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Functional connectivity between cerebral cortical regions during natural sleep has attracted a keen interest from both cognitive and clinical neuroscientists because of its importance in understanding the default mode network of human brain. Multiple recordings of functional near-infrared spectroscopy (fNIRS) in several sleep phases make it possible for us to detect potential differences of directional interactions between cortical areas from healthy subjects and patients with ADHD or sleep disorders. Namely we propose a computational method to estimate time-domain Granger causality among fNIRS time series using a Kolmogorov–Smirnov test based on F-statistics. In order to validate indication of directional interactions, we also apply convergent cross-mapping to the time series as an alternative approach to causality based on state space reconstruction of dynamical systems. Comparing the averaged heatmaps of significant causal pairs of regions, we show that the map of directional interactions varies for each sleep phase, e.g., REM, of the same subject. The observation suggests an unexplored source for non-invasive classification benchmark of the above cognitive disorders.

Decision-making is considered the most essential phase in volitional act, and in the “Theory of the Consumer” it is assumed that rational individuals maximize the consumption of real goods given a limited availability of nominal goods (money). The ultimatum game (UG) is a two-player game, in which Player_1 (P1) has a certain sum of money at his disposal and offers a share to Player_2 (P2). If P2 accepts the proposal, the share is done accordingly, but in case of rejection both players end up with nothing. If players were selfish income maximizers, P2 should accept any amount and P1 should offer small amounts Experimental results show that most humans do not behave like that. What happens in the brain while the game is ongoing? Decisions must be the result of a sort of calculation of costs and benefits that a human is capable of performing rather quickly. The working hypothesis is that the dynamics of the interactions within the brain network underpin decision-making and its investigation can be achieved by recordings brain signals. We study the correlation between unsupervised machine learning analysis of event-related potentials recorded during the whole decision-making process (\(N=50\) participants) with personality traits measured by the HEXACO questionnaire and with the brief mood introspection scale. Unsupervised feature extraction of ERPS found two very robust clusters of participants: (i) associated with emotionality, characteristic of people showing a greedy behavior; (ii) associated with honesty and agreeableness, for people expressing willingness-to-share. This approach is likely to open the way to new studies of the neural basis of where and how a “decision” is taken in the brain.

Patients affected by attention-deficit/hyperactivity disorder (ADHD) are characterized by impaired executive functioning and/or attentional deficits. Our study is aimed to determine whether the outcomes measured by the attentional network task (ANT), i.e., the reaction times (RT) to specific target and cueing conditions and alerting, orienting, and conflict effects, are affected by cognitive training with a dual N-Back task. We considered three groups of young adult participants: ADHD patients without medication, ADHD with medication (MADHD), and age/education-matched controls (CTL). Working memory training began the day after the pretest. Participants were asked to perform 20 trainings composed of 20 blocks during an entire month. They were told that they would have to practice the dual N-Back task for about 30 min per day during the week and to rest for two days in the weekend. Each experimental group was randomly assigned into two conditions, the first with a progressive level (PL) of difficulty training, while the second was blocked at the level 1 during the whole training phase (baseline training). We observed that PL training was beneficial with reduced RTs in all groups and reduced conflict effects. MADHD showed a positive effect already with baseline training, whereas ADHD showed no significant reduction of neither RTs nor conflict effect after baseline training. No group showed any effect of training on alerting and orienting effects.

We investigate the effects of network topology on the dynamical activity of a hierarchically organized network of simulated spiking neurons. With a fixed basic two-by-two grid structure of processing modules each composed by almost 6000 leaky integrate-and-fire neurons and different connectivity schemes in between these modules, we study how the activation and the biologically inspired processes of plasticity on the network shape its topology using invariants based on algebro-topological constructions. By definition, a clique is a fully connected directed subnetwork that means there is one source and one sink in the subnetwork. We define ‘k-clique hub cells’ for a positive integer k any cell which is sink and source cell of at least k 3-cliques. We show that there is a statistically different distribution of in- and out-degrees between clique hubs and other cells. Furthermore, we show that by identifying ‘clique hub cells’ we can find synfire chains that are involved in spatio-temporal firing patterns. Hence, the results suggest a link exists between an initial topological structure characterized by subnetworks cliques and a functional connectivity emerging at a later stage as the outcome of synaptic plasticity mechanisms.

