Functional brain networks: intra and inter-subject variability in healthy individuals and patients with neurological or neuropsychiatric diseases.

The projects of this thesis sits at the intersection between classical neuroscience and aspects related to engineering, signals’ and neuroimaging processing. Each of the three years has been dedicated to specific projects carried out on distinct datasets, groups of individuals/patients and methods, putting great emphasis on multidisciplinarity and international mobility. The studies carried out in Cagliari were based on EEG (electroencephalography), and the one conducted abroad was developed on functional magnetic resonance imaging (fMRI) data. The common thread of the project concerns variability and stability of individuals' features related primarily to functional connectivity and network, as well as to the periodic and aperiodic components of EEG power spectra, and their possible use for clinical purposes. In the first study (Fraschini et al., 2019) we aimed to investigate the impact of some of the most commonly used metrics to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-channel interaction. In the second study (Demuru et al., 2020) we performed a comparison between power spectral density and some widely used nodal network metrics, both at scalp and source level, with the aim of evaluating their possible association. The first first-authored study (Pani et al., 2020)was dedicated to investigate how the variability due to subject, session and task affects electroencephalogram(EEG) power, connectivity and network features estimated using source-reconstructed EEG time-series of healthy subjects. In the study carried out with the supervision of Prof. Fornito (https://doi.org/10.1016/j.pscychresns.2020.111202) during the experience at the Brain, Mind and Society Research Hub of Monash University, partial least square analysis has been applied on fMRI data of an healthy cohort to evaluate how different specific aspects of psychosis-like experiences related to functional connectivity. Due to the pandemic of Sars-Cov-2 it was impossible to continue recording the patients affected by neurological diseases (Parkinson’s, Diskynesia) involved in the study we planned for the third year, that should have replicated the design of the first first-authored one, with the aim of investigate how individual variability/stability of functional brain networks is affected by diseases. For the aforementioned reason, we carried out the last study on a dataset we finished to record in February 2020. The analysis has the aim of investigate whether it is possible by using 19 channels sleep scalp EEG to highlight differences in the brain of patients affected by non-rem parasomnias and sleep-related hypermotor epilepsy, when considering the periodic and aperiodic component of EEG power spectra

Robustness of functional connectivity metrics for EEG-based personal identification over task-induced intra-class and inter-class variations

Growing interest is devoted to understanding in which situations and with what accuracy brain signals recorded from scalp electroencephalography (EEG) may represent unique fingerprints of individual neural activity. In this context, the present paper aims to investigate the impact of some of the most commonly used metrics to estimate functional connectivity on the ability to unveil personal distinctive patterns of inter-channel interactions. Different metrics were compared in terms of equal error rate. It is widely accepted that each connectivity metric carries specific information in respect to the underlying interactions. Experimental results on publicly available EEG recordings show that different connectivity metrics define peculiar subjective profile of connectivity and show different mechanisms to detect subject-specific patterns of inter-channel interactions. Moreover, these findings highlight that some measures are more accurate and more robust than others, regardless of the task performed by the user. Finally, it is important to consider that frequency content and spurious connectivity may still play a relevant role in determining subject-specific characteristics

Evidence of Task-Independent Person-Specific Signatures in EEG using Subspace Techniques

Electroencephalography (EEG) signals are promising as alternatives to other biometrics owing to their protection against spoofing. Previous studies have focused on capturing individual variability by analyzing task/condition-specific EEG. This work attempts to model biometric signatures independent of task/condition by normalizing the associated variance. Toward this goal, the paper extends ideas from subspace-based text-independent speaker recognition and proposes novel modifications for modeling multi-channel EEG data. The proposed techniques assume that biometric information is present in the entire EEG signal and accumulate statistics across time in a high dimensional space. These high dimensional statistics are then projected to a lower dimensional space where the biometric information is preserved. The lower dimensional embeddings obtained using the proposed approach are shown to be task-independent. The best subspace system identifies individuals with accuracies of 86.4% and 35.9% on datasets with 30 and 920 subjects, respectively, using just nine EEG channels. The paper also provides insights into the subspace model's scalability to unseen tasks and individuals during training and the number of channels needed for subspace modeling.Comment: \copyright 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Desarrollo de una herramienta basada en redes de asociación para la ayuda al diagnóstico de la enfermedad del Alzheimer

La enfermedad de Alzheimer (EA) es el tipo de demencia más común en el mundo occidental y se espera que su prevalencia vaya en aumento como resultado del incremento en la esperanza de vida. Está asociada a un deterioro de las funciones cognitivas que afectan significativamente a la calidad de vida de las personas que la sufren. Obtener un diagnóstico temprano es clave para alargar la autonomía del paciente, darle tiempo para tomar decisiones acerca de su futuro y reducir significativamente los costes asociados a la enfermedad. Al no existir un biomarcador de la EA fácilmente accesible en la práctica clínica diaria, los especialistas en neurología se ven obligados a recurrir a un diagnóstico clínico, en base a los síntomas que presenta el paciente, una exploración neurológica y múltiples pruebas complementarias indicadas en el consenso médico vigente. Esto hace del diagnóstico un proceso complejo y con cierto grado de subjetividad. Además, algunas de estas pruebas tienen un coste elevado, no se encuentran ampliamente disponibles o son invasivas. Por lo tanto, la realización de pruebas a toda la población de riesgo para obtener un diagnóstico temprano es insostenible, tanto organizativa como económicamente. El objetivo de este Trabajo Fin de Grado consiste en encontrar nuevas aproximaciones metodológicas que permitan obtener un diagnóstico más económico, sencillo, objetivo, no invasivo y fácilmente escalable a toda la población de riesgo. Para ello, se han establecido cuatro sistemas de clasificación: (i) sujetos patológicos vs. no patológicos, (ii) controles vs. enfermos de Alzheimer, (iii) controles vs. pacientes con deterioro cognitivo leve vs. enfermos de Alzheimer, y (iv) controles vs. pacientes con deterioro cognitivo leve vs. enfermos de Alzheimer con distinto grado de severidad (leve, moderada y severa). Para cada sistema, en función de las variables incluidas, se distinguen tres modelos: reducido, ampliado y completo.Alzheimer’s disease (AD) is the most common type of dementia in the Western world and its prevalence is expected to grow because of prolongation in the average lifespan. It is associated with a deterioration of the cognitive functions that significantly affects the quality of life of patients. Early diagnosis is critical to extending patients’ autonomy, giving them time to make decisions about their future and thus reducing the costs associated with the disease. In the absence of an accessible AD biomarker in clinical practice, neurologists use a clinical diagnosis based on symptoms, neurological exploration, and multiple complementary tests prescribed by the current medical consensus. This makes diagnosis a complex and subjective process. Furthermore, some of these tests are expensive, poorly available, or invasive. Therefore, testing the entire at-risk population for an early diagnosis is not sustainable, neither economically nor organisationally. The objective of this final degree project is to find new methodological approaches to obtain a less expensive, simpler, more objective, and non-invasive diagnosis which is scalable to the entire risk population. For this purpose, four classification systems have been established: (i) pathological subjects vs. non-pathological, (ii) controls vs. AD patients, (iii) controls vs. mild cognitive impairment patients vs. AD patients, and (iv) controls vs. mild cognitive impairment patients vs. AD patients with different severity degrees (mild, moderate, and severe). For each system, depending on the variables included, we can distinguish three models: reduced, extended, and complete.Grado en Ingeniería de Tecnologías de Telecomunicació