Heart rate fragmentation and brain MRI markers of small vessel disease in MESA.

INTRODUCTION: Heart rate (HR) fragmentation indices quantify breakdown of HR regulation and are associated with atrial fibrillation and cognitive impairment. Their association with brain magnetic resonance imaging (MRI) markers of small vessel disease is unexplored. METHODS: In 606 stroke-free participants of the Multi-Ethnic Study of Atherosclerosis (mean age 67), HR fragmentation indices including percentage of inflection points (PIP) were derived from sleep study recordings. We examined PIP in relation to white matter hyperintensity (WMH) volume, total white matter fractional anisotropy (FA), and microbleeds from 3-Tesla brain MRI completed 7 years later. RESULTS: In adjusted analyses, higher PIP was associated with greater WMH volume (14% per standard deviation [SD], 95% confidence interval [CI]: 2, 27%, P = 0.02) and lower WM FA (-0.09 SD per SD, 95% CI: -0.16, -0.01, P = 0.03). DISCUSSION: HR fragmentation was associated with small vessel disease. HR fragmentation can be measured automatically from ambulatory electrocardiogram devices and may be useful as a biomarker of vascular brain injury.

Postmortem Brain Imaging in Alzheimer's Disease and Related Dementias: The South Texas Alzheimer's Disease Research Center Repository.

BACKGROUND: Neuroimaging bears the promise of providing new biomarkers that could refine the diagnosis of dementia. Still, obtaining the pathology data required to validate the relationship between neuroimaging markers and neurological changes is challenging. Existing data repositories are focused on a single pathology, are too small, or do not precisely match neuroimaging and pathology findings. OBJECTIVE: The new data repository introduced in this work, the South Texas Alzheimer's Disease research center repository, was designed to address these limitations. Our repository covers a broad diversity of dementias, spans a wide age range, and was specifically designed to draw exact correspondences between neuroimaging and pathology data. METHODS: Using four different MRI sequences, we are reaching a sample size that allows for validating multimodal neuroimaging biomarkers and studying comorbid conditions. Our imaging protocol was designed to capture markers of cerebrovascular disease and related lesions. Quantification of these lesions is currently underway with MRI-guided histopathological examination. RESULTS: A total of 139 postmortem brains (70 females) with mean age of 77.9 years were collected, with 71 brains fully analyzed. Of these, only 3% showed evidence of AD-only pathology and 76% had high prevalence of multiple pathologies contributing to clinical diagnosis. CONCLUSION: This repository has a significant (and increasing) sample size consisting of a wide range of neurodegenerative disorders and employs advanced imaging protocols and MRI-guided histopathological analysis to help disentangle the effects of comorbid disorders to refine diagnosis, prognosis and better understand neurodegenerative disorders.

Association of brain microbleeds with risk factors, cognition, and MRI markers in MESA.

INTRODUCTION: Little is known about the epidemiology of brain microbleeds in racially/ethnically diverse populations. METHODS: In the Multi-Ethnic Study of Atherosclerosis, brain microbleeds were identified from 3T magnetic resonance imaging susceptibility-weighted imaging sequences using deep learning models followed by radiologist review. RESULTS: Among 1016 participants without prior stroke (25% Black, 15% Chinese, 19% Hispanic, 41% White, mean age 72), microbleed prevalence was 20% at age 60 to 64.9 and 45% at ≥85 years. Deep microbleeds were associated with older age, hypertension, higher body mass index, and atrial fibrillation, and lobar microbleeds with male sex and atrial fibrillation. Overall, microbleeds were associated with greater white matter hyperintensity volume and lower total white matter fractional anisotropy. DISCUSSION: Results suggest differing associations for lobar versus deep locations. Sensitive microbleed quantification will facilitate future longitudinal studies of their potential role as an early indicator of vascular pathology.

Patterns of Regional Brain Atrophy and Brain Aging in Middle- and Older-Aged Adults With Type 1 Diabetes.

