NEUROTRANSMITTERS AND RESTING STATE NETWORKS: CLINICAL IMPLICATION FOR MAJOR PSYCHIATRIC DISORDER

Alterations in brain intrinsic activity \u2013 as organized in resting-state networks (RSNs) such as sensorimotor network (SMN), salience network (SN) and default-mode network (DMN) \u2013 and in neurotransmitters signaling \u2013 such as dopamine (DA) and serotonin (5-HT) \u2013 have been independently detected in psychiatric disorders like bipolar disorder and schizophrenia. Thus, the aim of this work was to investigate the relationship between such neurotransmitters and RSNs in healthy, by reviewing the relevant work on this topic and performing complementary analyses, in order to better understand their physiological link as well as their alterations in psychiatric disorders. According to the reviewed data, neurotransmitters nuclei diffusively project to subcortical and cortical regions of RSNs. In particular, the dopaminergic substantia nigra (SNc)-related nigrostriatal pathway is structurally and functionally connected with core regions of the SMN, while the ventral tegmental area (VTA)-related mesocorticolimbic pathway with core regions of the SN. The serotonergic raphe nuclei (RNi) connections involve regions of the SMN and DMN. Coherently, changes in neurotransmitters activity impact the functional configuration and level of activity of RSNs, as measured by functional connectivity (FC) and amplitude of low-frequency fluctuations/temporal variability of BOLD signal. Specifically, DA signaling is associated with increase in FC and activity in the SMN (hypothetically via the SNc-related nigrostriatal pathway) and SN (hypothetically via the VTA-related mesocorticolimbic pathway), as well as concurrent decrease in FC and activity in the DMN. By contrast, 5-HT signaling (via the RNi-related pathways) is associated with decrease in SMN activity along with increase in DMN activity. Complementally, our empirical data showed a positive correlation between SNc-related FC and SMN activity, while a negative correlation between RNi-related FC and SMN activity (along with tilting of networks balance toward the DMN). According to these data, we hypothesize that the activity of neurotransmitters-related neurons synchronize the low-frequency oscillations within different RSNs regions, thus affecting the baseline level of RSNs activity and their balancing. In our model, DA signaling favors the predominance of SMN-SN activity, while 5-HT signaling favors the predominance of DMN activity, manifesting in distinct behavioral patterns. In turn, alterations in neurotransmitters signaling (or its disconnection) may favor a correspondent functional reorganization of RSNs, manifesting in distinct psychopathological states. The here suggested model carries important implications for psychiatric disorders, providing novel and well testable hypotheses especially on bipolar disorder and schizophrenia

Resting-State Functional Connectivity in Late-Life Depression: Higher Global Connectivity and More Long Distance Connections

Functional magnetic resonance imaging recordings in the resting-state (RS) from the human brain are characterized by spontaneous low-frequency fluctuations in the blood oxygenation level dependent signal that reveal functional connectivity (FC) via their spatial synchronicity. This RS study applied network analysis to compare FC between late-life depression (LLD) patients and control subjects. Raw cross-correlation matrices (CM) for LLD were characterized by higher FC. We analyzed the small-world (SW) and modular organization of these networks consisting of 110 nodes each as well as the connectivity patterns of individual nodes of the basal ganglia. Topological network measures showed no significant differences between groups. The composition of top hubs was similar between LLD and control subjects, however in the LLD group posterior medial-parietal regions were more highly connected compared to controls. In LLD, a number of brain regions showed connections with more distant neighbors leading to an increase of the average Euclidean distance between connected regions compared to controls. In addition, right caudate nucleus connectivity was more diffuse in LLD. In summary, LLD was associated with overall increased FC strength and changes in the average distance between connected nodes, but did not lead to global changes in SW or modular organization

Functional Brain Organization in Space and Time

The brain is a network functionally organized at many spatial and temporal scales. To understand how the brain processes information, controls behavior and dynamically adapts to an ever-changing environment, it is critical to have a comprehensive description of the constituent elements of this network and how relationships between these elements may change over time. Decades of lesion studies, anatomical tract-tracing, and electrophysiological recording have given insight into this functional organization. Recently, however, resting state functional magnetic resonance imaging (fMRI) has emerged as a powerful tool for whole-brain non-invasive measurement of spontaneous neural activity in humans, giving ready access to macroscopic scales of functional organization previously much more difficult to obtain. This thesis aims to harness the unique combination of spatial and temporal resolution provided by functional MRI to explore the spatial and temporal properties of the functional organization of the brain. First, we establish an approach for defining cortical areas using transitions in correlated patterns of spontaneous BOLD activity (Chapter 2). We then propose and apply measures of internal and external validity to evaluate the credibility of the areal parcellation generated by this technique (Chapter 3). In chapter 4, we extend the study of functional brain organization to a highly sampled individual. We describe the idiosyncratic areal and systems-level organization of the individual relative to a standard group-average description. Further, we develop a model describing the reliability of BOLD correlation estimates across days that accounts for relevant sources of variability. Finally, in Chapter 5, we examine whether BOLD correlations meaningfully vary over the course of single resting-state scans

A working model on large-scale spatio-temporal organization of brain functioning and its implications for bipolar disorder

A working model on large-scale spatio-temporal organization of brain functioning and its implications for bipolar disorde

