Neural effective connectivity explains subjective fatigue in stroke

Persistent fatigue is a major debilitating symptom in many psychiatric and neurological conditions, including stroke. Post-stroke fatigue has been linked to low corticomotor excitability. Yet, it remains elusive what the neuronal mechanisms are that underlie motor cortex excitability and chronic persistence of fatigue. In this cross-sectional observational study, in two experiments we examined a total of 59 non-depressed stroke survivors with minimal motoric and cognitive impairments using 'resting state' magnetic resonance imaging (rs-fMRI), single-pulse and paired-pulse transcranial magnetic stimulation (pp-TMS). In the first session of Experiment 1, we assessed resting motor thresholds (RMTs) - a typical measure of cortical excitability-by applying TMS to the primary motor cortex (M1) and measuring motor-evoked potential in the hand affected by stroke. In the second session, we measured their brain activity with rs-fMRI to assess effective connectivity interactions at rest. In Experiment 2 we examined effective inter-hemispheric connectivity in an independent sample of patients using pp-TMS. We also assessed the levels of non-exercise induced, persistent fatigue using Fatigue Severity Scale (FSS-7), a self-report questionnaire which has been widely applied and validated across different conditions. We employed spectral dynamic causal modelling (sp-DCM) in Experiment 1 and pp-TMS in Experiment 2 to characterise how neuronal effective connectivity relates to self-reported post-stroke fatigue. In a multiple regression we used the balance in inhibitory connectivity between homologue regions in M1 as the main predictor, and have included lesioned hemisphere, RMT and levels of depression as additional predictors. Our novel index of inter-hemispheric inhibition balance was a significant predictor of post-stroke fatigue in Experiment 1 (β  =  1.524, p = 7.56e-05, CI[0.921, 2.127]) and in Experiment 2 (β  =  0.541, p = 0.049, CI[0.002, 1.080]). In experiment 2, depression scores and corticospinal excitability, a measure associated with subjective fatigue, also significantly accounted for variability in fatigue. We suggest that the balance in inter-hemispheric inhibitory effects between primary motor regions can explain subjective post-stroke fatigue. Findings provide novel insights into neural mechanisms that underlie persistent fatigue

Hippocampal neuroinflammation, functional connectivity, and depressive symptoms in multiple sclerosis

Depression, a condition commonly comorbid with multiple sclerosis (MS), is associated more generally with elevated inflammatory markers and hippocampal pathology. We hypothesized that neuroinflammation in the hippocampus is responsible for depression associated with MS. We characterized the relationship between depressive symptoms and hippocampal microglial activation in patients with MS using the 18-kDa translocator protein radioligand [18F]PBR111. To evaluate pathophysiologic mechanisms, we explored the relationships between hippocampal neuroinflammation, depressive symptoms, and hippocampal functional connectivities defined by resting-state functional magnetic resonance imaging. Methods The Beck Depression Inventory (BDI) was administered to 11 patients with MS and 22 healthy control subjects before scanning with positron emission tomography and functional magnetic resonance imaging. We tested for higher [18F]PBR111 uptake in the hippocampus of patients with MS relative to healthy control subjects and examined the correlations between [18F]PBR111 uptake, BDI scores, and hippocampal functional connectivities in the patients with MS. Results Patients with MS had an increased hippocampal [18F]PBR111 distribution volume ratio relative to healthy control subjects (p = .024), and the hippocampal distribution volume ratio was strongly correlated with the BDI score in patients with MS (r = .86, p = .006). Hippocampal functional connectivities to the subgenual cingulate and prefrontal and parietal regions correlated with BDI scores and [18F]PBR111 distribution volume ratio. Conclusions Our results provide evidence that hippocampal microglial activation in MS impairs the brain functional connectivities in regions contributing to maintenance of a normal affective state. Our results suggest a rationale for the responsiveness of depression in some patients with MS to effective control of brain neuroinflammation. Our findings also lend support to further investigation of the role of inflammatory processes in the pathogenesis of depression more generally