Importance: Little is known about structural brain changes in type 1 diabetes (T1D) and whether there are early manifestations of a neurodegenerative condition like Alzheimer disease (AD) or evidence of premature brain aging. Objective: To evaluate neuroimaging markers of brain age and AD-like atrophy in participants with T1D in the Diabetes Control and Complications Trial (DCCT)/Epidemiology of Diabetes Interventions and Complications (EDIC) study, identify which brain regions are associated with the greatest changes in patients with T1D, and assess the association between cognition and brain aging indices. Design, Setting, and Participants: This cohort study leveraged data collected during the combined DCCT (randomized clinical trial, 1983-1993) and EDIC (observational study, 1994 to present) studies at 27 clinical centers in the US and Canada. A total of 416 eligible EDIC participants and 99 demographically similar adults without diabetes were enrolled in the magnetic resonance imaging (MRI) ancillary study, which reports cross-sectional data collected in 2018 to 2019 and relates it to factors measured longitudinally in DCCT/EDIC. Data analyses were performed between July 2020 and April 2022. Exposure: T1D diagnosis. Main Outcomes and Measures: Psychomotor and mental efficiency were evaluated using verbal fluency, digit symbol substitution test, trail making part B, and the grooved pegboard. Immediate memory scores were derived from the logical memory subtest of the Wechsler memory scale and the Wechsler digit symbol substitution test. MRI and machine learning indices were calculated to predict brain age and quantify AD-like atrophy. Results: This study included 416 EDIC participants with a median (range) age of 60 (44-74) years (87 of 416 [21%] were older than 65 years) and a median (range) diabetes duration of 37 (30-51) years. EDIC participants had consistently higher brain age values compared with controls without diabetes, indicative of approximately 6 additional years of brain aging (EDIC participants: ß, 6.16; SE, 0.71; control participants: ß, 1.04; SE, 0.04; P < .001). In contrast, AD regional atrophy was comparable between the 2 groups. Regions with atrophy in EDIC participants vs controls were observed mainly in the bilateral thalamus and putamen. Greater brain age was associated with lower psychomotor and mental efficiency among EDIC participants (ß, -0.04; SE, 0.01; P < .001), but not among controls. Conclusions and Relevance: The findings of this study suggest an increase in brain aging among individuals with T1D without any early signs of AD-related neurodegeneration. These increases were associated with reduced cognitive performance, but overall, the abnormal patterns seen in this sample were modest, even after a mean of 38 years with T1D.

Deep Learning Based Detection of Enlarged Perivascular Spaces on Brain MRI.

Deep learning has been demonstrated effective in many neuroimaging applications. However, in many scenarios, the number of imaging sequences capturing information related to small vessel disease lesions is insufficient to support data-driven techniques. Additionally, cohort-based studies may not always have the optimal or essential imaging sequences for accurate lesion detection. Therefore, it is necessary to determine which imaging sequences are crucial for precise detection. This study introduces a deep learning framework to detect enlarged perivascular spaces (ePVS) and aims to find the optimal combination of MRI sequences for deep learning-based quantification. We implemented an effective lightweight U-Net adapted for ePVS detection and comprehensively investigated different combinations of information from SWI, FLAIR, T1-weighted (T1w), and T2-weighted (T2w) MRI sequences. The experimental results showed that T2w MRI is the most important for accurate ePVS detection, and the incorporation of SWI, FLAIR and T1w MRI in the deep neural network had minor improvements in accuracy and resulted in the highest sensitivity and precision (sensitivity =0.82, precision =0.83). The proposed method achieved comparable accuracy at a minimal time cost compared to manual reading. The proposed automated pipeline enables robust and time-efficient readings of ePVS from MR scans and demonstrates the importance of T2w MRI for ePVS detection and the potential benefits of using multimodal images. Furthermore, the model provides whole-brain maps of ePVS, enabling a better understanding of their clinical correlates compared to the clinical rating methods within only a couple of brain regions.

Assessment of Risk Factors and Clinical Importance of Enlarged Perivascular Spaces by Whole-Brain Investigation in the Multi-Ethnic Study of Atherosclerosis.

