Background: Methamphetamine (MA) abuse is a global crisis that exacerbates sociopolitico-economic burdens in South Africa. MA use is associated with a myriad of neural abnormalities of structure and function, with associated neurocognitive deficits, particularly executive function (EF). Working memory (WM) training has been identified as a potential adjunct to treatment of substance use disorder (SUD) to improve EF in the hope of reducing relapse rates. Neuroimaging suggests MA alters intrinsic resting state functional connectivity (rsFC), and this may contribute to neuropsychological deficits observed in methamphetamine use disorder (MUD). Methods: This nested study analysed data described in Brooks et al. (2016), in which WM training was used as an adjunct to inpatient treatment of MUD. Healthy controls (HC, N = 25) were compared to two MUD groups, one receiving treatment as usual (TAU, N = 17), and one receiving additional cognitive training (CT, N = 24) in the form of a modified version of the ‘N-back' task (C-Ya). This task was also used to assess WMA in the neural scanner, using conditions of 0-back and 1-back across groups. The current research explored these data in a novel manner through examining rsFC. Hypotheses: It was predicted that: 1) HC and MUD participants would differ on measures of WMA, but WMA would improve in MA groups at follow-up compared to baseline and this would be augmented in the CT group; 2) rsFC networks of neural regions supporting WM would be predictive of ability to perform well and improve on WM tasks; and 3) MA groups would display heightened rsFC activity within and between resting state neural networks of the default mode network (DMN) and canonical cognitive control networks (CCNs). Results: Significant differences were observed between HC and MA groups in race and level of education, but not on WMA as tested in the scanner. The CT group, who completed WMA 3-back conditions, demonstrated significant improvement on this task post- intervention. Exploratory regression models showed the WM rsFC network did not demonstrate significant relationships with any clinical, demographic, or WM variables when controlling for multiple comparisons. Heightened connectivity within and between the DMN and CCNs was observed in the MUD compared to the HC group, which provided support for hypothesis 3. Exploratory multivariate regression models demonstrated race, age, education, duration of drug use, and an interaction of group and abstinence may impact rsFC in these networks. Post-hoc analyses identified pairwise network combinations affected by these variables. Conclusions: Despite limitations of this small study, it offers tentative preliminary insights into the largely unexplored field of rsFC in MA populations. This study supports limited research demonstrating hyperconnectivity within and between CCNs and DMN of MA users. This study also offers support for recent research suggesting that easier conditions of the Nback task may not reliably test all aspects of WM function. Exploratory analyses of covariates potentially affecting rsFC provide a platform for directions of future research.

