100 research outputs found

    The Effects of Amygdalar Size Normalization on Group Analysis in Late-Life Depression

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    Structural MRI has been utilized in numerous ways to measure morphologic characteristics of subcortical brain regions. Volumetric analysis is frequently used to quantify the size of brain structures to ultimately compare size differences between individuals. In order to make such comparisons, inter-subject variability in brain and/or head size must be taken into consideration. A heterogeneous set of methods are commonly used to normalize regional volume by brain and/or head size yielding inconsistent findings making it diffcult to interpret and compare results from published volumetric studies. This study investigated the effect that various volume normalization methodologies might have on group analysis. Specifically, the amygdalae were the regions of interest in elderly, healthy and depressed individuals. Normalization methods investigated included spatial transformations, brain and head volume, and tissue volume techniques. Group analyses were conducted with independent t-tests by dividing amygdalar volumes by various volume measures, as well as with univariate analysis of covariance (ANCOVA) analyses by using amygdalar volumes as dependent variables and various volume measures as covariates. Repeated measures ANOVA was performed to assess the effect of each normalization procedure. Results indicate that volumetric differences between groups varied based on the normalization method utilized, which may explain, in part, the discrepancy found in amygdalar volumetric studies. We believe the findings of this study are extensible to other brain regions and demographics, and thus, investigators should carefully consider the normalization methods utilized in volumetric studies to properly interpret the results and conclusions

    Lower Digit Symbol Substitution Score in the Oldest Old is Related to Magnetization Transfer and Diffusion Tensor Imaging of the White Matter

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    Background: Slowing information processing is common among community-dwelling elderly and it predicts greater mortality and disability risk. Slowing information processing is related to brain macro-structural abnormalities. Specifically, greater global atrophy and greater small vessel disease of the white matter (WM) have been associated with slower processing speed. However, community-dwelling elderly with such macro-structural abnormalities can maintain processing speed. The roles of brain micro-structure for slow processing in very old adults living in the community is uncertain, as epidemiological studies relating these brain markers to cognition and in the context of other health characteristics are sparse. Hypothesis: Information processing is cross-sectionally associated with WM micro-structure independent of overt macro-structural abnormalities and also independent of health related characteristics. Methods: Imaging indices of micro-structure diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI), macro-structure white matter hyperintensities (WMH), gray matter (GM) volume, digit symbol substitution test (DSST), and health characteristics were measured in 272 elderly (mean age 83 years old, 43% men, 40% black) living in the community. Results: The DTI- and MTI-indices of micro-structure from the normal appearing WM and not from the normal appearing GM were associated with DSST score independent of WMH and GM volumes. Associations were also independent of age, race, gender, mini-mental score, systolic blood pressure, and prevalent myocardial infarction. Interpretation: DTI and MTI-indices of normal appearing WM are indicators of information processing speed in this cohort of very old adults living in the community. Since processing slowing is a potent index of mortality and disability, these indices may serve as biomarkers in prevention or treatment trials of disability

    Functional connectivity measured with magnetoencephalography identifies persons with HIV disease

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    There is need for a valid and reliable biomarker for HIV Associated Neurocognitive Disorder (HAND). The purpose of the present study was to provide preliminary evidence of the potential utility of neuronal functional connectivity measures obtained using magnetoencephalography (MEG) to identify HIV-associated changes in brain function. Resting state, eyes closed, MEG data from 10 HIV-infected individuals and 8 seronegative controls were analyzed using mutual information (MI) between all pairs of MEG sensors to ..

    Investigating white matter hyperintensities in a multicenter COVID-19 study using 7T MRI

