Neuroimaging is vital in quantifying brain atrophy due to typical aging and due to neurodegenerative diseases. To collect large samples necessary to model lifespan brain development, research consortiums aggregate images acquired across multiple study sites. Previous studies have demonstrated that this multi-site study design can lead to site-related bias, necessitating harmonization of these "site effects." However, current methodologies are unable to generalize to new sites outside the original harmonized sample, limiting translation to new sites or clinical practice. Here, we propose a method called ComBat-Predict (CB-Predict) building upon the ComBat method for site effect adjustment, which extends to data from a new site with smaller sample sizes and unknown site effects. In data from the Alzheimer's Disease Neuroimaging Initiative, our proposed method mitigates bias and yields high accuracy in predicting cortical thickness measures when generalizing the model to new data. Furthermore, we demonstrate that our proposed harmonization method can reduce site-related variance in centile scores estimated using data from the Lifespan Brain Chart Consortium. Altogether, our results demonstrate that CB-Predict effectively harmonizes new sites and thereby enables effective translation of neuroimaging models to additional samples.

Aging is associated with disruptions in non-rapid eye movement (NREM) sleep and memory decline. Cerebral small vessel disease (CSVD) increases with age and is associated with clinical sleep disturbance, but little is known about its relationship with local expression of NREM sleep. Here, we explore associations between CSVD burden, memory, and local electroencephalography (EEG) measures during NREM sleep in older adults. Fifteen cognitively intact older adults (mean age 71.8±6.3 years, 10 female, Apnea Hypopnea Index (AHI)=7.82±7.95) underwent T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging, an in-lab overnight polysomnography with 128-channel EEG, and a sleep-dependent mnemonic discrimination task was administered. Lobar white matter hyperintensity (WMH) volumes were calculated using a validated semi-automated toolbox. Topographical correlations were run with 5000-permutation threshold free cluster enhancement. Older age was associated with lower absolute posterior total (all p<0.016, r>-0.61) and slow sigma power (all p<0.018, r>-0.60) and slow wave activity (SWA) (all p<0.017, r>-0.61) over centro-posterior EEG derivations. Age was also positively associated with frontal (τ=0.51, p<0.01) and parietal (τ=0.49, p<0.01) but not occiptal (τ=0.33, p=0.092) or temporal (τ=0.36, p=0.064) WMH burden. Occipital WMH burden was also not associated with AHI (τ=0.038, p=0.843). Occipital WMH burden was associated with global reductions in alpha activity (all p<0.043, τ>-0.78), centro-posterior reductions in SWA (all p<0.043, τ>-0.63) and Delta (all p<0.009, τ>-0.63), frontal reductions in total (all p<0.043, τ>-0.71) and fast sigma power (all p<0.004, τ>-0.57), global reductions in slow sigma power (all p<0.043, τ>-0.71), and central reductions in theta activity (all p<0.042, τ>-0.57). Better lure discrimination for low similarity lures was significantly positively associated with higher relative slow oscillation expression over fronto-parietal regions, though this did not survive TFCE correction (all p<0.033, r>0.55). Collectively, these findings indicate that WMH burden in older adults is associated with local disruptions in NREM sleep expression in multiple frequency bands, of which lower frequencies were particularly vulnerable. This in turn may explain weakened memory performance. Future studies should examine the role of NREM sleep expression in cognitive decline associated with CSVD burden.