Introduction : L'électroconvulsivothérapie (ECT) est un traitement non pharmacologique du trouble dépressif résistant. Bien que son efficacité ait été démontrée dans cette indication, les mécanismes cérébraux qui sous-tendent ce processus restent très imprécis. Il n'existe actuellement pas de travail étudiant l'effet d'une ECT efficace au niveau des modifications structurofonctionnelles cérébrales. Il semble primordial de poursuivre l'étude des corrélats neuroanatomiques précoces et plus tardifs sous tendant les processus neurofonctionnels responsables de l'amélioration de la clinique. Méthodes : Il s'agit d'une étude mono centrique menée sur le CHU de Toulouse. Chez des patients présentant un trouble dépressif résistant, des évaluations cliniques et en IRM multimodale sont réalisées à 4 temps. La 1ère évaluation a lieu avant le début de la cure, la 2ème après une 1ère ECT, la 3ème après une 1ère ECT efficace et la 4ème après rémission.Résultats: Concernant le volume de l'hippocampe et de l'amygdale à la première visite n'était pas diffèrent du volume à la troisième visite (t(135) = .329, p = .94). Au contraire, il y avait une différence significatif entre le volume de deux structures entre la première et la quatrième visite (t(135) = -2.47, p = .039) et entre la troisième et la quatrième visite (t(135) = -3.51, p = .002). Concernant la diffusivité moyenne en tant que l'effet des visites tend vers la significativité pour la DM (F(2,136) = 2.67, p = .072). En IRM resting state, il existe une hypoconnectivité précoce entre (i) l'hippocampe Droit et le cortex Cingulaire antérieur dorsal (t = -6.20 ; pFDR : 0.0123) ; (ii) l'hippocampe Droit et le noyaux caudé gauche ( t = -7.69 ; pFDR : 0.0035) et (iii) le vermis cervelet et le precuneus (t = -5.93 p FDR : 0.0363). Il existe une hyperconnectivité entre V4 et V1 entre (i) le cortex orbito frontal médian droit et le gyrus occipital médian (t = 6.58 ; p FDR : 0.0146) et (ii) le gyrus frontal inférieur droit et le cortex fronto median gauche (t = 6.83 ; pFDR : 0.0104). Il existe une diminution significative des symptomes de depression entre la V4 et la V1 à l'échelle d'Hamilton (V4: 3,08 ET : 1,62 ; V1 : 23,17 ET : 3,21 ; p <0.001).Conclusion : Il semble exister des modifications structuro-fonctionnelle à l'issu de la cure d'ECT sans modifications structurelles et micro structurelles précoces. / Background: Electroconvulsive Therapy (ECT) is a non-pharmacological treatment of resistant depressive disorder. Although its efficacy has been demonstrated in this indication, the brain mechanisms underlying this process remain very imprecise. There is currently no work studying the effect of one effective ECT on cerebral structural changes. It seems essential to continue the study of the early and late neuroanatomical correlates underlying neurofunctional processes responsible for improving the clinic. Methods: This is a mono-centric study conducted on the Toulouse University Hospital. In patients with resistant depressive disorder, clinical and multimodal MRI assessments are performed at 4-step intervals. The first evaluation takes place before the beginning of the treatment, the 2nd after a 1st ECT, the 3rd after a 1st effective ECT and the 4th after remission. Results: Regarding the volume of the hippocampus and amygdala at the first visit was not different from the volume at the third visit (t (135) = .329, p = .94). On the contrary, there was a significant difference between the volume of two structures between the first and the fourth visit (t (135) = -2.47, p = .039) and between the third and fourth visits (t (135) = -3.51, p = .002). For mean diffusivity, the effect of visits showed a trend toward significance for MD (F (2.136) = 2.67, p = .072). In the MRI resting state, there is early hypoconnectivity between (i) the right hippocampus and the dorsal anterior cingulate cortex (t = -6.20, pFDR: 0.0123); (ii) right hippocampus and left caudate nucleus (t = -7.69, pFDR: 0.0035) and (iii) vermis cerebellum and precuneus (t = -5.93 p FDR: 0.0363). There is hyperconnectivity between V4 and V1 between (i) the right medial orbit frontal cortex and the medial occipital gyrus (t = 6.58; p FDR: 0.0146) and (ii) the right inferior frontal gyrus and left fronto medial cortex (t = 6.83, pFDR: 0.0104). There is a significant decrease in the symptoms of depression between V4 and V1 at the Hamilton scale (V4: 3.08 AND: 1.62, V1: 23.17 AND: 3.21, p <0.001). Conclusion: There appears to be structural-functional changes at the end of the ECT course. However, we do not find early structural and micro structural changes.

