Epidemiological studies suggest that insulin resistance accelerates progression of age-based cognitive impairment, which neuroimaging has linked to brain glucose hypometabolism. As cellular inputs, ketones increase Gibbs free energy change for ATP by 27% compared to glucose. Here we test whether dietary changes are capable of modulating sustained functional communication between brain regions (network stability) by changing their predominant dietary fuel from glucose to ketones. We first established network stability as a biomarker for brain aging using two large-scale (n= 292, ages 20 to 85 y;n= 636, ages 18 to 88 y) 3 T functional MRI (fMRI) datasets. To determine whether diet can influence brain network stability, we additionally scanned 42 adults, age < 50 y, using ultrahigh-field (7 T) ultrafast (802 ms) fMRI optimized for single-participant-level detection sensitivity. One cohort was scanned under standard diet, overnight fasting, and ketogenic diet conditions. To isolate the impact of fuel type, an independent overnight fasted cohort was scanned before and after administration of a calorie-matched glucose and exogenous ketone ester (d-β-hydroxybutyrate) bolus. Across the life span, brain network destabilization correlated with decreased brain activity and cognitive acuity. Effects emerged at 47 y, with the most rapid degeneration occurring at 60 y. Networks were destabilized by glucose and stabilized by ketones, irrespective of whether ketosis was achieved with a ketogenic diet or exogenous ketone ester. Together, our results suggest that brain network destabilization may reflect early signs of hypometabolism, associated with dementia. Dietary interventions resulting in ketone utilization increase available energy and thus may show potential in protecting the aging brain.

A large proportion (range of 44-75%) of women who experience intimate-partner violence (IPV) have been shown to sustain repetitive mild traumatic brain injuries (mTBIs) from their abusers. Further, despite requests for research on TBI-related health outcomes, there are currently only a handful of studies addressing this issue and only one prior imaging study that has investigated the neural correlates of IPV-related TBIs. In response, we examined specific regions of white matter microstructure in 20 women with histories of IPV. Subjects were imaged on a 3-Tesla Siemens Magnetom TrioTim scanner using diffusion magnetic resonance imaging. We investigated the association between a score reflecting number and recency of IPV-related mTBIs and fractional anisotropy (FA) in the posterior and superior corona radiata as well as the posterior thalamic radiation, brain regions shown previously to be involved in mTBI. We also investigated the association between several cognitive measures, namely learning, memory, and cognitive flexibility, and FA in the white matter regions of interest. We report a negative correlation between the brain injury score and FA in regions of the posterior and superior corona radiata. We failed to find an association between our cognitive measures and FA in these regions, but the interpretation of these results remains inconclusive due to possible power issues. Overall, these data build upon the small but growing literature demonstrating potential consequences of mTBIs for women experiencing IPV, and further underscore the urgent need for larger and more comprehensive studies in this area.

Alcohol Use Disorder (AUD) has been associated with abnormalities in hippocampal volumes, but these relationships have not been fully explored with respect to sub-regional volumes, nor in association with individual characteristics such as age, gender differences, drinking history, and memory. The present study examined the impact of those variables in relation to hippocampal subfield volumes in abstinent men and women with a history of AUD. Using Magnetic Resonance Imaging at 3 Tesla, we obtained brain images from 67 participants with AUD (31 women) and 64 nonalcoholic control (NC) participants (31 women). The average duration of the most recent period of sobriety for AUD participants was 7.1 years. We used Freesurfer 6.0 to segment the hippocampus into 12 regions. These were imputed into statistical models to examine the relationships of brain volume with AUD group, age, gender, memory, and drinking history. Interactions with gender and age were of particular interest. Compared to the NC group, the AUD group had approximately 5% smaller subiculum, CA1, molecular layer, and hippocampal tail regions. Age was negatively associated with volumes for the AUD group in the subiculum and the hippocampal tail, but no significant interactions with gender were identified. The relationships for delayed and immediate memory with hippocampal tail volume differed for AUD and NC groups: Higher scores on tests of immediate and delayed memory were associated with smaller volumes in the AUD group, but larger volumes in the NC group. Length of sobriety was associated with decreasing CA1 volume in women (0.19% per year) and increasing volume size in men (0.38% per year). The course of abstinence on CA1 volume differed for men and women, and the differential relationships of subfield volumes to age and memory could indicate a distinction in the impact of AUD on functions of the hippocampal tail. These findings confirm and extend evidence that AUD, age, gender, memory, and abstinence differentially impact volumes of component parts of the hippocampus.