The past decade has seen impressive advances in neuroimaging, moving from qualitative to quantitative outputs. Available techniques now allow for the inference of microscopic changes occurring in white and gray matter, along with alterations in physiology and function. These existing and emerging techniques hold the potential of providing unprecedented capabilities in achieving a diagnosis and predicting outcomes for traumatic brain injury (TBI) and a variety of other neurological diseases. To see this promise move from the research lab into clinical care, an understanding is needed of what normal data look like for all age ranges, sex, and other demographic and socioeconomic categories. Clinicians can only use the results of imaging scans to support their decision-making if they know how the results for their patient compare with a normative standard. This potential for utilizing magnetic resonance imaging (MRI) in TBI diagnosis motivated the American College of Radiology and Cohen Veterans Bioscience to create a reference database of healthy individuals with neuroimaging, demographic data, and characterization of psychological functioning and neurocognitive data that will serve as a normative resource for clinicians and researchers for development of diagnostics and therapeutics for TBI and other brain disorders. The goal of this article is to introduce the large, well-curated Normative Neuroimaging Library (NNL) to the research community. NNL consists of data collected from ∼1900 healthy participants. The highlights of NNL are (1) data are collected across a diverse population, including civilians, veterans, and active-duty service members with an age range (18-64 years) not well represented in existing datasets; (2) comprehensive structural and functional neuroimaging acquisition with state-of-the-art sequences (including structural, diffusion, and functional MRI; raw scanner data are preserved, allowing higher quality data to be derived in the future; standardized imaging acquisition protocols across sites reflect sequences and parameters often recommended for use with various neurological and psychiatric conditions, including TBI, post-traumatic stress disorder, stroke, neurodegenerative disorders, and neoplastic disease); and (3) the collection of comprehensive demographic details, medical history, and a broad structured clinical assessment, including cognition and psychological scales, relevant to multiple neurological conditions with functional sequelae. Thus, NNL provides a demographically diverse population of healthy individuals who can serve as a comparison group for brain injury study and clinical samples, providing a strong foundation for precision medicine. Use cases include the creation of imaging-derived phenotypes (IDPs), derivation of reference ranges of imaging measures, and use of IDPs as training samples for artificial intelligence-based biomarker development and for normative modeling to help identify injury-induced changes as outliers for precision diagnosis and targeted therapeutic development. On its release, NNL is poised to support the use of advanced imaging in clinician decision support tools, the validation of imaging biomarkers, and the investigation of brain-behavior anomalies, moving the field toward precision medicine.