Abstract Functional communication between brain regions is likely to play a key role in complex cognitive processes that require continuous integration of information across different regions of the brain. This makes the studying of functional connectivity in the human brain of high importance. It also provides new insights into the hierarchical organization of the human brain regions. Resting-state networks (RSNs) can be reliably and reproducibly detected using independent component analysis (ICA) at both individual subject and group levels. A growing number of ICA studies have reported altered functional connectivity in clinical populations. In the current work, it was hypothesized that ICA model order selection influences characteristics of RSNs as well as their functional connectivity. In addition, it was suggested that high ICA model order could be a useful tool to provide more detailed functional connectivity results. RSNs’ characteristics, i.e. spatial features, volume and repeatability of RSNs, were evaluated, and also differences in functional connectivity were investigated across different ICA model orders. ICA model order estimation had a significant impact on the spatial characteristics of the RSNs as well as their parcellation into sub-networks. Notably, at low model orders neuroanatomically and functionally different units tend to aggregate into large singular RSN components, while at higher model orders these units become separate RSN components. Disease-related differences in functional connectivity also seem to alter as a function of ICA model order. The volume of between-group differences reached maximum at high model orders. These findings demonstrate that fine-grained RSNs can provide detailed, disease-specific functional connectivity alterations. Finally, in order to overcome the multiple comparisons problem encountered at high ICA model orders, a new framework for group-ICA analysis was introduced. The framework involved concatenation of IC maps prior to permutation tests, which enables statistical inferences from all selected RSNs. In SAD patients, this new correction enabled the detection of significantly increased functional connectivity in eleven RSNs. / Tiivistelmä Toiminnallisten aivoalueiden välinen viestintä on todennäköisesti avainasemassa kognitiivisissa prosesseissa, jotka edellyttävät jatkuvaa tiedon integraatiota aivojen eri alueiden välillä. Tämä tekee ihmisaivojen toiminnallisen kytkennällisyyden tutkimuksesta erittäin tärkeätä. Kytkennälllisyyden tutkiminen antaa myös uutta tietoa ihmisaivojen osa-alueiden välisestä hierarkiasta. Aivojen hermoverkot voidaan luotettavasti ja toistettavasti havaita lepotilan toiminnasta yksilö- ja ryhmätasolla käyttämällä itsenäisten komponenttien analyysia (engl. Independent component analysis, ICA). Yhä useammat ICA-tutkimukset ovat raportoineet poikkeuksellisia toiminnallisen konnektiviteetin muutoksia kliinisissä populaatioissa. Tässä tutkimuksessa hypotetisoitiin, että ICA:lla laskettaujen komponenttien lukumäärä (l. asteluku) vaikuttaa tuloksena saatujen hermoverkkojen ominaisuuksiin kuten tilavuuteen ja kytkennällisyyteen. Lisäksi oletettiin, että korkea ICA-asteluku voisi olla herkempit tuottamaan yksityiskohtaisia toiminnallisen jaottelun tuloksia. Aivojen lepotilan hermoverkkojen ominaisuudet, kuten anatominen jakautuminen, volyymi ja lepohermoverkkojen havainnoinnin toistettavuus evaluoitin. Myös toiminnallisen kytkennällisyyden erot tutkitaan eri ICA-asteluvuilla. Havaittiin että asteluvulla on huomattava vaikutus aivojen lepotilan hermoverkkojen tilaominaisuuksiin sekä niiden jakautumiseen alaverkoiksi. Pienillä asteluvuilla hermoverkojen neuroanatomisesti erilliset yksiköt pyrkivät keräytymään laajoiksi yksittäisiksi komponenteiksi, kun taas korkeammilla asteluvuilla ne havaitaan erillisinä. Sairauksien aiheuttamat muutokset toiminnallisessa kytkennällisyydessä näyttävät muuttuvan myös ICA asteluvun mukaan saavuttaen maksiminsa korkeilla asteluvuilla. Korkeilla asteluvuilla voidaan havaita yksityiskohtaisia, sairaudelle ominaisia toiminnallisen konnektiviteetin muutoksia. Korkeisiin ICA asteluvun liittyvän tilastollisen monivertailuongelman ratkaisemiseksi kehitimme uuden menetelmän, jossa permutaatiotestejä edeltävien itsenäisten IC-karttoja yhdistämällä voidaan tehdä luotettava tilastollinen arvio yhtä aikaa lukuisista hermoverkoista. Kaamosmasennuspotilailla esimerkiksi kehittämämme korjaus paljastaa merkittävästi lisääntynyttä toiminnallista kytkennällisyyttä yhdessätoista hermoverkossa.

Alzheimer's disease (AD) is developing into the single greatest healthcare challenge in the coming decades. The development of early and effective treatments that can prevent the pathological damage responsible for AD-related dementia is of utmost priority for healthcare authorities. The role of the APOE-ε4 genotype, which has been shown to increase an individual's risk of developing AD, is of central interest to this goal. Understanding the mechanism by which possession of this gene modulates brain function, leading to a predisposition towards AD is an active area of research. Functional connectivity (FC) is an excellent candidate for linking APOE-related differences in brain function to sites of AD pathology. Magnetoencephalography (MEG) is a neuroimaging tool that can provide a unique insight into the electrophysiology underpinning resting-state networks (RSNs) - whose dysfunction is postulated to lead to a predisposition to AD. This thesis presents a range of methods for measuring functional connectivity in MEG data. We first develop a set of novel adaptations for preprocessing MEG data and performing source reconstruction using a beamformer (chapter 3). We then develop a range of analyses for measuring FC through correlations in the slow envelope oscillations of band-limited source-space MEG data (chapter 4). We investigate the optimum time scales for detecting FC. We then develop methods for extracting single networks (using seed-based correlation) and multiple networks (using ICA). We proceed to develop a group-statistical framework for detecting spatial differences in RSNs and present a preliminary finding for APOE-genotype-dependent differences in RSNs (chapter 5). We also develop a statistical framework for quantifying task-locked temporal differences in functional networks during task-positive experiments (chapter 6). Finally, we demonstrate a data-driven parcellation and network analysis pipeline that includes a novel correction for signal leakage between parcels. We use this framework to show evidence of stationary cross-frequency FC (chapter 7).

