The role of the broader autism phenotype in anxiety and depression in college-aged adults

The current study examines the relationship between the presence of autistic traits and anxiety and mood disorders in young adults from different racial groups. A representative sample from a predominately white university (2,791 non-Hispanic White (NHW) and 185 Black students) completed the broad autism phenotype questionnaire (BAPQ), a measure of depression (Patient Health Questionnaire, PHQ-9), and anxiety (Generalized Anxiety Disorder, GAD-7). Statistical Package for Social Sciences (SPSS) was used to perform two multiple regression analyses to determine the association between race, BAPQ score and anxiety and depression symptoms. The current study found a stronger association between autistic traits had depression and anxiety symptoms in Black participants than did NHW participants. These findings underscore the association between autistic traits and anxiety and depression in Black communities, and the need for further studies on this topic area. Additionally, it highlights the importance of improving access to mental health care for this population

The Timecourse of Activation Within the Cortical Network Associated with Visual Imagery

The current study examined the hemodynamic timecourse of activation within a network of regions that is thought to be associated with visual imagery. Two experimental conditions were examined that were designed to place differential demands on specific nodes within the visual imagery network. The two tasks were an object inspection task and a mental rotation task. The two conditions recruited overlapping cortical regions; however several regions revealed a differential response to object inspection and mental rotation. The mental rotation condition elicited greater activation in parietal cortex, lateral occipital/temporal regions, and bilateral prefrontal cortex. Conversely, the object inspection condition elicited greater activation in inferior extrastriate cortex, the inferior frontal gyrus, and the right cerebellum. When examining the timecourse of activation three different timecourse patterns were observed across cortical regions and conditions. The shape of the hemodynamic timecourse appears to correspond strongly with the cognitive processing taking place within the region, not the stimulus paradigm. The paper discusses the significance of those varying timecourse shapes and has implications for the appropriateness of using the canonical hrf during fMRI data analysis

Analyzing Complex Problem Solving by Dynamic Brain Networks

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution

The health of rural Black communities during COVID: Some affirmations, some surprises

Background and objectiveThere are overwhelming health disparities in the Deep South. It is important to include the voice of communities affected by these disparities when developing interventions. The goal of the current study was to develop an academic community engaged partnership to strengthen the ability to address priority health concerns of rural African American communities with a focus on health literacy and health advocacy.MethodsA community-based participatory research approach was used to administer a 15-item community health survey in five rural communities led by African American mayors in Alabama (N = 752). The survey assessed the health concerns and the potential behaviors that may be associated with those health concerns.ResultsThe five communities demonstrated similarities as well as differences in both the health concerns endorsed and the potential health behaviors that may contribute to those concerns. All five communities identified cardiovascular disease as a health concern with three endorsing mental health issues and 2 dental health. With respect to behaviors, all five communities identified either unhealthy eating/exercise and substance use as concerns with one community identifying racism as a risky behavior affecting health.ConclusionThe results presented replicate CBPR studies demonstrating that communities are important sources of information about local health priorities and concerns

Segmentation of the brain using direction-averaged signal of DWI images

Segmentation of brain tissue in diffusion MRI image space has some unique advantages. A novel segmentation method using the direction-averaged diffusion weighted imaging (DWI) signal is proposed. Two images can be obtained from the fitting of the direction-averaged DWI signal as a function of b-value: one with superior contrast between the gray matter and white matter; one with prominent CSF contrast. A pseudo T1 weighted image can be constructed and standard segmentation tools can be applied. The method was tested on the HCP dataset using SPM12, and showed good agreement with segmentation using the T1 weighted image with the same resolution. The Dice score was all greater than 0.88 for GM or WM with full DWI data and very stable against subsampling of the DWI data in number of diffusion directions, number of shells, and spatial resolution

Disturbances of postural sway components in cannabis users

Introduction A prominent effect of acute cannabis use is impaired motor coordination and driving performance. However, few studies have evaluated balance in chronic cannabis users, even though density of the CB1 receptor, which mediates the psychoactive effects of cannabis, is extremely high in brain regions critically involved in this fundamental behavior. The present study measured postural sway in regular cannabis users and used rambling and trembling analysis to quantify the integrity of central and peripheral nervous system contributions to the sway signal. Methods Postural sway was measured in 42 regular cannabis users (CB group) and 36 non-cannabis users (N-CB group) by asking participants to stand as still as possible on a force platform in the presence and absence of motor and sensory challenges. Center of pressure (COP) path length was measured, and the COP signal was decomposed into rambling and trembling components. Exploratory correlational analyses were conducted between sway variables, cannabis use history, and neurocognitive function. Results The CB group had significantly increased path length and increased trembling in the anterior-posterior (AP) direction. Exploratory correlational analyses suggested that AP rambling was significantly inversely associated with visuo-motor processing speed. Discussion Regular cannabis use is associated with increased postural sway, and this appears to be predominantly due to the trembling component, which is believed to reflect the peripheral nervous system’s contribution to the sway signal

Impaired Effective Connectivity During a Cerebellar-Mediated Sensorimotor Synchronization Task in Schizophrenia

Prominent conceptual models characterize schizophrenia as a dysconnectivity syndrome, with recent research focusing on the contributions of the cerebellum in this framework. The present study examined the role of the cerebellum and its effective connectivity to the cerebrum during sensorimotor synchronization in schizophrenia. Specifically, the role of the cerebellum in temporally coordinating cerebral motor activity was examined through path analysis. Thirty-one individuals diagnosed with schizophrenia and 40 healthy controls completed a finger-tapping fMRI task including tone-paced synchronization and self-paced continuation tapping at a 500 ms intertap interval (ITI). Behavioral data revealed shorter and more variable ITIs during self-paced continuation, greater clock (vs motor) variance, and greater force of tapping in the schizophrenia group. In a whole-brain analysis, groups showed robust activation of the cerebellum during self-paced continuation but not during tone-paced synchronization. However, effective connectivity analysis revealed decreased connectivity in individuals with schizophrenia between the cerebellum and primary motor cortex but increased connectivity between cerebellum and thalamus during self-paced continuation compared with healthy controls. These findings in schizophrenia indicate diminished temporal coordination of cerebral motor activity by cerebellum during the continuation tapping portion of sensorimotor synchronization. Taken together with the behavioral finding of greater temporal variability in schizophrenia, these effective connectivity results are consistent with structural and temporal models of dysconnectivity in the disorder

The sensitivity of diffusion MRI to microstructural properties and experimental factors

Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic