Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas

Visual category perception is thought to depend on brain areas that respond specifically when certain categories are viewed. These category-sensitive areas are often assumed to be modules (with some degree of processing autonomy) and to act predominantly on feedforward visual input. This modular view can be complemented by a view that treats brain areas as elements within more complex networks and as influenced by network properties. This network-oriented viewpoint is emerging from studies using either diffusion tensor imaging to map structural connections or effective connectivity analyses to measure how their functional responses influence each other. This literature motivates several hypotheses that predict category-sensitive activity based on network properties. Large, long-range fiber bundles such as inferior fronto-occipital, arcuate and inferior longitudinal fasciculi are associated with behavioural recognition and could play crucial roles in conveying backward influences on visual cortex from anterior temporal and frontal areas. Such backward influences could support top-down functions such as visual search and emotion-based visual modulation. Within visual cortex itself, areas sensitive to different categories appear well-connected (e.g., face areas connect to object- and motion sensitive areas) and their responses can be predicted by backward modulation. Evidence supporting these propositions remains incomplete and underscores the need for better integration of DTI and functional imaging

Untangling cross-frequency coupling in neuroscience

Cross-frequency coupling (CFC) has been proposed to coordinate neural dynamics across spatial and temporal scales. Despite its potential relevance for understanding healthy and pathological brain function, the standard CFC analysis and physiological interpretation come with fundamental problems. For example, apparent CFC can appear because of spectral correlations due to common non-stationarities that may arise in the total absence of interactions between neural frequency components. To provide a road map towards an improved mechanistic understanding of CFC, we organize the available and potential novel statistical/modeling approaches according to their biophysical interpretability. While we do not provide solutions for all the problems described, we provide a list of practical recommendations to avoid common errors and to enhance the interpretability of CFC analysis.Comment: 47 pages, 12 figures, including supplementary materia

The Perceived Benefits of Participation in Community Drum Circles

The purpose of this study was to explore the perspectives of adults who participate in community drum circles. The act of striking a vibrating membrane or drum surface to produce a sound with a group of people comprises a drum circle. Drumming has been utilized for many different reasons for a large portion of human history. A phenomenological study was conducted to examine perceived benefits of participation in a drum circle. Six participants from two different drum circles were interviewed and data was coded inductively and deductively. Interview questions gathered data related to participants’ perception of the benefits and meaning of drum circle participation, social components, and how they were initially introduced to drumming. Findings related to interview questions were used to organize deductive findings. Further data analysis revealed the following inductive themes. Participants experienced many positive physiological and emotional responses in anticipation, during, and after participation in drum circles. Participants experienced an increased connectedness to the group and the present moment. Differences were revealed in participants’ path to drum circle participation. Other differences include how the context of drumming influenced participant expectations. Despite differences, all participants were open to new learning and experiences, possessed a desire to share drumming with others, and experienced drumming as a valued and meaningful activity. The use of drumming in the context of drum circles may be useful as a therapeutic tool to promote, maintain, and restore engagement in meaningful occupations with beneficial outcomes related to physical and mental health

Un paradigma EEG di entrainment neurale per lo studio di strutture linguistiche gerarchiche.