Importance: Blast-related mild traumatic brain injuries (bTBI), the 'signature injury' of post-9/11 conflicts, are associated with clinically-relevant long-term cognitive, psychological, and behavioral dysfunction and disability; however, the underlying neural mechanisms remain unclear. Objective: To investigate associations between a history of remote bTBI and regional brain volume in a sample of United States (U.S.) Veterans and Active Duty Service Members (VADSM). Design: Prospective case-control study of U.S. VADSM of participants from the Long-term Impact of Military-relevant Brain Injury Consortium - Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC), which enrolled over 1,500 participants at five sites used in this analysis between 2014-2023. Setting: Participants were recruited from Veterans Affairs medical centers across the U.S. Participants: Seven hundred and seventy-four VADSM of the U.S. military met eligibility criteria for this analysis. Exposure: All participants had combat exposure, and 82% had one or more lifetime mild TBIs with variable injury mechanisms. Main Outcomes and Measures: Regional brain volume was calculated using tensor-based morphometry on 3D T1-weighted magnetic resonance imaging scans. TBI history, including history of blast-related injury (bTBI), was assessed by structured clinical interview. Cognitive performance and psychiatric symptoms were assessed with a battery of validated instruments. We hypothesized that regional volume would be smaller in the bTBI group, and that this would be associated with cognitive performance. Results: Individuals with a history of bTBI had smaller brain volumes in several clusters, with the largest centered bilaterally in the superior corona radiata and globus pallidus. Greater volume deficits were associated with a larger number of lifetime bTBIs. Additionally, causal mediation analysis revealed that these volume differences significantly mediated the association between bTBI and performance on measures of working memory and processing speed. Conclusions and Relevance: Our results reveal robust volume differences associated with bTBI. Magnetic resonance elastography atlases reveal that the specific regions affected include the stiffest tissues in the brain, which may underlie their vulnerability to pressure waves from blast exposures. Furthermore, these volume differences significantly mediated the association between bTBI and cognitive function, indicating that this may be a helpful biomarker in tracking outcome after bTBI and suggesting potential treatment targets to prevent or limit chronic dysfunction.### Competing Interest StatementDr. Hinds is an employee of SCS Consulting LLC, which has financial affiliations with Major League Soccer Players Association, Nano DX, Owl Therapeutics, Prevent Biometrics, Collaborative Neuropathology Network Characterizing Outcomes of TBI (CONNECT-TBI), United States Army Medical Research and Development Command's Congressionally Directed Medical Research Programs and the National Football League Players Association. Dr. Hinds has not received financial compensation for his work with LIMBIC-CENC. Dr. Hinds is a member of Concussion Legacy Foundation's Veterans Advisory Board; advisory Board member to the University of Michigan Concussion Center; advisor to Gryphon Bio; ad hoc reviewer for VA Brain Health Research; invited reviewer to Congressionally Directed Medical Research Programs; contributor to the National Academy of Science, Engineering, and Medicine 'Accelerating Progress in TBI Research and Care'; NASEM TBI Forum committee member (currently inactive); and former contributor to Post-traumatic Epilepsy Research Program. His former Department of Defense work includes: NFL Scientific Advisory Board member; NCAA-DoD CARE Medical Advisory Board Member; DoD Brain Health Research Coordinating Officer and Medical Advisor to the Principal Assistant for Research and Technology (PAR&T), United States Army Medical Research and; Development Command (USAMRDC); Ex Officio National Advisory Neurological. All other authors report no relevant disclosures.### Funding StatementThis work was supported by the Assistant Secretary of Defense for Health Affairs endorsed by the Department of Defense, through the Psychological Health/Traumatic Brain Injury Research Program LongTerm Impact of Military Relevant Brain Injury Consortium (LIMBIC) Award W81XWH18PH/TBIRPLIMBIC under Awards No. W81XWH1920067 and W81XWH1320095, and by the U.S. Department of Veterans Affairs Awards No. I01 CX002097, I01 CX002096, I01 HX003155, I01 RX003444, I01 RX003443, I01 RX003442, I01 CX001135, I01 CX001246, I01 RX001774, I01 RX 001135, I01 RX 002076, I01 RX 001880, I01 RX 002172, I01 RX 002173, I01 RX 002171, I01 RX 002174, I01 CX001820, and I01 RX 002170. Dr. Pugh was supported by VA Health Services Research and Development Service Research Career Scientist Award, 1 IK6 HX002608; RCS 17-297. ### Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:IRB of the University of Utah gave ethical approval for this work. IRB of the Richmond VA gave ethical approval for this work. IRB of the Houston VA gave ethical approval for this work. IRB of the Tampa VA gave ethical approval for this work. IRB of the Portland VA gave ethical approval for this work. IRB of the Minneapolis VA gave ethical approval for this work.I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesData are available upon reasonable request to the LIMBIC-CENC consortium.

Abstract Introduction Aging is associated with disruptions in non-rapid eye movement (NREM) sleep expression, but the mechanisms driving this effect remain unclear. Cerebral small vessel disease (CSVD) burden increases with age and is associated with clinical sleep disturbance, but little is known about its relationship with local expression of oscillatory activity during NREM sleep. Here, we explore associations between CSVD burden and local electroencephalography (EEG) measures during NREM sleep in older adults. Methods Fifteen cognitively intact older adults (mean age 71.8±6.3 years, 10 female, Apnea Hypopnea Index (AHI)=7.82±7.95) completed T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging on a 3T scanner to quantify total and lobar white matter hyperintensity (WMH) volumes using a validated semi-automated toolbox and an in-lab overnight polysomnography with 128-channel EEG, separated by 1.95±1.02 years. EEG was preprocessed and multi-tapered to derive absolute spectral power. Topographical correlations between NREM multi-tapered absolute spectra bands were run with 5000-permutation threshold free cluster enhancement. Results Older age was associated with lower absolute posterior total (all p< 0.016, r>-0.61) and slow sigma power (all p< 0.018, r>-0.60) and slow wave activity (SWA) (all p< 0.017, r>-0.61) over centro-posterior EEG derivations. Age was also positively associated with frontal (τ=0.51, p< 0.01) and parietal (τ=0.49, p< 0.01) but not occipital (τ=0.33, p=0.092) or temporal (τ=0.36, p=0.064) WMH burden. Occipital WMH burden was also not associated with AHI (τ=0.038, p=0.843). Occipital WMH burden was associated with global reductions in alpha activity (all p< 0.043, τ>-0.78), centro-posterior reductions in SWA (all p< 0.043, τ>-0.63) and Delta (all p< 0.009, τ>-0.63), frontal reductions in total (all p< 0.043, τ>-0.71) and fast sigma power (all p< 0.004, τ>-0.57), global reductions in slow sigma power (all p< 0.043, τ>-0.71), and central reductions in theta activity (all p< 0.042, τ>-0.57). Conclusion Collectively, these findings indicate that WMH burden in older adults is associated with local disruptions in NREM sleep expression in multiple frequency bands associated with sleep-dependent cognitive functions. Future studies should examine the role of NREM sleep expression in cognitive decline associated with CSVD burden. Support (if any) Supported by NIH grants R01AG053555, R21AG079552, K01AG068353, F31AG074703, P30AG066519, and the AASM Foundation SRA-1818

