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A comprehensive characterization of the brain’s white matter is critical for improving our understanding of healthy and diseased aging. Here we used diffusion-weighted magnetic resonance imaging (dMRI) to estimate age and sex effects on white matter microstructure in a cross-sectional sample of 15,628 adults aged 45–80 years old (47.6% male, 52.4% female). Microstructure was assessed using the following four models: a conventional single-shell model, diffusion tensor imaging (DTI); a more advanced single-shell model, the tensor distribution function (TDF); an advanced multi-shell model, neurite orientation dispersion and density imaging (NODDI); and another advanced multi-shell model, mean apparent propagator MRI (MAPMRI). Age was modeled using a data-driven statistical approach, and normative centile curves were created to provide sex-stratified white matter reference charts. Participant age and sex substantially impacted many aspects of white matter microstructure across the brain, with the advanced dMRI models TDF and NODDI detecting such effects the most sensitively. These findings and the normative reference curves provide an important foundation for the study of healthy and diseased brain aging. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-021-00548-y.

Automatic neuroimaging segmentation and parcellation tools provide convenient and systematic methods for extracting numerous features from brain MRI scans, and are becoming standard practice for large-scale coordinated studies. One such tool, FreeSurfer, provides an easy-to-use pipeline to extract metrics describing cortical and subcortical morphometry. Over the past two decades, there have been over 25 stable releases of FreeSurfer, and different versions are used across published works. Despite this, the reliability and compatibility of metrics derived from the most recent major version releases have yet to be assessed empirically. Here, we use test-retest data from three public brain MRI datasets to assess within-version reliability and between-version compatibility across 42 regional outputs from three versions of FreeSurfer: the latest, v7.1, and two previous stable releases - v5.3, and v6.0. We find v7.1 was less compatible with older versions for measuring cortical thickness. In particular, the thickness of the cingulate gyrus had low compatibility (intraclass correlation coefficient (ICC) between 0.37 and 0.61) between versions. Temporal and frontal poles, and the medial orbitofrontal surface area metrics, also showed low to moderate compatibility with v7.1. While our work compares all three versions, our sub-comparisons between the older versions (v5.3 and v6.0) replicates earlier findings of low compatibility of pallidum and putamen volumes. Low between-version compatibility was not always indicative of low within-version reliability – all versions showed good to excellent reliability across most regional measures (ICC>0.8). Age associations, quality control metrics, and Dice coefficients in an independent sample of 106 individual scans, processed with all three versions of FreeSurfer, revealed differences in results of downstream statistical analysis. As neuroimaging studies adopt more recently released software, we provide researchers with a reference to highlight the regions and metrics that may yield findings inconsistent with published works using older FreeSurfer software. An interactive viewer for the results is provided at http://data.brainescience.org/Freesurfer_Reliability/

