Emile D' Angremont

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

This study aimed to develop and validate a fully automated pipeline for vertebral level identification (C1–C3) of the upper cervical spinal cord (SC) in T1-weighted brain MRI with a quality control module to assess the generated masks. MRI data from 998 controls and 879 patients with hereditary ataxias (32 sites; age 5–85 years; 46% male) were used to train deep learning models (nnU-Net, U-Net with deep supervision, SwinUNETR) for automatic vertebral labeling of C1–C3 levels. A secondary model based on VGG, ResNet, and EfficientNet architectures was trained to identify vertebral labeling errors. The pipeline also generates image quality metrics including signal-to-noise ratio, contrast-to-noise ratio, coefficient of joint variation, and entropy focus criterion. External validation was performed using 2737 MRIs from four public datasets, and clinical utility was demonstrated in a longitudinal cohort of 51 patients with multiple sclerosis. The nnU-Net achieved the highest performance (Dice: C1 = 0.85, C2 = 0.89, C3 = 0.89), labeling 95.8% of mislabeled cases from the Spinal Cord Toolbox. The VGG-based classifier achieved 97.9% accuracy and 98.7% sensitivity. External validation showed labeling accuracies exceeding 92% in four of five datasets and sensitivity to longitudinal SC atrophy in multiple sclerosis. This pipeline enables scalable, reliable vertebral labeling and morphometric extraction of the upper cervical SC for multicenter studies and supports biomarker development in neurodegenerative diseases.

Differences between clinical questionnaires limit the pooling of symptom data across studies, particularly when participants have completed different instruments. We developed a framework that uses questionnaire item content to harmonize neuropsychiatric measures without requiring overlapping response data. Experts mapped items to shared symptom dimensions, which informed score transformation and the training of models to map new items automatically. We evaluated the framework in a multisite Parkinson’s disease sample and an independent depression cohort. Transformation improved score alignment across questionnaires, while additional analyses supported clinical relevance and cross-questionnaire convergence. Our fine-tuned embedding models performing the semantic mapping task generalize to unseen questionnaires with a mean accuracy of 85.3%. This semantic, dimensional harmonization approach supports retrospective pooling of heterogeneous questionnaire data and offers a practical and systematic path toward mega-analyses on retrospective datasets.

Objective: Disruption of the excitation/inhibition balance may contribute to the pathophysiology of bipolar disorder, with post-mortem studies reporting abnormalities in GABA-receptors, interneurons and inhibitory signaling in prefrontal areas. Transcranial magnetic stimulation with electroencephalography (TMS-EEG) enables in vivo assessment of cortical excitability/inhibition. This study examined short-latency intracortical inhibition (SICI) after left- and right-dorsolateral prefrontal cortex (DLPFC) stimulation in bipolar depression (BDep, n=10) and healthy controls (HC, n=22). Methods: SICI (paired-pulse TMS) and excitability (single-pulse TMS) were quantified using local- and global-mean-field-power. Associations with lithium use and between-group differences in TMS-evoked potential amplitudes were also explored. Results: For left-DLPFC stimulation, BDep showed weaker SICI than HC (local: 3.7%±12.5 vs 9.0%±20.3, p=0.05; global: 1.5%±11.7 vs 8.8%±20.6, p=0.10), driven by larger ppTMS responses (weaker inhibition). For right-DLPFC stimulation, BDep showed stronger SICI than HC (local: 17.3%±16.1 vs 0.75%±27.1, p=0.36; global: 16.2%±14.6 vs −1.0%±21.8, p=0.03), driven by a larger spTMS response (enlarged excitability). Stronger right-hemispheric global-SICI was most pronounced in BDep patients not using lithium (17.9%±7.0) vs lithium users (3.9%±11.9) and HC (pFDR=0.03). Conclusions: BDep is characterized by reduced cortical inhibition after left-DLPFC stimulation and enlarged cortical excitability after right-DLPFC stimulation; the latter partly normalized by lithium. Significance: To our knowledge, this is the first study to apply TMS-EEG to the bilateral DLPFC in BDep, revealing distinct patterns of hemispheric dysfunction. These findings warrant replication in larger samples, to further elucidate the underlying pathophysiology and inform the mechanisms of action of neuromodulation treatments, such as rTMS.

