The voxel-wise functional connectome can be efficiently derived from co-activations in a sparse spatio-temporal point-process

Large efforts are currently under way to systematically map functional connectivity between all pairs of millimeter-scale brain regions based on large neuroimaging databases. The exploratory unraveling of this "functional connectome" based on functional Magnetic Resonance Imaging (fMRI) can benefit from a better understanding of the contributors to resting state functional connectivity. In this work, we introduce a sparse representation of fMRI data in the form of a discrete point-process encoding high-amplitude events in the blood oxygenation level-dependent (BOLD) signal and we show it contains sufficient information for the estimation of functional connectivity between all pairs of voxels. We validate this method by replicating results obtained with standard whole-brain voxel-wise linear correlation matrices in two datasets. In the first one (n = 71), we study the changes in node strength (a measure of network centrality) during deep sleep. The second is a large database (n = 1147) of subjects in which we look at the age-related reorganization of the voxel-wise network of functional connections. In both cases it is shown that the proposed method compares well with standard techniques, despite requiring only data on the order of 1% of the original BOLD signal time series. Furthermore, we establish that the point-process approach does not reduce (and in one case increases) classification accuracy compared to standard linear correlations. Our results show how large fMRI datasets can be drastically simplified to include only the timings of large-amplitude events, while still allowing the recovery of all pair-wise interactions between voxels. The practical importance of this dimensionality reduction is manifest in the increasing number of collaborative efforts aiming to study large cohorts of healthy subjects as well as patients suffering from brain disease. Our method also suggests that the electrophysiological signals underlying the dynamics of fMRI time series consist of all-or-none temporally localized events, analogous to the avalanches of neural activity observed in recordings of local field potentials (LFP), an observation of potentially high neurobiological relevance.Fil: Tagliazucchi, Enzo. Christian Albrechts Universitat Zu Kiel.; Alemania. University Frankfurt am Main; AlemaniaFil: Siniatchkin, Michael. Christian Albrechts Universitat Zu Kiel.; AlemaniaFil: Laufs, Helmut. University Frankfurt am Main; Alemania. University Hospital Schleswig Holstein; AlemaniaFil: Chialvo, Dante Renato. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Martin. Escuela de Ciencia y Tecnologia. Centro de Estudios Multidisciplinarios En Sistemas Complejos y Ciencias del Cerebro.; Argentin

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Advancing Brain-Computer Interface System Performance in Hand Trajectory Estimation with NeuroKinect

Brain-computer interface (BCI) technology enables direct communication between the brain and external devices, allowing individuals to control their environment using brain signals. However, existing BCI approaches face three critical challenges that hinder their practicality and effectiveness: a) time-consuming preprocessing algorithms, b) inappropriate loss function utilization, and c) less intuitive hyperparameter settings. To address these limitations, we present \textit{NeuroKinect}, an innovative deep-learning model for accurate reconstruction of hand kinematics using electroencephalography (EEG) signals. \textit{NeuroKinect} model is trained on the Grasp and Lift (GAL) tasks data with minimal preprocessing pipelines, subsequently improving the computational efficiency. A notable improvement introduced by \textit{NeuroKinect} is the utilization of a novel loss function, denoted as $\mathcal{L}_{\text{Stat}}$. This loss function addresses the discrepancy between correlation and mean square error in hand kinematics prediction. Furthermore, our study emphasizes the scientific intuition behind parameter selection to enhance accuracy. We analyze the spatial and temporal dynamics of the motor movement task by employing event-related potential and brain source localization (BSL) results. This approach provides valuable insights into the optimal parameter selection, improving the overall performance and accuracy of the \textit{NeuroKinect} model. Our model demonstrates strong correlations between predicted and actual hand movements, with mean Pearson correlation coefficients of 0.92 ($\pm$0.015), 0.93 ($\pm$0.019), and 0.83 ($\pm$0.018) for the X, Y, and Z dimensions. The precision of \textit{NeuroKinect} is evidenced by low mean squared errors (MSE) of 0.016 ($\pm$0.001), 0.015 ($\pm$0.002), and 0.017 ($\pm$0.005) for the X, Y, and Z dimensions, respectively

Une nouvelle approche pour l’identification des états dynamiques de la parcellisation fonctionnelle cérébrale individuelle