BackgroundChronic pain is a global health priority. Mapping pain occurring at different body sites, and variability in brain circuitry related to widespread chronic pain, can elucidate nuanced roles of the central nervous system underlying chronic pain conditions. Chronic pain triples suicide risk; however, whether brain circuitry can inform this risk relationship has not been investigated.Methods11,298 participants (mean age: 64 years (range: 58-70), 55% female) with brain MRI from the UK Biobank with pain for more than 3 months in the head, neck/shoulders, back, abdomen, or hips and knees, were age-and-sex-matched to 11,298 pain-free controls. Regression models assessed cortical and subcortical structure differences between individuals reporting chronic pain and those without; mediation models determined the relationship between pain, brain structure and history of attempted suicide.OutcomesChronic pain, regardless of site was associated with, lower surface area throughout the cortex, lower volume in the brainstem, ventral diencephalon, cerebellum, and pallidum, lower cortical thickness in the anterior insula, and greater cortical thickness in the superior parietal cortex. When differentiated by pain site, participants with chronic headaches distinctly showed an overall thicker cortex compared with controls. Chronic pain was associated with an elevated risk for suicide attempt and this relationship was mediated by lower cerebellum volume.InterpretationThere are shared cortical mechanisms underlying chronic pain across body sites. An extensive thicker cortex in chronic headache was consistent with previous research. Cerebellum volume mediates the relationship between chronic pain and suicide attempt, serving as a potential biomarker prognostic for suicidal behaviors in chronic pain patients.FundingNational Science Foundation, National Institutes of HealthResearch in ContextEvidence before this studyChronic pain is the leading cause of disability and disease burden globally, and its prevalence is increasing. As perception of pain occurs in the brain, alterations in brain structure have been investigated in various chronic pain conditions. However, published works, to date, report inconsistent findings, and typically do not compare a wide range of chronic pain types within the same study. Chronic pain is a risk factor for suicidal ideation, which can occur in up to 41% of individuals with chronic pain, but the role of specific brain systems in mediating the relationship between chronic pain and suicide has not been investigated.Added value of this studyThe present study reports alterations of brain structure in the largest and most well-powered sample reporting chronic pain to date (N = 11,298) compared to 11,298 pain-free controls, while taking into account age, sex, socioeconomic status, anxiety and depression. The effect of chronic pain on the brain is also evaluated as a function of pain across one or more of six different sites in the body (i.e. headaches, neck and shoulder, back, abdominal, hip and knee pain). Lower cortical surface area throughout the brain was related to chronic pain, and shown to be far more extensive than previously recognized. We, for the first time, show that participants with chronic headaches compared to controls have, on average, thicker gray matter throughout the cortex, a distinct and opposite pattern of effects than when individuals with other systemic pain conditions are compared to controls. A higher prevalence of suicide attempt history was noted in participants reporting chronic pain than controls. The relationship between chronic pain and suicide attempt, was mediated by the volume of the cerebellum, implicating spinocerebellar mechanisms.Implications of all the available evidenceBrain structure plays a key role in chronic pain, and mediates the role between pain and suicidal behaviors, independent of commonly presenting comorbidities. Our results highlight the concept of central sensitization and the role of the brain’s interacting networks in the presence of chronic pain. The thicker cortical gray matter in chronic headaches vs. controls - compared to other chronic pain conditions - indicates different mechanisms underlie these conditions and suggests that a clinically different approach to treatment is warranted. The cerebellum volume is a reliable mediator between chronic pain and suicide attempt, a finding that provides insight into potential underlying spinocerebellar mechanisms and to how treatments such as ketamine infusions may be beneficial in chronic pain and suicidal risk behavior management. Our work shows reliable neurobiological support for the multiple brain networks impacted and in regulating mood in the chronic pain phenotype.

Large data initiatives and high-powered brain imaging analyses require the pooling of MR images acquired across multiple scanners, often using different protocols. Prospective cross-site harmonization often involves the use of a phantom or traveling subjects. However, as more datasets are becoming publicly available, there is a growing need for retrospective harmonization, pooling data from sites not originally coordinated together. Several retrospective harmonization techniques have shown promise in removing cross-site image variation. However, most unsupervised methods cannot distinguish between image-acquisition based variability and cross-site population variability, so they require that datasets contain subjects or patient groups with similar clinical or demographic information. To overcome this limitation, we consider cross-site MRI image harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a reference image directly, without knowing their site/scanner labelsa priori. We trained our model using data from five large-scale multi-site datasets with varied demographics. Results demonstrated that our styleencoding model can harmonize MR images, and match intensity profiles, successfully, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. Moreover, we further demonstrated that if we included diverse enough images into the training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising novel tool for ongoing collaborative studies.

As stroke mortality rates decrease, there has been a surge of effort to study poststroke dementia (PSD) to improve long‐term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in poststroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas‐based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network‐based hippocampal segmentation method, does not rely solely on a single atlas‐based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well‐accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric correlation with manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control. All three automated segmentation methods had good correlation with manual segmentations and no one method was significantly more correlated than the others. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy., In this study, we compared three automated hippocampal segmentation methods in a large stroke population in terms of quality control and segmentation accuracy compared to manual segmentations. While all three methods yielded similar volumes, new convolutional neural network based segmentation method Hippodeep had the lowest method‐wise quality control fail rate, suggesting it may be the most robust to post‐stroke anatomical distortions.

