.6%). Conclusion The findings indicate that Max-WLL, which represents the proportion of transected CST fibers, most accurately captures CST injury as it relates to motor and functional outcomes after stroke. Additionally, results suggest the importance of an age-appropriate template, a key consideration given that stroke is largely a disease of the elderly. Together, these findings provide an independently validated tool to optimize future research examining CST injury after stroke.
  • Structural brain abnormalities and aggression in schizophrenia: mega-analysis of data from 2095 patients and 2861 healthy controls via the ENIGMA consortium
    Jelle Lamsma, Adrian Raine, Seyed M. Kia, Wiepke Cahn, Dominic Arold, et al.
    Molecular Psychiatry, 2026
  • Cerebral cortical alterations in adolescent early-onset psychosis: a surface-based morphometry mega-analysis
    Stener Nerland, Claudia Barth, Kjetil Nordbø Jørgensen, Laura A. Wortinger, Josefina Castro-Fornieles, et al.
    Molecular Psychiatry, 2026
    Cortical brain morphology in early-onset psychosis (EOP; age of onset <19 years) is poorly understood, partly due to recruitment constraints linked to its low incidence. We pooled T1-weighted magnetic resonance imaging (MRI) data from 387 adolescents with EOP (mean age=16.1 ± 1.5; 49.6% female) and 338 healthy controls (CTR; mean age=15.8 ± 1.9, 54.4% female) from nine research sites worldwide. Using harmonized processing protocols, we extracted cortical brain metrics from 34 bilateral regions. Univariate regression analyses revealed widespread lower bilateral cortical thickness (left/right hemisphere: d = −0.36/−0.31), surface area (left/right: d = −0.42/−0.41), cortical volume (left/right: d = −0.58/−0.56), and Local Gyrification Index (LGI; left/right: d = −0.39/−0.52) in EOP relative to CTR. Diagnostic subgroup analyses showed broader and more pronounced case-control differences in early-onset schizophrenia for area, volume, and LGI. We found no associations with antipsychotic medication use, illness duration, age of onset, or positive symptoms. Negative symptoms were related to smaller left lingual volume (partial r = −0.21; p FDR = 0.014) and antidepressant users had smaller area ( d = −0.43; p FDR = 0.034) and volume ( d = −0.50; p FDR = 0.003) of the right rostral anterior cingulate compared to non-users. Cortical thickness alterations in EOP showed a similar pattern to those observed in prior studies on adults with schizophrenia (SCZ; r = 0.62) and bipolar disorders (BD; r = 0.61). However, surface area alterations were overall 1.5 times greater for EOP than adult SCZ and 4.6 times greater than adult BD. In the largest study of its kind, we observed a widespread pattern of cortical alterations in adolescents with psychotic disorders, highlighting the potential impact of aberrant neurodevelopment on cortical morphology in this clinical group.
  • Associations between contralesional neuroplasticity and motor impairment through deep learning-derived MRI regional brain age in chronic stroke (ENIGMA): a multicohort, retrospective, observational study
    Gilsoon Park, Mahir H Khan, Justin W Andrushko, Nerisa Banaj, Michael R Borich, et al.
    Lancet Digital Health, 2026
  • Regional Cerebellar Volumetrics in Obsessive-Compulsive Disorder: An ENIGMA-OCD Study
    Srinivas Balachander, Janardhanan C. Narayanaswamy, Venkataram Shivakumar, Yoshinari Abe, Pino Alonso, et al.
    Biological Psychiatry Cognitive Neuroscience and Neuroimaging, 2026
  • Contribution of Cerebellar Glutamatergic and GABAergic Systems in Premotor and Early Stages of Parkinson’s Disease
    Clelia Pellicano, Daniela Vecchio, Federico Giove, Lucia Macchiusi, Marco Clemenzi, et al.
    International Journal of Molecular Sciences, 2025
    Parkinson’s disease (PD) is a multisystem disorder, with early changes extending beyond basal ganglia circuitries and involving non-dopaminergic pathways, including cerebellar networks. Whether cerebellar dysfunction reflects a compensatory mechanism or an intrinsic hallmark of disease progression remains unresolved. In this cross-sectional study, we examined how cerebellar γ-aminobutyric acid (GABA) and glutamate/glutamine (Glx) systems, as well as their excitatory/inhibitory (E/I) balance, are modulated along the disease course. As to ascertain how these mechanisms contribute to motor and non-motor features in the premotor and early stages of PD, 18 individuals with isolated REM sleep behavior disorder (iRBD), 20 de novo, drug-naïve PD (dnPD), and 18 matched healthy controls underwent clinical, cognitive, and neuropsychiatric assessments alongside cerebellar magnetic resonance spectroscopy (MRS, MEGA-PRESS, 3T). While cerebellar neurotransmitter levels did not differ significantly across groups, dnPD patients exhibited a shift toward hyperexcitability in the E/I ratio, without correlation to clinical or cognitive measures. In contrast, in iRBD, an inverse relationship between heightened GABAergic activity and neuropsychiatric symptoms emerged. These findings suggest an early, dynamic cerebellar involvement, potentially reflecting compensatory modulation of altered basal ganglia output. Our results support cerebellar GABA MRS as a promising biomarker and open perspectives for targeting non-dopaminergic pathways in PD.
  • Thalamocortical Structural Covariation Networks Are Related to Familial Risk for Schizophrenia in the Context of Lower Nuclei Volume Estimates in Patients: An ENIGMA Study
    Annalisa Lella, Linda A. Antonucci, Roberta Passiatore, Loredana Bellantuono, Pierluigi Selvaggi, et al.
    Biological Psychiatry, 2025
  • Effectiveness of Omega-3 Fatty Acids Versus Placebo in Subjects at Ultra-High Risk for Psychosis: The PURPOSE Randomized Clinical Trial
    Inge Winter-van Rossum, Margot I E Slot, Hendrika H van Hell, Matthijs G Bossong, Gregor Berger, et al.
    Schizophrenia Bulletin, 2025
    Background and Hypotheses In the past 2 decades, substantial effort has been put into research on therapeutic options for people at ultra-high risk (UHR) for developing a first episode of psychosis (FEP), focusing on omega-3 polyunsaturated fatty acids (PUFAs) in preventing transition to psychosis. Despite an initial positive finding, subsequent studies failed to find a beneficial effect. The current study aimed to further investigate the effect of omega-3 PUFAs in UHR, to determine whether this line of research is worth pursuing. Study Design A double-blind, randomized, placebo-controlled study testing the efficacy of 6-month treatment with omega-3 PUFAs in 135 subjects at UHR for FEP, aged 13 to 20 years on the prevention of a transition to psychosis, followed up for 18 months post-treatment. The trial was conducted at 16 general hospitals and psychiatric specialty centers located in 8 European countries and Israel. Study Results There was no beneficial effect of treatment with omega-3 PUFAs compared to placebo; the rate of transition over 2 years did not differ between treatment arms nor was there a difference in change in symptom severity after 6-month treatment. Dropout rates and serious adverse events were similar across the groups. Conclusions This is the third study that fails to replicate the original finding on the protective effect of omega-3 PUFAs in UHR subjects for transition to psychosis. The accumulating evidence therefore suggests that omega-3 PUFAs do not reduce transition rates to psychosis in those at increased risk at 2 years follow-up. Clinical Trials This trial is registered with ClinicalTrials.gov (NCT02597439; Study Details | Placebo-controlled Trial in Subjects at Ultra-high Risk for Psychosis With Omega-3 Fatty Acids in Europe | ClinicalTrials.gov).
  • Evaluating conversion from mild cognitive impairment to Alzheimer’s disease with structural MRI: a machine learning study
    Daniela Vecchio, Federica Piras, Federica Natalizi, Nerisa Banaj, Clelia Pellicano, et al.
