Multi-level prediction of substance use: Interaction of white matter integrity, resting-state connectivity and inhibitory control measured repeatedly in every-day life.

Substance use disorders are characterized by inhibition deficits related to disrupted connectivity in white matter pathways, leading via interaction to difficulties in resisting substance use. By combining neuroimaging with smartphone-based ecological momentary assessment (EMA), we questioned how biomarkers moderate inhibition deficits to predict use. Thus, we aimed to assess white matter integrity interaction with everyday inhibition deficits and related resting-state network connectivity to identify multi-dimensional predictors of substance use. Thirty-eight patients treated for alcohol, cannabis or tobacco use disorder completed 1 week of EMA to report substance use five times and complete Stroop inhibition testing twice daily. Before EMA tracking, participants underwent resting state functional MRI and diffusion tensor imaging (DTI) scanning. Regression analyses were conducted between mean Stroop performances and whole-brain fractional anisotropy (FA) in white matter. Moderation testing was conducted between mean FA within significant clusters as moderator and the link between momentary Stroop performance and use as outcome. Predictions between FA and resting-state connectivity strength in known inhibition-related networks were assessed using mixed modelling. Higher FA values in the anterior corpus callosum and bilateral anterior corona radiata predicted higher mean Stroop performance during the EMA week and stronger functional connectivity in occipital-frontal-cerebellar regions. Integrity in these regions moderated the link between inhibitory control and substance use, whereby stronger inhibition was predictive of the lowest probability of use for the highest FA values. In conclusion, compromised white matter structural integrity in anterior brain systems appears to underlie impairment in inhibitory control functional networks and compromised ability to refrain from substance use.

Contributions of Cerebral White Matter Hyperintensities to Postural Instability in Aging with and without Alcohol Use Disorder.

BACKGROUND: Postural instability and brain white matter hyperintensities (WMH) are both noted markers of normal aging and alcohol use disorder (AUD). Here, we questioned what variables contribute to sway path/WMH relations in individuals with AUD and healthy control participants. METHOD: The data comprised 404 balance platform sessions, yielding sway path length and MRI acquired cross-sectionally or longitudinally, in 102 control and 158 AUD participants, ages 25-80 years. Balance sessions were typically conducted on the same day as MRI FLAIR acquisitions, permitting WMH volume quantification. Factors considered in multiple regression analyses as potential contributors to relations between WMH volumes and postural instability were age, sex, socioeconomic status, education, pedal 2-point discrimination, systolic and diastolic blood pressure, body mass index, depressive symptoms, total alcohol consumed in the past year, and race. RESULTS: Initial analysis identified diagnosis, age, sex, and race as significant contributors to observed sway path/WMH relations. Inclusion of these factors as predictors in multiple regression analysis substantially attenuated the sway/WMH relations in both AUD and healthy control groups. Women, irrespective of diagnosis or race, had shorter sway paths than men. Black participants, irrespective of diagnosis or sex, had shorter sway paths than non-Black participants despite having modestly larger WMH volumes than non-Black participants, possibly a reflection of the younger age of the Black sample. DISCUSSION: Longer sway paths were related to larger WMH volumes in healthy men and women, with and without AUD. Critically, however, age nearly fully accounted for these relations.

Federated learning with knowledge distillation for multi-organ segmentation with partially labeled datasets.

The state-of-the-art multi-organ CT segmentation relies on deep learning models, which only generalize when trained on large samples of carefully curated data. However, it is challenging to train a single model that can segment all organs and types of tumors since most large datasets are partially labeled or are acquired across multiple institutes that may differ in their acquisitions. A possible solution is Federated learning, which is often used to train models on multi-institutional datasets where the data is not shared across sites. However, predictions of federated learning can be unreliable after the model is locally updated at sites due to 'catastrophic forgetting'. Here, we address this issue by using knowledge distillation (KD) so that the local training is regularized with the knowledge of a global model and pre-trained organ-specific segmentation models. We implement the models in a multi-head U-Net architecture that learns a shared embedding space for different organ segmentation, thereby obtaining multi-organ predictions without repeated processes. We evaluate the proposed method using 8 publicly available abdominal CT datasets of 7 different organs. Of those datasets, 889 CTs were used for training, 233 for internal testing, and 30 volumes for external testing. Experimental results verified that our proposed method substantially outperforms other state-of-the-art methods in terms of accuracy, inference time, and the number of parameters.