Disrupted iron homeostasis is associated with several neurodegenerative diseases, including Alzheimer's disease (AD), and may be partially modulated by genetic risk factors. Here we evaluated whether subcortical iron deposition is associated with ApoE genotype, which substantially affects risk for late-onset AD. We evaluated differences in subcortical quantitative susceptibility mapping (QSM), a type of MRI sensitive to cerebral iron deposition, between either ApoE4 (E3E4+E4E4) or ApoE2 (E2E3+E2E2) carriers and E3 homozygotes (E3E3) in 27,535 participants from the UK Biobank (age: 45-82 years). We found that ApoE4 carriers had higher hippocampal (d=0.036; p=0.012) and amygdalar (d=0.035; p=0.013) magnetic susceptibility, particularly individuals aged 65 years or older, while those carrying ApoE2 (which protects against AD) had higher QSM only in the hippocampus (d=0.05; p=0.006), particularly those under age 65. Secondary diffusion MRI microstructural associations in these regions revealed greater diffusivity and less diffusion restriction in E4 carriers, however no differences were detected in E2 carriers. Disease risk conferred by ApoE4 may be linked with higher subcortical iron burden in conjunction with inflammation or neuronal loss in aging individuals, while ApoE2 associations may not necessarily reflect unhealthy iron deposits earlier in life.

A major challenge in multiple sclerosis (MS) research is the understanding of silent progression and Progressive MS. Using a novel method to accurately capture upper cervical cord area from legacy brain MRI scans we aimed to study the role of spinal cord and brain atrophy for silent progression and conversion to secondary progressive disease (SPMS).From a single-center observational study, all RRMS (n = 360) and SPMS (n = 47) patients and 80 matched controls were evaluated. RRMS patient subsets who converted to SPMS (n = 54) or silently progressed (n = 159), respectively, during the 12-year observation period were compared to clinically matched RRMS patients remaining RRMS (n = 54) or stable (n = 147), respectively. From brain MRI, we assessed the value of brain and spinal cord measures to predict silent progression and SPMS conversion.Patients who developed SPMS showed faster cord atrophy rates (-2.19%/yr) at least 4 years before conversion compared to their RRMS matches (-0.88%/yr, p 0.001). Spinal cord atrophy rates decelerated after conversion (-1.63%/yr, p = 0.010) towards those of SPMS patients from study entry (-1.04%). Each 1% faster spinal cord atrophy rate was associated with 69% (p 0.0001) and 53% (p 0.0001) shorter time to silent progression and SPMS conversion, respectively.Silent progression and conversion to secondary progressive disease are predominantly related to cervical cord atrophy. This atrophy is often present from the earliest disease stages and predicts the speed of silent progression and conversion to Progressive MS. Diagnosis of SPMS is rather a late recognition of this neurodegenerative process than a distinct disease phase. ANN NEUROL 2022;91:268-281.

The human brain is a complex organ underlying many cognitive and physiological processes, affected by a wide range of diseases. Genetic associations with macroscopic brain structure are emerging, providing insights into genetic sources of brain variability and risk for functional impairments and disease. However, specific associations with measures of local brain folding, associated with both brain development and decline, remain under-explored. Here we carried out detailed large-scale genome-wide associations of regional brain cortical sulcal measures derived from magnetic resonance imaging data of 40,169 individuals in the UK Biobank. Combining both genotyping and whole-exome sequencing data (∼12 million variants), we discovered 388 regional brain folding associations across 77 genetic loci at p−8, which replicated at pKCNK2 locus with a cortex-specific KCNK2 eQTL. Genetic correlations with neuropsychiatric conditions highlighted emerging patterns across distinct sulcal parameters and related phenotypes. We provide an interactive 3D visualisation of our summary associations, making complex association patterns easier to interpret, and emphasising the added resolution of regional brain analyses compared to global brain measures. Our results offer new insights into the genetic architecture underpinning brain folding and provide a resource to the wider scientific community for studies of pathways driving brain folding and their role in health and disease.