    Brain Communications, 2025
    Alzheimer’s disease is a disabling neurodegenerative disorder for which no effective treatment currently exists. To predict the diagnosis of Alzheimer’s disease could be crucial for patients’ outcome, but current Alzheimer’s disease biomarkers are invasive, time consuming or expensive. Thus, developing MRI-based computational methods for Alzheimer’s disease early diagnosis would be essential to narrow down the phenotypic measures predictive of cognitive decline. Amnestic mild cognitive impairment (aMCI) is associated with higher risk for Alzheimer’s disease, and here, we aimed to identify MRI-based quantitative rules to predict aMCI to possible Alzheimer’s disease conversion, applying different machine learning algorithms sequentially. At baseline, T1-weighted brain images were collected for 104 aMCI patients and processed to obtain 146 volumetric measures of cerebral grey matter regions [regions of interest (ROIs)]. One year later, patients were classified as converters (aMCI-c = 32) or non-converters, i.e. clinically and neuropsychologically stable (aMCI-s = 72) based on cognitive performance. Feature selection was performed by random forest (RF), and the identified seven ROIs volumetric data were used to implement support vector machine (SVM) and decision tree (DT) classification algorithms. Both SVM and DT reached an average accuracy of 86% in identifying aMCI-c and aMCI-s. DT found a critical threshold volume of the right entorhinal cortex (EC-r) as the first feature for differentiating aMCI-c/aMCI-s. Almost all aMCI-c had an EC-r volume &amp;lt;1286 mm3, while more than half of the aMCI-s patients had a volume above the identified threshold for this structure. Other key regions for the classification between aMCI-c/aMCI-s were the left lateral occipital (LOC-l), the middle temporal gyrus and the temporal pole cortices. Our study reinforces previous evidence suggesting that the morphometry of the EC-r and LOC-l best predicts aMCI to Alzheimer’s disease conversion. Further investigations are needed prior to deeming our findings as a broadly applicable predictive framework. However, here, a first indication was derived for volumetric thresholds that, being easy to obtain, may assist in early identification of Alzheimer’s disease in clinical practice, thus contributing to establishing MRI as a useful non-invasive prognostic instrument for dementia onset.
  • Neuroimaging-based data-driven subtypes of spatiotemporal atrophy due to Parkinson's disease
    Zeena Shawa, Cameron Shand, Beatrice Taylor, Henk W Berendse, Chris Vriend, et al.
    Brain Communications, 2025
    Parkinson’s disease is the second most common neurodegenerative disease. Despite this, there are no robust biomarkers to predict progression, and understanding of disease mechanisms is limited. We used the Subtype and Stage Inference algorithm to characterize Parkinson’s disease heterogeneity in terms of spatiotemporal subtypes of macroscopic atrophy detectable on T1-weighted MRI—a successful approach used in other neurodegenerative diseases. We trained the model on covariate-adjusted cortical thicknesses and subcortical volumes from the largest known T1-weighted MRI dataset in Parkinson’s disease, Enhancing Neuroimaging through Meta-Analysis consortium Parkinson’s Disease dataset (n = 1100 cases). We tested the model by analyzing clinical progression over up to 9 years in openly-available data from people with Parkinson’s disease from the Parkinson’s Progression Markers Initiative (n = 584 cases). Under cross-validation, our analysis supported three spatiotemporal atrophy subtypes, named for the location of the earliest affected regions as: ‘Subcortical’ (n = 359, 33%), ‘Limbic’ (n = 237, 22%) and ‘Cortical’ (n = 187, 17%). A fourth subgroup having sub-threshold/no atrophy was named ‘Sub-threshold atrophy’ (n = 317, 29%). Statistical differences in clinical scores existed between the no-atrophy subgroup and the atrophy subtypes, but not among the atrophy subtypes. This suggests that the prime T1-weighted MRI delineator of clinical differences in Parkinson’s disease is atrophy severity, rather than atrophy location. Future work on unravelling the biological and clinical heterogeneity of Parkinson’s disease should leverage more sensitive neuroimaging modalities and multimodal data.
