26 research outputs found
Investigation of neural activity in Schizophrenia during resting-state MEG : using non-linear dynamics and machine-learning to shed light on information disruption in the brain
Environ 25% de la population mondiale est atteinte de troubles psychiatriques qui sont typiquement associés à des problèmes comportementaux, fonctionnels et/ou cognitifs et dont les corrélats neurophysiologiques sont encore très mal compris. Non seulement ces dysfonctionnements réduisent la qualité de vie des individus touchés, mais ils peuvent aussi devenir un fardeau pour les proches et peser lourd dans l’économie d’une société. Cibler les mécanismes responsables du fonctionnement atypique du cerveau en identifiant des biomarqueurs plus robustes permettrait le développement de traitements plus efficaces. Ainsi, le premier objectif de cette thèse est de contribuer à une meilleure caractérisation des changements dynamiques cérébraux impliqués dans les troubles mentaux, plus précisément dans la schizophrénie et les troubles d’humeur. Pour ce faire, les premiers chapitres de cette thèse présentent, en intégral, deux revues de littératures systématiques que nous avons menées sur les altérations de connectivité cérébrale, au repos, chez les patients schizophrènes, dépressifs et bipolaires. Ces revues révèlent que, malgré des avancées scientifiques considérables dans l’étude de l’altération de la connectivité cérébrale fonctionnelle, la dimension temporelle des mécanismes cérébraux à l’origine de l’atteinte de l’intégration de l’information dans ces maladies, particulièrement de la schizophrénie, est encore mal comprise. Par conséquent, le deuxième objectif de cette thèse est de caractériser les changements cérébraux associés à la schizophrénie dans le domaine temporel. Nous présentons deux études dans lesquelles nous testons l’hypothèse que la « disconnectivité temporelle » serait un biomarqueur important en schizophrénie. Ces études explorent les déficits d’intégration temporelle en schizophrénie, en quantifiant les changements de la dynamique neuronale dite invariante d’échelle à partir des données magnétoencéphalographiques (MEG) enregistrés au repos chez des patients et des sujets contrôles. En particulier, nous utilisons (1) la LRTCs (long-range temporal correlation, ou corrélation temporelle à longue-distance) calculée à partir des oscillations neuronales et (2) des analyses multifractales pour caractériser des modifications de l’activité cérébrale arythmique. Par ailleurs, nous développons des modèles de classification (en apprentissage-machine supervisé) pour mieux cerner les attributs corticaux et sous-corticaux permettant une distinction robuste entre les patients et les sujets sains. Vu que ces études se basent sur des données MEG spontanées enregistrées au repos soit avec les yeux ouvert, ou les yeux fermées, nous nous sommes par la suite intéressés à la possibilité de trouver un marqueur qui combinerait ces enregistrements. La troisième étude originale explore donc l’utilité des modulations de l’amplitude spectrale entre yeux ouverts et fermées comme prédicteur de schizophrénie. Les résultats de ces études démontrent des changements cérébraux importants chez les patients schizophrènes au niveau de la dynamique d’invariance d’échelle. Elles suggèrent une dégradation du traitement temporel de l’information chez les patients, qui pourrait être liée à leurs symptômes cognitifs et comportementaux. L’approche multimodale de cette thèse, combinant la magétoencéphalographie, analyses non-linéaires et apprentissage machine, permet de mieux caractériser l’organisation spatio-temporelle du signal cérébrale au repos chez les patients atteints de schizophrénie et chez des individus sains. Les résultats fournissent de nouvelles preuves supportant l’hypothèse d’une « disconnectivité temporelle » en schizophrénie, et étendent les recherches antérieures, en explorant la contribution des structures cérébrales profondes et en employant des mesures non-linéaires avancées encore sous-exploitées dans ce domaine. L’ensemble des résultats de cette thèse apporte une contribution significative à la quête de nouveaux biomarqueurs de la schizophrénie et démontre l’importance d’élucider les altérations des propriétés temporelles de l’activité cérébrales intrinsèque en psychiatrie. Les études présentées offrent également un cadre méthodologique pouvant être étendu à d’autres psychopathologie, telles que la dépression.Psychiatric disorders affect nearly a quarter of the world’s population. These typically bring about debilitating behavioural, functional and/or cognitive problems, for which the underlying neural mechanisms are poorly understood. These symptoms can significantly reduce the quality of life of affected individuals, impact those close to them, and bring on an economic burden on society. Hence, targeting the baseline neurophysiology associated with psychopathologies, by identifying more robust biomarkers, would improve the development of effective treatments. The first goal of this thesis is thus to contribute to a better characterization of neural dynamic alterations in mental health illnesses, specifically in schizophrenia and mood disorders. Accordingly, the first