TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain

Cerebello-cortical functional connectivity may regulate reactive balance control in older adults with mild cognitive impairment

BackgroundOlder adults with mild cognitive impairment (OAwMCI) experience a two-fold increased risk of falling compared to their cognitively intact counterparts. This increased risk could be attributed to impairments in balance control mechanisms (both volitional and reactive), however, the exact neural substrates contributing to the balance impairments remain unclear. While changes in functional connectivity (FC) networks in volitional balance control tasks have been well highlighted, the relationship between these changes and reactive balance control has not been examined. Therefore, this study aims to explore the relationship between FC networks of the brain obtained during resting state fMRI (no visualization or active task performed) and behavioral measures on a reactive balance task in OAwMCI.MethodsEleven OAwMCI (< 25/30 on MoCA, > 55 years) underwent fMRI and were exposed to slip-like perturbations on the Activestep treadmill. Postural stability, i.e., dynamic center of mass motion state (i.e., its position and velocity) was computed to determine reactive balance control performance. The relationship between reactive stability and FC networks was explored using the CONN software.ResultsOAwMCI with greater FC in default mode network-cerebellum (r2 = 0.43, p < 0.05), and sensorimotor-cerebellum (r2 = 0.41, p < 0.05) network exhibited lower reactive stability. Further, people with lower FC in middle frontal gyrus-cerebellum (r2 = 0.37, p < 0.05), frontoparietal-cerebellum (r2 = 0.79, p < 0.05) and cerebellar network-brainstem (r2 = 0.49, p < 0.05) exhibited lower reactive stability.ConclusionOlder adults with mild cognitive impairment demonstrate significant associations between reactive balance control and cortico-subcortical regions involved in cognitive-motor control. Results indicate that the cerebellum and its communications with higher cortical centers could be potential substrates contributing to impaired reactive responses in OAwMCI

TempoCave: Visualizing Dynamic Connectome Datasets to Support Cognitive Behavioral Therapy

We introduce TempoCave, a novel visualization application for analyzing dynamic brain networks, or connectomes. TempoCave provides a range of functionality to explore metrics related to the activity patterns and modular affiliations of different regions in the brain. These patterns are calculated by processing raw data retrieved functional magnetic resonance imaging (fMRI) scans, which creates a network of weighted edges between each brain region, where the weight indicates how likely these regions are to activate synchronously. In particular, we support the analysis needs of clinical psychologists, who examine these modular affiliations and weighted edges and their temporal dynamics, utilizing them to understand relationships between neurological disorders and brain activity, which could have a significant impact on the way in which patients are diagnosed and treated. We summarize the core functionality of TempoCave, which supports a range of comparative tasks, and runs both in a desktop mode and in an immersive mode. Furthermore, we present a real-world use case that analyzes pre- and post-treatment connectome datasets from 27 subjects in a clinical study investigating the use of cognitive behavior therapy to treat major depression disorder, indicating that TempoCave can provide new insight into the dynamic behavior of the human brain

EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy $6$--$7\%$ higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs

Digital Clock Drawing: Differentiating “Thinking” versus “Doing” in Younger and Older Adults with Depression

Psychomotor slowing has been documented in depression. The digital Clock Drawing Test (dCDT) provides: (i) a novel technique to assess both cognitive and motor aspects of psychomotor speed within the same task and (ii) the potential to uncover subtleties of behavior not previously detected with non-digitized modes of data collection. Using digitized pen technology in 106 participants grouped by Age (younger/older) and Affect (euthymic/unmedicated depressed), we recorded cognitive and motor output by capturing how the clock is drawn rather than focusing on the final product. We divided time to completion (TTC) for Command and Copy conditions of the dCDT into metrics of percent of drawing (%Ink) versus non-drawing (%Think) time. We also obtained composite Z-scores of cognition, including attention/information processing (AIP), to explore associations of %Ink and %Think times to cognitive and motor performance. Despite equivalent TTC, %Ink and %Think Command times (Copy n.s.) were significant (AgeXAffect interaction: p=.03)—younger depressed spent a smaller proportion of time drawing relative to thinking compared to the older depressed group. Command %Think time negatively correlated with AIP in the older depressed group (r=−.46; p=.02). Copy %Think time negatively correlated with AIP in the younger depressed (r=−.47; p=.03) and older euthymic groups (r=−.51; p=.01). The dCDT differentiated aspects of psychomotor slowing in depression regardless of age, while dCDT/cognitive associates for younger adults with depression mimicked patterns of older euthymics

