Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros–cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.

There are many cases in which the separation of different sources from single channel recordings is important, for example, in fluorescence spectral overlap compensation, electrical impedance signaling, intramuscular electromyogram decomposition or in the case of spike sorting of neuron potentials from microelectrode arrays (MEA). Focusing on the latter, the problem can be faced by identifying spikes emerging from the background and clustering into different groups, indicating the activity of different neurons. Problems are found when more spikes are superimposed in overlapped waveforms. We discuss the application of Biogeography-Based Optimization (BBO) to resolve this specific problem. Our algorithm is compared with three spike-sorting methods (SpyKING Circus, Common Basis Pursuit and Klusta), showing statistically better performance (in terms of F1 score, True Positive Rate—TPR and Positive Predictive Value—PPV) in resolving overlaps in realistic, simulated data. Specifically, BBO showed median F1, TPR and PPV of 100%, 100% and about 75%, respectively, considering a simulated noise with the same spectral density as the experimental one and a similar power with highly statistically significant improvements of at least two performance indexes over each of the other three tested algorithms.

The aim of this work was to monitor the effects of extracellular α-synuclein on the firing activity of midbrain neurons dissociated from substantia nigra TH-GFP mice embryos and cultured on microelectrode arrays (MEA). We monitored the spontaneous firing discharge of the network for 21 days after plating and the role of glutamatergic and GABAergic inputs in regulating burst generation and network synchronism. Addition of GABAA, AMPA and NMDA antagonists did not suppress the spontaneous activity but allowed to identify three types of neurons that exhibited different modalities of firing and response to applied L-DOPA: high-rate (HR) neurons, low-rate pacemaking (LR-p), and low-rate non-pacemaking (LR-np) neurons. Most HR neurons were insensitive to L-DOPA, while the majority of LR-p neurons responded with a decrease of the firing discharge; less defined was the response of LR-np neurons. The effect of exogenous α-synuclein (α-syn) on the firing discharge of midbrain neurons was then studied by varying the exposure time (0–48 h) and the α-syn concentration (0.3–70 μM), while the formation of α-syn oligomers was monitored by means of AFM. Independently of the applied concentration, acute exposure to α-syn monomers did not exert any effect on the spontaneous firing rate of HR, LR-p, and LR-np neurons. On the contrary, after 48 h exposure, the firing activity was drastically altered at late developmental stages (14 days in vitro, DIV, neurons): α-syn oligomers progressively reduced the spontaneous firing discharge (IC50 = 1.03 μM), impaired burst generation and network synchronism, proportionally to the increased oligomer/monomer ratio. Different effects were found on early-stage developed neurons (9 DIV), whose firing discharge remained unaltered, regardless of the applied α-syn concentration and the exposure time. Our findings unravel, for the first time, the variable effects of exogenous α-syn at different stages of midbrain network development and provide new evidence for the early detection of neuronal function impairment associated to aggregated forms of α-syn.

The electroencephalogram (EEG) of patients suffering from inflammatory diseases of the brain may show specific waveforms called slow biphasic complexes (SBC). Recent studies indicated a correlation between the severity of encephalitis and some features of SBCs, such as location, amplitude and frequency of appearance. Moreover, EEG rhythms were found to vary before the onset of an SBC, as if the brain was preparing to the discharge (actually with a slowing down of the EEG oscillation). Here, we investigate possible variations of EEG functional connectivity (FC) in EEGs from pediatric patients with different levels of severity of encephalitis. FC was measured by the maximal crosscorrelation of EEG rhythms in different bipolar channels. Then, the indexes of network patterns (namely strength, clustering coefficient, efficiency and characteristic path length) were estimated to characterize the global behavior when they are measured during SBCs or far from them. EEG traces showed statistical differences in the two conditions: clustering coefficient, efficiency and strength are higher close to an SBC, whereas the characteristic path length is lower. Moreover, for more severe conditions, an increase in clustering coefficient, efficiency and strength and a decrease in characteristic path length were observed in the delta–theta band. These outcomes support the hypothesis that SBCs result from the anomalous coordination of neurons in different brain areas affected by the inflammation process and indicate FC as an additional key for interpreting the EEG in encephalitis patients.

The brain–computer interfaces (BCI) are interfaces that put the user in communication with an electronic device based on signals originating from the brain. In this paper, we describe a proof of concept that took place within the context of BciAi4Sla, a multidisciplinary project involving computer scientists, physiologists, biomedical engineers, neurologists, and psychologists with the aim of designing and developing a BCI system following a user-centered approach, involving domain experts and users since initial prototyping steps in a design–test–redesign development cycle. The project intends to develop a software platform able to restore a communication channel in patients who have compromised their communication possibilities due to illness or accidents. The most common case is the patients with amyotrophic lateral sclerosis (ALS). In this paper, we describe the background and the main development steps of the project, also reporting some initial and promising user evaluation results, including real-time performance classification and a proof-of-concept prototype.