Functional Magnetic Resonance Imaging (fMRI) has consistently highlighted aberrant functional connectivity across brain regions of autism spectrum disorder (ASD) patients. However, the manifestation and neural substrates of these alterations are highly heterogeneous and often conflicting. Moreover, their neurobiological under- pinnings and etiopathological significance remain largely unknown. A deeper understanding of the complex pathophysiological cascade leading to impaired connectivity in ASD can greatly benefit from the use of model organisms where individual pathophysiological or phenotypic components of ASD can be recreated and investigated via approaches that are either off limits or confounded by clinical heterogeneity. In this work, we first describe the intrinsic organization of the mouse brain at the macroscale as seen through resting-state fMRI (rsfMRI). The analysis of a large rsfMRI dataset revealed the presence of six distinct functional modules related to known brainwide functional partitions, including a homologue of the human default-mode network (DMN). Consistent with human studies, interconnected functional hubs were identified in several sub-regions of the DMN, in the thalamus, and in small foci within integrative cortical structures such as the insular and temporal association cortices. We then study the effects of mutations in contactin associated protein-like 2 (Cntnap2), a neurexin-related cell-adhesion protein, on functional connectivity. Homozygous mutations in this gene are strongly linked to autism and epilepsy in humans, and using rsfMRI, we showed that homozygous mice lacking Cntnap2 exhibit aberrant functional connectivity in prefrontal and midline functional hubs, an effect that was associated with reduced social investigation, a core “autism trait” in mice. Notably, viral tracing revealed reduced frequency of prefrontal-projecting neural clusters in the cingulate cortex of Cntnap2−/− mutants, suggesting a possible contribution of defective mesoscale axonal wiring to the observed functional impairments. Macroscale cortico-cortical white-matter organization appeared to be otherwise preserved in these animals. These findings revealed a key contribution of ASD-associated gene CNTNAP2 in modulating macroscale functional connectivity, and suggest that homozygous loss-of-function mutations in this gene may predispose to neurodevelopmental disorders and autism through a selective dysregulation of connectivity in integrative prefrontal areas. Finally, we discuss the role mouse models could play in generating and testing mechanistic hypotheses about the elusive origin and significance of connectional aberrations observed in autism and recent progress towards this goal.

This thesis stems from the need to implement the existing technology in the rehabilitation. Smartphones, touch screen technology, apps, which are a common part of our daily life don’t find an application in clinical practice. The aim of this work is to verify the effectiveness of using this technology both in the hospital and outside. The exergames we developed can be played on usual touchscreen devices, on personal computers and on the custom device built in our laboratory. The device used during our experimentation is now installed in a medical facility with other latest generation medical devices like the Armeo and two different types of exoskeletons. In the first part of this project we focused on developing some exergames oriented to the rehabilitation of persons affected by strokes and in the characterization of people affected by Parkinson. In the second part, because of the strong correlation between physical activity and neurocognitive functions we decided to use the device since the very beginning of the rehabilitation process, developing some kind of exergames used also to monitor patients during this phase. Thanks to the collaboration with the medical staff we analysed and summarized the macro areas and the neurocognitive functions involved during the rehabilitation process. After that, we analysed some of the usual exercises given to the patients highlighting the neurocognitive functions involved, and for each exercise we defined some indicators, like touch precision and its standard deviation, mean of force and its standard deviation, total time to execute the test, number of errors, etc... To develop the exergames based on what we decided with the medical staff we used the game engine called Unity3D and we wrote the code of the exergames in C#. After a first test phase in which the medical staff tried the exergames and gave us a feedback, we fixed the bugs and decided to integrate all the exergames in a common platform. Then, we defined an official procedure for the rehabilitation program based on this new method in order to submit it to the ethics committee. In a second phase, the medical staff selected the group of patients to be assigned to the test program, and defined some useful indicators about the neurocognitive functions involved. Finally, to validate the efficiency of this protocol, patients need to executed the exergames for a certain time, after which the medical staff measured the indicators. In this way we were able to validate the efficiency of the exergames and of the device installed. By working in the European project NoTremor we developed two different type of test with the aim of characterize a model of people affected by Parkinson.

Quantifying the amount of information communicated between neural population is crucial to understand brain dynamics. To address this question, many tools for the analysis of time series of neural activity, such as Granger causality, Transfer Entropy, Directed Information have been proposed. However, none of these popular model-free measures can reveal what information has been exchanged. Yet, understanding what information is exchanged is key to be able to infer, from brain recordings, the nature and the mechanisms of brain computation. To provide the mathematical tools needed to address this issue, we developed a new measure, exploiting benefits of novel Partial Information Decomposition framework, that determines how much information about each specific stimulus or task feature has been transferred between two neuronal populations. We tested this methodology on simulated neural data and showed that it captures the specific information being transmitted very well, and it is also highly robust to several of the confounds that have proven to be problematic for previous methods. Moreover, the measure was significantly better in detection of the temporal evolution of the information transfer and the directionality of it than the previous measures. We also applied the measure to an EEG dataset acquired during a face detection task that revealed interesting patterns of interhemispheric phase-specific information transfer. We finally analyzed high gamma activity in an MEG dataset of a visuomotor associations. Our measure allowed for tracing of the stimulus information flow and it confirmed the notion that dorsal fronto-parietal network is crucial for the visuomotor computations transforming visual information into motor plans. Altogether our work suggests that our new measure has potential to uncover previously hidden specific information transfer dynamics in neural communication.