Soon after the advent of signal-recording techniques in the brain, functional connectivity (FC), a measure of interregional neural interactions, became an important tool to assess brain function and its relation to structure. It was discovered that certain groups of regions in the brain corresponding to behavioural domains are organized into intrinsic networks of connectivity (ICNs). These networks were shown to exhibit high FC during rest, and also during task. ICNs are not only delineated by areas which correspond to various behaviours, but can be modulated in the long and short-term in their connectivity by disease conditions, learning, and task performance. The significance of changes in FC, permanent and transient, is poorly understood with respect to even the simplest ICNs corresponding to motor and visual regions. A better grasp on how to interpret these changes could elucidate the mechanisms and implications of patterns in FC changes during therapy and basic tasks. The aim of this work is to examine long-term changes in the connectivity of several ICNs as a result of modulation by stroke and rehabilitation, and to assess short term changes due to simple, continuous task performance in healthy volunteers. To explore long-term changes in ICN connectivity, fifteen hemiparetic stroke patients underwent resting state scanning and behavioural testing before and after a two-week session of Constraint Induced Movement Therapy (CIMT). It was found that therapy led to localized increases in FC within the sensorimotor ICN. To assess transient changes in FC with task, sixteen healthy volunteers underwent a series of scans during rest, continuous performance of a non-demanding finger-tapping task, viewing of a continuous visual stimulus, and a combined (but uncoupled) visual and motor task. Group Independent Component Analysis (ICA) revealed that canonical ICNs remained robustly connected during task conditions as well as during rest, and dual regression/seed analyses showed that visual and sensorimotor ICNs showed divergent patterns of changes in FC, with the former showing increased intra-network connectivity and the latter decreased intra-network connectivity. Additionally, it was found that task activation within ICNs has a relationship to these changes in FC. Overall, these results suggest that modulation of functional connectivity is a valuable and informative tool in the study of disease recovery and task performance.

Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).

Despite the success of available medical and psychosocial treatments, a sizable subgroup of individuals with commonly co-occurring disorders, generalized anxiety disorder (GAD) and major depressive disorder (MDD), fail to make sufficient treatment gains thereby prolonging their deficits in life functioning and satisfaction. Clinically, these patients often display temperamental features reflecting heightened sensitivity to underlying motivational systems related to threat/safety and reward/loss (e.g., somatic anxiety) as well as inordinate negative self-referential processing (e.g., worry, rumination). This profile may reflect disruption in two important neural networks associated with emotional/motivational salience (e.g., salience network) and self-referentiality (e.g., default network, DN). Emotion Regulation Therapy (ERT) was developed to target this hypothesized profile and its neurobehavioral markers. In the present study, 22 GAD patients (with and without MDD) completed resting state MRI scans before receiving 16 sessions of ERT. To test study these hypotheses, we examined the associations between baseline patterns of intrinsic functional connectivity (iFC) of the insula and of hubs within the DN (anterior and dorsal medial prefrontal cortex [MPFC] and posterior cingulate cortex [PCC]) and treatment-related changes in worry, somatic anxiety symptoms and decentering. Results suggest that greater treatment linked reductions in worry were associated with iFC clusters in both the insular and parietal cortices. Greater treatment linked gains in decentering, a metacognitive process that involves the capacity to observe items that arise in the mind with healthy psychological distance that is targeted by ERT, was associated with iFC clusters in the anterior and posterior DN. The current study adds to the growing body of research implicating disruptions in the default and salience networks as promising targets of treatment for GAD with and without co-occurring MDD.