  • A worldwide study of subcortical shape as a marker for clinical staging in Parkinson’s disease
    Max A. Laansma, Yuji Zhao, Eva M. van Heese, Joanna K. Bright, Conor Owens-Walton, et al.
    Npj Parkinson S Disease, 2024
  • A worldwide study of white matter microstructural alterations in people living with Parkinson’s disease
    Conor Owens-Walton, Talia M. Nir, Sarah Al-Bachari, Sonia Ambrogi, Tim J. Anderson, et al.
    Npj Parkinson S Disease, 2024
  • Neurostructural subgroup in 4291 individuals with schizophrenia identified using the subtype and stage inference algorithm
    Yuchao Jiang, Cheng Luo, Jijun Wang, Lena Palaniyappan, Xiao Chang, et al.
    Nature Communications, 2024
  • Brain-age prediction: Systematic evaluation of site effects, and sample age range and size
    Yuetong Yu, Hao‐Qi Cui, Shalaila S. Haas, Faye New, Nicole Sanford, et al.
    Human Brain Mapping, 2024
  • Verbal Learning and Memory Deficits across Neurological and Neuropsychiatric Disorders: Insights from an ENIGMA Mega Analysis
    Eamonn Kennedy, Spencer W. Liebel, Hannah M. Lindsey, Shashank Vadlamani, Pui-Wa Lei, et al.
    Brain Sciences, 2024
  • Correction to: White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group (Molecular Psychiatry, (2024), 29, 4, (1063-1074), 10.1038/s41380-023-02392-6)
    Bo-Gyeom Kim, Gakyung Kim, Yoshinari Abe, Pino Alonso, Stephanie Ameis, et al.
    Molecular Psychiatry, 2024
  • White matter diffusion estimates in obsessive-compulsive disorder across 1653 individuals: machine learning findings from the ENIGMA OCD Working Group
    Bo-Gyeom Kim, Gakyung Kim, Yoshinari Abe, Pino Alonso, Stephanie Ameis, et al.
    Molecular Psychiatry, 2024
  • Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation
    Ruiyang Ge, Yuetong Yu, Yi Xuan Qi, Yu-nan Fan, Shiyu Chen, et al.
    Lancet Digital Health, 2024
  • Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study
    Shuqing Si, Anbreen Bi, Zhaoying Yu, Cheryl See, Sinead Kelly, et al.
    Molecular Psychiatry, 2024
  • The Role of the Anterior Thalamic Nuclei in the Genesis of Memory Disorders in Alzheimer's Disease: An Exploratory Study
    Journal of Alzheimer S Disease, 2024
  • Data-driven biomarkers better associate with stroke motor outcomes than theory-based biomarkers
    Emily R Olafson, Christoph Sperber, Keith W Jamison, Mark D Bowren, Aaron D Boes, et al.
    Brain Communications, 2024
  • Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study
    Rebecca Kerestes, Max A. Laansma, Conor Owens‐Walton, Andrew Perry, Eva M. van Heese, et al.
    Movement Disorders, 2023
  • Cortical morphology in patients with the deficit and non-deficit syndrome of schizophrenia: a worldwide meta- and mega-analyses
    Nerisa Banaj, Daniela Vecchio, Fabrizio Piras, Pietro De Rossi, Juan Bustillo, et al.
    Molecular Psychiatry, 2023
  • Game Transfer Phenomena in a Clinical Case with Psychosis and Gaming Disorder
    Huu Kim Le, Angelica B. Ortiz de Gortari, Annabel Callan, Daragh Poynton, Daniela Vecchio, et al.
    Psychiatry International, 2023
  • Association of Brain Age, Lesion Volume, and Functional Outcome in Patients With Stroke
    Sook-Lei Liew, Nicolas Schweighofer, James H. Cole, Artemis Zavaliangos-Petropulu, Bethany P. Lo, et al.
    Neurology, 2023