Contributions of cerebral white matter hyperintensities, age, and pedal perception to postural sway in people living with HIV.

OBJECTIVE: With aging, people living with HIV (PLWH) have diminishing postural stability that increases liability for falls. Factors and neuromechanisms contributing to instability are incompletely known. Brain white matter abnormalities seen as hyperintense (WMH) signals have been considered to underlie instability in normal aging and PLWH. We questioned whether sway-WMH relations endured after accounting for potentially relevant demographic, physiological, and HIV-related variables. DESIGN: Mixed cross-sectional/longitudinal data acquired over 15 years in 141 PLWH and 102 age-range matched controls, 25-80 years old. METHODS: Multimodal structural MRI data were quantified for 7 total and regional WMH volumes. Static posturography acquired with a force platform measured sway path length separately with eyes closed and eyes open. Statistical analyses used multiple regression with mixed modeling to test contributions from non-MRI and non-path data on sway path-WMH relations. RESULTS: In simple correlations, longer sway paths were associated with larger WMH volumes in PWLH and controls. When demographic, physiological, and HIV-related variables were entered into multiple regressions, the sway-WMH relations under both vision conditions in the controls were attenuated when accounting for age and 2-point pedal discrimination. Although the sway-WMH relations in PLWH were influenced by age, 2-point pedal discrimination, and years with HIV infection, the sway-WMH relations endured for 5 of the 7 regions in the eyes-open condition. CONCLUSIONS: The constellation of age-related increasing instability while standing, degradation of brain white matter integrity, and peripheral pedal neuropathy is indicative of advancing fraility and liability for falls as people age with HIV infection.

Frontal cortical volume deficits as enduring evidence of childhood abuse in community adults with AUD and HIV infection comorbidity.

Background: Childhood abuse is an underappreciated source of stress, associated with adverse mental and physical health consequences. Childhood abuse has been directly associated with risky behavior thereby increasing the likelihood of alcohol misuse and risk of HIV infection, conditions associated with brain structural and functional deficits. Here, we examined the neural and behavioral correlates of childhood trauma history in alcohol use disorder (AUD), HIV infection (HIV), and their comorbidity (AUD+HIV). Methods: Occurrence of childhood trauma was evaluated by retrospective interview. Cortical (frontal, temporal, parietal, and occipital), subcortical (hippocampus, amygdala), and regional frontal volumes were derived from structural MRI, adjusted for intracranial volume and age. Test scores of executive functioning, attention/working memory, verbal/visual learning, verbal/visual memory, and motor speed functional domains were standardized on age and education of a laboratory control group. Results: History of childhood abuse was associated with smaller frontal lobe volumes regardless of diagnosis. For frontal subregional volumes, history of childhood abuse was selectively associated with smaller orbitofrontal and supplementary motor volumes. In participants with a child abuse history, poorer verbal/visual memory performance was associated with smaller orbitofrontal and frontal middle volumes, whereas in those without childhood abuse, poorer verbal/visual memory performance was associated with smaller orbitofrontal, frontal superior, and supplemental motor volumes. Conclusions: Taken together, these results comport with and extend the findings that childhood abuse is associated with brain and behavioral sequelae in AUD, HIV, and AUD+HIV comorbidity. Further, these findings suggest that sequelae of abuse in childhood may be best conceptualized as a spectrum disorder as significant deficits may be present in those who may not meet criteria for a formal trauma-related diagnosis yet may be suffering enduring stress effects on brain structural and functional health.