Importance: Enlarged perivascular spaces (ePVSs) have been associated with cerebral small-vessel disease (cSVD). Although their etiology may differ based on brain location, study of ePVSs has been limited to specific brain regions; therefore, their risk factors and significance remain uncertain. Objective: Toperform a whole-brain investigation of ePVSs in a large community-based cohort. Design, Setting, and Participants: This cross-sectional study analyzed data from the Atrial Fibrillation substudy of the population-based Multi-Ethnic Study of Atherosclerosis. Demographic, vascular risk, and cardiovascular disease data were collected from September 2016 to May 2018. Brain magnetic resonance imaging was performed from March 2018 to July 2019. The reported analysis was conducted between August and October 2022. A total of 1026 participants with available brain magnetic resonance imaging data and complete information on demographic characteristics and vascular risk factors were included. Main Outcomes and Measures: Enlarged perivascular spaces were quantified using a fully automated deep learning algorithm. Quantified ePVS volumes were grouped into 6 anatomic locations: basal ganglia, thalamus, brainstem, frontoparietal, insular, and temporal regions, and were normalized for the respective regional volumes. The association of normalized regional ePVS volumes with demographic characteristics, vascular risk factors, neuroimaging indices, and prevalent cardiovascular disease was explored using generalized linear models. Results: In the 1026 participants, mean (SD) age was 72 (8) years; 541 (53%) of the participants were women. Basal ganglia ePVS volume was positively associated with age (ß = 3.59 × 10-3; 95% CI, 2.80 × 10-3 to 4.39 × 10-3), systolic blood pressure (ß = 8.35 × 10-4; 95% CI, 5.19 × 10-4 to 1.15 × 10-3), use of antihypertensives (ß = 3.29 × 10-2; 95% CI, 1.92 × 10-2 to 4.67 × 10-2), and negatively associated with Black race (ß = -3.34 × 10-2; 95% CI, -5.08 × 10-2 to -1.59 × 10-2). Thalamic ePVS volume was positively associated with age (ß = 5.57 × 10-4; 95% CI, 2.19 × 10-4 to 8.95 × 10-4) and use of antihypertensives (ß = 1.19 × 10-2; 95% CI, 6.02 × 10-3 to 1.77 × 10-2). Insular region ePVS volume was positively associated with age (ß = 1.18 × 10-3; 95% CI, 7.98 × 10-4 to 1.55 × 10-3). Brainstem ePVS volume was smaller in Black than in White participants (ß = -5.34 × 10-3; 95% CI, -8.26 × 10-3 to -2.41 × 10-3). Frontoparietal ePVS volume was positively associated with systolic blood pressure (ß = 1.14 × 10-4; 95% CI, 3.38 × 10-5 to 1.95 × 10-4) and negatively associated with age (ß = -3.38 × 10-4; 95% CI, -5.40 × 10-4 to -1.36 × 10-4). Temporal region ePVS volume was negatively associated with age (ß = -1.61 × 10-2; 95% CI, -2.14 × 10-2 to -1.09 × 10-2), as well as Chinese American (ß = -2.35 × 10-1; 95% CI, -3.83 × 10-1 to -8.74 × 10-2) and Hispanic ethnicities (ß = -1.73 × 10-1; 95% CI, -2.96 × 10-1 to -4.99 × 10-2). Conclusions and Relevance: In this cross-sectional study of ePVSs in the whole brain, increased ePVS burden in the basal ganglia and thalamus was a surrogate marker for underlying cSVD, highlighting the clinical importance of ePVSs in these locations.

Association of Intensive vs Standard Blood Pressure Control With Regional Changes in Cerebral Small Vessel Disease Biomarkers: Post Hoc Secondary Analysis of the SPRINT MIND Randomized Clinical Trial.