Individual variation in brain network topology predicts emotional intelligence

BACKGROUND: Social cognitive ability is a significant determinant of functional outcome and deficits in social cognition are a disabling symptom of psychotic disorders. The neurobiological underpinnings of social cognition are not well understood, hampering our ability to ameliorate these deficits. Using ‘resting-state’ fMRI (functional magnetic resonance imaging) and a trans-diagnostic, data-driven analytic strategy, we sought to identify the brain network basis of emotional intelligence, a key domain of social cognition. METHODS: Subjects included 60 participants with a diagnosis of schizophrenia or schizoaffective disorder and 46 healthy comparison participants from three different sites: Beth Israel Deaconess Medical Center, Boston, MA, McLean Hospital, Belmont, MA, and University of Pittsburgh, Pittsburgh, PA. All participants underwent a structural T1/MPRAGE and resting-state fMRI scan. Emotional Intelligence was measured using the Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). A connectome-wide analysis of brain connectivity examined how each individual brain voxel’s connectivity correlated with emotional intelligence using multivariate distance matrix regression (MDMR). RESULTS: We identified a region in the left superior parietal lobule (SPL) where individual network topology predicted emotional intelligence. Specifically, the association of this region with the Default Mode Network (DMN) predicted higher emotional intelligence (r = 0.424, p < 0.001) and association with the Dorsal Attention Network (DAN) predicted lower emotional intelligence (r = -0.504, p < 0.001). This correlation was observed in both schizophrenia and healthy comparison participants. These results held true despite corrections for sex, age, race, medication dosage (chlorpromazine equivalents), and full scale IQ (FSIQ), and was replicable per site. Post-hoc analyses showed that membership of the left SPL was entirely within the DMN in high scorers and within the DAN in low scorers. This relationship was also shown to be specific to the identified left SPL region when compared to adjacent regions. Sulcal depth analysis of the left SPL revealed a correlation to emotional intelligence (r = 0.269, p = 0.0075). CONCLUSIONS: Previous studies have demonstrated individual variance in brain network topology but the cognitive or behavioral relevance of these differences was undetermined. We observe that the left SPL, a region of high individual variance at the cytoarchitectonic level, also demonstrates individual variance in its association with large scale brain networks and that network topology predicts emotional intelligence. This is the first demonstration of a clinical phenotype in individual brain network topology.2019-07-03T00:00:00

Intrinsic Inter-Subject Variability in Functional Neuroimaging: Verification Using Blind Source Separation Features

The holy grail of brain imaging is the identification of a biomarker, which can identify an abnormality that can be used to diagnose disease and track the effectiveness of treatment and disease progression. Typically approaches that search for biomarkers start by identifying mean differences between groups of patients and healthy controls. However, combining data from different subjects and groups to be able to make meaningful inferences is not trivial. The structure of the brain in each individual is unique in size and shape as well as in the relative location of anatomical landmarks (e.g. sulci and gyri). When looking for mean differences in functional images, this issue is exacerbated by the presence of variability in functional localization, i.e. variability in the location of functional regions in the brain. This is notably an important reason to focus on looking for inter-individual differences or variability. Inter-subject variability in neuroimaging experiments is often viewed as noise. The analyses are setup in a manner to ignore this variability assuming that a global spatial normalization brings the data into the same space. Nonetheless, functional activation patterns can be impacted by variability in multiple ways for e.g., there could be spatial variability of the maps or variability in the spectral composition of the timecourses or variability in the connectivity between the activation patterns identified. The overarching problem this thesis seeks to contribute to, is seeking improved measures to quantify biologically significant spatial, spectral and connectivity based variability and to identify associated cognitive or behavioral differences in the distribution of brain networks. We have successfully shown that different (spatial and spectral) measures of variability in blind source separated functional activation patterns underline previously unexplained characteristics that help in discerning schizophrenia patients from healthy controls. Additionally, we show that variance measures in dynamic connectivity between networks in healthy controls can justify relationship between connectivity patterns and executive functioning abilities

Sex Commonalities and Differences in Obesity-Related Alterations in Intrinsic Brain Activity and Connectivity.

OBJECTIVE:This study aimed to characterize obesity-related sex differences in the intrinsic activity and connectivity of the brain's reward networks. METHODS:Eighty-six women (n = 43) and men (n = 43) completed a 10-minute resting functional magnetic resonance imaging scan. Sex differences and commonalities in BMI-related frequency power distribution and reward seed-based connectivity were investigated by using partial least squares analysis. RESULTS:For whole-brain activity in both men and women, increased BMI was associated with increased slow-5 activity in the left globus pallidus (GP) and substantia nigra. In women only, increased BMI was associated with increased slow-4 activity in the right GP and bilateral putamen. For seed-based connectivity in women, increased BMI was associated with reduced slow-5 connectivity between the left GP and putamen and the emotion and cortical regulation regions, but in men, increased BMI was associated with increased connectivity with the medial frontal cortex. In both men and women, increased BMI was associated with increased slow-4 connectivity between the right GP and bilateral putamen and the emotion regulation and sensorimotor-related regions. CONCLUSIONS:The stronger relationship between increased BMI and decreased connectivity of core reward network components with cortical and emotion regulation regions in women may be related to the greater prevalence of emotional eating. The present findings suggest the importance of personalized treatments for obesity that consider the sex of the affected individual