Background: Individuals with asthma can vary widely in clinical presentation, severity, and pathobiology. Hyperpolarized xenon-129 (Xe129) MRI is a novel imaging method to provide 3-D mapping of both ventilation and gas exchange in the human lung. Purpose: To evaluate the functional changes in adults with asthma as compared to healthy controls using Xe129 MRI. Methods: All subjects (20 controls and 20 asthmatics) underwent lung function measurements and Xe129 MRI on the same day. Outcome measures included the pulmonary ventilation defect and transfer of inspired Xe129 into two soluble compartments: tissue and blood. Ten asthmatics underwent Xe129 MRI before and after bronchodilator to test whether gas transfer measures change with bronchodilator effects. Results: Initial analysis of the results revealed striking differences in gas transfer measures based on age, hence we compared outcomes in younger (n = 24, <= 35 years) versus older (n = 16, > 45 years) asthmatics and controls. The younger asthmatics exhibited significantly lower Xe129 gas uptake by lung tissue (Asthmatic: 0.98% +/- 0.24%, Control: 1.17% +/- 0.12%, P = 0.035), and higher Xe129 gas transfer from tissue to the blood (Asthmatic: 0.40 +/- 0.10, Control: 0.31% +/- 0.03%, P = 0.035) than the younger controls. No significant difference in Xe129 gas transfer was observed in the older group between asthmatics and controls (P > 0.05). No significant change in Xe129 transfer was observed before and after bronchodilator treatment. Conclusions: By using Xe129 MRI, we discovered heterogeneous alterations of gas transfer that have associations with age. This finding suggests a heretofore unrecognized physiological derangement in the gas/tissue/blood interface in young adults with asthma that deserves further study.