Age-Accelerated Increase of White Matter Hyperintensity Volumes Is Exacerbated by Heavy Alcohol Use in People Living With HIV.

BACKGROUND: Antiretroviral treatment has enabled people living with HIV infection to have a near-normal life span. With longevity comes opportunities for engaging in risky behavior, including initiation of excessive drinking. Given that both HIV infection and alcohol use disorder (AUD) can disrupt brain white matter integrity, we questioned whether HIV infection, even if successfully treated, or AUD alone results in signs of accelerated white matter aging and whether HIV+AUD comorbidity further accelerates brain aging. METHODS: Longitudinal magnetic resonance imaging-FLAIR data were acquired over a 15-year period from 179 control individuals, 204 participants with AUD, 70 participants with HIV, and 75 participants with comorbid HIV+AUD. White matter hyperintensity (WMH) volumes were quantified and localized, and their functional relevance was examined with cognitive and motor testing. RESULTS: The 3 diagnostic groups each had larger WMH volumes than the control group. Although all 4 groups exhibited accelerating volume increases with aging, only the HIV groups showed faster WMH enlargement than control individuals; the comorbid group showed faster acceleration than the HIV-only group. Sex and HIV infection length, but not viral suppression status, moderated acceleration. Correlations emerged between WMH volumes and attention/working memory and executive function scores of the AUD and HIV groups and between WMH volumes and motor skills in the 3 diagnostic groups. CONCLUSIONS: Even treated HIV can show accelerated aging, possibly from treatment sequelae or legacy effects, and notably from AUD comorbidity. WMH volumes may be especially relevant for tracking HIV and AUD brain health because each condition is associated with liability for hypertensive processes, for which WMHs are considered a marker.

Brain structural covariance network features are robust markers of early heavy alcohol use.

BACKGROUND AND AIMS: Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies. DESIGN AND SETTING: Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (n = 400, age range = 14-22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (n = 272, age range = 17-22 years) and the Human Connectome Project (HCP) (n = 375, age range = 22-37 years). CASES: Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected. MEASUREMENTS: Graph theory metrics of segregation and integration were used to summarize SCN. FINDINGS: Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = -0.029, P = 0.002], lower modularity (AUC = -0.14, P = 0.004), lower average shortest path length (AUC = -0.078, P = 0.017) and higher global efficiency (AUC = 0.007, P = 0.010). Local efficiency differences were marginal (AUC = -0.017, P = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar. CONCLUSION: Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.

Brain Volume in Fetal Alcohol Spectrum Disorders Over a 20-Year Span.