Les parcellations cérébrales sont appliquées en neuroimagerie pour aider les chercheurs à ré- duire la haute dimensionnalité des données d’IRM fonctionnelle. L’objectif principal est une meilleure compréhension de l’organisation fonctionnelle du cerveau tant chez les sujets sains que chez les sujets souffrant de troubles neurologiques, dont la maladie d’Alzheimer. Malgré la vague d’approches de parcellations précédentes, les mesures de performance doivent en- core être améliorées pour générer des parcellations fiables, même avec de longues acquisitions. Autrement dit, une reproductibilité plus élevée qui permet aux chercheurs de reproduire des parcellations et de comparer leurs études. Il est également important de minimiser la perte d’informations entre les données compressées et les données brutes pour représenter avec précision l’organisation d’un cerveau individuel. Dans cette thèse, j’ai développé une nou- velle approche pour parcellaire le cerveau en reconfigurations spatiales distinctes appelées «états dynamiques de parcellations». J’ai utilisé une méthode d’agrégation de cluster simple DYPAC1.0 de parcelles basées sur des semences sur plusieurs fenêtres de temps. J’ai émis l’hypothèse que cette nouvelle façon de formaliser le problème de parcellisation améliorera les mesures de performance par rapport aux parcellations statiques. Le premier chapitre de ce document est une introduction générale au contexte des réseaux à grande échelle du cerveau humain. Je montre également l’importance des parcellations pour une meilleure compréhension du cerveau humain à l’aide de connectomes fonctionnels afin de prédire les schémas de progression de la maladie. Ensuite, j’explique pourquoi le problème de parcelli- sation cérébrale est difficile et les différentes questions de recherche ouvertes associées à ce domaine. Mes contributions à la recherche sont subdivisées en deux articles. Les deuxième et troisième chapitres sont consacrés au premier article principal et à son supplément publié dans Network Neuroscience Journal. Le quatrième chapitre représente le deuxième document en préparation. Le cinquième chapitre conclut mes contributions et ses implications dans le domaine de la neuroimagerie, ainsi que des orientations de recherche ouvertes. En un mot, la principale conclusion de ce travail est l’existence de reconfigurations spatiales distinctes dans tout le cerveau avec des scores de reproductibilité presque parfaits sur les données de test-retest (jusqu’à 0,9 coefficient de corrélation de Pearson). Un algorithme d’agrégation de cluster simple et évolutif appelé DYPAC 1.0 est expliqué pour identifier ces reconfigu- rations ou «états dynamiques de parcellations» pour des sous-réseaux de départ spécifiques (deuxième chapitre). L’analyse de ces états a montré l’existence d’un répertoire plus riche «d’états dynamiques» dans le cas des cortex hétéromodaux (ex: cortex cingulaire posté- rieur et cortex cingulaire antérieur dorsal) par rapport aux cortex unimodaux (ex: cortex visuel). En outre, les résultats de l’analyse de reproductibilité ont montré que DYPAC 1.0 a de meilleurs résultats de reproductibilité (en termes de corrélation de Pearson) par rapport aux parcelles statiques (deuxième chapitre). Plusieurs analyses démontrent que DYPAC 1.0 est robuste au choix de ses paramètres (troisième chapitre). Ces résultats et l’évolutivité de DYPAC 1.0 ont motivé une analyse complète du niveau cérébral. Je présente DYPAC 2.0 comme une approche au niveau cérébral complet pour fragmenter le cerveau en «états dynamiques de parcellations». Des reconfigurations spatiales distinctes et se chevauchant ou «états dynamiques» sont identifiées pour différentes régions du cerveau (quatrième chapitre). Ces états ont des scores de compression prometteurs qui montrent une faible perte d’infor- mations entre les cartes de stabilité d’état réduit et les données d’origine dans les cortex cérébraux, c’est-à-dire jusqu’à seulement 20% de perte de la variance expliquée. Cette thèse présente ainsi de nouvelles contributions dans le domaine de la parcellisation fonctionnelle qui pourraient avoir un impact sur la manière dont les chercheurs modélisent les interactions riches et dynamiques entre les réseaux cérébraux dans la santé et la maladie.Brain parcellations are applied in neuroimaging to help researchers reduce the high dimen- sionality of the functional MRI data. The main objective is a better understanding of the brain functional organization in both healthy subjects and subjects having neurological dis- orders, including Alzheimer disease. Despite the flurry of previous parcellation approaches, the performance measures still need improvement to generate reliable parcellations even with long acquisitions. That is, a higher reproducibility that allows researchers to replicate par- cellations and compare their studies. It is also important to minimize the information loss between the compressed data and the raw data to accurately represent the organization of an individual brain. In this thesis, I developed a new approach to parcellate the brain into distinct spatial reconfigurations called “dynamic states of parcellations”. I used a simple cluster aggregation method DYPAC1.0 of seed based parcels over multiple time windows. I hypothesized this new way to formalize the parcellation problem will improve performance measures over static parcellations. The first chapter of this document is a general context introduction to the human brain large scale networks. I also show the importance of par- cellations for a better understanding of the human brain using functional connectomes in order to predict patterns of disease progression. Then, I explain why the brain parcellation problem is hard and the different open research questions associated with this field. My research contributions are subdivided into two papers. The second and the third chapters are dedicated to the first main paper and its supplementary published in Network Neuro- science Journal. The fourth chapter represents the second paper under preparation. The fifth chapter concludes my contributions and its implications in the neuroimaging field, along with open research directions. In a nutshell, the main finding of this work is the existence of distinct spatial reconfigurations throughout the brain with near perfect reproducibility scores across test-retest data (up to .9 Pearson correlation coefficient). A simple and scalable clus- ter aggregation algorithm called DYPAC 1.0 is explained to identify these reconfigurations or “dynamic states of parcellations” for specific seed subnetworks (second chapter). The analysis of these states showed the existence of a richer repertoire of “dynamic states” in the case of heteromodal cortices (e.g., posterior cingulate cortex and the dorsal anterior cingulate cortex) compared to unimodal cortices (e.g., visual cortex). Also, the reproducibility analysis results showed that DYPAC 1.0 has better reproducibility results (in terms of Pearson corre- lation) compared to static parcels (second chapter). Several analyses demonstrate DYPAC 1.0 is robust to the choice of its parameters (third chapter). These findings and the scalabil- ity of DYPAC 1.0 motivated a full brain level analysis. I present DYPAC 2.0 as the full brain level approach to parcellate the brain into “dynamic states of parcellations”. Distinct and overlapping spatial reconfigurations or “dynamic states” are identified for different regions throughout the brain (fourth chapter). These states have promising compression scores that show low information loss between the reduced state stability maps and the original data throughout the cerebral cortices, i.e. up to only 20% loss in explained variance. This thesis thus presents new contributions in the functional parcellation field that may impact how researchers model the rich and dynamic interactions between brain networks in health and disease

