Monoamine Oxidase A is Required for Rapid Dendritic Remodeling in Response to Stress

Background: Acute stress triggers transient alterations in the synaptic release and metabolism of brain monoamine neurotransmitters. These rapid changes are essential to activate neuroplastic processes aimed at the appraisal of the stressor and enactment of commensurate defensive behaviors. Threat evaluation has been recently associated with the dendritic morphology of pyramidal cells in the orbitofrontal cortex (OFC) and basolateral amygdala (BLA); thus, we examined the rapid effects of restraint stress on anxiety-like behavior and dendritic morphology in the BLA and OFC of mice. Furthermore, we tested whether these processes may be affected by deficiency of monoamine oxidase A (MAO-A), the primary enzyme catalyzing monoamine metabolism. Methods: Following a short-term (1–4h) restraint schedule, MAO-A knockout (KO) and wild-type (WT) mice were sacrificed, and histological analyses of dendrites in pyramidal neurons of the BLA and OFC of the animals were performed. Anxiety-like behaviors were examined in a separate cohort of animals subjected to the same experimental conditions. Results: In WT mice, short-term restraint stress significantly enhanced anxiety-like responses, as well as a time-dependent proliferation of apical (but not basilar) dendrites of the OFC neurons; conversely, a retraction in BLA dendrites was observed. None of these behavioral and morphological changes were observed in MAO-A KO mice. Conclusions: These findings suggest that acute stress induces anxiety-like responses by affecting rapid dendritic remodeling in the pyramidal cells of OFC and BLA; furthermore, our data show that MAO-A and monoamine metabolism are required for these phenomena

Associations of resting-state perfusion and auditory verbal hallucinations with and without emotional content in schizophrenia.

Auditory Verbal Hallucinations (AVH) are highly prevalent in patients with schizophrenia. AVH with high emotional content lead to particularly poor functional outcome. Increasing evidence shows that AVH are associated with alterations in structure and function in language and memory related brain regions. However, neural correlates of AVH with emotional content remain unclear. In our study (n = 91), we related resting-state cerebral perfusion to AVH and emotional content, comparing four groups: patients with AVH with emotional content (n = 13), without emotional content (n = 14), without hallucinations (n = 20) and healthy controls (n = 44). Patients with AVH and emotional content presented with increased perfusion within the amygdala and the ventromedial and dorsomedial prefrontal cortex (vmPFC/ dmPFC) compared to patients with AVH without emotional content. In addition, patients with any AVH showed hyperperfusion within the anterior cingulate gyrus, the vmPFC/dmPFC, the right hippocampus, and the left pre- and postcentral gyrus compared to patients without AVH. Our results indicate metabolic alterations in brain areas critical for the processing of emotions as key for the pathophysiology of AVH with emotional content. Particularly, hyperperfusion of the amygdala may reflect and even trigger emotional content of AVH, while hyperperfusion of the vmPFC/dmPFC cluster may indicate insufficient top-down amygdala regulation in patients with schizophrenia

Zinc Nanoparticles Enhance Brain Connectivity in the Canine Olfactory Network: Evidence From an fMRI Study in Unrestrained Awake Dogs

Prior functional Magnetic Resonance Imaging (fMRI) studies have indicated increased neural activation when zinc nanoparticles are added to odorants in canines. Here we demonstrate that zinc nanoparticles up-regulate directional brain connectivity in parts of the canine olfactory network. This provides an explanation for previously reported enhancement in the odor detection capability of the dogs in the presence of zinc nanoparticles. In this study, we obtained fMRI data from awake and unrestrained dogs while they were being exposed to odorants with and without zinc nanoparticles, zinc nanoparticles suspended in water vapor, as well as just water vapor alone. We obtained directional connectivity between the brain regions of the olfactory network that were significantly stronger for the condition of odorant + zinc nanoparticles compared to just odorants, water vapor + zinc nanoparticles and water vapor alone. We observed significant strengthening of the paths of the canine olfactory network in the presence of zinc nanoparticles. This result indicates that zinc nanoparticles could potentially be used to increase canine detection capabilities in the environments of very low concentrations of the odorants, which would have otherwise been undetected

Sliding-window analysis tracks fluctuations in amygdala functional connectivity associated with physiological arousal and vigilance during fear conditioning