Background:Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD) are the most common neurodegenerative conditions. These central nervous system disorders impact both the structure and function of the brain and may lead to imaging changes that precede symptoms. Patients with ADRD or PD have long asymptomatic phases that exhibit significant heterogeneity. Hence, quantitative measures that can provide early disease indicators are necessary to improve patient stratification, clinical care, and clinical trial design. This work uses machine learning techniques to derive such a quantitative marker from T1-weighted (T1w) brain Magnetic resonance imaging (MRI). Methods:In this retrospective study, we developed machine learning (ML) based disease-specific scores based on T1w brain MRI utilizing Parkinson's Disease Progression Marker Initiative (PPMI) and Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. We evaluated the potential of ML-based scores for early diagnosis, prognosis, and monitoring of ADRD and PD in an independent large-scale population-based longitudinal cohort, UK Biobank. Findings:1,826 dementia images from 731 participants, 3,161 healthy control images from 925 participants from the ADNI cohort, 684 PD images from 319 participants, and 232 healthy control images from 145 participants from the PPMI cohort were used to train machine learning models. The classification performance is 0.94 [95% CI: 0.93-0.96] area under the ROC Curve (AUC) for ADRD detection and 0.63 [95% CI: 0.57-0.71] for PD detection using 790 extracted structural brain features. The most predictive regions include the hippocampus and temporal brain regions in ADRD and the substantia nigra in PD. The normalized ML model's probabilistic output (ADRD and PD imaging scores) was evaluated on 42,835 participants with imaging data from the UK Biobank. There are 66 cases for ADRD and 40 PD cases whose T1 brain MRI is available during pre-diagnostic phases. For diagnosis occurrence events within 5 years, the integrated survival model achieves a time-dependent AUC of 0.86 [95% CI: 0.80-0.92] for dementia and 0.89 [95% CI: 0.85-0.94] for PD. ADRD imaging score is strongly associated with dementia-free survival (hazard ratio (HR) 1.76 [95% CI: 1.50-2.05] per S.D. of imaging score), and PD imaging score shows association with PD-free survival (hazard ratio 2.33 [95% CI: 1.55-3.50]) in our integrated model. HR and prevalence increased stepwise over imaging score quartiles for PD, demonstrating heterogeneity. As a proxy for diagnosis, we validated AD/PD polygenic risk scores of 42,835 subjects against the imaging scores, showing a highly significant association after adjusting for covariates. In both the PPMI and ADNI cohorts, the scores are associated with clinical assessments, including the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-cognitive subscale (ADAS-Cog), and pathological markers, which include amyloid and tau. Finally, imaging scores are associated with polygenic risk scores for multiple diseases. Our results suggest that we can use imaging scores to assess the genetic architecture of such disorders in the future. Interpretation:Our study demonstrates the use of quantitative markers generated using machine learning techniques for ADRD and PD. We show that disease probability scores obtained from brain structural features are useful for early detection, prognosis prediction, and monitoring disease progression. To facilitate community engagement and external tests of model utility, an interactive app to explore summary level data from this study and dive into external data can be found here https://ndds-brainimaging-ml.streamlit.app. As far as we know, this is the first publicly available cloud-based MRI prediction application. Funding:US National Institute on Aging, and US National Institutes of Health.

Importance Blast-related mild traumatic brain injuries (TBIs), the "signature injury" of post-9/11 conflicts, are associated with clinically relevant, long-term cognitive, psychological, and behavioral dysfunction and disability; however, the underlying neural mechanisms remain unclear. Objective To investigate associations between a history of remote blast-related mild TBI and regional brain volume in a sample of US veterans and active duty service members. Design, Setting, and Participants Prospective cohort study of US veterans and active duty service members from the Long-Term Impact of Military-Relevant Brain Injury Consortium-Chronic Effects of Neurotrauma Consortium (LIMBIC-CENC), which enrolled more than 1500 participants at 5 sites used in this analysis between 2014 and 2023. Participants were recruited from Veterans Affairs medical centers across the US; 774 veterans and active duty service members of the US military met eligibility criteria for this secondary analysis. Assessment dates were from January 6, 2015, to March 31, 2023; processing and analysis dates were from August 1, 2023, to January 15, 2024. Exposure All participants had combat exposure, and 82% had 1 or more lifetime mild TBIs with variable injury mechanisms. Main Outcomes and Measures Regional brain volume was calculated using tensor-based morphometry on 3-dimensional, T1-weighted magnetic resonance imaging scans; history of TBI, including history of blast-related mild TBI, was assessed by structured clinical interview. Cognitive performance and psychiatric symptoms were assessed with a battery of validated instruments. We hypothesized that regional volume would be smaller in the blast-related mild TBI group and that this would be associated with cognitive performance. Results A total of 774 veterans (670 [87%] male; mean [SD] age, 40.1 [9.8] years; 260 [34%] with blast-related TBI) were included in the sample. Individuals with a history of blast-related mild TBI had smaller brain volumes than individuals without a history of blast-related mild TBI (which includes uninjured individuals and those with non-blast-related mild TBI) in several clusters, with the largest centered bilaterally in the superior corona radiata and subcortical gray and white matter (cluster peak Cohen d range, -0.23 to -0.38; mean [SD] Cohen d, 0.28 [0.03]). Additionally, causal mediation analysis revealed that these volume differences significantly mediated the association between blast-related mild TBI and performance on measures of working memory and processing speed. Conclusions and Relevance In this cohort study of 774 veterans and active duty service members, robust volume differences associated with blast-related TBI were identified. Furthermore, these volume differences significantly mediated the association between blast-related mild TBI and cognitive function, indicating that this pattern of brain differences may have implications for daily functioning.

