Sara Sommariva

Summary: From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.

Dementia with Lewy Bodies (DLB) is a heterogeneous disease characterized by four core clinical features: visual hallucinations, REM sleep behaviour disorder (RBD), cognitive fluctuations and Parkinsonism. We study the relationship between these features and functional connectivity from high-density electroencephalographic data of 33 subjects with early-stage DLB and 21 healthy controls. We used two connectivity metrics averaged on the individual theta and alpha bands, defined by determining the individual theta-to-alpha transition frequency through the package transfreq. After showing that DLB determines a shift toward lower frequencies of posterior dominant rhythm and theta-alpha transition, we used Network Based Statistics to explore differential connectivity networks between subgroups of DLB patients with different features. We found that both visual hallucinations and RBD are associated with increased connectivity in early DLB patients mostly in the left hemisphere, while cognitive fluctuations and Parkinsonism appear to have a non-significant impact on functional connectivity metrics in our population.

In real world applications dealing with compositional datasets, it is easy to face the presence of structural zeros. The latter arise when, due to physical limitations, one or more variables are intrinsically zero for a subset of the population under study. The classical Aitchison approach requires all the components of a composition to be strictly positive, since the adaptation of the most widely used statistical techniques to the compositional framework relies on computing the logratios of these components. Therefore, datasets containing structural zeros are usually split in two subsets, the one containing the observations with structural zeros and the one containing all the other data. Then statistical analysis is performed on the two subsets separately, assuming the two datasets are drawn from two different subpopulations. However, this approach may lead to incomplete results when the split into two populations is merely artificial. To overcome this limitation and increase the robustness of such an approach, we introduce a statistical test to check whether the first K principal components of the two datasets generate the same vector space. An approximation of the corresponding null distribution is derived analytically when data are normally distributed on the simplex and through a nonparametric bootstrap approach in the other cases. Results from simulated data demonstrate that the proposed procedure can discriminate scenarios where the subpopulations share a common subspace from those where they are actually distinct. The performance of the proposed method is also tested on an experimental dataset concerning microbiome measurements.

Dementia with Lewy Bodies (DLB) is a heterogeneous disease characterized by four core clinical features, namely visual hallucinations, REM sleep behaviour disorder (RBD), cognitive fluctuations and parkinsonism. In this paper, we perform for the first time a comprehensive study on the relationship between all these features and functional connectivity derived from high-density electroencephalographic data. To quantify functional connectivity, we used two different connectivity metrics, each one averaged on individual theta and alpha frequency bands. The latter were defined by determining the individual theta-to-alpha transition frequency through a previously validated package called transfreq. The study was performed using a cohort comprising 33 subjects affected by early stage DLB (mean age 82 ± 6 years, males/females: 23/10) and 21 healthy controls (mean age 71 ± 9 years, males/female: 10/11). After showing that overall DLB determines a shift toward lower frequencies of posterior dominant rhythm and theta-alpha transition, we used Network Based Statistic (NBS) to explore differential connectivity networks between subgroups of DLB patients with different core features. We consistently found that the presence of both visual hallucinations and RBD is associated with increased connectivity in early DLB patients mostly in the left hemisphere, while cognitive fluctuations and parkinsonism appear to have a non-significant impact on functional connectivity metrics. These findings may represent an initial compensatory mechanism in response to underlying neurodegeneration.