ObjectiveIdentifying brain differences associated with suicidal thoughts and behaviors (STBs) in young people is critical to understanding their development and generating effective approaches to early intervention and prevention. The ENIGMA Suicidal Thoughts and Behaviours (ENIGMA-STB) consortium analyzed neuroimaging data harmonized across sites to examine brain morphology associated with STBs in youth.MethodsFirst, we examined associations among regional brain structure and STBs, which were assessed in six samples of youth with mood disorders, using the Columbia Suicide Severity Rating Scale (C-SSRS;N=577). Second, we combined this sample with a larger sample (total 21 sites) in which STBs were assessed using various instruments. MRI metrics were compared among healthy controls without STBs (HC;N=688), clinical controls without STBs (CC;N=648), and young people with psychiatric diagnoses and current suicidal ideation (N=406). In separate analyses, MRI metrics were compared among HCs (N=335), CCs (N=768), and suicide attempters (N=254).ResultsIn the homogeneous C-SSRS sample, surface area of the frontal pole was lower in young people with mood disorders and history of actual suicide attempts (N=163) than those without (N=394; FDR-pd=.334). When expanding to more clinically heterogeneous samples, we also found lower surface area of the frontal pole in those with a history of suicide attempts (Cohen’sd=.22).ConclusionsLower frontal pole surface area may represent a vulnerability for a suicide attempt; however, more research is needed to understand the nature of its relationship to suicide risk.

Author(s): University of California, San Francisco MS-EPIC Team; Cree, Bruce AC; Hollenbach, Jill A; Bove, Riley; Kirkish, Gina; Sacco, Simone; Caverzasi, Eduardo; Bischof, Antje; Gundel, Tristan; Zhu, Alyssa H; Papinutto, Nico; Stern, William A; Bevan, Carolyn; Romeo, Andrew; Goodin, Douglas S; Gelfand, Jeffrey M; Graves, Jennifer; Green, Ari J; Wilson, Michael R; Zamvil, Scott S; Zhao, Chao; Gomez, Refujia; Ragan, Nicholas R; Rush, Gillian Q; Barba, Patrick; Santaniello, Adam; Baranzini, Sergio E; Oksenberg, Jorge R; Henry, Roland G; Hauser, Stephen L | Abstract: ObjectiveRates of worsening and evolution to secondary progressive multiple sclerosis (MS) may be substantially lower in actively treated patients compared to natural history studies from the pretreatment era. Nonetheless, in our recently reported prospective cohort, more than half of patients with relapsing MS accumulated significant new disability by the 10th year of follow-up. Notably, "no evidence of disease activity" at 2 years did not predict long-term stability. Here, we determined to what extent clinical relapses and radiographic evidence of disease activity contribute to long-term disability accumulation.MethodsDisability progression was defined as an increase in Expanded Disability Status Scale (EDSS) of 1.5, 1.0, or 0.5 (or greater) from baseline EDSS = 0, 1.0-5.0, and 5.5 or higher, respectively, assessed from baseline to year 5 (±1 year) and sustained to year 10 (±1 year). Longitudinal analysis of relative brain volume loss used a linear mixed model with sex, age, disease duration, and HLA-DRB1*15:01 as covariates.ResultsRelapses were associated with a transient increase in disability over 1-year intervals (p = 0.012) but not with confirmed disability progression (p = 0.551). Relative brain volume declined at a greater rate among individuals with disability progression compared to those who remained stable (p l 0.05).InterpretationLong-term worsening is common in relapsing MS patients, is largely independent of relapse activity, and is associated with accelerated brain atrophy. We propose the term silent progression to describe the insidious disability that accrues in many patients who satisfy traditional criteria for relapsing-remitting MS. Ann Neurol 2019;85:653-666.

Quality control (QC) is a vital step for all scientific data analyses and is critically important in the biomedical sciences. Image segmentation is a common task in medical image analysis, and automated tools to segment many regions from human brain MRIs are now well established. However, these methods do not always give anatomically correct labels. Traditional methods for QC tend to reject statistical outliers, which may not necessarily be inaccurate. Here, we make use of a large database of over 12,000 brain images that contain 68 parcellations of the human cortex, each of which was assessed for anatomical accuracy by a human rater. We trained three machine learning models to determine if a region was anatomically accurate (as ‘pass’, or ‘fail’) and tested the performance on an independent dataset. We found good performance for the majority of labeled regions. This work will facilitate more anatomically accurate large-scale multi-site research.