Exposure to blast overpressure has been a pervasive feature of combat-related injuries. Studies exploring the neurological correlates of repeated low-level blast exposure in career "breachers" demonstrated higher levels of tumor necrosis factor alpha (TNFα) and interleukin (IL)-6 and decreases in IL-10 within brain-derived extracellular vesicles (BDEVs). The current pilot study was initiated in partnership with the U.S. Special Operations Command (USSOCOM) to explore whether neuroinflammation is seen within special operators with prior blast exposure. Data were analyzed from 18 service members (SMs), inclusive of 9 blast-exposed special operators with an extensive career history of repeated blast exposures and 9 controls matched by age and duration of service. Neuroinflammation was assessed utilizing positron emission tomography (PET) imaging with [18F]DPA-714. Serum was acquired to assess inflammatory biomarkers within whole serum and BDEVs. The Blast Exposure Threshold Survey (BETS) was acquired to determine blast history. Both self-report and neurocognitive measures were acquired to assess cognition. Similarity-driven Multi-view Linear Reconstruction (SiMLR) was used for joint analysis of acquired data. Analysis of BDEVs indicated significant positive associations with a generalized blast exposure value (GBEV) derived from the BETS. SiMLR-based analyses of neuroimaging demonstrated exposure-related relationships between GBEV, PET-neuroinflammation, cortical thickness, and volume loss within special operators. Affected brain networks included regions associated with memory retrieval and executive functioning, as well as visual and heteromodal processing. Post hoc assessments of cognitive measures failed to demonstrate significant associations with GBEV. This emerging evidence suggests neuroinflammation may be a key feature of the brain response to blast exposure over a career in operational personnel. The common thread of neuroinflammation observed in blast-exposed populations requires further study.

Obstructive sleep apnea (OSA) is common in older adults and is associated with medial temporal lobe (MTL) degeneration and memory decline in aging and Alzheimer’s disease (AD). However, the underlying mechanisms linking OSA to MTL degeneration and impaired memory remains unclear. By combining magnetic resonance imaging (MRI) assessments of cerebrovascular pathology and MTL structure with clinical polysomnography and assessment of overnight emotional memory retention in older adults at risk for AD, cerebrovascular pathology in fronto-parietal brain regions was shown to statistically mediate the relationship between OSA-related hypoxemia, particularly during rapid eye movement (REM) sleep, and entorhinal cortical thickness. Reduced entorhinal cortical thickness was, in turn, associated with impaired overnight retention in mnemonic discrimination ability across emotional valences for high similarity lures. These findings identify cerebrovascular pathology as a contributing mechanism linking hypoxemia to MTL degeneration and impaired sleep-dependent memory in older adults.

INTRODUCTION:Virtually all people with Down syndrome (DS) develop neuropathology associated with Alzheimer's disease (AD). Atrophy of the hippocampus and entorhinal cortex (EC), as well as elevated plasma concentrations of neurofilament light chain (NfL) protein, are markers of neurodegeneration associated with late-onset AD. We hypothesized that hippocampus and EC gray matter loss and increased plasma NfL concentrations are associated with memory in adults with DS. METHODS:T1-weighted structural magnetic resonance imaging (MRI) data were collected from 101 participants with DS. Hippocampus and EC volume, as well as EC subregional cortical thickness, were derived. In a subset of participants, plasma NfL concentrations and modified Cued Recall Test scores were obtained. Partial correlation and mediation were used to test relationships between medial temporal lobe (MTL) atrophy, plasma NfL, and episodic memory. RESULTS:Hippocampus volume, left anterolateral EC (alEC) thickness, and plasma NfL were correlated with each other and were associated with memory. Plasma NfL mediated the relationship between left alEC thickness and memory as well as hippocampus volume and memory. DISCUSSION:The relationship between MTL gray matter and memory is mediated by plasma NfL levels, suggesting a link between neurodegenerative processes underlying axonal injury and frank gray matter loss in key structures supporting episodic memory in people with DS.