Resting state fMRI study of brain activation using rTMS in rats

Background and purpose: Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive neuromodulation technique used to treat many neurological and psychiatric conditions. However, not much is known about the mechanisms underlying its efficacy because human rTMS studies are mostly non-invasive while most animal studies are invasive. Invasive animal studies allow for cellular and molecular changes to be detected and hence, have been able to show that rTMS may alter synaptic plasticity in the form of long-term potentiation. This is the first rodent study using non-invasive resting state functional magnetic resonance imaging (rs-fMRI) to examine the effects of low-intensity rTMS (LI-rTMS) in order to provide a more direct comparison to human studies. Methods: rs-fMRI data were acquired before and after 10 minutes of LI-rTMS intervention at one of four frequencies—1 Hz, 10 Hz, biomimetic high frequency stimulation (BHFS) and continuous theta burst stimulation (cTBS)—in addition to sham. We used independent component analysis to uncover changes in the default mode network (DMN) induced by each rTMS protocol. Results: There were considerable rTMS-related changes in the DMN. Specifically, (1) the synchrony of resting activity of the somatosensory cortex was decreased ipsilaterally following 10 Hz stimulation, increased ipsilaterally following cTBS, and decreased bilaterally following 1 Hz stimulation and BHFS; (2) the motor cortex showed bilateral changes following 1 Hz and 10 Hz stimulation, an ipsilateral increase in synchrony of resting activity following cTBS, and a contralateral decrease following BHFS; and (3) in the hippocampus, 10 Hz stimulation caused an ipsilateral decrease while 1 Hz and BHFS caused a bilateral decrease in synchrony. There was no change in the correlation of the hippocampus induced by cTBS. Conclusion: The present findings suggest that LI-rTMS can modulate functional links within the DMN of rats. LI-rTMS can induce changes in the cortex, as well as in remote brain regions such as the hippocampus when applied to anaesthetised rats and the pattern of these changes depends on the frequency used, with 10 Hz stimulation, BHFS and cTBS causing mostly ipsilateral changes in synchrony of activity in the DMN and 1 Hz stimulation causing bilateral changes in synchrony, with the contralateral changes being more prominent than ipsilateral changes. Hence, combined rTMS-fMRI emerges as a powerful tool to visualise rTMS-induced cortical connectivity changes at a high spatio-temporal resolution and help unravel the physiological processes underlying these changes in the cortex and interconnected brain regions