The anatomy of excitement:Understanding and improving the effectiveness of electroconvulsive therapy

Electroconvulsive therapy (ECT) is an effective treatment for severe depression. In this thesis, I studied electroconvulsive therapy (ECT) research with the objective to improve the clinical outcome after treatment and to gain a better understanding of its working mechanisms. Multiple methods in ECT research were explored, varying with respect to sample selection (i.e., single- versus multi-center data), study design (i.e., observational retrospective study versus prospective RCT, controlled versus non-controlled), type of data (i.e., clinical, EEG, and [f]MRI), and the applied statistical models to analyze the data (i.e., frequentist versus Bayesian models). Additionally, I proposed a taxonomy of ECT research. The main chapters can be considered as specific case-examples of the child-nodes of this taxonomy. Thereby, this thesis contributes to improving the clinical outcome and understanding of the working mechanisms of ECT. Based on the findings in this thesis, I have discussed the methods that are commonly used in ECT research and which future directions this may take

Positron emissiontomography imaging of neuroinflammation in Multiple Sclerosis with a second generation translocator protein PET radioligand

This thesis describes a new approach for molecular imaging of neuroinflammation in Multiple Sclerosis (MS). My aim was to use the 2nd generation TSPO radioligand 18F-PBR111 to explore the potential of Positron Emission Tomography (PET) targeting the 18-kDa Translocator Protein (TSPO), as an in vivo biomarker of activated microglia in MS patients. This thesis addresses three research objectives. First, I characterised 18F-PBR111 PET signal in healthy controls’ brains and tested how it is affected by the TSPO gene polymorphism at rs6971. Second, I measured 18F-PBR111 uptake across white matter volumes segmented using structural MRI measures related to MS neuropathology. Third, I explored how 18F-PBR111 uptake in the hippocampus correlated with depressive symptoms and to the brain functional connectivity of the hippocampus. Eleven patients with relapsing-remitting MS and 22 age-matched healthy controls underwent 18F-PBR111 PET and MRI scans. Structural and functional MRI sequences were used to define conventional MS neuropathological markers and for the assessment of functional connectivity, respectively. I discovered that white matter 18F-PBR111 PET signal in healthy volunteers varied with TSPO genotype and correlated positively with age. In patients with MS, signal intensity in MRI-defined lesions was higher than that in normal-appearing white matter and correlated with the historical rate of progression of their disability. Hippocampal 18F-PBR111 uptake was higher in the MS patient group than in healthy volunteers and correlated with both depressive symptoms and functional connectivity of the hippocampus with frontal, temporal and parietal cortex. I thus discovered that this 2nd generation TSPO PET radiotracer, used in humans for the first time in our study, is sensitive to MS neuropathology consistent with recognized patterns of microglial activation and that differences between subjects can be related to disability progression. I also have discovered a novel relationship between this measure of hippocampal microglial activation and affective symptoms of MS.Open Acces

Superior longitudinal fasciculus microstructure and its functional triple-network mechanisms in depressive rumination

Depressive rumination, which involves a repetitive focus on one's distress, is associated with function connectivity disturbances of Default-Mode, Salience, and Executive-Control networks, comprising the so-called "triple-network" of attention. Missing, however, is a multimodal account of rumination that neuroanatomically explains the perseveration of these dysfunctional networks as a stable human trait. Using diffusion and functional Magnetic Resonance Imaging, we explored multimodal relationships between rumination severity, white-matter microstructure, and resting-state functional connectivity in N=39 depressed adults, and then directly replicated our findings in a demographically-matched, independent sample (N=39). Among the fully-replicated results, three core findings emerged. First, rumination severity is associated with both disintegrated and desegregated functional connectivity of the triple-network. Second, global microstructural inefficiency of the right Superior Longitudinal Fasciculus (SLF) provides a neuroanatomical connectivity basis for rumination and accounts for anywhere between 25-37% of the variance in rumination (Discovery: p corr<0.01; Replication: p corr<0.01; MSE=0.05). Finally, microstructure of the right SLF and auxiliary white-matter is strongly associated with functional connectivity biomarkers of rumination, both within and between components of the triple-network (Discovery: R²=0.36, p corr<0.05; Replication: R²=0.25, p corr<0.05; MSE=0.04-0.06). By cross-validating discovery with replication, our findings advance a reproducible microstructural-functional brain connectivity model of depressive rumination that unifies neurodevelopmental and neurocognitive perspectives.Psycholog