Importance: Little is known about the associations of strict blood pressure (BP) control with microstructural changes in small vessel disease markers. Objective: To investigate the regional associations of intensive vs standard BP control with small vessel disease biomarkers, such as white matter lesions (WMLs), fractional anisotropy (FA), mean diffusivity (MD), and cerebral blood flow (CBF). Design, Setting, and Participants: The Systolic Blood Pressure Intervention Trial (SPRINT) is a multicenter randomized clinical trial that compared intensive systolic BP (SBP) control (SBP target <120 mm Hg) vs standard control (SBP target <140 mm Hg) among participants aged 50 years or older with hypertension and without diabetes or a history of stroke. The study began randomization on November 8, 2010, and stopped July 1, 2016, with a follow-up duration of approximately 4 years. A total of 670 and 458 participants completed brain magnetic resonance imaging at baseline and follow-up, respectively, and comprise the cohort for this post hoc analysis. Statistical analyses for this post hoc analysis were performed between August 2020 and October 2022. Interventions: At baseline, 355 participants received intensive SBP treatment and 315 participants received standard SBP treatment. Main Outcomes and Measures: The main outcomes were regional changes in WMLs, FA, MD (in white matter regions of interest), and CBF (in gray matter regions of interest). Results: At baseline, 355 participants (mean [SD] age, 67.7 [8.0] years; 200 men [56.3%]) received intensive BP treatment and 315 participants (mean [SD] age, 67.0 [8.4] years; 199 men [63.2%]) received standard BP treatment. Intensive treatment was associated with smaller mean increases in WML volume compared with standard treatment (644.5 mm3 vs 1258.1 mm3). The smaller mean increases were observed specifically in the deep white matter regions of the left anterior corona radiata (intensive treatment, 30.3 mm3 [95% CI, 16.0-44.5 mm3]; standard treatment, 80.5 mm3 [95% CI, 53.8-107.2 mm3]), left tapetum (intensive treatment, 11.8 mm3 [95% CI, 4.4-19.2 mm3]; standard treatment, 27.2 mm3 [95% CI, 19.4-35.0 mm3]), left superior fronto-occipital fasciculus (intensive treatment, 3.2 mm3 [95% CI, 0.7-5.8 mm3]; standard treatment, 9.4 mm3 [95% CI, 5.5-13.4 mm3]), left posterior corona radiata (intensive treatment, 26.0 mm3 [95% CI, 12.9-39.1 mm3]; standard treatment, 52.3 mm3 [95% CI, 34.8-69.8 mm3]), left splenium of the corpus callosum (intensive treatment, 45.4 mm3 [95% CI, 25.1-65.7 mm3]; standard treatment, 83.0 mm3 [95% CI, 58.7-107.2 mm3]), left posterior thalamic radiation (intensive treatment, 53.0 mm3 [95% CI, 29.8-76.2 mm3]; standard treatment, 106.9 mm3 [95% CI, 73.4-140.3 mm3]), and right posterior thalamic radiation (intensive treatment, 49.5 mm3 [95% CI, 24.3-74.7 mm3]; standard treatment, 102.6 mm3 [95% CI, 71.0-134.2 mm3]). Conclusions and Relevance: This study suggests that intensive BP treatment, compared with standard treatment, was associated with a slower increase of WMLs, improved diffusion tensor imaging, and FA and CBF changes in several brain regions that represent vulnerable areas that may benefit from more strict BP control. Trial Registration: ClinicalTrials.gov Identifier: NCT01206062.

ADCoC: Adaptive Distribution Modeling Based Collaborative Clustering for Disentangling Disease Heterogeneity from Neuroimaging Data.

Conventional clustering techniques for neuroimaging applications usually focus on capturing differences between given subjects, while neglecting arising differences between features and the potential bias caused by degraded data quality. In practice, collected neuroimaging data are often inevitably contaminated by noise, which may lead to errors in clustering and clinical interpretation. Additionally, most methods ignore the importance of feature grouping towards optimal clustering. In this paper, we exploit the underlying heterogeneous clusters of features to serve as weak supervision for improved clustering of subjects, which is achieved by simultaneously clustering subjects and features via nonnegative matrix tri-factorization. In order to suppress noise, we further introduce adaptive regularization based on coefficient distribution modeling. Particularly, unlike conventional sparsity regularization techniques that assume zero mean of the coefficients, we form the distributions using the data of interest so that they could better fit the non-negative coefficients. In this manner, the proposed approach is expected to be more effective and robust against noise. We compared the proposed method with standard techniques and recently published methods demonstrating superior clustering performance on synthetic data with known ground truth labels. Furthermore, when applying our proposed technique to magnetic resonance imaging (MRI) data from a cohort of patients with Parkinson's disease, we identified two stable and highly reproducible patient clusters characterized by frontal and posterior cortical/medial temporal atrophy patterns, respectively, which also showed corresponding differences in cognitive characteristics.

SPARE-Tau: A flortaucipir machine-learning derived early predictor of cognitive decline.