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

Neuroimaging data management and processing are tedious and error-prone, prompting reproducibility concerns. Globally, studies with heterogeneous infrastructure and governance policies lead to eclectic data processing and sharing, necessitating standardization of data workflows to ensure reusability and comparability of multi-centric datasets. The Nipoppy neuroinformatics framework facilitates such standardization by combining specification, protocol, and software to manage study-level data workflows. With its adoption, researchers can share standardized, derived datasets enabling efficient, reproducible, and inclusive research.

International data privacy regulations impede the pooling of research data for collaborative analysis. We introduce Neurobagel, a federated network enabling cohort discovery across locally governed, access-controlled datasets. Through intuitive graphical tools and a decentralized query infrastructure, Neurobagel facilitates harmonization, control, and discovery of data according to local regulations. Today, Neurobagel is deployed by consortia and data platforms in Europe, North America, Asia, and Australia, supporting diverse and evolving regulatory frameworks.

Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.

The adoption of open science principles for data has been instrumental in enabling the creation and reuse of large datasets and efficient collaboration, but requires a dedicated open data sharing infrastructure. Although clinical neuroscience research increasingly involves large-scale collaboration on controlled, sensitive data, open science principles can bring key benefits in this setting. The unique privacy and access requirements of clinical research create challenges that require a specialized data infrastructure, focused on local governance. Here we present a narrative account of a pilot project to establish such an infrastructure for the data of the ENIGMA Parkinson’s Disease working group by adopting the decentralized Nipoppy and Neurobagel tool stack. Our collaboration has led to the adoption of community data standards among international sites of the working group and has launched an open portal that enables the cross-site discovery of cohorts based on harmonized metadata. We discuss our approach to this collaboration, the social and technical challenges we have encountered and how we addressed them in the hope that groups in similar situations may benefit from our experiences.

Objective: The phenotype of patients with Parkinson's disease carrying GBA1 variants (GBA‐PD) suggest similarities to symptomatology associated with early cholinergic system degeneration. Therefore, this study aims to investigate the clinical features and the cholinergic innervation pattern in patients with early GBA‐PD versus those without the GBA1 mutation (non‐GBA‐PD).Methods: A total of 46 GBA‐PD and 104 non‐GBA‐PD subjects were included. Clinical assessments included motor and non‐motor evaluation, as well as a comprehensive neuropsychological examination. Cholinergic system integrity was assessed using ¹⁸F‐Fluoroethoxybenzovesamicol (¹⁸F‐FEOBV) positron emission tomography (PET) to investigate the differences between GBA‐PD and non‐GBA‐PD. Given the higher prevalence of females in GBA‐PD, analyses were repeated when stratified by sex. Additionally, we examined the association between cognitive domains and whole‐brain cholinergic binding in both groups. Exploratory analyses examined clinical and ¹⁸F‐FEOBV binding differences among GBA1 variants.Results: GBA‐PD patients exhibited a higher burden of non‐motor symptoms and lower cognitive performance on executive functions and attention. We observed a more pronounced cholinergic denervation in GBA‐PD, compared to non‐GBA‐PD, primarily in the anterior, central, and limbic regions. However, the distribution of cholinergic loss and its association with attention and executive dysfunction was comparable between GBA‐PD and non‐GBA‐PD. In addition, the clinical presentation and cholinergic binding differed significantly between sexes.Interpretation: These results suggest an important role of early cholinergic denervation in GBA‐PD patients, which is related to more severe cognitive dysfunction. ANN NEUROL 2025;98:398–409