BACKGROUND AND PURPOSE:Identifying patients with MS at higher risk of clinical progression is essential to inform clinical management. We aimed to build prognostic models by using machine learning (ML) algorithms predicting long-term clinical outcomes based on a systematic mapping of volumetric, radiomic, and macrostructural disconnection features from routine brain MRI scans of patients with MS. MATERIALS AND METHODS:In this longitudinal monocentric study, 3T structural MRI scans of patients with MS were retrospectively analyzed. Based on a 10-year clinical follow-up (average duration = 9.4 ± 1.1 years), patients were classified according to confirmed disability progression (CDP) and cognitive impairment (CI) as assessed through the Expanded Disability Status Scale and the Brief International Cognitive Assessment of Multiple Sclerosis battery, respectively. Three-dimensional T1-weighted and FLAIR images were automatically segmented to obtain volumes, disconnection scores (estimated based on lesion masks and normative tractography data), and radiomic features from 116 GM regions defined according to the automated anatomic labeling atlas. Three ML algorithms (Extra Trees, Logistic Regression, and Support Vector Machine) were used to build models predicting long-term CDP and CI based on MRI-derived features. Feature selection was performed on the training set with a multistep process, and models were validated with a holdout approach, randomly splitting the patients into training (75%) and test (25%) sets. RESULTS:We studied 177 patients with MS (men/women = 51/126; mean ± standard deviation age: 35.2 ± 8.7 years). Long-term CDP and CI were observed in 71 and 55 patients, respectively. Regarding the CDP class prediction analysis, the feature selection identified 13-, 12-, and 10-feature subsets obtaining an accuracy on the test set of 0.71, 0.69, and 0.67 for the Extra Trees, Logistic Regression, and Support Vector Machine classifiers, respectively. Similarly, for the CI prediction, subsets of 16, 17, and 19 features were selected, with 0.69, 0.64, and 0.62 accuracy values on the test set, respectively. There were no significant differences in accuracy between ML models for CDP (P = .65) or CI (P = .31). CONCLUSIONS:Building on quantitative features derived from conventional MRI scans, we obtained long-term prognostic models, potentially informing patients' stratification and clinical decision-making.

A healthy brain exhibits a rich dynamical repertoire, with flexible spatiotemporal patterns replaying on both microscopic and macroscopic scales. We hypothesize that the observed relationship between empirical structure and functional patterns is best explained when the microscopic neuronal dynamics is close to a critical regime. Using a modular spiking neuronal network model based on empirical connectomes, we posit that multiple stored functional patterns can transiently reoccur when the system operates near a critical regime, generating realistic brain dynamics and structural-functional relationships. The connections in the model are chosen so as to force the network to learn and propagate suited modular spatiotemporal patterns. To test our hypothesis, we employ magnetoencephalography and tractography data from five healthy individuals. We show that the extended critical region of the model maximizes the structure-function correlation and generates realistic features, demonstrating the relevance of near-critical regimes for physiological brain activity.

BACKGROUND AND OBJECTIVES:Although multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses typically require advanced MRI sequences not commonly acquired in clinical practice. Using conventional MRI, we assessed cross-sectional and longitudinal structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability. METHODS:In this longitudinal monocentric study, 3T structural MRI of pwMS and healthy controls (HC) was retrospectively analyzed. Physical and cognitive disabilities were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Demyelinating lesions were automatically segmented, and the corresponding masks were used to assess pairwise structural disconnection between atlas-defined brain regions based on normative tractography data. Using the Morphometric Inverse Divergence method, we computed morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its extension NBS-predict, we tested whether subject-level connectomes were associated with disease status, progression, clinical disability, and long-term confirmed disability progression (CDP), independently from global lesion burden and atrophy. RESULTS:We studied 461 pwMS (age = 37.2 ± 10.6 years, F/M = 324/137), corresponding to 1,235 visits (mean follow-up time = 1.9 ± 2.0 years, range = 0.1-13.3 years), and 55 HC (age = 42.4 ± 15.7 years; F/M = 25/30). Long-term clinical follow-up was available for 285 pwMS (mean follow-up time = 12.4 ± 2.8 years), 127 of whom (44.6%) exhibited CDP. At baseline, structural disconnection in pwMS was mostly centered around the thalami and cortical sensory and association hubs, while morphometric similarity was extensively disrupted (pFWE < 0.01). EDSS was related to frontothalamic disconnection (pFWE < 0.01) and disrupted morphometric similarity around the left perisylvian cortex (pFWE = 0.02), while SDMT was associated with cortico-subcortical disconnection in the left hemisphere (pFWE < 0.01). Longitudinally, both structural disconnection and morphometric similarity disruption significantly progressed (pFWE = 0.04 and pFWE < 0.01), correlating with EDSS increase (ρ = 0.07, p = 0.02 and ρ = 0.11, p < 0.001), while baseline disconnection predicted long-term CDP (accuracy = 59% [58-60], p = 0.03). DISCUSSION:Structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its progression. They explain disease-related clinical disability and predict its long-term evolution independently from global lesion burden and atrophy, potentially adding to established MRI measures as network-based biomarkers of disease severity and progression.

Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patients, and it remains not fully understood. Accurate classification of FOG in PD is crucial for tailoring effective interventions and is necessary for a better understanding of its underlying mechanisms. In the present work, we applied four Machine Learning (ML) classifiers (Decision Tree - DT, Random Forest - RF, Multilayer Perceptron - MLP, Logistic Regression - LOG) to different four metrics derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data processing to assess their accuracy in automatically classifying PD patients based on the presence or absence of Freezing of Gait (FOG). To validate our approach, we applied the same methodologies to distinguish PD patients from a group of Healthy Subject (HS). The performance of the four ML algorithms was validated by repeated k-fold cross-validation on randomly selected independent training and validation subsets. The results showed that when discriminating PD from HS, the best performance was achieved using RF applied to fractional Amplitude of Low-Frequency Fluctuations (fALFF) data (AUC 96.8 ± 2 %). Similarly, when discriminating PD-FOG from PD-nFOG, the RF algorithm was again the best performer on all four metrics, with AUCs above 90 %. Finally, trying to unbox how AI system black-box choices were made, we extracted features' importance scores for the best-performing method(s) and discussed them based on the results obtained to date in rs-fMRI studies on FOG in PD and, more generally, in PD. In summary, regions that were more frequently selected when differentiating both PD from HS and PD-FOG from PD-nFOG patients were mainly relevant to the extrapyramidal system, as well as visual and default mode networks. In addition, the salience network and the supplementary motor area played an additional major role in differentiating PD-FOG from PD-nFOG patients.

Multiple sclerosis (MS) is a clinically heterogeneous, multifactorial autoimmune disorder affecting the central nervous system. Structural damage to the myelin sheath, resulting in the consequent slowing of the conduction velocities, is a key pathophysiological mechanism. In fact, the conduction velocities are closely related to the degree of myelination, with thicker myelin sheaths associated to higher conduction velocities. However, how the intensity of the structural lesions of the myelin translates to slowing of nerve conduction delays is not known. In this work, we use large-scale brain models and Bayesian model inversion to estimate how myelin lesions translate to longer conduction delays across the damaged tracts. A cohort of 38 subjects (20 healthy and 18 with MS) underwent MEG recordings during an eyes-closed resting-state condition, along with MRI acquisitions and detailed white matter tractography analysis. We observed that MS patients consistently showed decreased power within the alpha frequency band (8-13 Hz) as compared to the healthy group. We also derived a lesion matrix indicating the percentage of lesions for each tract in every patient. Using large-scale brain modeling, the neural activity of each region was represented as a Stuart-Landau oscillator operating in a regime showing damped oscillations, and the regions were coupled according to subject-specific connectomes. We propose a linear formulation to the relationship between the conduction delays and the amount of structural damage in each white matter tract. Dependent upon the parameter γ $$ \upgamma $$ , this function translates lesions into edge-specific conduction delays (leading to shifts in the power spectra). Using deep neural density estimators, we found that the estimation of γ $$ \upgamma $$ showed a strong correlation with the alpha peak in MEG recordings. The most probable inferred γ $$ \upgamma $$ for each subject is inversely proportional to the observed peaks, while power peaks themselves do not correlate with total lesion volume. Furthermore, the estimated parameters were predictive (cross-sectionally) of individual clinical disability. This study represents the initial exploration showcasing the location-specific impact of myelin lesions on conduction delays, thereby enhancing the customization of models for individuals with multiple sclerosis.