BACKGROUND: Recently, tau PET tracers have shown strong associations with clinical outcomes in individuals with cognitive impairment and cognitively unremarkable elderly individuals. flortaucipir PET scans to measure tau deposition in multiple brain areas as the disease progresses. This information needs to be summarized to evaluate disease severity and predict disease progression. We, therefore, sought to develop a machine learning-derived index, SPARE-Tau, which successfully detects pathology in the earliest disease stages and accurately predicts progression compared to a priori-based region of interest approaches (ROI). METHODS: 587 participants of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort had flortaucipir scans, structural MRI scans, and an Aß biomarker test (CSF or florbetapir PET) performed on the same visit. We derived the SPARE-Tau index in a subset of 367 participants. We evaluated associations with clinical measures for CSF p-tau, SPARE-MRI, and flortaucipir PET indices (SPARE-Tau, meta-temporal, and average Braak ROIs). Bootstrapped multivariate adaptive regression splines linear regression analyzed the association between the biomarkers and baseline ADAS-Cog13 scores. Bootstrapped multivariate linear regression models evaluated associations with clinical diagnosis. Cox-hazards and mixed-effects models investigated clinical progression and longitudinal ADAS-Cog13 changes. The Aß positive cognitively unremarkable participants, not included in the SPARE-Tau training, served as an independent validation group. RESULTS: Compared to CSF p-tau, meta-temporal, and averaged Braak tau PET ROIs, SPARE-Tau showed the strongest association with baseline ADAS-cog13 scores and diagnosis. SPARE-Tau also presented the strongest association with clinical progression in cognitively unremarkable participants and longitudinal ADAS-Cog13 changes. Results were confirmed in the Aß+ cognitively unremarkable hold-out sample participants. CSF p-tau showed the weakest cross-sectional associations and longitudinal prediction. DISCUSSION: Flortaucipir indices showed the strongest clinical association among the studied biomarkers (flortaucipir, florbetapir, structural MRI, and CSF p-tau) and were predictive in the preclinical disease stages. Among the flortaucipir indices, the machine-learning derived SPARE-Tau index was the most sensitive clinical progression biomarker. The combination of different biomarker modalities better predicted cognitive performance.

Left Atrial Function and Arrhythmias in Relation to Small Vessel Disease on Brain MRI: The Multi-Ethnic Study of Atherosclerosis.

Background Atrial fibrillation (AF) is associated with increased stroke risk and accelerated cognitive decline, but the association of early manifestations of left atrial (LA) impairment with subclinical changes in brain structure is unclear. We investigated whether abnormal LA structure and function, greater supraventricular ectopy, and intermittent AF are associated with small vessel disease on magnetic resonance imaging of the brain. Methods and Results In the Multi-Ethnic Study of Atherosclerosis, 967 participants completed 14-day ambulatory electrocardiographic monitoring, speckle tracking echocardiography and, a median 17 months later, magnetic resonance imaging of the brain. We assessed associations of LA volume index and reservoir strain, supraventricular ectopy, and prevalent AF with brain magnetic resonance imaging measures of small vessel disease and atrophy. The mean age of participants was 72 years; 53% were women. In multivariable models, LA enlargement was associated with lower white matter fractional anisotropy and greater prevalence of microbleeds; reduced LA strain, indicating worse LA function, was associated with more microbleeds. More premature atrial contractions were associated with lower total gray matter volume. Compared with no AF, intermittent AF (prevalent AF with <100% AF during electrocardiographic monitoring) was associated with lower white matter fractional anisotropy (-0.25 SDs [95% CI, -0.44 to -0.07]) and greater prevalence of microbleeds (prevalence ratio: 1.42 [95% CI, 1.12-1.79]). Conclusions In individuals without a history of stroke or transient ischemic attack, alterations of LA structure and function, including enlargement, reduced strain, frequent premature atrial contractions, and intermittent AF, were associated with increased markers of small vessel disease. Detailed assessment of LA structure and function and extended ECG monitoring may enable early identification of individuals at greater risk of small vessel disease.

Disentangling Alzheimer's disease neurodegeneration from typical brain ageing using machine learning.

Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer's disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer's Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48-95 years) with Alzheimer's disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer's disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer's disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer's disease continuum group (n = 718; consisting of amyloid-positive Alzheimer's disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer's disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer's disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56-0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer's disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer's disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer's disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer's disease.

Disentangling tau and brain atrophy cluster heterogeneity across the Alzheimer's disease continuum.

