Functional connectivity alterations in epilepsy from resting-state functional MRI

The study of functional brain connectivity alterations induced by neurological disorders and their analysis from resting state functional Magnetic Resonance Imaging (rfMRI) is generally considered to be a challenging task. The main challenge lies in determining and interpreting the large-scale connectivity of brain regions when studying neurological disorders such as epilepsy. We tackle this challenging task by studying the cortical region connectivity using a novel approach for clustering the rfMRI time series signals and by identifying discriminant functional connections using a novel difference statistic measure. The proposed approach is then used in conjunction with the difference statistic to conduct automatic classification experiments for epileptic and healthy subjects using the rfMRI data. Our results show that the proposed difference statistic measure has the potential to extract promising discriminant neuroimaging markers. The extracted neuroimaging markers yield 93.08% classification accuracy on unseen data as compared to 80.20% accuracy on the same dataset by a recent state-of-the-art algorithm. The results demonstrate that for epilepsy the proposed approach confirms known functional connectivity alterations between cortical regions, reveals some new connectivity alterations, suggests potential neuroimaging markers, and predicts epilepsy with high accuracy from rfMRI scans.Scopu

Altered Brain Networks In Patients with Psychogenic Non-Epileptic Seizures (PNES) Using Ultra High Field MRI

Background: Psychogenic Non-epileptic Seizures (PNES) are attacks that appear similar to epileptic attacks. However, they lack abnormal electrical discharges in the brain and have psychological underpinnings and causes. The gold standard of diagnosis is video-EEG which is not widely accessible, creating a poor prognosis for patients. Resting state functional magnetic resonance imaging can aid in the diagnosis and treatment of PNES by helping better understand brain networks in patients with PNES. This study examines brain networks in patients with PNES with a focus on the default mode network and salience network. Methods: Twelve patients with PNES between the ages of 18-56 and twelve age- and sex- matched healthy participants between the ages of 18-59 were recruited. Participants underwent 7T resting-state fMRI scanning. Independent Components Analysis (ICA) and whole brain functional connectivity making use of region of interest analysis (ROI) was used to study the default mode network and the salience network. Results: No Significant differences in functional connectivity between regions in the default mode network (DMN) as well as the salience network (SN) were found when comparing patients with PNES to healthy control participants. Conclusions: In the current study patients with PNES do not show altered connectivity between brain regions in the default mode network as well as the salience network. Limitations and future directions of the current study will be discussed

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

Neuroplasticity hypothesis of the mechanism of electroconvulsive therapy: a proton magnetic resonance and functional connectivity investigation