Increased age has arguably one of the largest known effects on brain structure in healthy adult populations, often affecting different brain regions at different rates, and showing numerous interactions. Modeling age appropriately can be challenging. Tensor-based morphometry produces voxelwise maps of regional inter-subject differences in the brain, often in relation to a single study-specific (3D) template. In studies with a wide age range, this single template may not be sufficient. Here, we create age-specific templates within smaller age bins (4D) to compare to the standard 3D model and evaluate the potential biases. We analyzed the morphological changes of nearly 26,000 subjects from the UK Biobank. We found that age-related biases that existed with a 3D template were minimized with the 4D template. For effective modeling of age across the lifespan, a single template appears suboptimal.

Highlights • RVI for MDD and AD was derived based on large meta-analytical findings. • RVI-MDD and AD were significantly elevated in UKBB subjects with respective illnesses. • There was no elevation of RVI-MDD in subjects with AD or RVI-AD in subjects with MDD. • RVI captures neuroanatomic deviation patterns. • RVI is a useful biomarker for assessing similarity to neuropsychiatric illnesses., Neurological and psychiatric illnesses are associated with regional brain deficit patterns that bear unique signatures and capture illness-specific characteristics. The Regional Vulnerability Index (RVI) was developed to quantify brain similarity by comparing individual white matter microstructure, cortical gray matter thickness and subcortical gray matter structural volume measures with neuroanatomical deficit patterns derived from large-scale meta-analytic studies. We tested the specificity of the RVI approach for major depressive disorder (MDD) and Alzheimer’s disease (AD) in a large epidemiological sample of UK Biobank (UKBB) participants (N = 19,393; 9138 M/10,255F; age = 64.8 ± 7.4 years). Compared to controls free of neuropsychiatric disorders, participants with MDD (N = 2,248; 805 M/1443F; age = 63.4 ± 7.4) had significantly higher RVI-MDD values (t = 5.6, p = 1·10−8), but showed no detectable difference in RVI-AD (t = 2.0, p = 0.10). Subjects with dementia (N = 7; 4 M/3F; age = 68.6 ± 8.6 years) showed significant elevation in RVI-AD (t = 4.2, p = 3·10−5) but not RVI-MDD (t = 2.1, p = 0.10) compared to controls. Even within affective illnesses, participants with bipolar disorder (N = 54) and anxiety disorder (N = 773) showed no significant elevation in whole-brain RVI-MDD. Participants with Parkinson’s disease (N = 37) showed elevation in RVI-AD (t = 2.4, p = 0.01) while subjects with stroke (N = 247) showed no such elevation (t = 1.1, p = 0.3). In summary, we demonstrated elevation in RVI-MDD and RVI-AD measures in the respective illnesses with strong replicability that is relatively specific to the respective diagnoses. These neuroanatomic deviation patterns offer a useful biomarker for population-wide assessments of similarity to neuropsychiatric illnesses.

The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intracellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extracellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.

Modeling of structural brain variation over the lifespan is important to better understand factors contributing to healthy aging and risk for neurological conditions such as Alzheimer’s disease. Even so, we lack normative data on brain morphometry across the adult lifespan in large, well-powered samples. Here, in a large population-based sample of 26,440 adults from the UK Biobank (age: 44-81 yrs.), we created normative percentile charts for MRI-derived subcortical volumes. Next, we investigated associations between these morphometric measures and the strongest known genetic risk factor for late-onset Alzheimer’s disease (APOE genotype) and mapped the spatial distribution of age-by-sex interactions using computational surface mesh modeling and shape analysis. Vertex-wise shape mapping supplements traditional gross volumetric approaches to reveal finer-grained variations across functionally important brain subcompartments. Normative curves revealed volumetric loss with age, as expected, for all subcortical brain structures except for the lateral ventricles, which expanded with age. Surprisingly, no volumetric associations with APOE genotype were detected, despite the very large sample size. Age-related trajectories for volumes differed in women versus men, and surface-based statistical maps revealed the spatial distribution of the age-by-sex interaction. Subcortical volumes declined faster in men than women over the full age range, but after age 60, fewer structures showed sex-dependent trajectories, indicating similar volumetric changes in older men and women. Large-scale statistical modeling of age effects on brain structures may drive new insights into individual differences in brain aging and help to identify factors that promote healthy brain aging and risk for disease.