Introduction: Neuroimaging heterogeneity in dementia has been examined using single modalities. We evaluated the associations of magnetic resonance imaging (MRI) atrophy and flortaucipir positron emission tomography (PET) clusters across the Alzheimer's disease (AD) spectrum. Methods: We included 496 Alzheimer's Disease Neuroimaging Initiative participants with brain MRI, flortaucipir PET scan, and amyloid beta biomarker measures obtained. We applied a novel robust collaborative clustering (RCC) approach on the MRI and flortaucipir PET scans. We derived indices for AD-like (SPARE-AD index) and brain age (SPARE-BA) atrophy. Results: We identified four tau (I-IV) and three atrophy clusters. Tau clusters were associated with the apolipoprotein E genotype. Atrophy clusters were associated with white matter hyperintensity volumes. Only the hippocampal sparing atrophy cluster showed a specific association with brain aging imaging index. Tau clusters presented stronger clinical associations than atrophy clusters. Tau and atrophy clusters were partially associated. Conclusions: Each neuroimaging modality captured different aspects of brain aging, genetics, vascular changes, and neurodegeneration leading to individual multimodal phenotyping.

Association Between Obstructive Sleep Apnea and Brain White Matter Hyperintensities in a Population-Based Cohort in Germany.

Importance: Underlying pathomechanisms of brain white matter hyperintensities (WMHs), commonly observed in older individuals and significantly associated with Alzheimer disease and brain aging, have not yet been fully elucidated. One potential contributing factor to WMH burden is chronic obstructive sleep apnea (OSA), a disorder highly prevalent in the general population with readily available treatment options. Objective: To investigate potential associations between OSA and WMH burden. Design, Setting, and Participants: Analyses were conducted in 529 study participants of the Study of Health in Pomerania-Trend baseline (SHIP-Trend-0) study with complete WMH, OSA, and important clinical data available. SHIP-Trend-0 is a general population-based, cross-sectional, observational study to facilitate the investigation of a large spectrum of common risk factors, subclinical disorders, and clinical diseases and their relationships among each other with patient recruitment from Western Pomerania, Germany, starting on September 1, 2008, with data collected until December 31, 2012. Data analysis was performed from February 1, 2019, to January 31, 2021. Exposures: The apnea-hypopnea index (AHI) and oxygen desaturation index (ODI) were assessed during a single-night, laboratory-based polysomnography measurement. Main Outcomes and Measures: The primary outcome was WMH data automatically segmented from 1.5-T magnetic resonance images. Results: Of 529 study participants (mean [SD] age, 52.15 [13.58] years; 282 female [53%]), a total of 209 (40%) or 102 (19%) individuals were diagnosed with OSA according to AHI or ODI criteria (mean [SD] AHI, 7.98 [12.55] events per hour; mean [SD] ODI, 3.75 [8.43] events per hour). Both AHI (ß = 0.024; 95% CI, 0.011-0.037; P <.001) and ODI (ß = 0.033; 95% CI, 0.014-0.051; P <. 001) were significantly associated with brain WMH volumes. These associations remained even in the presence of additional vascular, metabolic, and lifestyle WMH risk factors. Region-specific WMH analyses found the strongest associations between periventricular frontal WMH volumes and both AHI (ß = 0.0275; 95% CI, 0.013-0.042, P < .001) and ODI (ß = 0.0381; 95% CI, 0.016-0.060, P < .001) as well as periventricular dorsal WMH volumes and AHI (ß = 0.0165; 95% CI, 0.004-0.029, P = .008). Conclusions and Relevance: This study found significant associations between OSA and brain WMHs, indicating a novel, potentially treatable WMH pathomechanism.

DEEPMIR: a deep neural network for differential detection of cerebral microbleeds and iron deposits in MRI.