Importance: Anomalous brain development and mental health problems are prevalent in fetal alcohol spectrum disorders (FASD), but there is a paucity of longitudinal brain imaging research into adulthood. This study presents long-term follow-up of brain volumetrics in a cohort of participants with FASD. Objective: To test whether brain tissue declines faster with aging in individuals with FASD compared with control participants. Design, Setting, and Participants: This cohort study used magnetic resonance imaging (MRI) data collected from individuals with FASD and control individuals (age 13-37 years at first magnetic resonance imaging [MRI1] acquired 1997-2000) compared with data collected 20 years later (MRI2; 2018-2021). Participants were recruited for MRI1 through the University of Washington Fetal Alcohol Syndrome (FAS) Follow-Up Study. For MRI2, former participants were recruited by the University of Washington Fetal Alcohol and Drug Unit. Data were analyzed from October 2022 to August 2023. Main Outcomes and Measures: Intracranial volume (ICV) and regional cortical and cerebellar gray matter, white matter, and cerebrospinal fluid volumes were quantified automatically and analyzed, with group and sex as between-participant factors and age as a within-participant variable. Results: Of 174 individuals with MRI1 data, 48 refused participation, 36 were unavailable, and 24 could not be located. The remaining 66 individuals (37.9%) were rescanned for MRI2, including 26 controls, 18 individuals with nondysmorphic heavily exposed fetal alcohol effects (FAE; diagnosed prior to MRI1), and 22 individuals with FAS. Mean (SD) age was 22.9 (5.6) years at MRI1 and 44.7 (6.5) years at MRI2, and 35 participants (53%) were male. The FAE and FAS groups exhibited enduring stepped volume deficits at MRI1 and MRI2; volumes among control participants were greater than among participants with FAE, which were greater than volumes among participants with FAS (eg, mean [SD] ICV: control, 1462.3 [119.3] cc at MRI1 and 1465.4 [129.4] cc at MRI2; FAE, 1375.6 [134.1] cc at MRI1 and 1371.7 [120.3] cc at MRI2; FAS, 1297.3 [163.0] cc at MRI1 and 1292.7 [172.1] cc at MRI2), without diagnosis-by-age interactions. Despite these persistent volume deficits, the FAE participants and FAS participants showed patterns of neurodevelopment within reference ranges: increase in white matter and decrease in gray matter of the cortex and decrease in white matter and increase in gray matter of the cerebellum. Conclusions and Relevance: The findings of this cohort study support a nonaccelerating enduring, brain structural dysmorphic spectrum following prenatal alcohol exposure and a diagnostic distinction based on the degree of dysmorphia. FASD was not a progressive brain structural disorder by middle age, but whether accelerated decline occurs in later years remains to be determined.

Imputing Brain Measurements Across Data Sets via Graph Neural Networks.

Publicly available data sets of structural MRIs might not contain specific measurements of brain Regions of Interests (ROIs) that are important for training machine learning models. For example, the curvature scores computed by Freesurfer are not released by the Adolescent Brain Cognitive Development (ABCD) Study. One can address this issue by simply reapplying Freesurfer to the data set. However, this approach is generally computationally and labor intensive (e.g., requiring quality control). An alternative is to impute the missing measurements via a deep learning approach. However, the state-of-the-art is designed to estimate randomly missing values rather than entire measurements. We therefore propose to re-frame the imputation problem as a prediction task on another (public) data set that contains the missing measurements and shares some ROI measurements with the data sets of interest. A deep learning model is then trained to predict the missing measurements from the shared ones and afterwards is applied to the other data sets. Our proposed algorithm models the dependencies between ROI measurements via a graph neural network (GNN) and accounts for demographic differences in brain measurements (e.g. sex) by feeding the graph encoding into a parallel architecture. The architecture simultaneously optimizes a graph decoder to impute values and a classifier in predicting demographic factors. We test the approach, called Demographic Aware Graph-based Imputation (DAGI), on imputing those missing Freesurfer measurements of ABCD (N=3760; minimum age 12 years) by training the predictor on those publicly released by the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA, N=540). 5-fold cross-validation on NCANDA reveals that the imputed scores are more accurate than those generated by linear regressors and deep learning models. Adding them also to a classifier trained in identifying sex results in higher accuracy than only using those Freesurfer scores provided by ABCD.

LSOR: Longitudinally-Consistent Self-Organized Representation Learning.

Interpretability is a key issue when applying deep learning models to longitudinal brain MRIs. One way to address this issue is by visualizing the high-dimensional latent spaces generated by deep learning via self-organizing maps (SOM). SOM separates the latent space into clusters and then maps the cluster centers to a discrete (typically 2D) grid preserving the high-dimensional relationship between clusters. However, learning SOM in a high-dimensional latent space tends to be unstable, especially in a self-supervision setting. Furthermore, the learned SOM grid does not necessarily capture clinically interesting information, such as brain age. To resolve these issues, we propose the first self-supervised SOM approach that derives a high-dimensional, interpretable representation stratified by brain age solely based on longitudinal brain MRIs (i.e., without demographic or cognitive information). Called Longitudinally-consistent Self-Organized Representation learning (LSOR), the method is stable during training as it relies on soft clustering (vs. the hard cluster assignments used by existing SOM). Furthermore, our approach generates a latent space stratified according to brain age by aligning trajectories inferred from longitudinal MRIs to the reference vector associated with the corresponding SOM cluster. When applied to longitudinal MRIs of the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=632), LSOR generates an interpretable latent space and achieves comparable or higher accuracy than the state-of-the-art representations with respect to the downstream tasks of classification (static vs. progressive mild cognitive impairment) and regression (determining ADAS-Cog score of all subjects). The code is available at https://github.com/ouyangjiahong/longitudinal-som-single-modality.

