Apathy and Striatal Gray Matter Patterns in Schizophrenia and Huntington’s Disease

Apathy is a symptom of many neurodegenerative and neuropsychiatric disorders, such as Huntington\u27s disease and schizophrenia. Apathy is often conceptualized as a combination of three domains, cognitive, behavioral, and emotional, characterized by impaired goal-directed behavior. The striatum has been shown to be significantly associated with executive functions and planned motor behavior via projection to the prefrontal cortex (PFC). Due to its connection to the PFC and its involvement in the basal ganglia motor circuit, the striatum is thought to be a significant part of the circuit that controls goal-directed behavior. The purpose of this study was to investigate the relationship between apathy severity and dorsal striatal grey matter concentration across several disorders, specifically Huntington\u27s disease and schizophrenia. With access to the PREDICT-HD and FBIRN datasets, structural MRI images and clinical assessments were collected from 823 and 178 participants, respectively. We employed the use of SBM to isolate relevant basal ganglia components and used the resulting loading coefficients for a multivariate analysis. In parallel, we also conducted a univariate analysis using segmented subcortical volumetric data. We then constructed a mixed linear model to examine the relationship between apathy and any gray matter patterns in the striatum. In Huntington’s disease, our results indicate that apathy is significantly related to the caudate and putamen atrophy with covarying in the medial PFC. In schizophrenia, our results indicate that apathy is significantly related to the putamen with covarying regions in the gyrus rectus and orbital medial PFC. We concluded that Huntington’s disease and schizophrenia manifest apathy in different ways in unique structures

Redox-sensitive DNA Binding by Homodimeric Methanosarcina Acetivorans MsvR is Modulated by Cysteine Residues

Background: Methanoarchaea are among the strictest known anaerobes, yet they can survive exposure to oxygen. The mechanisms by which they sense and respond to oxidizing conditions are unknown. MsvR is a transcription regulatory protein unique to the methanoarchaea. Initially identified and characterized in the methanogen Methanothermobacter thermautotrophicus (Mth), MthMsvR displays differential DNA binding under either oxidizing or reducing conditions. Since MthMsvR regulates a potential oxidative stress operon in M. thermautotrophicus, it was hypothesized that the MsvR family of proteins were redox-sensitive transcription regulators. Results: An MsvR homologue from the methanogen Methanosarcina acetivorans, MaMsvR, was overexpressed and purified. The two MsvR proteins bound the same DNA sequence motif found upstream of all known MsvR encoding genes, but unlike MthMsvR, MaMsvR did not bind the promoters of select genes involved in the oxidative stress response. Unlike MthMsvR that bound DNA under both non-reducing and reducing conditions, MaMsvR bound DNA only under reducing conditions. MaMsvR appeared as a dimer in gel filtration chromatography analysis and site-directed mutagenesis suggested that conserved cysteine residues within the V4R domain were involved in conformational rearrangements that impact DNA binding. Conclusions: Results presented herein suggest that homodimeric MaMsvR acts as a transcriptional repressor by binding Ma PmsvR under non-reducing conditions. Changing redox conditions promote conformational changes that abrogate binding to Ma PmsvR which likely leads to de-repression

Evaluation of a coastal acoustic buoy for cetacean detections, bearing accuracy and exclusion zone monitoring

The Maryland Department of Natural Resources and the Maryland Offshore Wind Development Fund at the Maryland Energy Administration cosponsored this work. This report was prepared as an account of work sponsored by an agency of the United States Government.There is strong socio-political support for offshore wind development in US territorial waters and construction is planned off several east coast states. Some of the planned development sites coincide with important habitat for critically endangered North Atlantic right whales. Both exclusion zones and passive acoustic monitoring are important tools for managing interactions between marine mammals and human activities. Understanding where animals are with respect to exclusion zones is important to avoid costly construction delays while minimizing the potential for negative impacts. Impact piling from construction of hundreds of offshore wind turbines likely require exclusion zones as large as 10 km. We have developed a three-hydrophone passive acoustic monitoring system that provides bearing information along with marine mammal detections to allow for informed management decisions in real-time. Multiple units form a monitoring system designed to determine whether marine mammal calls originate from inside or outside of an exclusion zone. In October 2021, we undertook a full system validation, with a focus on evaluating the detection range and bearing accuracy of the system with respect to right whale upcalls. Five units were deployed in Mid-Atlantic waters and we played more than 3500 simulated right whale upcalls at known locations to characterize the detection function and bearing accuracy of each unit. The modelled results of the detection function error were then used to compare the effectiveness of a bearing-based system to a single sensor that can only detect a signal but not ascertain directivity. Field trials indicated maximum detection ranges from 4-7.3 km depending on source and ambient noise levels. Simulations showed that incorporating bearing detections provide a substantial improvement in false alarm rates (6 to 12 times depending on number of units, placement and signal to noise conditions) for a small increase in the risk of missed detections inside of an exclusion zone (1%-3%). We show that the system can be used for monitoring exclusion zones and clearly highlight the value of including bearing estimation into exclusion zone monitoring plans while noting that placement and configuration of units should reflect anticipated ambient noise conditions.Publisher PDFPeer reviewe

Deep Learning for Neuroimaging: a Validation Study

Deep learning methods have recently made notable advances in the tasks of classification and representation learning. These tasks are important for brain imaging and neuroscience discovery, making the methods attractive for porting to a neuroimager’s toolbox. Success of these methods is, in part, explained by the flexibility of deep learning models. However, this flexibility makes the process of porting to new areas a difficult parameter optimization problem. In this work we demonstrate our results (and feasible parameter ranges) in application of deep learning methods to structural and functional brain imaging data. These methods include deep belief networks and their building block the restricted Boltzmann machine. We also describe a novel constraint-based approach to visualizing high dimensional data. We use it to analyze the effect of parameter choices on data transformations. Our results show that deep learning methods are able to learn physiologically important representations and detect latent relations in neuroimaging data

