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

Impaired Prefrontal Synaptic Gain in People with Psychosis and Their Relatives during the Mismatch Negativity

The mismatch negativity (MMN) evoked potential, a preattentive brain response to a discriminable change in auditory stimulation, is significantly reduced in psychosis. Glutamatergic theories of psychosis propose that hypofunction of NMDA receptors (on pyramidal cells and inhibitory interneurons) causes a loss of synaptic gain control. We measured changes in neuronal effective connectivity underlying the MMN using dynamic causal modeling (DCM), where the gain (excitability) of superficial pyramidal cells is explicitly parameterised. EEG data were obtained during a MMN task—for 24 patients with psychosis, 25 of their first‐degree unaffected relatives, and 35 controls—and DCM was used to estimate the excitability (modeled as self‐inhibition) of (source‐specific) superficial pyramidal populations. The MMN sources, based on previous research, included primary and secondary auditory cortices, and the right inferior frontal gyrus. Both patients with psychosis and unaffected relatives (to a lesser degree) showed increased excitability in right inferior frontal gyrus across task conditions, compared to controls. Furthermore, in the same region, both patients and their relatives showed a reversal of the normal response to deviant stimuli; that is, a decrease in excitability in comparison to standard conditions. Our results suggest that psychosis and genetic risk for the illness are associated with both context‐dependent (condition‐specific) and context‐independent abnormalities of the excitability of superficial pyramidal cell populations in the MMN paradigm. These abnormalities could relate to NMDA receptor hypofunction on both pyramidal cells and inhibitory interneurons, and appear to be linked to the genetic aetiology of the illness, thereby constituting potential endophenotypes for psychosis

Magnetoencephalography as a tool in psychiatric research: current status and perspective

The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. In this review, we propose that recent technological and analytic advances in Magnetoencephalography (MEG), a technique which allows the measurement of neuronal events directly and non-invasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as Schizophrenia, Autism Spectrum Disorders, and the dementias. In our paper, we summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then survey recent studies that have utilized MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This is followed by links with preclinical research, which have highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, which could account for measured changes in neural oscillations. In the final section of the paper, challenges as well as novel methodological developments are discussed which could pave the way for a widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis

Intrinsic functional brain networks in health and disease

6 Introduction   6  6.1   Imaging  cognitive  processes  with  functional  magnetic  resonance  imaging   7  6.2   Imaging  the  brain’s  resting  state   8  6.3   Intrinsic  connectivity  networks  in  the  resting  state   9  6.4   Investigating  modulations  and  plasticity  of  intrinsic  connectivity  networks   12 7 Paper  1:   Towards  discovery  science  of  human  brain  function  (PNAS  2010)   14 8 Paper  2:   Repeated  pain  induces  adaptations  of  intrinsic  brain  activity  to  reflect  past  and  predict future pain  (Neuroimage  2011)   30 9 Paper  3:   Intrinsic  network  connectivity  reflects  consistency  of  synesthetic  experience

Network based statistical analysis detects changes induced by continuous theta-burst stimulation on brain activity at rest

We combined continuous theta-burst stimulation (cTBS) and resting state (RS)-fMRI approaches to investigate changes in functional connectivity (FC) induced by right dorsolateral prefrontal cortex (DLPFC)-cTBS at rest in a group of healthy subjects. Seed-based fMRI analysis revealed a specific pattern of correlation between the right prefrontal cortex and several brain regions: based on these results, we defined a 29-node network to assess changes in each network connection before and after, respectively, DLPFC-cTBS and sham sessions. A decrease of correlation between the right prefrontal cortex and right parietal cortex (Brodmann areas 46 and 40, respectively) was detected after cTBS, while no significant result was found when analyzing sham-session data. To our knowledge, this is the first study that demonstrates within-subject changes in FC induced by cTBS applied on prefrontal area. The possibility to induce selective changes in a specific region without interfering with functionally correlated area could have several implications for the study of functional properties of the brain, and for the emerging therapeutic strategies based on transcranial stimulation

Biophysical Modulations of Functional Connectivity

Resting-state low frequency oscillations have been detected in many functional magnetic resonance imaging (MRI) studies and appear to be synchronized between functionally related areas. Converging evidence from MRI and other imaging modalities suggest that this activity has an intrinsic neuronal origin. Multiple consistent networks have been found in large populations, and have been shown to be stable over time. Further, these patterns of functional connectivity have been shown to be altered in healthy controls under various physiological challenges. This review will present the biophysical characterization of functional connectivity, and examine the effects of physical state manipulations (such as anesthesia, fatigue, and aging) in healthy controls.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90432/1/brain-2E2011-2E0039.pd