Healthy brain exhibits a rich dynamical repertoire, with flexible spatiotemporal patterns replays on both microscopic and macroscopic scales. How do fixed structural connections yield a diverse range of dynamic patterns in spontaneous brain activity? We hypothesize that the observed relationship between empirical structure and functional patterns is best explained when the microscopic neuronal dynamics is close to a critical regime. Using a modular Spiking Neuronal Network model based on empirical connectomes, we posit that multiple stored functional patterns can transiently reoccur when the system operates near a critical regime, generating realistic brain dynamics and structural-functional relationships. The connections in the model are chosen as to force the network to learn and propagate suited modular spatiotemporal patterns. To test our hypothesis, we employ magnetoencephalography and tractography data from five healthy individuals. We show that the critical regime of the model is able to generate realistic features, and demonstrate the relevance of near-critical regimes for physiological brain activity. ### Competing Interest Statement The authors have declared no competing interest.

AbstractWhilst multiple sclerosis (MS) can be conceptualized as a network disorder, brain network analyses are typically dependent on advanced MRI sequences not commonly acquired in clinical practice. Here, we used conventional MRI to assess cross-sectional and longitudinal modifications of structural disconnection and morphometric similarity networks in people with MS (pwMS), along with their relationship with clinical disability.In this longitudinal monocentric study, 3T structural MRI scans of pwMS and healthy controls (HC) were retrospectively analysed. Physical and cognitive disabilities were assessed with the expanded disability status scale (EDSS) and the symbol digit modalities test (SDMT), respectively. Demyelinating lesions were automatically segmented on 3D-T1w and FLAIR images and, based on normative tractography data, the corresponding masks were used to compute pairwise structural disconnection between atlas-defined brain regions (100 cortical and 14 subcortical). Using the Morphometric Inverse Divergence (MIND) method, we built matrices of morphometric similarity between cortical regions based on FreeSurfer surface reconstruction. Using network-based statistics (NBS) and its prediction-based extension NBS-predict, we tested whether subject-level connectomes were associated with disease status, progression, clinical disability, and long-term confirmed disability progression (CDP), independently from global lesion burden and atrophy. The coupling between structural disconnection and morphometric similarity was assessed at different scales.We studied 461 pwMS (age 37.2±10.6 years, F/M 324/137), corresponding to 1235 visits (mean follow-up time 1.9±2.0 years, range 0.1-13.3 years), and 55 HC (age 42.4±15.7 years; F/M 25/30). Long-term clinical follow-up was available for 285 pwMS (mean follow-up time 12.4±2.8 years), 127 of whom (44.6%) exhibited CDP. At baseline, structural disconnection in pwMS was mostly centered around the thalami and cortical sensory and association hubs, while morphometric similarity was extensively disrupted (pFWE<0.01). EDSS was related to fronto-thalamic disconnection (pFWE<0.01) and disrupted morphometric similarity around the left perisylvian cortex (pFWE0.02), whilst SDMT was associated with cortico-subcortical disconnection in the left hemisphere (pFWE<0.01). Longitudinally, both structural disconnection and morphometric similarity disruption significantly progressed (pFWE0.04 andpFWE<0.01), correlating with EDSS increase (rho 0.07, p 0.02 and rho 0.11,p<0.001), whilst baseline disconnection predicted long-term CDP with nearly 60% accuracy (p0.03). On average, structural disconnection and morphometric similarity were positively associated at both the edge (rho 0.18,p<0.001) and node (rho 0.16,p<0.001) levels.Structural disconnection and morphometric similarity networks, as assessed through conventional MRI, are sensitive to MS-related brain damage and its progression. They explain disease-related clinical disability and predict its long-term evolution independently from global lesion burden and atrophy, potentially adding to established MRI measures as network-based biomarkers of disease severity and progression.

Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests focus on specific white-matter tracts, which may not reflect overall impairment accurately. In this study, we integrate diffusion tensor immaging (DTI) and magnetoencephalography (MEG) data into individualized virtual brain models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in patients. Remarkably, these velocities proved superior predictors of clinical disability compared to structural damage. Our findings underscore a nuanced relationship between conduction delays and large-scale brain dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute causatively to clinical outcomes in MS.

Motivation: Multiple sclerosis can be modelled as a network disorder. Progressive demyelination and neurodegeneration lead to structural disconnection and disruption of the morphometric similarity between gray matter regions. Goal(s): To obtain measures of structural disconnection and morphometric similarity networks from conventional MRI sequences and test whether they are sensitive to disease status and clinical disability. Approach: 461 patients were imaged. Using publicly available software, we computed structural disconnection using white matter lesions masks and normative tractography atlases. Likewise, morphometric similarity was computed from standard FreeSurfer outputs. Results: Structural disconnection and morphometric similarity networks are sensitive to disease status and explain clinical disability. Impact: Measures of structural disconnection and morphometric similarity networks obtained from conventional MRI sequences are sensitive to multiple sclerosis and its related physical and cognitive disability. Our approach could represent a way to overcome the limitations of the standard network analyses.

