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

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

wmh_seg: Transformer based U-Net for Robust and Automatic White Matter Hyperintensity Segmentation across 1.5T, 3T and 7T

White matter hyperintensity (WMH) remains the top imaging biomarker for neurodegenerative diseases. Robust and accurate segmentation of WMH holds paramount significance for neuroimaging studies. The growing shift from 3T to 7T MRI necessitates robust tools for harmonized segmentation across field strengths and artifacts. Recent deep learning models exhibit promise in WMH segmentation but still face challenges, including diverse training data representation and limited analysis of MRI artifacts' impact. To address these, we introduce wmh_seg, a novel deep learning model leveraging a transformer-based encoder from SegFormer. wmh_seg is trained on an unmatched dataset, including 1.5T, 3T, and 7T FLAIR images from various sources, alongside with artificially added MR artifacts. Our approach bridges gaps in training diversity and artifact analysis. Our model demonstrated stable performance across magnetic field strengths, scanner manufacturers, and common MR imaging artifacts. Despite the unique inhomogeneity artifacts on ultra-high field MR images, our model still offers robust and stable segmentation on 7T FLAIR images. Our model, to date, is the first that offers quality white matter lesion segmentation on 7T FLAIR images

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

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

Low thalamic activity during a digit-symbol substitution task is associated with symptoms of subjective cognitive decline

IntroductionSubjective cognitive decline (SCD) may represent the earliest preclinical stage of Alzheimer's Disease (AD) for some older adults. However, the underlying neurobiology of SCD is not completely understood. Since executive function may be affected earlier than memory function in the progression of AD, we aimed to characterize SCD symptoms in terms of fMRI brain activity during the computerized digit-symbol substitution task (DSST), an executive function task. We also explored associations of DSST task performance with brain activation, SCD severity, and amyloid-ß (Aß) load.MethodsWe analyzed data from 63 cognitively normal older individuals (mean age 73.6 ± 7.2) with varying degree of SCD symptoms. Participants completed a computerized version of DSST in the MR scanner and a Pittsburgh Compound-B (PiB)-PET scan to measure global cerebral Aß load.ResultsA voxel-wise analysis revealed that greater SCD severity was associated with lower dorsomedial thalamus activation. While task performance was not associated with brain activation nor Aß load, slower reaction time was associated with greater SCD severity.DiscussionThe observed lower dorsomedial thalamus activation may reflect declining familiarity-based working memory and the trans-thalamic executive function pathway in SCD. SCD symptoms may reflect altered neural function and subtle decline of executive function, while Aß load may have an indirect impact on neural function and performance. Self-perceived cognitive decline may serve as a psychological/subjective marker reflecting subtle brain changes

Functional connectivity measured with magnetoencephalography identifies persons with HIV disease

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 ..

Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression

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

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