INTRODUCTION: Major depressive disorder (MDD) is characterized by ongoing feelings of guilt, sadness, and memory and cognition impairment. It is a multidimensional illness that affects many functionally integrated pathways of the brain. Understanding the underlying brain dysfunction that gives rise to this complex illness has been challenging, and by extension the search for appropriate treatments. MDD patients who are considered treatment resistant make up the primary population that receives electroconvulsive therapy (ECT). Remarkably, ECT shows a 75% remission rate in this patient population and is considered the “gold standard” treatment for major depression. Although the exact mechanism of its function is unknown, it is well accepted that the induced grand-mal seizure confers its therapeutic effect. The seizure likely has broad effect that somehow corrects the underlying dysfunction in brain circuitry. Here, we specifically examined studies of functional connectivity and metabolite changes. METHODS: Through literature search, we examined six studies in functional connectivity and four studies in magnetic resonance spectroscopy (MRS). RESULTS: Functional Connectivity: Studies have found that after bilateral ECT treatments, patients with major depression showed reduction of functional connectivity (FC) from the left dorsolateral prefrontal cortex (DLPFC) to other cortical and limbic structures. Correlated activity between the superior frontal gyri, middle frontal gyri and angular gyri were significantly increased after ECT. Hyperdeactivation of the orbitofrontal cortex to negative emotional stimuli in patients was decreased, and it was associated with improvement in depressive symptoms. Regional activity in the subgenual anterior cingulate cortex (sgACC) and functional connectivity between the sgACC and left hippocampus in treatment naïve patients after ECT were increased and correlated to reduction of depressive symptoms. Reduced connectivity between the amygdale and sgACC and increased connectivity between the amygdale and DLPFC was found by sequential assessments over a course of ECT treatments. Lastly, ECT increased the functional connectivity between DLPFC and the default mode network. MRS: Studies found decreased levels of glutamate or glx (glutamate/glutamine/ GABA) in patients in the anterior cingulate cortex and dorsolateral prefrontal cortex (DLPFC) compared to healthy controls. Additionally, it was found that glx levels increased after ECT treatments and that this increase was only in those who responded to treatment. Lastly, GABA level increased after ECT treatment in the occipital cortex. Discussion: Results from functional connectivity and brain metabolite studies in patients with major depression point to induced neuroplasticity as part of ECT’s therapeutic mechanism. Remodeling connectivity and mediating metabolite changes both will require modifications at the synaptic level. The wide spread changes seen in several different brain regions that have been implicated in depression further suggests that ECT’s effects are both highly specific and broad. CONCLUSION: Electroconvulsive therapy has consistently demonstrated impressive efficacy among the most severely depressed patients and is known to produce widely distributed effects in the brain. However, this also makes assessing its therapeutic mechanism challenging. Magnetic resonance imaging studies assessing functional connectivity and brain metabolite levels have demonstrated that ECT likely produces neuroplastic changes to remodel aberrant connectivity and dysfunctional excitatory and inhibitory neurotransmission in cortical and limbic areas. Although these findings should be interpreted with caution, this field of research has provided an unprecedented opportunity to examine the living brain in great detail. Further studies with larger sample sizes and improved technical specifications will likely yield greater results

ENIGMA and global neuroscience: A decade of large-scale studies of the brain in health and disease across more than 40 countries.

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors

The association of psychotic disorders, dopaminergic agents and resting-state EEG/MEG functional connectivity

Psychotic disorders are complex and heterogeneous mental disorders with low recovery rates despite a great amount of research on the topic. Various hypotheses exist as to the etiology of psychotic disorders. Amongst these, the dopamine hypothesis and the dysconnectivity hypothesis have been the most enduring in the last six decades. Little is known on how the dopamine and the dysconnectivity hypothesis are associated. The overarching research question of this thesis is to investigate this knowledge gap. Resting-state magneto- and electroencephalography (MEG, EEG) were chosen as non-invasive measurement modalities of dysconnectivity at the source and sensor level of the brain in publication 1. Parameters of resting-state EEG microstate classes A-D were used as a global analysis method of functional connectivity at the sensor level of the brain in publications 2 and 3. The first research question focused on finding systematic evidence on the association of the two hypotheses and was addressed by means of a systematic review (publication 1) of 20 studies published since 2000. Based on the review, no definite conclusion on the association of antipsychotic medication (that mainly acts on the dopamine system) and source- and sensor-level EEG/MEG functional connectivity could be drawn. The second research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated to antipsychotic medication. It was addressed by a study (publication 2) that compared 19-channel clinical EEG recordings of medicated (mFEP, n = 17) and medication-naïve (untreated; uFEP, n = 30) patients with first-episode psychotic disorders (FEP). The study results revealed significant decrease of microstate class A and significant increase of microstate class B to differentiate mFEP from uFEP. The third research question focused on whether differences in parameters of resting-state EEG microstate classes A-D are associated with psychosis illness progression and transition to psychosis in FEP and ultra-high-risk (UHR) patients. It was addressed by a study (publication 3) that found significantly increased microstate class A to differentiate a combined group of medication-naïve FEP (n = 29) and UHR patients (n = 54) together from healthy controls (HC, n = 25); significantly decreased microstate class B to differentiate FEP from all UHR patients combined; and significantly decreased microstate class D to differentiate UHR-T patients with (n = 20) from UHR-NT patients without (n = 34) later transition to psychotic disorders using 19-channel EEG recordings. In conclusion across all three publications, an association between the dopamine and the dysconnectivity hypothesis could be demonstrated by means of resting-state EEG microstates assessed in publication 2 and 3. No definite conclusion could be drawn by the systematic review (publication 1). More studies with longitudinal designs are needed to rule-out between-subject differences, track response trajectories, pre-post effects of antipsychotic medication and their association with dysconnectivity. With increased effort, resting-state EEG microstates could contribute to establishing a robust biomarker in a multi- domain approach in order to inform clinicians for the diagnosis, treatment and outcome prediction of psychotic disorders