Objective:Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer's disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. Furthermore, when imaging data are unavailable in clinical practice, patients may not benefit from effective application of biomarkers for disease diagnosis and monitoring. Methods:To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. Results:Experimental results show that our method can generate high-quality individual 3D structural MRI with high similarity to ground truth, observed images. Additionally, images generated using ReMiND exhibit relatively lower error rates and more accurately estimated rates of atrophy over time in important anatomical brain regions compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Conclusion:Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

UK Biobank is a large-scale epidemiological resource for investigating prospective correlations between various lifestyle, environmental, and genetic factors with health and disease progression. In addition to individual subject information obtained through surveys and physical examinations, a comprehensive neuroimaging battery consisting of multiple modalities provides imaging-derived phenotypes (IDPs) that can serve as biomarkers in neuroscience research. In this study, we augment the existing set of UK Biobank neuroimaging structural IDPs, obtained from well-established software libraries such as FSL and FreeSurfer, with related measurements acquired through the Advanced Normalization Tools Ecosystem. This includes previously established cortical and subcortical measurements defined, in part, based on the Desikan-Killiany-Tourville atlas. Also included are morphological measurements from two recent developments: medial temporal lobe parcellation of hippocampal and extra-hippocampal regions in addition to cerebellum parcellation and thickness based on the Schmahmann anatomical labeling. Through predictive modeling, we assess the clinical utility of these IDP measurements, individually and in combination, using commonly studied phenotypic correlates including age, fluid intelligence, numeric memory, and several other sociodemographic variables. The predictive accuracy of these IDP-based models, in terms of root-mean-squared-error or area-under-the-curve for continuous and categorical variables, respectively, provides comparative insights between software libraries as well as potential clinical interpretability. Results demonstrate varied performance between package-based IDP sets and their combination, emphasizing the need for careful consideration in their selection and utilization.

Importance: While the hallmark pathologies of amyloid-beta and tau in Alzheimer's disease (AD) are well documented and even part of the definition, upstream neuroinflammation is thought to play an important role but remains poorly understood. Objectives: We tested whether two distinct neuroinflammatory markers are associated with cerebrovascular injury and amyloid-beta, and whether these markers are associated with plasma phosphorylated tau (pTau) concentration, medial temporal lobe (MTL) cortical and hippocampal atrophy, and memory deficits. We examined neuroinflammatory markers plasma YKL-40 and GFAP, due to previous conflicting evidence relating YKL-40 and GFAP to AD pathogenic markers. Design: Cross-sectional data from a community observational study (Biomarker Exploration in Aging, Cognition, and Neurodegeneration - BEACoN) were included. Setting: All participants were enrolled in a single site, at University of California, Irvine. Participants: 126 participants were included if they had at least one of the following measures available: neuropsychological data, MRI, amyloid-PET, or plasma. Exposures: Plasma YKL-40 and plasma glial fibrillary acidic protein (GFAP) levels. Main outcomes and measures: White matter hyperintensity (WMH) volume, 18F-florbetapir (FBP) PET mean SUVR, plasma phosphorylated tau (pTau-217) concentration, MTL cortical thickness, hippocampal volume, and memory function assessed by Rey Auditory Verbal Learning Test. Using path analysis, we tested whether higher plasma YKL-40 and GFAP are associated with WMH and amyloid-beta, and whether these converge to downstream markers of tauopathy, MTL neurodegeneration, and memory deficits. Results: In older adults without dementia (N=126, age=70.60+6.29, 62% women), we found that higher plasma YKL-40 concentration was associated with greater WMH volume, while higher plasma GFAP concentration was related to increased FBP SUVR. Further, higher plasma GFAP, WMH and FBP SUVR were independently associated with increased pTau-217. In turn, plasma pTau-217 was associated with reduced MTL cortical thickness and hippocampal volume. Subsequently, only reduced hippocampal volume was related to lower memory function. Conclusions and Relevance: Neuroinflammatory markers contribute to parallel pathways of cerebrovascular injury and amyloid-beta, which converge to tau-associated neurodegeneration and memory deficits in older adults. These observations underscore the need for a more comprehensive approach to developing an AD framework and treatment strategies. ### Competing Interest Statement MAY is co-founder and scientific advisor for Augnition Labs, LLC. AMB is a paid consultant for IQVIA and Cognition Therapeutics, Inc.. He serves on the Scientific Advisory Boards of CogState and Cognito Therapeutics. He is an inventor on a patent for quantification of white matter hyperintensities (US patent #9867566).