chapter of this thesis presents two systematic literature reviews, which investigate the resting-state changes in brain connectivity in schizophrenia, depression and bipolar disorder patients. Great strides have been made in neuroimaging research in identifying alterations in functional connectivity. However, these two reviews reveal a gap in the knowledge about the temporal basis of the neural mechanisms involved in the disruption of information integration in these pathologies, particularly in schizophrenia. Therefore, the second goal of this thesis is to characterize the baseline temporal neural alterations of schizophrenia. We present two studies for which we hypothesize that the resting temporal dysconnectivity could serve as a key biomarker in schizophrenia. These studies explore temporal integration deficits in schizophrenia by quantifying neural alterations of scale-free dynamics using resting-state magnetoencephalography (MEG) data. Specifically, we use (1) long-range temporal correlation (LRTC) analysis on oscillatory activity and (2) multifractal analysis on arrhythmic brain activity. In addition, we develop classification models (based on supervised machine-learning) to detect the cortical and sub-cortical features that allow for a robust division of patients and healthy controls. Given that these studies are based on MEG spontaneous brain activity, recorded at rest with either eyes-open or eyes-closed, we then explored the possibility of finding a distinctive feature that would combine both types of resting-state recordings. Thus, the third study investigates whether alterations in spectral amplitude between eyes-open and eyes-closed conditions can be used as a possible marker for schizophrenia. Overall, the three studies show changes in the scale-free dynamics of schizophrenia patients at rest that suggest a deterioration of the temporal processing of information in patients, which might relate to their cognitive and behavioural symptoms. The multimodal approach of this thesis, combining MEG, non-linear analyses and machine-learning, improves the characterization of the resting spatiotemporal neural organization of schizophrenia patients and healthy controls. Our findings provide new evidence for the temporal dysconnectivity hypothesis in schizophrenia. The results extend on previous studies by characterizing scale-free properties of deep brain structures and applying advanced non-linear metrics that are underused in the field of psychiatry. The results of this thesis contribute significantly to the identification of novel biomarkers in schizophrenia and show the importance of clarifying the temporal properties of altered intrinsic neural dynamics. Moreover, the presented studies offer a methodological framework that can be extended to other psychopathologies, such as depression
Emotional Memory Moderates the Relationship Between Sigma Activity and Sleep-Related Improvement in Affect
Sleep is essential for regulating mood and affect, and it also consolidates emotional memories. The mechanisms underlying these effects may overlap. Here, we investigated whether the influence of sleep on affect may be moderated by emotional memory consolidation. Young adults viewed 45 negative and 45 neutral pictures before taking an afternoon nap measured with polysomnography. Following the nap period, participants viewed the same pictures intermixed with novel ones and indicated whether they remembered each picture. Affect was measured with the Positive and Negative Affect Schedule (PANAS) at baseline before the initial picture viewing task, immediately following the initial picture viewing task, and following the nap. The ratio of positive to negative affect declined over the task period and recovered over the nap period. When controlling for pre-nap affect, NREM sigma activity significantly predicted post-nap affect. Memory for negative pictures moderated this relationship such that a positive association between sigma activity and affect occurred when memory was low but not when memory was high. These results indicate that emotional memory consolidation influences the relationship between nap physiology and mood
Variability and stability of large-scale cortical oscillation patterns
Individual differences in brain organization exist at many spatiotemporal scales and underlie the diversity of human thought and behavior. Oscillatory neural activity is crucial for these processes, but how such rhythms are expressed across the cortex within and across individuals is poorly understood. We conducted a systematic characterization of brain-wide activity across frequency bands and oscillatory features during rest and task execution. We found that oscillatory profiles exhibit sizable group-level similarities, indicating the presence of common templates of oscillatory organization. Nonetheless, well-defined subject-specific network profiles were discernible beyond the structure shared across individuals. These individualized patterns were sufficiently stable to recognize individuals several months later. Moreover, network structure of rhythmic activity varied considerably across distinct oscillatory frequencies and features, indicating the existence of several