Pattern separation and frontal EEG change as markers for responsiveness to electroconvulsive therapy

There is still a great deal that is unknown about various depressive conditions, though it is a very common affliction and cause of disability throughout the world. Not only do the underlying mechanisms of various types of depression remain uncertain, but the mystery of how different treatment options work and who will respond to them also persists. The aim of this study was to identify potential non-invasive biomarkers, to predict responsiveness to electroconvulsive therapy. Two hypotheses were investigated in this study. The first was that patient improvement from baseline on the neurocognitive, computer based pattern separation task prior to the third ECT treatment will correlate with a clinical antidepressant response. The second was that increased prefrontal slowing relative to baseline will correlate with a decrease in depressive symptoms. As a first step to validate this approach, a healthy control group performed both the pattern separation and EEG tasks once per week over the course of three weeks. Patient participants completed both tasks before their first ECT treatment, prior to their third treatment, and prior to their last treatment. A spectral analysis of EEG data was then conducted. Results indicated good test-retest reliability for the pattern separation task and EEG measurements across all three trials in the healthy control group. Results from patient data are inconclusive, but indicates that there is a change from baseline to subsequent trials for at least the EEG measurements. However, a larger sample size is needed to determine this. The limited results from this small patient sample suggest that these measurements may have clinical value in refining ECT treatment, and merit further study

Common and Specific Functional Activity Features in Schizophrenia, Major Depressive Disorder, and Bipolar Disorder

Objectives: Schizophrenia (SZ), major depressive disorder (MDD), and bipolar disorder (BD) are serious mental disorders with distinct diagnostic criteria. They share common clinical and biological features. However, there are still only few studies on the common and specific brain imaging changes associated with the three mental disorders. Therefore, the aim of this study was to identify the common and specific functional activity and connectivity changes in SZ, MDD, and BD.Methods: A total of 271 individuals underwent functional magnetic resonance imaging: SZ (n = 64), MDD (n = 73), BD (n = 41), and healthy controls (n = 93). The symptoms of SZ patients were evaluated by the Positive and Negative Syndrome Scale. The Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) were used to evaluate the symptoms of MDD patients. The BDI, BAI, and Young Mania Rating Scale were used to evaluate the symptoms of MDD and BD patients. In addition, we compared the fALFF and functional connectivity between the three mental disorders and healthy controls using two sample t-tests.Results: Significantly decreased functional activity was found in the right superior frontal gyrus, middle cingulate gyrus, left middle frontal gyrus, and decreased functional connectivity (FC) of the insula was found in SZ, MDD, and BD. Specific fALFF changes, mainly in the ventral lateral pre-frontal cortex, striatum, and thalamus were found for SZ, in the left motor cortex and parietal lobe for MDD, and the dorsal lateral pre-frontal cortex, orbitofrontal cortex, and posterior cingulate cortex in BD.Conclusion: Our findings of common abnormalities in SZ, MDD, and BD provide evidence that salience network abnormality may play a critical role in the pathogenesis of these three mental disorders. Meanwhile, our findings also indicate that specific alterations in SZ, MDD, and BD provide neuroimaging evidence for the differential diagnosis of the three mental disorders