Multimodal patient representation learning aims to integrate information from multiple modalities and generate comprehensive patient representations for subsequent clinical predictive tasks. However, many existing approaches either presuppose the availability of all modalities and labels for each patient or only deal with missing modalities. In reality, patient data often comes with both missing modalities and labels for various reasons (i.e., the missing modality and label issue). Moreover, multimodal models might over-rely on certain modalities, causing sub-optimal performance when these modalities are absent (i.e., the modality collapse issue). To address these issues, we introduce MUSE: a mutual-consistent graph contrastive learning method. MUSE uses a flexible bipartite graph to represent the patient-modality relationship, which can adapt to various missing modality patterns. To tackle the modality collapse issue, MUSE learns to focus on modality-general and label-decisive features via a mutual-consistent contrastive learning loss. Notably, the unsupervised component of the contrastive objective only requires self-supervision signals, thereby broadening the training scope to incorporate patients with missing labels. We evaluate MUSE on three publicly available datasets: MIMIC-IV, eICU, and ADNI. Results show that MUSE outperforms all baselines, and MUSE+ further elevates the absolute improvement to ~4% by extending the training scope to patients with absent labels.

AbstractThe Parkinson’s Progression Markers Initiative (PPMI) delivers multiple modality MRI (M3RI) and biomarker data for a comprehensive longitudinal study of Parkinson’s Disease (PD). These provide quantitative indices of deep brain and cortical structure (T1-weighted MRI), microstructural integrity of brain tissue (diffusion-weighted imaging) and resting brain function (resting state functional MRI). Integrating and uniformly analyzing M3RI alongside non-imaging biological and clinical data is challenging due to the distinct nature of each modality. This study systematically organizes these complex data into a structured format, provides a PD-focused evaluation of the methodologies and evidence for technical robustness of the approach. The cohort encompasses 841 idiopathic PD, 309 genetic PD, 1364 presymptomatic PD and 240 control subjects at baseline with followup at a mean of 1.83 years.

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

AbstractRegistration is the process of establishing spatial correspondences between images. It allows for the alignment and transfer of key information across subjects and atlases. Registration is thus a central technique in many medical imaging applications. This chapter first introduces the fundamental concepts underlying image registration. It then presents recent developments based on machine learning, specifically deep learning, which have advanced the three core components of traditional image registration methods—the similarity functions, transformation models, and cost optimization. Finally, it describes the key application of these techniques to brain disorders.

The U-Net deep learning network architecture is generally effective in segmenting, or delineating, medical images into various regions, or classes, of interest. However, the segmentation performance of U-Net can be poor under certain circumstances, such as when different classes have sufficiently similar voxel properties and/or have overlapping spatial locations. While multiple plausible solutions to this problem are known, it is not possible to determine a priori which solution is appropriate for a given set of images, because the optimal solution depends greatly on image properties. In this brief report, we demonstrate that it is feasible to use the spatial probability maps, or 'anatomical priors', of various individual anatomical regions of the human hand to obtain highly accurate segmentation performance by the U-Net.