We evaluated whether sliding-window analysis can reveal functionally relevant brain network dynamics during a well-established fear conditioning paradigm. To this end, we tested if fMRI fluctuations in amygdala functional connectivity (FC) can be related to task-induced changes in physiological arousal and vigilance, as reflected in the skin conductance level (SCL). Thirty-two healthy individuals participated in the study. For the sliding-window analysis we used windows that were shifted by one volume at a time. Amygdala FC was calculated for each of these windows. Simultaneously acquired SCL time series were averaged over time frames that corresponded to the sliding-window FC analysis, which were subsequently associated with the whole-brain seed-based amygdala sliding-window FC using the GLM. Surrogate time series were generated to test whether connectivity dynamics could have occurred by chance. In addition, results were contrasted against static amygdala FC and sliding-window FC of the primary visual cortex, which was chosen as a control seed, while a physio-physiological interaction (PPI) was performed as cross-validation. During periods of increased SCL, the left amygdala became more strongly coupled with the bilateral insula and medial prefrontal cortex, core areas of the salience network. The sliding-window analysis yielded a connectivity pattern that was unlikely to have occurred by chance, was spatially distinct from static amygdala FC and from sliding-window FC of the primary visual cortex, but was highly comparable to that of the PPI analysis. We conclude that sliding-window analysis can reveal functionally relevant fluctuations in connectivity in the context of an externally cued task

The influence of pain-related fear levels on structural brain changes in pediatric complex regional pain syndrome

Complex Regional Pain Syndrome (CRPS) is a chronic neuropathic pain condition associated with significant alterations in the somatosensory and motor cortex brain regions. Cognitive-affective alterations have recently been recognized in patients suffering with CRPS, however, relatively little neuroimaging research has been done to examine these dimensions. Moreover, many children and adolescents suffer from CRPS, but very little is known about the impact of this condition on brain states in the pediatric population. The aim of this paper is to assess the structural brain differences between children with CRPS and healthy controls and to examine to what degree fear level influences such differences. This study is part of a larger investigation that integrates functional and structural brain differences to evaluate fear-related brain circuitry in patients with CRPS. Thirty-seven patients with CRPS were age and gender matched with 35 healthy controls. The two groups underwent structural magnetic resonance imaging (MRI) scans as well as completed the Fear of Pain Questionnaire, child report (FOPQ). To examine gray matter differences, voxel-based morphometry (VBM) and cortical thickness (CT) analysis was completed. Patients with CRPS in this study had an average age of 13.2 (SD=2.7) and were predominantly female (73%). Of the 35 patients who completed FOPQ, 49% reported clinically significant pain-related fear. Compared with healthy controls, CRPS patients had significantly less in gray matter (GM) volume in pain- and fear-related brain regions, including the dorsolateral prefrontal gyrus, motor and somatosensory cortex, anterior and posterior cingulate cortex, nucleus accumbens, putamen, amygdala, and hippocampus. Furthermore, gray matter decreases in regions such as anterior midcingulate cortex, nucleus accumbens, and putamen were associated with elevated pain-related fear in patients. Differences in gray matter volume in fear-circuitry areas could potentially be one mechanism by which abnormal fear learning and extinction develops in youth suffering with CRPS. Further research examining brain changes post-treatment is needed to determine if treatments that target improving pain and fear levels are associated with concomitant normalization of brain structures

Neuroimage

Threat-related emotional function is supported by a neural circuit that includes the prefrontal cortex (PFC), hippocampus, and amygdala. The function of this neural circuit is altered by negative life experiences, which can potentially affect threat-related emotional processes. Notably, Black-American individuals disproportionately endure negative life experiences compared to White-American individuals. However, the relationships among negative life experiences, race, and the neural substrates that support threat-related emotional function remains unclear. Therefore, the current study investigated whether the brain function that supports threat-related emotional processes varies with racial differences in negative life experiences. In the present study, adolescent violence exposure, family income, and neighborhood disadvantage were measured prospectively (i.e., at 11-19 years of age) for Black-American and White-American volunteers. Participants then, as young adults (i.e., 18-23 years of age), completed a Pavlovian fear conditioning task during functional magnetic resonance imaging (fMRI). Cued and non-cued threats were presented during the conditioning task and behavioral (threat expectancy) and psychophysiological responses (skin conductance response; SCR) were recorded simultaneously with fMRI. Racial differences were observed in neural (fMRI activity), behavioral (threat expectancy), and psychophysiological (SCR) responses to threat. These threat-elicited responses also varied with negative life experiences (violence exposure, family income, and neighborhood disadvantage). Notably, racial differences in brain activity to threat were smaller after accounting for negative life experiences. The present findings suggest that racial differences in the neural and behavioral response to threat are due, in part, to exposure to negative life experiences and may provide new insight into the mechanisms underlying racial disparities in mental health.U19 DP002664/DP/NCCDPHP CDC HHS/United StatesU48 DP000046/DP/NCCDPHP CDC HHS/United StatesU48 DP000057/DP/NCCDPHP CDC HHS/United StatesU19 DP002663/DP/NCCDPHP CDC HHS/United StatesU48 DP000056/DP/NCCDPHP CDC HHS/United StatesR01 MH098348/MH/NIMH NIH HHS/United StatesT32 MH100019/MH/NIMH NIH HHS/United StatesU19 DP002665/DP/NCCDPHP CDC HHS/United States2020-11-15T00:00:00Z31401241PMC6819267866

Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches

In the past two decades, functional Magnetic Resonance Imaging has been used to relate neuronal network activity to cognitive processing and behaviour. Recently this approach has been augmented by algorithms that allow us to infer causal links between component populations of neuronal networks. Multiple inference procedures have been proposed to approach this research question but so far, each method has limitations when it comes to establishing whole-brain connectivity patterns. In this work, we discuss eight ways to infer causality in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality, Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and Transfer Entropy. We finish with formulating some recommendations for the future directions in this area

Neuroscience

Exposure to violence during childhood can lead to functional changes in brain regions that are important for emotion expression and regulation, which may increase susceptibility to internalizing disorders in adulthood. Specifically, childhood violence exposure can disrupt the functional connectivity among brain regions that include the prefrontal cortex (PFC), hippocampus, and amygdala. Together, these regions are important for modulating autonomic responses to stress. However, it is unclear to what extent changes in brain connectivity relate to autonomic stress reactivity and how the relationship between brain connectivity and autonomic responses to stress varies with childhood violence exposure. Thus, the present study examined whether stress-induced changes in autonomic responses (e.g., heart rate, skin conductance level (SCL)) varied with amygdala-, hippocampus-, and ventromedial prefrontal cortex (vmPFC)-whole brain resting-state functional connectivity (rsFC) as a function of violence exposure. Two hundred and ninety-seven participants completed two resting-state functional magnetic resonance imaging scans prior to (pre-stress) and after (post-stress) a psychosocial stress task. Heart rate and SCL were recorded during each scan. Post-stress heart rate varied negatively with post-stress amygdala-inferior parietal lobule rsFC and positively with post-stress hippocampus-anterior cingulate cortex rsFC among those exposed to high, but not low, levels of violence. Results from the present study suggest that post-stress fronto-limbic and parieto-limbic rsFC modulates heart rate and may underlie differences in the stress response among those exposed to high levels of violence.U19 DP002664/DP/NCCDPHP CDC HHSUnited States/U48 DP000046/DP/NCCDPHP CDC HHSUnited States/U19 DP002665/DP/NCCDPHP CDC HHSUnited States/U48 DP000057/DP/NCCDPHP CDC HHSUnited States/U19 DP002663/DP/NCCDPHP CDC HHSUnited States/U48 DP000056/DP/NCCDPHP CDC HHSUnited States/R01 MH098348/MH/NIMH NIH HHSUnited States

A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks

Investigating the Correspondence of Clinical Diagnostic Grouping With Underlying Neurobiological and Phenotypic Clusters Using Unsupervised Machine Learning

Many brain-based disorders are traditionally diagnosed based on clinical interviews and behavioral assessments, which are recognized to be largely imperfect. Therefore, it is necessary to establish neuroimaging-based biomarkers to improve diagnostic precision. Resting-state functional magnetic resonance imaging (rs-fMRI) is a promising technique for the characterization and classification of varying disorders. However, most of these classification methods are supervised, i.e., they require a priori clinical labels to guide classification. In this study, we adopted various unsupervised clustering methods using static and dynamic rs-fMRI connectivity measures to investigate whether the clinical diagnostic grouping of different disorders is grounded in underlying neurobiological and phenotypic clusters. In order to do so, we derived a general analysis pipeline for identifying different brain-based disorders using genetic algorithm-based feature selection, and unsupervised clustering methods on four different datasets; three of them—ADNI, ADHD-200, and ABIDE—which are publicly available, and a fourth one—PTSD and PCS—which was acquired in-house. Using these datasets, the effectiveness of the proposed pipeline was verified on different disorders: Attention Deficit Hyperactivity Disorder (ADHD), Alzheimer's Disease (AD), Autism Spectrum Disorder (ASD), Post-Traumatic Stress Disorder (PTSD), and Post-Concussion Syndrome (PCS). For ADHD and AD, highest similarity was achieved between connectivity and phenotypic clusters, whereas for ASD and PTSD/PCS, highest similarity was achieved between connectivity and clinical diagnostic clusters. For multi-site data (ABIDE and ADHD-200), we report site-specific results. We also reported the effect of elimination of outlier subjects for all four datasets. Overall, our results suggest that neurobiological and phenotypic biomarkers could potentially be used as an aid by the clinician, in additional to currently available clinical diagnostic standards, to improve diagnostic precision. Data and source code used in this work is publicly available at https://github.com/xinyuzhao/identification-of-brain-based-disorders.git