The dynamic functional connectome: State-of-the-art and perspectives

Resting-state functional magnetic resonance imaging (fMRI) has highlighted the rich structure of brain activity in absence of a task or stimulus. A great effort has been dedicated in the last two decades to investigate functional connectivity (FC), i.e. the functional interplay between different regions of the brain, which was for a long time assumed to have stationary nature. Only recently was the dynamic behaviour of FC revealed, showing that on top of correlational patterns of spontaneous fMRI signal fluctuations, connectivity between different brain regions exhibits meaningful variations within a typical resting-state fMRI experiment. As a consequence, a considerable amount of work has been directed to assessing and characterising dynamic FC (dFC), and several different approaches were explored to identify relevant FC fluctuations. At the same time, several questions were raised about the nature of dFC, which would be of interest only if brought back to a neural origin. In support of this, correlations with electroencephalography (EEG) recordings, demographic and behavioural data were established, and various clinical applications were explored, where the potential of dFC could be preliminarily demonstrated. In this review, we aim to provide a comprehensive description of the dFC approaches proposed so far, and point at the directions that we see as most promising for the future developments of the field. Advantages and pitfalls of dFC analyses are addressed, helping the readers to orient themselves through the complex web of available methodologies and tools

Early soft and flexible fusion of electroencephalography and functional magnetic resonance imaging via double coupled matrix tensor factorization for multisubject group analysis

Data fusion refers to the joint analysis of multiple datasets that provide different (e.g., complementary) views of the same task. In general, it can extract more information than separate analyses can. Jointly analyzing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) measurements has been proved to be highly beneficial to the study of the brain function, mainly because these neuroimaging modalities have complementary spatiotemporal resolution: EEG offers good temporal resolution while fMRI is better in its spatial resolution. The EEG–fMRI fusion methods that have been reported so far ignore the underlying multiway nature of the data in at least one of the modalities and/or rely on very strong assumptions concerning the relation of the respective datasets. For example, in multisubject analysis, it is commonly assumed that the hemodynamic response function is a priori known for all subjects and/or the coupling across corresponding modes is assumed to be exact (hard). In this article, these two limitations are overcome by adopting tensor models for both modalities and by following soft and flexible coupling approaches to implement the multimodal fusion. The obtained results are compared against those of parallel independent component analysis and hard coupling alternatives, with both synthetic and real data (epilepsy and visual oddball paradigm). Our results demonstrate the clear advantage of using soft and flexible coupled tensor decompositions in scenarios that do not conform with the hard coupling assumption

Detecting multineuronal temporal patterns in parallel spike trains

We present a non-parametric and computationally efficient method that detects spatiotemporal firing patterns and pattern sequences in parallel spike trains and tests whether the observed numbers of repeating patterns and sequences on a given timescale are significantly different from those expected by chance. The method is generally applicable and uncovers coordinated activity with arbitrary precision by comparing it to appropriate surrogate data. The analysis of coherent patterns of spatially and temporally distributed spiking activity on various timescales enables the immediate tracking of diverse qualities of coordinated firing related to neuronal state changes and information processing. We apply the method to simulated data and multineuronal recordings from rat visual cortex and show that it reliably discriminates between data sets with random pattern occurrences and with additional exactly repeating spatiotemporal patterns and pattern sequences. Multineuronal cortical spiking activity appears to be precisely coordinated and exhibits a sequential organization beyond the cell assembly concept

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