Anche se la comprensione del linguaggio è apparentemente svolta senza sforzo esplicito, si è mostrato come questa sia il prodotto di un sofisticalo sistema di interconnessioni neurali. Si pensa che il nostro cervello sia in grado di costruire meticolosamente strutture gerarchiche sintattiche e semantiche a partire dal semplice flusso sonoro. Per entrainment a strutture linguistiche gerarchiche si intende il fatto che il cervello, partendo dall’analisi e rappresentazioni degli elementi di base del parlato come sillabe e parole, li combina incrementalmente in strutture sintattiche più ampie e complesse quali sintagmi e frasi. In questa tesi riporto un paradigma per una replica concettuale di uno studio di Ding et al. (2017) che ha studiato, utilizzando l’elettroencefalografia (EEG), la sincronizzazione dell’attività corticale in relazione ad una presentazione ritmica di queste unità linguistiche (sillabe, sintagmi e frasi) aggiungendo una condizione sperimentale che atta a chiarire l’interpretazione dei picchi di criticata da Frank et al. (2018). L’adattamento del paradigma alla lingua italiana ha richiesto di affrontare specifiche problematiche, offrendo al contempo l’opportunità di costruire un disegno sperimentale più pulito e complesso.Although language comprehension is apparently performed without explicit effort, it has been shown to be the product of an intertwined sophisticated neural system. It is thought that our brain succeeds in meticulously rendering a mere flow of sound into hierarchically organized syntactic and semantic structures. Entrainment into hierarchical linguistic structures means that the brain starts by analyzing and representing smaller elements of speech, syllables for example, and then incrementally combining them into larger, syntactically more complex structures such as words, phrases and sentences. In this thesis I report a conceptual replication of the study of Ding et al. (2017) which studies cortical activity synchronization in correlation with rhythmic presentation of these hierarchical linguistic units (syllables, phrases, sentences), using Electroencephalography (EEG). The adaptation of the paradigm to Italian language need to face some specific challenges, yet it offered the possibility of conducting a more complex and a clean experimental design

EEG-based visual deviance detection in freely behaving mice

The mouse is widely used as an experimental model to study visual processing. To probe how the visual system detects changes in the environment, functional paradigms in freely behaving mice are strongly needed. We developed and validated the first EEG-based method to investigate visual deviance detection in freely behaving mice. Mice with EEG implants were exposed to a visual deviant detection paradigm that involved changes in light intensity as standard and deviant stimuli. By subtracting the standard from the deviant evoked waveform, deviant detection was evident as bi-phasic negativity (starting around 70 ms) in the difference waveform. Additionally, deviance-associated evoked (beta/gamma) and induced (gamma) oscillatory responses were found. We showed that the results were stimulus-independent by applying a "flip-flop " design and the results showed good repeatability in an independent measurement. Together, we put forward a validated, easy-to-use paradigm to measure visual deviance processing in freely behaving mice.Functional Genomics of Muscle, Nerve and Brain Disorder

EEG, MEG and neuromodulatory approaches to explore cognition: Current status and future directions

Neural oscillations and their association with brain states and cognitive functions have been object of extensive investigation over the last decades. Several electroencephalography (EEG) and magnetoencephalography (MEG) analysis approaches have been explored and oscillatory properties have been identified, in parallel with the technical and computational advancement. This review provides an up-to-date account of how EEG/MEG oscillations have contributed to the understanding of cognition. Methodological challenges, recent developments and translational potential, along with future research avenues, are discussed. Keywords: Cognition; Electrophysiology; Event-related-potentials; Neural oscillations; Neural synchronisation; Neuromodulatio

Neurophysiological assessments of low-level and high-level interdependencies between auditory and visual systems in the human brain

This dissertation investigates the functional interplay between visual and auditory systems and its degree of experience-dependent plasticity. To function efficiently in everyday life, we must rely on our senses, building complex hierarchical representations about the environment. Early sensory deprivation, congenital (from birth) or within the first year of life, is a key model to study sensory experience and the degree of compensatory reorganizations (i.e., neuroplasticity). Neuroplasticity can be intramodal (within the sensory system) and crossmodal (the recruitment of deprived cortical areas for remaining senses). However, the exact role of early sensory experience and the mechanisms guiding experience-driven plasticity need further investigation. To this aim, we performed three electroencephalographic studies, considering the aspects: 1) sensory modality (auditory/visual), 2) hierarchy of the brain functional organization (low-/high-level), and 3)sensory deprivation (deprived/non-deprived cortices). The first study explored how early auditory experience affects low-level visual processing, using time-frequency analysis on the data of early deaf individuals and their hearing counterparts. The second study investigated experience- dependent plasticity in hierarchically organized face processing, applying fast periodic visual stimulation in congenitally deaf signers and their hearing controls. The third study assessed neural responses of blindfolded participants, using naturalistic stimuli together with temporal response function, and evaluated neural tracking in hierarchically organized speech processing when retinal input is absent, focusing on the role of the visual cortex. The results demonstrate the importance of atypical early sensory experience in shaping (via intra-and crossmodal changes) the brain organization at various hierarchical stages of sensory processing but also support the idea that some crossmodal effects emerge even with typical experience. This dissertation provides new insights into understanding the functional interplay between visual and auditory systems and the related mechanisms driving experience-dependent plasticity and may contribute to the development of sensory restoration tools and rehabilitation strategies for sensory-typical and sensory-deprived populations