Lobar cerebral microbleeds (CMBs) and localized non-hemorrhage iron deposits in the basal ganglia have been associated with brain aging, vascular disease and neurodegenerative disorders. Particularly, CMBs are small lesions and require multiple neuroimaging modalities for accurate detection. Quantitative susceptibility mapping (QSM) derived from in vivo magnetic resonance imaging (MRI) is necessary to differentiate between iron content and mineralization. We set out to develop a deep learning-based segmentation method suitable for segmenting both CMBs and iron deposits. We included a convenience sample of 24 participants from the MESA cohort and used T2-weighted images, susceptibility weighted imaging (SWI), and QSM to segment the two types of lesions. We developed a protocol for simultaneous manual annotation of CMBs and non-hemorrhage iron deposits in the basal ganglia. This manual annotation was then used to train a deep convolution neural network (CNN). Specifically, we adapted the U-Net model with a higher number of resolution layers to be able to detect small lesions such as CMBs from standard resolution MRI. We tested different combinations of the three modalities to determine the most informative data sources for the detection tasks. In the detection of CMBs using single class and multiclass models, we achieved an average sensitivity and precision of between 0.84-0.88 and 0.40-0.59, respectively. The same framework detected non-hemorrhage iron deposits with an average sensitivity and precision of about 0.75-0.81 and 0.62-0.75, respectively. Our results showed that deep learning could automate the detection of small vessel disease lesions and including multimodal MR data (particularly QSM) can improve the detection of CMB and non-hemorrhage iron deposits with sensitivity and precision that is compatible with use in large-scale research studies.

Evaluating the role of 1.5T quantitative susceptibility mapping for subthalamic nucleus targeting in deep brain stimulation surgery.

BACKGROUND AND PURPOSE: Quantitative susceptibility mapping (QSM) has been shown to be valuable in direct targeting for subthalamic nucleus (STN) DBS, given its higher quality of contrast between the STN border and adjacent anatomical structures. The objective is to demonstrate the feasibility of using 1.5T QSM for direct targeting in STN DBS planning. MATERIAL AND METHODS: Eleven patients underwent MRI acquisitions using a 1.5T scanner, including multi-echo gradient echo sequences for generating QSM images. 22 STN targets were planned with direct targeting method using QSM images by one stereotactic neurosurgeon and indirect targeting method using standard protocol by a second stereotactic neurosurgeon. The two physicians were blinded to each other's results. RESULTS: The mean coordinates for the STN using direct targeting relative to the mid-commissural point (MCP) was 11.41±2.43mm lateral, 2.48±0.53mm posterior and 4.45±0.95mm inferior. The mean coordinates for the STN using indirect targeting was 11.79±2.51mm lateral, 2.55±0.54mm posterior, and 4.84±1.03mm inferior. The mean (±SEM) radial error between the direct and indirect target was 0.67±0.14mm. In cases where DBS electrodes were implanted, the radial difference between the indirect and actual target (1.19±0.30mm) was statistically equivalent to the radial difference between the direct and actual target (1.0±0.27mm). CONCLUSIONS: Direct targeting of the STN for DBS implantation using 1.5T QSM was found to be statistically equivalent to standard protocol surgery planning. This may offer a simpler, more intuitive alternative for DBS surgery planning at centers with 1.5T MRIs.

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

INTRODUCTION: Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects). METHODS: Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD. RESULTS: WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aß) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD. DISCUSSION: A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

Use of Functional Magnetic Resonance Imaging to Assess How Motor Phenotypes of Parkinson's Disease Respond to Deep Brain Stimulation.

BACKGROUND: Deep brain stimulation (DBS) is a well-accepted treatment of Parkinson's disease (PD). Motor phenotypes include tremor-dominant (TD), akinesia-rigidity (AR), and postural instability gait disorder (PIGD). The mechanism of action in how DBS modulates motor symptom relief remains unknown. OBJECTIVE: Blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) was used to determine whether the functional activity varies in response to DBS depending on PD phenotypes. MATERIALS AND METHODS: Subjects underwent an fMRI scan with DBS cycling ON and OFF. The effects of DBS cycling on BOLD activation in each phenotype were documented through voxel-wise analysis. For each region of interest, ANOVAs were performed using T-values and covariate analyses were conducted. Further, a correlation analysis was performed comparing stimulation settings to T-values. Lastly, T-values of subjects with motor improvement were compared to those who worsened. RESULTS: As a group, BOLD activation with DBS-ON resulted in activation in the motor thalamus (p < 0.01) and globus pallidus externa (p < 0.01). AR patients had more activation in the supplementary motor area (SMA) compared to PIGD (p < 0.01) and TD cohorts (p < 0.01). Further, the AR cohort had more activation in primary motor cortex (MI) compared to the TD cohort (p = 0.02). Implanted nuclei (p = 0.01) and phenotype (p = <0.01) affected activity in MI and phenotype alone affected SMA activity (p = <0.01). A positive correlation was seen between thalamic activation and pulse-width (p = 0.03) and between caudate and total electrical energy delivered (p = 0.04). CONCLUSIONS: These data suggest that DBS modulates network activity differently based on patient motor phenotype. Improved understanding of these differences may further our knowledge about the mechanisms of DBS action on PD motor symptoms and to optimize treatment.

