Parameter clustering in Bayesian functional principal component analysis of neuroscientific data

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 PDFPeer reviewe

Sympathetic skin response in multiple sclerosis : a meta-analysis of case-control studies

The study was supported by a grant of the Italian Ministry of Health, Ricerca Corrente funding program 2014–2015 [RC2014].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 < 0.0001) but with considerable heterogeneity across studies (I2 = 90.3%). Patients' age explained 22% of the overall variability and positive correlations were found with Expanded Disability Status Scale and disease duration. The pooled mean difference of SSR latency showed a significant increase in patients on both upper (193 ms; 95% CI 120-270 ms) and lower (350 ms; 95% CI 190-510 ms) extremities. We tested the discriminatory value of SSR latency thresholds defined as the 95% confidence interval (CI) upper bound of the healthy controls, and validated the results on a new dataset. The lower limb threshold of 1.964 s produces the best results in terms of sensitivity 0.86, specificity 0.67, positive predicted value 0.75 and negative predicted value 0.80. Despite a considerable heterogeneity of findings, there is evidence that SSR is a useful tool in MS.Publisher PDFPeer reviewe

Exploring the predictive value of the evoked potentials score in MS within an appropriate patient population : a hint for an early identification of benign MS?

This study was supported by the Italian Ministry of Health, Ricerca Corrente funding plan to the institutional research activity of the Scientific Institute S. Maria Nascente of the Don C. Gnocchi Foundation.Background: The prognostic value of evoked potentials (EPs) in multiple sclerosis (MS) has not been fully established. The correlations between the Expanded Disability Status Scale (EDSS) at First Neurological Evaluation (FNE) and the duration of the disease, as well as between EDSS and EPs, have influenced the outcome of most previous studies. To overcome this confounding relations, we propose to test the prognostic value of EPs within an appropriate patient population which should be based on patients with low EDSS at FNE and short disease duration. Methods: We retrospectively selected a sample of 143 early relapsing remitting MS (RRMS) patients with an EDSS < 3.5 from a larger database spanning 20 years. By means of bivariate logistic regressions, the best predictors of worsening were selected among several demographic and clinical variables. The best multivariate logistic model was statistically validated and prospectively applied to 50 patients examined during 2009-2011. Results: The Evoked Potentials score (EP score) and the Time to EDSS 2.0 (TT2) were the best predictors of worsening in our sample (Odds Ratio 1.10 and 0.82 respectively, p=0.001). Low EP score (below 15-20 points), short TT2 (lower than 3-5 years) and their interaction resulted to be the most useful for the identification of worsening patterns. Moreover, in patients with an EP score at FNE below 6 points and a TT2 greater than 3 years the probability of worsening was 10% after 4-5 years and rapidly decreased thereafter. Conclusions: In an appropriate population of early RRMS patients, the EP score at FNE is a good predictor of disability at low values as well as in combination with a rapid buildup of disability. Interestingly, an EP score at FNE under the median together with a clinical stability lasting more than 3 years turned out to be a protective pattern. This finding may contribute to an early identification of benign patients, well before the term required to diagnose Benign MS (BMS).Publisher PDFPeer reviewe

Changing lifestyle of persons with multiple sclerosis : development, feasibility and preliminary results of a novel high-impact collaborative intervention in leisure environments

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 PDFPeer reviewe

Brain networks construction using Bayes FDR and average power function

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.</p