An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment.

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS-Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explainability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT.

White matter microstructural integrity continues to develop from adolescence to young adulthood in mice and humans: Same phenotype, different mechanism.

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

An Explainable Geometric-Weighted Graph Attention Network for Identifying Functional Networks Associated with Gait Impairment.

One of the hallmark symptoms of Parkinson's Disease (PD) is the progressive loss of postural reflexes, which eventually leads to gait difficulties and balance problems. Identifying disruptions in brain function associated with gait impairment could be crucial in better understanding PD motor progression, thus advancing the development of more effective and personalized therapeutics. In this work, we present an explainable, geometric, weighted-graph attention neural network (xGW-GAT) to identify functional networks predictive of the progression of gait difficulties in individuals with PD. xGW-GAT predicts the multi-class gait impairment on the MDS-Unified PD Rating Scale (MDS-UPDRS). Our computational- and data-efficient model represents functional connectomes as symmetric positive definite (SPD) matrices on a Riemannian manifold to explicitly encode pairwise interactions of entire connectomes, based on which we learn an attention mask yielding individual- and group-level explain-ability. Applied to our resting-state functional MRI (rs-fMRI) dataset of individuals with PD, xGW-GAT identifies functional connectivity patterns associated with gait impairment in PD and offers interpretable explanations of functional subnetworks associated with motor impairment. Our model successfully outperforms several existing methods while simultaneously revealing clinically-relevant connectivity patterns. The source code is available at https://github.com/favour-nerrise/xGW-GAT.

Episodic memory deficit in HIV infection: common phenotype with Parkinson's disease, different neural substrates.

Episodic memory deficits occur in people living with HIV (PLWH) and individuals with Parkinson's disease (PD). Given known effects of HIV and PD on frontolimbic systems, episodic memory deficits are often attributed to executive dysfunction. Although executive dysfunction, evidenced as retrieval deficits, is relevant to mnemonic deficits, learning deficits may also contribute. Here, the California Verbal Learning Test-II, administered to 42 PLWH, 41 PD participants, and 37 controls, assessed learning and retrieval using measures of free recall, cued recall, and recognition. Executive function was assessed with a composite score comprising Stroop Color-Word Reading and Backward Digit Spans. Neurostructural correlates were examined with MRI of frontal (precentral, superior, orbital, middle, inferior, supplemental motor, medial) and limbic (hippocampus, thalamus) volumes. HIV and PD groups were impaired relative to controls on learning and free and cued recall trials but did not differ on recognition or retention of learned material. In no case did executive functioning solely account for the observed mnemonic deficits or brain-performance relations. Critically, the shared learning and retrieval deficits in HIV and PD were related to different substrates of frontolimbic mnemonic neurocircuitry. Specifically, diminished learning and poorer free and cued recall were related to smaller orbitofrontal volume in PLWH but not PD, whereas diminished learning in PD but not PLWH was related to smaller frontal superior volume. In PD, poorer recognition correlated with smaller thalamic volume and poorer retention to hippocampal volume. Although memory deficits were similar, the neural correlates in HIV and PD suggest different pathogenic mechanisms.

Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.