Functional MRI Safety and Artifacts during Deep Brain Stimulation: Experience in 102 Patients.

BackgroundWith growing numbers of patients receiving deep brain stimulation (DBS), radiologists are encountering these neuromodulation devices at an increasing rate. Current MRI safety guidelines, however, limit MRI access in these patients.PurposeTo describe an MRI (1.5 T and 3 T) experience and safety profile in a large cohort of participants with active DBS systems and characterize the hardware-related artifacts on images from functional MRI.Materials and MethodsIn this prospective study, study participants receiving active DBS underwent 1.5- or 3-T MRI (T1-weighted imaging and gradient-recalled echo [GRE]-echo-planar imaging [EPI]) between June 2017 and October 2018. Short- and long-term adverse events were tracked. The authors quantified DBS hardware-related artifacts on images from GRE-EPI (functional MRI) at the cranial coil wire and electrode contacts. Segmented artifacts were then transformed into standard space to define the brain areas affected by signal loss. Two-sample t tests were used to assess the difference in artifact size between 1.5- and 3-T MRI.ResultsA total of 102 participants (mean age ± standard deviation, 60 years ± 11; 65 men) were evaluated. No MRI-related short- and long-term adverse events or acute changes were observed. DBS artifacts were most prominent near the electrode contacts and over the frontoparietal cortical area where the redundancy of the extension wire is placed subcutaneously. The mean electrode contact artifact diameter was 9.3 mm ± 1.6, and 1.9% ± 0.8 of the brain was obscured by the coil artifact. The coil artifacts were larger at 3 T than at 1.5 T, obscuring 2.1% ± 0.7 and 1.4% ± 0.7 of intracranial volume, respectively (P < .001). The superficial frontoparietal cortex and deep structures neighboring the electrode contacts were most commonly obscured.ConclusionWith a priori local safety testing, patients receiving deep brain stimulation may safely undergo 1.5- and 3-T MRI. Deep brain stimulation hardware-related artifacts only affect a small proportion of the brain.© RSNA, 2019Online supplemental material is available for this article.See also the editorial by Martin in this issue.

Functional MRI Signature of Chronic Pain Relief From Deep Brain Stimulation in Parkinson Disease Patients.

BACKGROUND: Chronic pain occurs in 83% of Parkinson disease (PD) patients and deep brain stimulation (DBS) has shown to result in pain relief in a subset of patients, though the mechanism is unclear. OBJECTIVE: To compare functional magnetic resonance imaging (MRI) data in PD patients with chronic pain without DBS, those whose pain was relieved (PR) with DBS and those whose pain was not relieved (PNR) with DBS. METHODS: Functional MRI (fMRI) with blood oxygen level-dependent activation data was obtained in 15 patients in control, PR, and PNR patients. fMRI was obtained in the presence and absence of a mechanical stimuli with DBS ON and DBS OFF. Voxel-wise analysis using pain OFF data was used to determine which regions were altered during pain ON periods. RESULTS: At the time of MRI, pain was scored a 5.4 ± 1.2 out of 10 in the control, 4.25 ± 1.18 in PNR, and 0.8 ± 0.67 in PR cohorts. Group analysis of control and PNR groups showed primary somatosensory (SI) deactivation, whereas PR patients showed thalamic deactivation and SI activation. DBS resulted in more decreased activity in PR than PNR (P < .05) and more activity in anterior cingulate cortex (ACC) in PNR patients (P < .05). CONCLUSION: Patients in the control and PNR groups showed SI deactivation at baseline in contrast to the PR patients who showed SI activation. With DBS ON, the PR cohort had less activity in SI, whereas the PNR had more anterior cingulate cortex activity. We provide pilot data that patients whose pain responds to DBS may have a different fMRI signature than those who do not, and PR and PNR cohorts produced different brain responses when DBS is employed.

Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

PURPOSE: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T2 MR images. METHODS: We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. RESULTS: The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII-CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution. CONCLUSION: This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.