The field of neuroimaging has truly become data rich, and as such, novel analytical methods capable of gleaning meaningful information from large stores of imaging data are in high demand. Those methods that might also be applicable on the level of individual subjects, and thus potentially useful clinically, are of special interest. In this dissertation we introduce just such a method, called nonlinear functional mapping (NFM), and demonstrate its application in the analysis of resting state fMRI (functional Magnetic Resonance Imaging) from a 242-subject subset of the IMAGEN project, a European study of risk-taking behavior in adolescents that includes longitudinal phenotypic, behavioral, genetic, and neuroimaging data. Functional mapping employs a computational technique inspired by biological evolution to discover and mathematically characterize interactions among ROI (regions of interest), without making linear or univariate assumptions. Statistics of the resulting interaction relationships comport with recent independent work, constituting a preliminary cross-validation. Furthermore, nonlinear terms are ubiquitous in the models generated by NFM, suggesting that some of the interactions characterized here are not discoverable by standard linear methods of analysis. One such nonlinear interaction is discussed in the context of a direct comparison with a procedure involving pairwise correlation, designed to be an analogous linear version of functional mapping. Another such interaction suggests a novel distinction in brain function between drinking and non-drinking adolescents: a tighter coupling of ROI associated with emotion, reward, and interceptive processes such as thirst, among drinkers. Finally, we outline many improvements and extensions of the methodology to reduce computational expense, complement other analytical tools like graph-theoretic analysis, and possibly allow for voxel level functional mapping to eliminate the necessity of ROI selection.

This thesis investigated transcranial direct current stimulation (tDCS) as applied to the motor system, and its ability to modulate underlying cortical processes and resultant motor behaviours. Functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) were employed to assess the extent to which tDCS induces quantifiable changes in neural structure and function in controls and stroke patients. Modifications in the connectivity of intrinsic functional networks following tDCS application were examined using resting state fMRI. Polarity-specific changes were found: cathodal (inhibitory) tDCS increased the strength of the default mode network and increased functional coupling between major nodes within the motor network. No significant effects were found following anodal (excitatory) tDCS. Although anodal tDCS elicited only subtle changes in resting activity, it is known to produce robust modifications of behaviour. Single and paired-pulse TMS were used to investigate the neurophysiological underpinnings of these changes. Consistent with the theory of homeostatic plasticity, anodal tDCS applied prior to task performance increased GABAA-mediated cortical inhibition and worsened behaviour. The specificity of these changes suggests a central role for the mechanism of surround inhibition. A longitudinal clinical trial in chronic stroke patients was conducted to determine the utility of tDCS as an adjunct in motor rehabilitation. Serial MRI scans revealed that, when combined with motor training, anodal tDCS increased functional activity and grey matter in primarily ipsilesional motor areas. These brain changes were correlated with behavioural improvements in the stroke-affected upper limb. The laterality of connectivity at baseline, as measured by resting state activity and corticospinal tract integrity, was predictive of response to the rehabilitation program, particularly in those stroke patients who received tDCS. Asymmetry favouring the contralesional hemisphere predicted greater behavioural gains. Such results underscore the importance of re-normalisation of structure and functional activity toward the lesioned hemisphere in stroke rehabilitation.

The development of functional connectivity is often described as changing from local to distributed connections which give rise to the functional brain networks observed in adulthood. In contrast to the well-explored pattern found in functional connectivity, no research has been published describing effective connectivity development. Also, there is a plethora of literature describing functional connectivity patterns in a variety of neurodevelopmental and internalizing disorders, but there is little consistency in the connectivity patterns discovered for each disorder. Hence, this study aimed to describe functional and effective resting-state connectivity during adolescent development in a typically developing adolescent (TDA) group (n = 128) and to determine how adolescents with comorbid neurodevelopmental disorders (CND) (n = 46) differed. This was accomplished through functional and effective connectivity analysis within and between four networks: the Default Mode Network (DMN), the Salience Network (SN), the Dorsal Attention Network (DAN), and the Frontal Parietal Control Network (FPCN). The results from this study indicate that within-network connectivity decreased across age in the TDA group, which is in opposition to previous work which suggests strengthening within-network connectivity. The CND group displayed hyper-connectivity compared to the TDA group in between-network connectivity with no effect of age. The effective connectivity in the TDA group displayed decreasing connectivity within networks with increasing age, a novel effect not previously reported in the literature. The CND group’s effective connectivity was overall hyper-connected (for within- and between-networks). The functional connectivity patterns in the TDA group suggest that functional connectivity has subtle developmental change during adolescence. Further, the CND group consistently displayed hyper-connectivity in functional and effective connectivity. The CND group, and perhaps similar comorbid groups, may have less efficient networks which could contribute to their disorder(s).