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    Background: Emerging evidence indicates that COVID-19 can negatively impact patient’s brain health (Douaud et al., 2022) (Cecchetti et al., 2022). Common clinical symptoms include brain fog, headaches, difficulty concentrating, and loss of sense of smell or taste. Some studies suggest that SARS-CoV-2 infection can damage the blood brain barrier either directly or through immune-inflammatory mechanisms (Zhang, et al. 2021). White matter hyperintensities (WMH) are imaging biomarkers of brain vascular or inflammatory injury. We investigated the association between severity of COVID-19 infection and burden of white matter hyperintensity volumes within a diverse multi-nation, multi-racial cohort using 7 Tesla (7T) MRI that can detect more subtle injury than conventional 1.5 or 3T MRI. Method: Participants were recruited at 4 sites: Pittsburgh, San Antonio and Houston, USA, and Nottingham, UK. To date, we have scanned and included the following participants in our analysis (Table 1). Detailed cognitive, neurological, mood and functional assessments and high-resolution MRI scans were collected. Subsequent WMH segmentation was performed using our in-house built deep learning based model (Figure 1). All segmentations were visually inspected and manually corrected before statistical analysis. Normalized WMH is calculated as a ratio of the WMH volume and the intracranial volume (WMH/ICV). Imaging data for an additional 36 age-matched controls were retrieved from the 7 Tesla Bioengineering Research Program (7TBRP) imaging bank at Pittsburgh. Result: Figure 1 shows the WMH segmentation outputs from our deep learning based model on images acquired at the 3 sites. Our Linear regression models along with our non-parametric Kruskal-Wallis test result suggests that compared to mild COVID cases and healthy control, COVID infected individuals that were ICU admitted show elevated WMH burden (Figure 2). Conclusion: Our results demonstrate that white matter hyperintensity volumes were higher among patients who had severe acute COVID infection that required ICU admission, compared to healthy age-matched controls. In contrast, no difference in white matter burden was observed in patients with mild COVID infection compared to healthy controls. Additional data (both cross-sectional and longitudinal), including more sensitive MRI measures is being collected to define the full spectrum of brain injury associated with sequelae of COVID infection

    QUANTIFICATION OF NORMAL BRAIN AGING USING FULLY DEFORMABLE REGISTRATION

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    Over the next twenty-five years, the proportion of the population over age 65 will increase 76%; therefore understanding both the normal and pathological processes involved in the aging of the human brain is of the highest public health priority. We report here the use of a computational method that provides estimates of the “brain age” of individuals that is based solely on a high resolution Magnetic Resonance Image (MRI) of the brain of the individual, and is blinded to his or her true chronological age. The method proceeds in two phases: first, a statistical learning algorithm is used to determine the numerical MRI-based features that predict true age on a training set of 198 healthy elderly individuals; second, these features are used to predict the true age of previously-unseen individuals. In cross-validation experiments, the brain age estimates differed from true age by a mean absolute error of 5.35 years in an elderly cohort, reflecting the broad heterogeneity in structural integrity of the elderly brain. The “brain age” of female subjects was significantly lower than that of male subjects who had the same true age (3.0 years younger for 50-year-olds and 1.6 years younger for 79 year olds), reflecting the longer life expectancy of females. Across the elderly age spectrum, the “brain age” of individuals with Alzheimer's Disease (AD) was significantly higher than that of cognitively-healthy elderly subjects with equivalent true age; however, this was not the case for the subjects with mild cognitive impairment (MCI), a possible AD prodrome

    Initial evidence regarding the neurobiological basis of psychological symptoms in dementia caregivers

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    Abstract Mood symptoms and disorders are common in dementia caregivers, who can be exposed to a myriad of potential stressors including their care recipient’s neuropsychiatric symptoms. Existing evidence indicates that the effects of potentially stressful exposures on mental health depend on the caregiver’s individual characteristics and responses. Specifically, prior studies indicate that risk factors measured on psychological (e.g., emotion-focused/behaviorally disengaged coping responses) and behavioral (e.g., sleep and activity restriction) levels of analysis may confer the effects of caregiving exposures on mental health. Theoretically, this process from caregiving stressors and other risk factors to mood symptoms is neurobiologically mediated. This article reviews recent studies that used brain imaging to identify neurobiological factors that are related to psychological outcomes in caregivers. Available observational data indicate that psychological outcomes in caregivers are related to differences in the structure/function of regions involved in socio-affective information processing (prefrontal), autobiographical memory (the posterior cingulate), and stress (amygdala). In addition, two small randomized controlled trials using repeated brain imaging showed that Mentalizing Imagery Therapy (a mindfulness program) increased prefrontal network connectivity and reduced mood symptoms. These studies raise the possibility that, in the future, brain imaging may be useful to detect the neurobiological basis of a given caregiver’s mood vulnerability and guide the selection of interventions that are known to modify it. However, there remains a need for evidence on whether brain imaging improves on simpler/inexpensive measurement modalities like self-report for identifying vulnerable caregivers and matching them with efficacious interventions. In addition, to target interventions, more evidence is needed regarding the effects that both risk factors and interventions have on mood neurobiology (e.g., how persistent emotion-focused coping, sleep disruption, and mindfulness affect brain function)

    Studying depression using imaging and machine learning methods

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    Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies
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