Attention Deficit/Hyperactivity Disorder (ADHD) is a behavioral disorder of childhood and adolescence characterized by symptoms that include impulsiveness, inattention, hyperactivity, impaired decision making, and primary deficits of executive functions. In a vast proportion of the diagnosed adolescents, the clinical symptoms may persist into adulthood and ADHD patients are characterized by Working Memory (WM) impairment. In the present study, we analyze brain dynamics by EEG recordings during the dual n-back task in a population of young adults with ADHD and healthy controls. The WM capacity and attention span are tested by n-back task, and divided attention is tested by running the task in the visual and auditory modalities concurrently. We analyzed the event-related potentials (ERPs) triggered by the onset of the audio-visual stimuli. In ADHD the amplitude of N200 wave component was only slightly reduced and the peak latency was unaffected. The amplitude of P300 peak was reduced in ADHD with respect to controls at all sites along the midline. The latency of P300 peak in ADHD was reduced at Fz and Cz. In particular, at Fz the latency of ADHD was reduced after a response that required matching the visual cue 1 or 2 trials back in time. These results support the hypothesis that the P300 component, associated with a cognitive workload, peaked earlier in the ADHD than in controls and it may be used to follow the outcome of cognitive training.

Event-related potentials (ERP) have been repeatedly used to study the spatiotemporal dynamics of the attentional response in the well-known oddball paradigm. We combined electroencephalography (EEG) with frequency-domain near-infrared spectroscopy (fNIRS) of the frontal cortex to measure neuronal activity with a high spatial and temporal resolution. The aim of this study was to determine the precise chronology of event-related optical signals (EROS) and their consistency with ERPs. In agreement with previous studies, the oddball condition produced larger waveforms for rare (1500 Hz pure tone) with respect to frequent stimuli (1000 Hz), with N1, P2, N2, P3a, and P3b components. At a latency corresponding to the mismatch negativity/N2 wave component, EROS showed the organization of a complex activity in a functional network of frontal areas, with rare tones activating the left premotor dorsal cortex and the left inferior frontal cortex and decreasing the activity of the right superior frontal gyrus. Rare tones elicited also a strong N500 (N400-like) wave component that EROS contributed to localize at the level of the right medial frontal gyrus by EROS. The simultaneous recording of fNIRS and EEG measurements with high temporal accuracy over the human prefrontal cortex supports the potential for this approach to unravel the functional cortical network involved in cognitive processing.

In the analysis of raw electrophysiological recordings, signal filtering is a crucial step to eliminate frequency components associated with noise and recording artifacts. Two problems have to be addressed: determining the optimal frequency range of the signal and which frequency values (extreme values, maximum and minimum) that are characteristic of the raw signal power spectrum. We developed an algorithm called ERFo (extractor of range for filtering optimization) that determines the frequency range boundaries expected to be optimal for the observation of the spectrum with the largest power in the range of interest. The regular differentiations (first and second derivatives) of the raw signal are used to detect the maximum and minimum amplitudes, which are reported in the algorithm to determine the frequency range of the raw signal filtration.

Impaired decision making and working memory (WM) are among the characteristic symptoms of patients affected by attention deficit hyperactivity disorder (ADHD). In order to investigate whether a WM training can affect the attitude toward risky decision making, we designed a study where participants had to perform a probabilistic gambling task. Our study has demonstrated that WM training affects in a different way controls and ADHD patients, who showed an increased tendency toward a risk-taking attitude in case of the adaptive variant of the memory task. In ADHD patients, the frontal sites appeared the most affected, whereas global brain activity was likely to be affected in controls. This study shows also the benefits of cognitive training in ADHD patients and healthy subjects.

According to game theory, a human subject playing the ultimatum game should choose more for oneself and offer the least amount possible for co-players (assumption of selfish rationality) (Rubinstein in J Econ Behav Organ 3(4):367–388, [1]). However, economy, sociology and neurology communities repeatedly claim non-rationality of the human behaviour (Werner et al. in Theory of Games and Economic Behavior. Princeton University Press, Princeton, [2]), following the observation that responders reject offers they find too low and proposers often offer more than the smallest amount, thus suggesting that humans’ behaviour is significantly influenced by social norms. We also assume human rationality, but our model describes a human responder via decision process with a reward function respecting fairness as much as the economic profit. This model is positively tested against a set of original experimental data, thus providing an insight into human’s motivation as a social being.