Population models for complex non-linear phenomena in biology: from mitochondrial dynamics to brain networks

The human brain is as much fascinating as complicated: this is the reason why it has always captured scientists’ attention in several fields of research, from biology to medicine, from psychology to engineering. In this context various non-invasive technologies have been optimized in order to allow the measure of signals, able to describe brain activities. These data, derived from measurement methods that largely differ in their nature, have opened the door to new characterizations of this organ, that highlighted the main features of its operating principles. Brain signals indeed have revealed to be fluctuating during time, both during a specific task, and when we are not carrying on any activities. Furthermore, a selective coordination among different regions of the brain has emerged. As engineers, we are particularly attracted by the description of our brain as a graph, whose nodes and edges can be representative of several different elements, at distinct spatial scales (from single neurons to large brain areas). In the last decades, wide attention has been devoted to reproduce and explain the complex dynamics of the brain elements by means of computational models. Graph theory tools, as well as the design of population models, allow the exploitation of many mathematical tools, helpful to enlarge the knowledge of healthy and damaged brains functioning, by means of brain networks. Interestingly, the incapability of human brains to work properly in case of disease, has found to be correlated with dysfunctions in the activity of mitochondria, the organelles that produce large part of the cells’ energy. In particular, specific relationships have been reported among neurological diseases and impairments in mitochondrial dynamics, which refers to the continuous change in shape of mitochondria, by means of fusion and fission processes. Although the existing link between brain and mitochondria is still ambiguous and under debate, the huge amount of energy required by our brain to work properly suggests a larger mitochondrial-dependence of the brain than of the other organs. In this thesis we report the results of our research, aimed to investigate a few aspects of this complex brain-mitochondria relationship. We focus on mitochondrial dynamics and brain network, as well as on suitable mathematical models used to describe them. Specifically, the main topics handled in this work can be summarized as follows. Population models for mitochondrial dynamics. We propose a modified preypredator non-linear population model to simulate the main processes, which take part in the mitochondrial dynamics, and the ones that are strongly related to it, without neglecting the energy production process. We present two possible setups, which differ in the inclusion of a feedback link between the available energy and the formation of new mitochondria. We discuss their dynamics, and their potential in reproducing biological behaviors. Brain signals: comparison of datasets derived through different technologies. We analyze two different datasets of brain signals, recorded with various methods (functional magnetic resonance imaging, fMRI, and magnetoencephalography, MEG), both in condition of no activity and during an attentional task. The aim of the analysis is twofold: the investigation of the spontaneous activity of the brain, and the exploration of possible relationships between the two different techniques. Brain network: a Kuramoto-based description. We analyze empirical brain data by means of their oscillatory features, with the purpose of highlighting the characteristics that a computational phase-model should be able to reproduce. Hence, we use a modified version of the classic Kuramoto model to reproduce the empirical oscillatory characteristics. Analysis and control of Kuramoto networks. Most of the theoretical contribution of this thesis refers to analytical results on Kuramoto networks. We analyze the topological and intrinsic conditions required to achieve a desired pattern of synchronization, represented by fully or clustered synchronized configuration of oscillators