Multiple sclerosis (MS) is an autoimmune chronic disease characterized by inflammation and demyelination of the central nervous system (CNS). Despite numerous studies conducted, valid biomarkers enabling a definitive diagnosis of MS are not yet available. The aim of our study was to identify a marker from a blood sample to ease the diagnosis of MS. In this study, since there is evidence connecting the serotonin pathway to MS, we used an ELISA (Enzyme-Linked Immunosorbent Assay) to detect serum MS-specific auto-antibodies (auto-Ab) against the extracellular loop 1 (ECL-1) of the 5-hydroxytryptamine (5-HT) receptor subtype 2A (5-HT2A). We utilized an ELISA format employing poly-D-lysine as a pre-coating agent. The binding of 208 serum samples from controls, both healthy and pathological, and of 104 serum samples from relapsing–remitting MS (RRMS) patients was tested. We observed that the serum-binding activity in control cohort sera, including those with autoimmune and neurological diseases, was ten times lower compared to the RRMS patient cohort (p = 1.2 × 10−47), with a sensitivity and a specificity of 98% and 100%, respectively. These results show that in the serum of patients with MS there are auto-Ab against the serotonin receptor type 2A which can be successfully used in the diagnosis of MS due to their high sensitivity and specificity.

ABSTRACT Background Large-scale brain activity has long been investigated under the erroneous assumption of stationarity. Nowadays, we know that resting state functional connectivity is characterised by aperiodic, scale-free bursts of activity (i.e. neuronal avalanches) that intermittently recruit different brain regions. These different patterns of activity represent a measure of brain flexibility, whose reduction has been found to predict clinical impairment in multiple neurodegenerative diseases such as Parkinson’s disease, Amyotrophic Lateral Sclerosis, and Alzheimer’s disease. Brain flexibility has been recently found increased in Multiple Sclerosis (MS) but its relationship with clinical disability remains elusive. Also, potential differences in brain dynamics according to the MS clinical phenotypes remain unexplored so far. Methods We studied brain flexibility through source-reconstruction of magnetoencephalography (MEG) signals in a cohort of 25 MS patients (10 RRMS and 15 SPMS) and 25 healthy controls (HC). Results RRMS patients showed greater brain flexibility than HC. On the contrary, no differences in brain dynamics were found in SPMS patients as compared to HC. Finally, brain dynamics showed a different predictive power on clinical disability according to the MS type. Conclusion For the first time, we investigated brain dynamics in MS patients through high temporal resolution techniques, unveiling differences in brain flexibility according to the MS phenotype and its relationship with clinical disability.

Craniosynostoses (CRS) are caused by the premature fusion of one or more cranial sutures, with isolated nonsyndromic CRS accounting for most of the clinical manifestations. Such premature suture fusion impacts both skull and brain morphology and involves regions far beyond the immediate area of fusion. The combined use of different neuroimaging tools allows for an accurate depiction of the most prominent clinical–radiological features in nonsyndromic CRS but can also contribute to a deeper investigation of more subtle alterations in the underlying nervous tissue organization that may impact normal brain development. This review paper aims to provide a comprehensive framework for a better understanding of the present and future potential applications of neuroimaging techniques for evaluating nonsyndromic CRS, highlighting strategies for optimizing their use in clinical practice and offering an overview of the most relevant technological advancements in terms of diagnostic performance, radiation exposure, and cost-effectiveness.

An innovative multicompartmental anatomic brain phantom (StepBrain) is described to simulate the in vivo tracer uptake of gray matter, white matter, and striatum, overcoming the limitations of currently available phantoms. Methods: StepBrain was created by exploiting the potential of fused deposition modeling 3-dimensional printing to replicate the real anatomy of the brain compartments, as modeled through ad hoc processing of healthy-volunteer MR images. Results: A realistic simulation of 18F-FDG PET brain studies, using target activity to obtain the real concentration ratios, was obtained, and the results of postprocessing with partial-volume effect correction tools developed for human PET studies confirmed the accuracy of these methods in recovering the target activity concentrations. Conclusion: StepBrain compartments (gray matter, white matter, and striatum) can be simultaneously filled, achieving different concentration ratios and allowing the simulation of different (e.g., amyloid, tau, or 6-fluoro-l-dopa) tracer distributions, with a potentially valuable role for multicenter PET harmonization studies.