The impact of ischemic stroke on connectivity gradients

The functional organization of the brain can be represented as a low-dimensional space that reflects its macroscale hierarchy. The dimensions of this space, described as connectivity gradients, capture the similarity of areas' connections along a continuous space. Studying how pathological perturbations with known effects on functional connectivity affect these connectivity gradients provides support for their biological relevance. Previous work has shown that localized lesions cause widespread functional connectivity alterations in structurally intact areas, affecting a network of interconnected regions. By using acute stroke as a model of the effects of focal lesions on the connectome, we apply the connectivity gradient framework to depict how functional reorganization occurs throughout the brain, unrestricted by traditional definitions of functional network boundaries. We define a three-dimensional connectivity space template based on functional connectivity data from healthy controls. By projecting lesion locations into this space, we demonstrate that ischemic strokes result in dimension-specific alterations in functional connectivity over the first week after symptom onset. Specifically, changes in functional connectivity were captured along connectivity Gradients 1 and 3. The degree of functional connectivity change was associated with the distance from the lesion along these connectivity gradients (a measure of functional similarity) regardless of the anatomical distance from the lesion. Together, these results provide support for the biological validity of connectivity gradients and suggest a novel framework to characterize connectivity alterations after stroke

The Electrophysiology of Resting State fMRI Networks

Traditional research in neuroscience has studied the topography of specific brain functions largely by presenting stimuli or imposing tasks and measuring evoked brain activity. This paradigm has dominated neuroscience for 50 years. Recently, investigations of brain activity in the resting state, most frequently using functional magnetic resonance imaging (fMRI), have revealed spontaneous correlations within widely distributed brain regions known as resting state networks (RSNs). Variability in RSNs across individuals has found to systematically relate to numerous diseases as well as differences in cognitive performance within specific domains. However, the relationship between spontaneous fMRI activity and the underlying neurophysiology is not well understood. This thesis aims to combine invasive electrophysiology and resting state fMRI in human subjects to better understand the nature of spontaneous brain activity. First, we establish an approach to precisely coregister intra-cranial electrodes to fMRI data (Chapter 2). We then created a novel machine learning approach to define resting state networks in individual subjects (Chapter 3). This approach is validated with cortical stimulation in clinical electrocorticography (ECoG) patients (Chapter 4). Spontaneous ECoG data are then analyzed with respect to fMRI time-series and fMRI-defined RSNs in order to illustrate novel ECoG correlates of fMRI for both local field potentials and band-limited power (BLP) envelopes (Chapter 5). In Chapter 6, we show that the spectral specificity of these resting state ECoG correlates link classic brain rhythms with large-scale functional domains. Finally, in Chapter 7 we show that the frequencies and topographies of spontaneous ECoG correlations specifically recapitulate the spectral and spatial structure of task responses within individual subjects

Human subsystems of medial temporal lobes extend locally to amygdala nuclei and globally to an allostatic-interoceptive system

In mammals, the hippocampus, entorhinal, perirhinal, and parahippocampal cortices (i.e., core regions of the human medial temporal lobes, MTL) are locally interlaced with the adjacent amygdala nuclei at the structural and functional levels. At the global brain level, the human MTL has been described as part of the default mode network whereas amygdala nuclei as parts of the salience network, with both networks forming collectively a large-scale brain system supporting allostatic-interoceptive functions. We hypothesized (i) that intrinsic functional connectivity of slow activity fluctuations would reveal human MTL subsystems locally extending to the amygdala; and (ii) that these extended local subsystems would be globally embedded in large-scale brain systems supporting allostatic-interoceptive functions. From the resting-state fMRI data of three independent samples of cognitively healthy adults (one main and two replication samples: Ns = 101, 61, and 29, respectively), we analyzed the functional connectivity of fluctuating ongoing BOLD-activity within and outside the amygdala-MTL in a data-driven way using masked independent component and dual-regression analyses. We found that at the local level MTL subsystems extend to the amygdala and are functionally organized along the longitudinal amygdala-MTL axis. These subsystems were characterized by a consistent involvement of amygdala, hippocampus, and entorhinal cortex, but a variable participation of perirhinal and parahippocampal regions. At the global level, amygdala-MTL subsystems selectively connected to salience, thalamic-brainstem, and default mode networks – the major cortical and subcortical parts of the allostatic-interoceptive system. These results provide evidence for integrated amygdala-MTL subsystems in humans, which are embedded within a larger allostatic-interoceptive system

Resting State Functional Magnetic Resonance and Diffusion Tensor Imaging of Hemiplegic Cerebral Palsy Patients Treated with Constraint-Induced Movement Therapy: Predictors and Clinically Correlated Evidence of Neuroplasticity

Hemiplegic cerebral palsy is characterized by unilateral upper limb impairment and patients often compensate by performing most tasks with their unaffected arm. Constraint-induced movement therapy (CIMT) directly combats this learned non-use by casting the unaffected arm and forcing the patient to repetitively practice skills with the hemiplegic limb. Subjects with hemiplegic cerebral palsy were recruited from Holland Bloorview Kids Rehabilitation Hospital, Thames Valley Children’s Centre and McMaster Children’s Hospital. MRI acquisitions and clinical evaluations were collected at baseline, 1 and 6-months later. The case group participated in a CIMT camp after baseline evaluations and was compared to an untreated control group. Resting state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) acquisitions quantify global network organization and neural integrity, respectively, and found alterations in multiple resting state network connectivity patterns and significantly different fractional anisotropy and mean diffusivity in the affected corticospinal tract. Asymmetric baseline sensorimotor network organization was predictive of a positive and continuous functional response to CIMT. Clinically correlated network reorganization provides further evidence of neuroplastic mechanisms related to CIMT