Electroconvulsive Therapy Response in Major Depressive Disorder: a Pilot Functional Network Connectivity Resting State FMRI Investigation

Major depressive disorder (MDD) is associated with increased functional connectivity in specific neural networks. Electroconvulsive therapy (ECT), the gold-standard treat- ment for acute, treatment-resistant MDD, but temporal dependencies between networks associated with ECT response have yet to be investigated. In the present longitudinal, case–control investigation, we used independent component analysis to identify distinct networks of brain regions with temporally coherent hemodynamic signal change and func- tional network connectivity (FNC) to assess component time course correlations across these networks. MDD subjects completed imaging and clinical assessments immediately prior to the ECT series and a minimum of 5 days after the last ECT treatment. We focused our analysis on four networks affected in MDD: the subcallosal cingulate gyrus, default mode, dorsal lateral prefrontal cortex, and dorsal medial prefrontal cortex (DMPFC). In an older sample of ECT subjects (n = 12) with MDD, remission associated with the ECT series reverses the relationship from negative to positive between the posterior default mode (p_DM) and two other networks: the DMPFC and left dorsal lateral prefrontal cortex (l_DLPFC). Relative to demographically healthy subjects (n = 12), the FNC between the p_DM areas and the DMPFC normalizes with ECT response. The FNC changes following treatment did not correlate with symptom improvement; however, a direct comparison between ECT remitters and non-remitters showed the pattern of increased FNC between the p_DM and l_DLPFC following ECT to be specific to those who responded to the treat- ment.The differences between ECT remitters and non-remitters suggest that this increased FNC between p_DM areas and the left dorsolateral prefrontal cortex is a neural correlate and potential biomarker of recovery from a depressed episode

Functional MRI Evaluation of Multiple Neural Networks Underlying Auditory Verbal Hallucinations in Schizophrenia Spectrum Disorders.

Functional MRI studies have identified a distributed set of brain activations to be asso­ ciated with auditory verbal hallucinations (AVH). However, very little is known about how activated brain regions may be linked together into AVH-generating networks. Fifteen volunteers with schizophrenia or schizoaffective disorder pressed buttons to indicate onset and offset of AVH during fMRI scanning. When a general linear model was used to compare blood oxygenation level dependence signals during periods in which subjects indicated that they were versus were not experiencing AVH ( AVH-on versus AVH-off ), it revealed AVH-related activity in bilateral inferior frontal and superior temporal regions; the right middle temporal gyrus; and the left insula, supramarginal gyrus, inferior parietal lobule, and extranuclear white matter. In an effort to identify AVH-related networks, the raw data were also processed using independent component analyses (ICAs). Four ICA components were spatially consistent with an a priori network framework based upon published meta-analyses of imaging correlates of AVH. Of these four components, only a network involving bilateral auditory cortices and posterior receptive language areas was significantly and positively correlated to the pattern of AVH-on versus AVH-off. The ICA also identified two additional networks (occipital-temporal and medial prefrontal), not fully matching the meta-analysis framework, but nevertheless containing nodes reported as active in some studies of AVH. Both networks showed significant AVH-related profiles, but both were most active during AVH-off periods. Overall, the data suggest that AVH generation requires specific and selective activation of auditory cortical and posterior language regions, perhaps coupled to a release of indirect influence by occipital and medial frontal structures

The genetics-BIDS extension: Easing the search for genetic data associated with human brain imaging

Metadata are what makes databases searchable. Without them, researchers would have difficulty finding data with features they are interested in. Brain imaging genetics is at the intersection of two disciplines, each with dedicated dictionaries and ontologies facilitating data search and analysis. Here, we present the genetics Brain Imaging Data Structure extension, consisting of metadata files for human brain imaging data to which they are linked, and describe succinctly the genomic and transcriptomic data associated with them, which may be in different databases. This extension will facilitate identifying micro-scale molecular features that are linked to macro-scale imaging repositories, facilitating data aggregation across studies

Evaluation of recombinant influenza virus-simian immunodeficiency virus vaccines in macaques

There is an urgent need for human immunodeficiency virus (HIV) vaccines that induce robust mucosal immunity. Influenza A viruses (both H1N1 and H3N2) were engineered to express simian immunodeficiency virus (SIV) CD8 T-cell epitopes and evaluated following administration to the respiratory tracts of 11 pigtail macaques. Influenza virus was readily detected from respiratory tract secretions, although the infections were asymptomatic. Animals seroconverted to influenza virus and generated CD8 and CD4 T-cell responses to influenza virus proteins. SIV-specific CD8 T-cell responses bearing the mucosal homing marker 7 integrin were induced by vaccination of naïve animals. Further, SIV-specific CD8 T-cell responses could be boosted by recombinant influenza virus-SIV vaccination of animals with already-established SIV infection. Sequential vaccination with influenza virus-SIV recombinants of different subtypes (H1N1 followed by H3N2 or vice versa) produced only a limited boost in immunity, probably reflecting T-cell immunity to conserved internal proteins of influenza A virus. SIV challenge of macaques vaccinated with an influenza virus expressing a single SIV CD8 T cell resulted in a large anamnestic recall CD8 T-cell response, but immune escape rapidly ensued and there was no impact on chronic SIV viremia. Although our results suggest that influenza virus-HIV vaccines hold promise for the induction of mucosal immunity to HIV, broader antigen cover will be needed to limit cytotoxic T-lymphocyte escape