BACKGROUND AND PURPOSE: There are no validated imaging criteria for the diagnosis of progressive multifocal leukoencephalopathy in the cerebellum. Here we introduce the MR imaging shrimp sign, a cerebellar white matter lesion identifiable in patients with cerebellar progressive multifocal leukoencephalopathy, and we evaluate its sensitivity and specificity. MATERIALS AND METHODS:We first identified patients with progressive multifocal leukoencephalopathy seen at Massachusetts General Hospital between 1998 and 2019 whose radiology reports included the term "cerebellum." Drawing on a priori knowledge, 2 investigators developed preliminary diagnostic criteria for the shrimp sign. These criteria were revised and validated in 2 successive stages by 4 additional blinded investigators. After defining the MR imaging shrimp sign, we assessed its sensitivity, specificity, positive predictive value, and negative predictive value. RESULTS:We identified 20 patients with cerebellar progressive multifocal leukoencephalopathy: 16 with definite progressive multifocal leukoencephalopathy (mean, 46.4 [SD, 9.2] years of age; 5 women), and 4 with possible progressive multifocal leukoencephalopathy (mean, 45.8 [SD, 8.5] years of age; 1 woman). We studied 40 disease controls (mean, 43.6 [SD, 21.0] years of age; 16 women) with conditions known to affect the cerebellar white matter. We defined the MR imaging shrimp sign as a T2-and FLAIR-hyperintense, T1-hypointense, discrete cerebellar white matter lesion abutting-but-sparing the dentate nucleus. MR imaging shrimp sign sensitivity was 0.85; specificity, 1; positive predictive value, 1; and negative predictive value, 0.93. The shrimp sign was also seen in fragile X-associated tremor ataxia syndrome, but radiographic and clinical features distinguished it from progressive multifocal leukoencephalopathy. CONCLUSIONS:In the right clinical context, the MR imaging shrimp sign has excellent sensitivity and specificity for cerebellar progressive multifocal leukoencephalopathy, providing a new radiologic marker of the disease. ABBREVIATIONS: JCV ¼ JC polyomavirus; MS ¼ multiple sclerosis; NPV ¼ negative predictive value; PML ¼ progressive multifocal leukoencephalopathy; PPV ¼ positive predictive value; PRES ¼ posterior reversible encephalopathy syndrome P rogressive multifocal leukoencephalopathy (PML) is an opportunistic demyelinating disease in which the human JC polyomavirus (JCV) causes lytic infection of oligodendrocytes, astrocytes, and, rarely, neurons. 1 It affects immunosuppressed patients with an

Alcohol use disorder (AUD) has been associated with abnormalities in hippocampal volumes, but these relationships have not been fully explored with respect to sub-regional volumes, nor in association with individual characteristics such as gender differences, age, and memory. The present study examined the impact of those variables in relation to hippocampal subfield volumes in abstinent men and women with a history of AUD. Using Magnetic Resonance Imaging at 3 Tesla, we obtained brain images from 67 participants (31 women) with AUD and 63 healthy control (NC) participants (30 women) without AUD. We used Freesurfer 6.0 to segment the hippocampus into 12 regions. These were imputed into mixed models to examine the relationships of brain volume with AUD group, gender, age, drinking history, and memory.The AUD group had approximately 5% smaller CA1, hippocampal tail, and molecular layer regions than the NC group. Age was negatively associated with volumes for the AUD group in the hippocampal tail, subiculum, and presubiculum. The relationships for delayed and immediate memory with hippocampal tail volume differed for AUD and NC groups: Higher scores were associated with smaller volumes in the AUD group, but larger volumes in the NC group. Length of sobriety was associated with decreasing CA1 volume in women (0.02% per year) and increasing volume size in men (0.03% per year). These findings confirm and extend evidence that AUD, gender, age, and abstinence differentially impact volumes of component parts of the hippocampus. The course of abstinence on CA1 volume differed for men and women, and the differential relationships of subregional volumes to age and memory could indicate a distinction in the impact of AUD on functions of the hippocampal tail.

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Study Objectives Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. Methods Adult patients (n=167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. Results Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r=0.503) and age-adjusted fluid cognition scores (r=0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. Conclusion Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.

Background. Full polysomnography, the gold standard of sleep measurement, is impractical for widespread use in the intensive care unit (ICU). Wrist-worn actigraphy and subjective sleep assessments do not measure sleep physiology adequately. Here, we explore the feasibility of estimating conventional sleep indices in the ICU with heart rate variability (HRV) and respiration signals using artificial intelligence methods. Methods. We used deep learning models to stage sleep with HRV (through electrocardiogram) and respiratory effort (through a wearable belt) signals in critically ill adult patients admitted to surgical and medical ICUs, and in covariate-matched sleep laboratory patients. We analyzed the agreement of the determined sleep stages between the HRV- and breathing-based models, computed sleep indices, and quantified breathing variables during sleep. Results. We studied 102 adult patients in the ICU across multiple days and nights, and 220 patients in a clinical sleep laboratory. We found that sleep stages predicted by HRV- and breathing-based models showed agreement in 60% of the ICU data and in 81% of the sleep laboratory data. In the ICU, deep NREM (N2 + N3) proportion of total sleep duration was reduced (ICU 39%, sleep laboratory 57%, p<0.01), REM proportion showed heavy-tailed distribution, and the number of wake transitions per hour of sleep (median = 3.6) was comparable to sleep laboratory patients with sleep-disordered breathing (median = 3.9). Sleep in the ICU was also fragmented, with 38% of sleep occurring during daytime hours. Finally, patients in the ICU showed faster and less variable breathing patterns compared to sleep laboratory patients. Conclusions. Cardiovascular and respiratory signals encode sleep state information, which can be utilized to measure sleep state in the ICU. Using these easily measurable variables can provide automated information about sleep in the ICU.