Cognitive disorders are prevalent in people with HIV (PWH) despite antiretroviral therapy. Given the heterogeneity of cognitive disorders in PWH in the current era and evidence that these disorders have different etiologies and risk factors, scientific rationale is growing for using data-driven models to identify biologically defined subtypes (biotypes) of these disorders. Here, we discuss the state of science using machine learning to understand cognitive phenotypes in PWH and their associated comorbidities, biological mechanisms, and risk factors. We also discuss methods, example applications, challenges, and what will be required from the field to successfully incorporate machine learning in research on cognitive disorders in PWH. These topics were discussed at the National Institute of Mental Health meeting on "Biotypes of CNS Complications in People Living with HIV" held in October 2021. These ongoing research initiatives seek to explain the heterogeneity of cognitive phenotypes in PWH and their associated biological mechanisms to facilitate clinical management and tailored interventions.

Postural instability in HIV infection: relation to central and peripheral nervous system markers.

OBJECTIVES: Determine the independent contributions of central nervous system (CNS) and peripheral nervous system (PNS) metrics to balance instability in people with HIV (PWH) compared with people without HIV (PWoH). METHODS: Volumetric MRI (CNS) and two-point pedal discrimination (PNS) were tested as substrates of stance instability measured with balance platform posturography. DESIGN: 125 PWH and 88 PWoH underwent balance testing and brain MRI. RESULTS: The PWH exhibited stability deficits that were disproportionately greater with eyes closed than eyes open compared with PWoH. Further analyses revealed that greater postural imbalance measured as longer sway paths correlated with smaller cortical and cerebellar lobular brain volumes known to serve sensory integration; identified brain/sway path relations endured after accounting for contributions from physiological and disease factors as potential moderators; and multiple regression identified PNS and CNS metrics as independent predictors of postural instability in PWH that differed with the use of visual information to stabilize balance. With eyes closed, temporal volumes and two-point pedal discrimination were significant independent predictors of sway; with eyes open, occipital volume was an additional predictor of sway. These relations were selective to PWH and were not detected in PWoH. CONCLUSION: CNS and PNS factors were independent contributors to postural instability in PWH. Recognizing that myriad inputs must be detected by peripheral systems and brain networks to integrate sensory and musculoskeletal information for maintenance of postural stability, age- or disease-related degradation of either or both nervous systems may contribute to imbalance and liability for falls.

Brain volumetrics differ by Fiebig stage in acute HIV infection.

OBJECTIVE: People with chronic HIV exhibit lower regional brain volumes compared to people without HIV (PWOH). Whether imaging alterations observed in chronic infection occur in acute HIV infection (AHI) remains unknown. DESIGN: Cross-sectional study of Thai participants with AHI. METHODS: One hundred and twelve Thai males with AHI (age 20-46) and 18 male Thai PWOH (age 18-40) were included. Individuals with AHI were stratified into early (Fiebig I-II; n  = 32) and late (Fiebig III-V; n  = 80) stages of acute infection using validated assays. T1-weighted scans were acquired using a 3 T MRI performed within five days of antiretroviral therapy (ART) initiation. Volumes for the amygdala, caudate nucleus, hippocampus, nucleus accumbens, pallidum, putamen, and thalamus were compared across groups. RESULTS: Participants in late Fiebig stages exhibited larger volumes in the nucleus accumbens (8% larger; P  = 0.049) and putamen (19%; P  < 0.001) when compared to participants in the early Fiebig. Compared to PWOH, participants in late Fiebig exhibited larger volumes of the amygdala (9% larger; P  = 0.002), caudate nucleus (11%; P  = 0.005), nucleus accumbens (15%; P  = 0.004), pallidum (19%; P  = 0.001), and putamen (31%; P  < 0.001). Brain volumes in the nucleus accumbens, pallidum, and putamen correlated modestly with stimulant use over the past four months among late Fiebig individuals ( P s < 0.05). CONCLUSIONS: Findings indicate that brain volume alterations occur in acute infection, with the most prominent differences evident in the later stages of AHI. Additional studies are needed to evaluate mechanisms for possible brain disruption following ART, including viral factors and markers of neuroinflammation.