Clinical Applications of Resting State Functional Connectivity

During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm

The Role of Alpha Oscillations among the Main Neuropsychiatric Disorders in the Adult and Developing Human Brain: Evidence from the Last 10 Years of Research

Alpha oscillations (7–13 Hz) are the dominant rhythm in both the resting and active brain. Accordingly, translational research has provided evidence for the involvement of aberrant alpha activ- ity in the onset of symptomatological features underlying syndromes such as autism, schizophrenia, major depression, and Attention Deficit and Hyperactivity Disorder (ADHD). However, findings on the matter are difficult to reconcile due to the variety of paradigms, analyses, and clinical phenotypes at play, not to mention recent technical and methodological advances in this domain. Herein, we seek to address this issue by reviewing the literature gathered on this topic over the last ten years. For each neuropsychiatric disorder, a dedicated section will be provided, containing a concise account of the current models proposing characteristic alterations of alpha rhythms as a core mechanism to trigger the associated symptomatology, as well as a summary of the most relevant studies and scientific con- tributions issued throughout the last decade. We conclude with some advice and recommendations that might improve future inquiries within this field

From rest to task

A primary goal of neuroscience research on psychiatric disorders such as schizophrenia is to enhance the current understanding of underlying biological mechanisms in order to develop novel interventions. Human brain functions are maintained through activity of large-scale brain networks. Accordingly, deficient perceptual and cognitive processing can be caused by failures of functional integration within networks, as reflected by the disconnection hypothesis of schizophrenia. Various neuroimaging techniques can be applied to study functional brain networks, each having different strengths. Frequently used complementary methods are the electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), which were shown to have a common basis. Given the feasibility of combined EEG and fMRI measurement, EEG signatures of functional networks have been described, providing complimentary information about the functional state of networks. Both at rest and during task completion, many independent EEG and fMRI studies confirmed deficient network connectivity in schizophrenia. However, a rather diffuse picture with hyper- and hypo- activations within and between specific networks was reported. Furthermore, the theory of state dependent information processing argues that spontaneous and prestimulus brain activity interacts with upcoming task-related processes. Consequently, observed network deficits that vary according to task conditions could be caused by differences in resting or prestimulus state in schizophrenia. Based on that background, the present thesis aimed to increase the understanding of aberrant functional networks in schizophrenia by using simultaneous EEG-fMRI under different conditions. One study investigated integrative mechanisms of networks during eyes-open (EO) resting state using a common-phase synchronization measure in an EEG-informed fMRI analysis (study 3). The other two studies (studies 1&2) used an fMRI-informed EEG analysis: The second study was an extension of the first, which was performed in healthy subjects only. Hence, the same methodologies and analyses were applied in both studies, but in the second study schizophrenia patients were compared to healthy controls. The associations between four temporally coherent networks (TCNs) – the default mode network (DMN), the dorsal attention network (dAN), left and right working memory networks (WMNs) – and power of three EEG frequency bands (theta, alpha, and beta band) during a verbal working memory (WM) task were investigated. Both resting state and task-related studies performed in schizophrenia patients (studies 2&3) revealed altered activation strength, functional states and interaction of TCNs, especially of the DMN. During rest (study 3), the DMN was differently integrated through common-phase synchronization in the delta (0.5 – 3.5Hz) and beta (13 – 30Hz) band. At prestimulus states of a verbal WM task, however, study 2 did not reveal differences in the activation level of the DMN between groups. Furthermore, from pre-to-post stimulus, the association of the DMN with frontal-midline (FM) theta (3 – 7Hz) band was altered, and a reduced suppression of the DMN during WM retention was detected. Schizophrenia patients also demonstrated abnormal interactions between networks: the DMN and dAN showed a reduced anti-correlation and the WMNs demonstrated an absent lateralization effect (study 2). The view that schizophrenia patients display TCN deficiencies is supported by the results of the present thesis. Especially the DMN and its interaction to the task-positive dAN showed specific alterations at different mental states and their interaction (during rest and from pre-to-post stimulus). Those alterations might at least partly explain observed symptomatology as attentional orientation deficits in patients. To conclude, functional networks as the DMN might represent promising targets for novel treatment options such as neurofeedback or transcranial direct current stimulation (tDCS)