parallel information processing streams embedded in distributed electrophysiological activity. These findings suggest that network similarity analyses may be useful for understanding the role of large-scale brain oscillations in physiology and behavior. Neural oscillations are critical for the human brain’s ability to optimally respond to complex environmental input. However, relatively little is known about the network properties of these oscillatory rhythms. We used electroencephalography (EEG) to analyze large-scale brain wave patterns, focusing on multiple frequency bands and several key features of oscillatory communication. We show that networks defined in this manner are, in fact, distinct, suggesting that EEG activity encompasses multiple, parallel information processing streams. Remarkably, the same networks can be used to uniquely identify individuals over a period of approximately half a year, thus serving as neural fingerprints. These findings indicate that investigating oscillatory dynamics from a network perspective holds considerable promise as a tool to understand human cognition and behavior
Bursts of beta oscillations across the brain as a neurophysiological correlate of contextual novelty
The retrosplenial cortex and hippocampus are brain regions which have been shown to be highly involved in contextual memory. In order to discover neurophysiological correlates of contextual memory in these regions, we used in vivo electrophysiology in awake, behaving mice while they explored a series of novel and familiar environments. Additionally, in order to better understand the specific neurophysiological effects of Alzheimer’s disease-associated amyloid pathology on the retrosplenial cortex and hippocampus, we compared network activity between wild-type mice and J20 mice, a transgenic mouse model which develops widespread age-related amyloid pathology and memory impairments. We detected transient bursts of beta oscillations in both the retrosplenial cortex and hippocampus that were synchronous between these regions and upregulated during contextual novelty. Moreover, spiking of neurons in the retrosplenial cortex was significantly increased during beta bursts. In J20 mice, we noted numerous examples of altered network activity, including aberrant beta bursting which is not coupled to neuronal spiking. Through the use of EEG recordings in mice, we demonstrated that beta bursts can be detected across the cortex, and are highly synchronous between different brain regions. Finally, we demonstrated that it is possible to pharmacologically induce beta bursting in the retrosplenial cortex in vitro through the use of carbachol, a muscarinic acetylcholine receptor agonist, providing an assay for better understanding the mechanisms underlying beta bursting. These findings suggest that transient beta bursting across the brain provides brief windows of effective communication between brain regions, which may underlie the formation of cortical representation of contexts, and may be impaired in Alzheimer’s disease
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
Propriétés de l'activité de décharge neuronale de masse chez les humains mesurée par EEG non invasive
Abstract : Electroencephalography (EEG) is a non-invasive neuroimaging modality that was first introduced over 80 years ago. Surface EEG does not directly measure neuronal activity, and it is often assumed that it cannot provide indications on the underlying neuronal firing. However, recent studies based on invasive measurements in monkeys have shown that the coupling between two EEG frequency bands, namely the Gamma (25-45 Hz) and Delta (2-4 Hz) bands, is a good predictor of underlying mass-spiking activity. Specifically, when the Delta signal is in its trough and Gamma power is high, the probability of mass- firing of neurons is large. Here, we investigate this property in healthy human EEG acquired during resting-state. Using the interaction between Delta phase and Gamma power, we derived a modeled spike signal (MSS) from the recorded EEG. We found the power spectrum density (PSD) pattern of the MSS to be similar to that observed in animal studies. Specifically, between 1-10 Hz that the PSD deviates from a 1/[florin] trend and exhibits a small peak at about 2-3Hz. In addition, an inter-hemispheric correlation was found between the MSS of the different pairs of electrode in opposite hemispheres. Our results open the possibility of studying underlying neuronal output with non-invasive EEG. // Résumé : L'électroencéphalographie (EEG) est une modalité de neuro-imagerie non invasive qui a été introduite il y a plus de 80 ans. L’EEG de surface ne mesure pas directement l’activité neuronale et il est généralement supposé qu’elle ne donne pas d’indications sur la décharge neuronale sous-jacente. Cependant des études récentes ont montré à l’aide de mesures invasives que le couplage entre deux bandes de fréquences EEG, soit les bandes Gamma (25-45 Hz) et Delta (2-4 Hz), est un bon indicateur de l’activité neuronale de masse sous-jacente chez les singes. Plus précisément, lorsque le signal Delta est dans un creux (phase de π) et que la puissance dans le signal Gamma est élevée, la probabilité de décharge