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The extraordinary advancements in neuroscientific technology for brain recordings over the last decades have led to increasingly complex spatiotemporal data sets. To reduce oversimplifications, new models have been developed to be able to identify meaningful patterns and new insights within a highly demanding data environment. To this extent, we propose a new model called parameter clustering functional principal component analysis (PCl-fPCA) that merges ideas from functional data analysis and Bayesian nonparametrics to obtain a flexible and computationally feasible signal reconstruction and exploration of spatiotemporal neuroscientific data. In particular, we use a Dirichlet process Gaussian mixture model to cluster functional principal component scores within the standard Bayesian functional PCA framework. This approach captures the spatial dependence structure among smoothed time series (curves) and its interaction with the time domain without imposing a prior spatial structure on the data. Moreover, by moving the mixture from data to functional principal component scores, we obtain a more general clustering procedure, thus allowing a higher level of intricate insight and understanding of the data. We present results from a simulation study showing improvements in curve and correlation reconstruction compared with different Bayesian and frequentist fPCA models and we apply our method to functional magnetic resonance imaging and electroencephalogram data analyses providing a rich exploration of the spatiotemporal dependence in brain time series. Publisher PDF

Brain functional connectivity is a widely investigated topic in neuroscience. In recent years, the study of brain connectivity has been largely aided by graph theory. The link between time series recorded at multiple locations in the brain and the construction of a graph is usually an adjacency matrix. The latter converts a measure of the connectivity between two time series, typically a correlation coefficient, into a binary choice on whether the two brain locations are functionally connected or not. As a result, the choice of a threshold τ over the correlation coefficient is key. In the present work, we propose a multiple testing approach to the choice of τ that uses the Bayes false discovery rate and a new estimator of the statistical power called average power function to balance the two types of statistical error. We show that the proposed average power function estimator behaves well both in case of independence and weak dependence of the tests and it is reliable under several simulated dependence conditions. Moreover, we propose a robust method for the choice of τ using the 5% and 95% percentiles of the average power function and False Discovery Rate bootstrap distributions, respectively, to improve stability. We applied our approach to functional magnetic resonance imaging and high density electroencephalogram data.

Objective: Only a limited percentage of persons with MS (pwMS) participate to multidisciplinary rehabilitation (MDR) because of poor support, knowledge and motivation. We reasoned that pwMS should be more effectively prepared to increase their adherence. We implemented an innovative collaborative approach, called “brief highimpact preparatory experience” (b-HIPE), inspired by an overarching model based on the interplay between competence, motivation and opportunity to increase in a short time awareness and motivation of pwMS. Methods: B-HIPE integrates physiotherapy, mindfulness, sailing, healthy diet, and cultural activities to be experienced in a convivial form at a beautiful seaside location in Sardinia. Sixteen pwMS participated to 3 successive one-week editions of the b-HIPE, co-sponsored by the Rotary Club of Milan and supported by researchers of our Institute and of partner associations. The feasibility was assessed with structured questionnaires and free reports concerning accommodation, logistics, coordination, social climate and the specific activities proposed. For this pilot study we used a single-group design with repeated measurements at baseline and post-intervention. The SF-36 QoL scale was the main outcome measure, the Fatigue Severity Scale (FSS), the Berg Balance scale (BBS) and the 9 hole peg test (9HPT) were the secondary outcomes. Results: The approach was feasible. Scores on several FS-36 scales and secondary outcomes were significantly improved. Participants’ satisfaction with all aspects of the experience was above expectations. PwMS became more motivated and aware of physical and mental resources, all learned to sail adapted monohulls, strategies to master stress and to modify their diet according to specific recommendations. Conclusion: B-HIPE is safe and feasible. The interplay of multiple factors produced in a very short time the expected changes in participants’ attitude toward a healthier lifestyle. A monitoring program is ongoing to assess long-term effects including adherence to hospital-based MDR. Publisher PDF