The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density.

Objective.To document the bias of thesimplifiedfree water model of diffusion MRI (dMRI) signal vis-à-vis aspecificmodel which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Approach.Both models assume that volume fractionfof the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). Thespecificandsimplifiedmodels are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to anin vivohealthy brain dMRI dataset. For bothsyntheticandin vivodata we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.Main results.From the simulations we see that the difference between the estimatedsimplified fandspecific fis largest for mid-range ground-truthf, and it increases as SNR increases. The estimation of volume fractionfis sensitive to the choice of model,simplifiedorspecific, but the estimated diffusion parameters are robust to small perturbations in the simulation.Specific fis more accurate and precise thansimplified f. In the white matter (WM) regions of thein vivoimages,specific fis lower thansimplified f.Significance.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.

Multi-atlas thalamic nuclei segmentation on standard T1-weighed MRI with application to normal aging.

Specific thalamic nuclei are implicated in healthy aging and age-related neurodegenerative diseases. However, few methods are available for robust automated segmentation of thalamic nuclei. The threefold aims of this study were to validate the use of a modified thalamic nuclei segmentation method on standard T1 MRI data, to apply this method to quantify age-related volume declines, and to test functional meaningfulness by predicting performance on motor testing. A modified version of THalamus Optimized Multi-Atlas Segmentation (THOMAS) generated 22 unilateral thalamic nuclei. For validation, we compared nuclear volumes obtained from THOMAS parcellation of white-matter-nulled (WMn) MRI data to T1 MRI data in 45 participants. To examine the effects of age/sex on thalamic nuclear volumes, T1 MRI available from a second data set of 121 men and 117 women, ages 20-86 years, were segmented using THOMAS. To test for functional ramifications, composite regions and constituent nuclei were correlated with Grooved Pegboard test scores. THOMAS on standard T1 data showed significant quantitative agreement with THOMAS from WMn data, especially for larger nuclei. Sex differences revealing larger volumes in men than women were accounted for by adjustment with supratentorial intracranial volume (sICV). Significant sICV-adjusted correlations between age and thalamic nuclear volumes were detected in 20 of the 22 unilateral nuclei and whole thalamus. Composite Posterior and Ventral regions and Ventral Anterior/Pulvinar nuclei correlated selectively with higher scores from the eye-hand coordination task. These results support the use of THOMAS for standard T1-weighted data as adequately robust for thalamic nuclear parcellation.

Risk for depression tripled during the COVID-19 pandemic in emerging adults followed for the last 8 years.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has significantly increased depression rates, particularly in emerging adults. The aim of this study was to examine longitudinal changes in depression risk before and during COVID-19 in a cohort of emerging adults in the U.S. and to determine whether prior drinking or sleep habits could predict the severity of depressive symptoms during the pandemic. METHODS: Participants were 525 emerging adults from the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA), a five-site community sample including moderate-to-heavy drinkers. Poisson mixed-effect models evaluated changes in the Center for Epidemiological Studies Depression Scale (CES-D-10) from before to during COVID-19, also testing for sex and age interactions. Additional analyses examined whether alcohol use frequency or sleep duration measured in the last pre-COVID assessment predicted pandemic-related increase in depressive symptoms. RESULTS: The prevalence of risk for clinical depression tripled due to a substantial and sustained increase in depressive symptoms during COVID-19 relative to pre-COVID years. Effects were strongest for younger women. Frequent alcohol use and short sleep duration during the closest pre-COVID visit predicted a greater increase in COVID-19 depressive symptoms. CONCLUSIONS: The sharp increase in depression risk among emerging adults heralds a public health crisis with alarming implications for their social and emotional functioning as this generation matures. In addition to the heightened risk for younger women, the role of alcohol use and sleep behavior should be tracked through preventive care aiming to mitigate this looming mental health crisis.