de masse des neurones est grande. Cette propriété est ici étudiée dans les signaux EEG d’humains sains en état de repos. En se basant sur l'interaction entre la phase du signal Delta et la puissance du signal Gamma, nous avons dérivé un modèle de l’activité neuronale de masse sous-jacente (modeled spike signal-MSS) obtenu à partir du signal l'EEG enregistrée. On trouve que la densité spectrale de puissance (power spectal density-PSD) du MSS est similaire à celle observée dans les études animales. Plus spécifiquement, entre 1-10 Hz la PSD s’écarte d’une tendance en 1 / [florin] et présente un pic de faible amplitude à environ 2-3Hz. En outre, une corrélation inter-hémisphérique a été observée entre les MSS de différentes paires d'électrodes positionnées sur les hémisphères opposés. Nos résultats ouvrent la possibilité d'étudier l’activité neuronale sous-jacente par EEG non-invasive
Spatiotemporal Dynamics of Neural Activity During Human Episodic Memory Encoding and Retrieval
Throughout literary history, the ability to travel in time has been a source of wonder and amusement. Why this fascination with moving through time? One reason may be that people are especially attuned to the concept of time travel because we each possess our own personal mental time machine: episodic memory. Through episodic memory, we transport ourselves back in time to re-live experiences from our past. This allows us to reflect on our own self-knowledge, effectively placing ourselves in context of our lives. This dissertation investigates how our brains accomplish this highly sophisticated cognitive operation. Using a laboratory model of episodic memory (free recall) and a particularly powerful neuroimaging tool (intracranial EEG), I document the changes that occur in the brain as episodic memories are first formed and then later retrieved. I find that the episodic memory system is best conceptualized as stage-wise process consisting of distinct brain regions that activate at highly conserved times relative to memory formation/retrieval. These discrete activations are used to construct a novel neurological model of episodic memory, the Neurological Stages of Episodic Retrieval and Formation (N-SERF) model. Future work should be aimed at verifying the hypotheses put forward by the N-SERF model, we well as relating the N-SERF model to prominent computational models of episodic memory
29th Annual Computational Neuroscience Meeting: CNS*2020
Meeting abstracts
This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests.
Virtual | 18-22 July 202
Characterizing dynamically evolving functional networks in humans with application to speech
Understanding how communication between brain areas evolves to support dynamic function remains a fundamental challenge in neuroscience. One approach to this question is functional connectivity analysis, in which statistical coupling measures are employed to detect signatures of interactions between brain regions. Because the brain uses multiple communication mechanisms at different temporal and spatial scales, and because the neuronal signatures of communication are often weak, powerful connectivity inference methodologies require continued development specific to these challenges.
Here we address the challenge of inferring task-related functional connectivity in brain voltage recordings. We first develop a framework for detecting changes in statistical coupling that occur reliably in a task relative to a baseline period. The framework characterizes the dynamics of connectivity changes, allows inference on multiple spatial scales, and assesses statistical uncertainty. This general framework is modular and applicable to a wide range of tasks and research questions.
We demonstrate the flexibility of the framework in the second part of this thesis, in which we refine the coupling statistics and hypothesis tests to improve statistical power and test different proposed connectivity mechanisms. In particular, we introduce frequency domain coupling measures and define test statistics that exploit theoretical properties and capture known sampling variability. The resulting test statistics use correlation, coherence, canonical correlation, and canonical coherence to infer task-related changes in coupling. Because canonical correlation and canonical coherence are not commonly used in functional connectivity analyses, we derive the theoretical values and statistical estimators for these measures.
In the third part of this thesis, we present a sample application of these techniques to electrocorticography data collected during an overt reading task. We discuss the challenges that arise with task-related human data, which is often noisy and underpowered, and present functional connectivity results in the context of traditional and contemporary within-electrode analytics. In two of nine subjects we observe time-domain and frequency-domain network changes that accord with theoretical models of information routing during motor processing.
Taken together, this work contributes a methodological framework for inferring task-related functional connectivity across spatial and temporal scales, and supports insight into the rapid, dynamic functional coupling of human speech