The aim of this retrospective study was to preliminarily assess whether the EP-score, a summary score derived from multimodal evoked potentials tests, might be used as a measure of treatment efficacy in multiple sclerosis (MS).A sample of 56 relapsing remitting MS (RRMS) patients who at diagnosis started treatment with interferon β (INFβ, n = 19), glatiramer acetate (GA, n = 15) or refused any chronic treatment were assessed at baseline (before treatment) and at a median of 1.7 and 3.6 years thereafter. Outcome variables were Expanded Disability Status Scale (EDSS), EP-Score, visual evoked potentials (VEP) and somatosensory evoked potentials (SEP) scores measured as differences between baseline and follow-ups. Statistical differences between groups and follow-ups were assessed using non-parametric analyses.Treatment effects were not significant for EDSS both at the first and at the second follow-up, while a trend toward significance was observed in the EP-score only in the first follow-up (p = 0.07). Post-hoc analysis showed a greater decrease in median VEP-score for the IFNβ group compared to the GA and DF groups at both the first and second follow-ups.We found no evidence that either INFβ or GA significantly improved disability in RRMS patients. Using the EP-score as an outcome measure, we found that it was improved at both follow-ups in the INFβ group mainly due to a decrease in the VEP-score. This finding supports the proposal to include the EP-score as an additional outcome variable in future studies of treatment efficacy in MS.

The usefulness of sympathetic skin responses (SSR) in multiple sclerosis (MS) has been advocated by several studies in the last 20 years; however, due to a great heterogeneity of findings, a comprehensive meta-analysis of case-control studies is in order to pinpoint consistencies and investigate the causes of discrepancies. We searched MEDLINE, EMBASE and Cochrane databases for case-control studies comparing SSR absence frequency and latency between patients with MS and healthy controls. Thirteen eligible studies including 415 MS patients and 331 healthy controls were identified. The pooled analysis showed that SSR can be always obtained in healthy controls while 34% of patients had absent SSRs in at least one limb (95% CI 22–47%; p

Background. Alzheimer’s disease (AD) is a highly prevalent neurodegenerative disorder. Rate of decline and functional restoration in AD greatly depend on the capacity for neural plasticity within residual neural tissues; this is at least partially influenced by polymorphisms in genes that determine neural plasticity, including Apolipoprotein E4 ( ApoE4) and synaptosomal-associated protein of 25 kDa ( SNAP-25). Objective. We investigated whether correlations could be detected between polymorphisms of ApoE4 and SNAP-25 and the outcome of a multidimensional rehabilitative approach, based on cognitive stimulation, behavioral, and functional therapy (multidimensional stimulation therapy [MST]). Methods. Fifty-eight individuals with mild-to-moderate AD underwent MST for 10 weeks. Neuro-psychological functional and behavioral evaluations were performed blindly by a neuropsychologist at baseline and after 10 weeks of therapy using Mini-Mental State Examination (MMSE), Functional Living Skill Assessment (FLSA), and Neuropsychiatric Inventory (NPI) scales. Molecular genotyping of ApoE4 and SNAP-25 rs363050, rs363039, rs363043 was performed. Results were correlated with ΔMMSE, ΔNPI and ΔFLSA scores by multinomial logistic regression analysis. Results. Polymorphisms in both genes correlated with the outcome of MST for MMSE and NPI scores. Thus, higher overall MMSE scores after rehabilitation were detected in ApoE4 negative compared to ApoE4 positive patients, whereas the SNAP-25 rs363050(G) and rs363039(A) alleles correlated with significant improvements in behavioural parameters. Conclusions. Polymorphisms in genes known to modulate neural plasticity might predict the outcome of a multistructured rehabilitation protocol in patients with AD. These data, although needing confirmation on larger case studies, could help optimizing the clinical management of individuals with AD, for example defining a more intensive treatment in those subjects with a lower likelihood of success.

To devise a multivariate parametric model for short-term prediction of disability using the Expanded Disability Status Scale (EDSS) and multimodal sensory EP (mEP). A total of 221 multiple sclerosis (MS) patients who underwent repeated mEP and EDSS assessments at variable time intervals over a 20-year period were retrospectively analyzed. Published criteria were used to compute a cumulative score (mEPS) of abnormalities for each of 908 individual tests. Data of a statistically balanced sample of 58 patients were fed to a parametrical regression analysis using time-lagged EDSS and mEPS along with other clinical variables to estimate future EDSS scores at 1 year. Whole sample cross-sectional mEPS were moderately correlated with EDSS, whereas longitudinal mEPS were not. Using the regression model, lagged mEPS and lagged EDSS along with clinical variables provided better future EDSS estimates. The R 2 measure of fit was significant and 72% of EDSS estimates showed an error value of ±0.5. A parametrical regression model combining EDSS and mEPS accurately predicts short-term disability in MS patients and could be used to optimize decisions concerning treatment.