EEG Classification based on Image Configuration in Social Anxiety Disorder

The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting of five frequency bands and further decomposed with a filter bank. The data are collected from 64 subjects consisting of healthy controls and patients with SAD. Validity of our hypothesis that model 2 will significantly outperform model 1 is borne out in the results, with accuracy 6– 7% higher for model 2 for each machine learning algorithm we investigated. Convolutional Neural Networks (CNN) were found to provide much better performance than SVM and kNNs. Index Terms— EEG, deep learning, classification

What metabolic syndrome contributes to brain outcomes in African Americans and Caucasian cohorts

Metabolic syndrome (MetS), i.e., meeting criteria for any three of the following: hyperglycemia, hypertension, hypertriglyceridemia, low high-density lipoprotein and/or abdominal obesity, is associated with negative health outcomes. For example, MetS negatively impacts cognition; however, less is known about incremental MetS risk, i.e., meeting 1 or 2 as opposed to 3 or more criteria. We hypothesized incremental MetS risk would negatively contribute to cognition and relevant neuroanatomy, e.g., memory and hippocampal volumes, and that this risk extends to affective functioning. 119 non-demented/non-depressed participants (age=60.1+12.9;~50% African American) grouped by incremental MetS risk-no (0 criteria met), low (1-2 criteria met), or high (3+ criteria met)-were compared across cognition, affect and relevant neuroanatomy using multivariable linear regressions. Exploratory analyses, stratified by race, consider the role of health disparities in disease severity of individual MetS component (e.g., actual blood pressure readings) on significant results from primary analyses. Incremental MetS risk contributed to depressive symptomatology (nolow=high) after controlling for age, race (n.s.) and IQ. Different indices of disease severity contributed to different aspects of brain structure and function by race providing empirical support for future studies of the impact distinct health disparities in vascular risk have on brain aging. MetS compromised mood, cognition and hippocampal structure with incremental risk applying to some but not all of these outcomes. Care providers may wish to monitor a broader spectrum of risk including components of MetS like blood pressure and cholesterol levels when considering brain-behavior relationships in adults from diverse populations

Divergent Influences of Cardiovascular Disease Risk Factor Domains on Cognition, Grey and White Matter Morphology

OBJECTIVE: Hypertension, diabetes, dyslipidemia, and obesity are associated with preclinical alterations in cognition and brain structure; however, this often comes from studies of comprehensive risk scores or single isolated factors. We examined associations of empirically derived cardiovascular disease risk factor domains with cognition and brain structure. METHODS: A total of 124 adults (age, 59.8 [13.1] years; 41% African American; 50% women) underwent neuropsychological and cardiovascular assessments and structural magnetic resonance imaging. Principal component analysis of nine cardiovascular disease risk factors resulted in a four-component solution representing 1, cholesterol; 2, glucose dysregulation; 3, metabolic dysregulation; and 4, blood pressure. Separate linear regression models for learning, memory, executive functioning, and attention/information processing were performed, with all components entered at once, adjusting for age, sex, and education. MRI analyses included whole-brain cortical thickness and tract-based fractional anisotropy adjusted for age and sex. RESULTS: Higher blood pressure was associated with poorer learning (B = -0.19; p = .019), memory (B = -0.22; p = .005), and executive functioning performance (B = -0.14; p = .031), and lower cortical thickness within the right lateral occipital lobe. Elevated glucose dysregulation was associated with poorer attention/information processing performance (B = -0.21; p = .006) and lower fractional anisotropy in the right inferior and bilateral superior longitudinal fasciculi. Cholesterol was associated with higher cortical thickness within left caudal middle frontal cortex. Metabolic dysfunction was positively associated with right superior parietal lobe, left inferior parietal lobe, and left precuneus cortical thickness. CONCLUSIONS: Cardiovascular domains were associated with distinct cognitive, gray, and white matter alterations and distinct age groups. Future longitudinal studies may assist in identifying vulnerability profiles that may be most important for individuals with multiple cardiovascular disease risk factors

Differential prefrontal and subcortical circuitry engagement during encoding of semantically related words in patients with late‐life depression

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109350/1/gps4165.pd