The Vital Role of Clinical Examination in Unmasking Bell's Palsy: Beyond Pattern Recognition.

While multiple sclerosis (MS) commonly manifests with optic nerve involvement, it can also masquerade as diverse cranial nerve (CN) palsies. We present the case of a young male initially diagnosed with Bell's palsy based on unilateral facial nerve paralysis. Despite the presence of typical clinical features, the patient's evaluation took an unexpected turn. Subsequent brain MRI revealed demyelinating lesions, ultimately confirming the diagnosis of MS. This case underscores the importance of maintaining vigilance in diagnosing atypical presentations of MS, illustrating how meticulous evaluation and neuroimaging play pivotal roles in uncovering underlying pathologies when conventional diagnoses such as Bell's palsy raise uncertainties.

Sex differences in anhedonia in bipolar depression: a resting-state fMRI study.

Previous studies about anhedonia symptoms in bipolar depression (BD) ignored the unique role of gender on brain function. This study aims to explore the regional brain neuroimaging features of BD with anhedonia and the sex differences in these patients. The resting-fMRI by applying fractional amplitude of low-frequency fluctuation (fALFF) method was estimated in 263 patients with BD (174 high anhedonia [HA], 89 low anhedonia [LA]) and 213 healthy controls. The effects of two different factors in patients with BD were analyzed using a 3 (group: HA, LA, HC) × 2 (sex: male, female) ANOVA. The fALFF values were higher in the HA group than in the LA group in the right medial cingulate gyrus and supplementary motor area. For the sex-by-group interaction, the fALFF values of the right hippocampus, left medial occipital gyrus, right insula, and bilateral medial cingulate gyrus were significantly higher in HA males than in LA males but not females. These results suggested that the pattern of high activation could be a marker of anhedonia symptoms in BD males, and the sex differences should be considered in future studies of BD with anhedonia symptoms.

Brain and cognitive changes in patients with long COVID compared with infection-recovered control subjects.

Between 2.5 and 28% of people infected with SARS-CoV-2 suffer Long COVID or persistence of symptoms for months after acute illness. Many symptoms are neurological, but the brain changes underlying the neuropsychological impairments remain unclear. This study aimed to provide a detailed description of the cognitive profile, the pattern of brain alterations in Long COVID and the potential association between them. To address these objectives, 83 patients with persistent neurological symptoms after COVID-19 were recruited, and 22 now healthy controls chosen because they had suffered COVID-19 but did not experience persistent neurological symptoms. Patients and controls were matched for age, sex and educational level. All participants were assessed by clinical interview, comprehensive standardized neuropsychological tests and structural MRI. The mean global cognitive function of patients with Long COVID assessed by ACE III screening test (Overall Cognitive level - OCLz= -0.39± 0.12) was significantly below the infection recovered-controls (OCLz= +0.32± 0.16, p< 0.01). We observed that 48% of patients with Long COVID had episodic memory deficit, with 27% also impaired overall cognitive function, especially attention, working memory, processing speed and verbal fluency. The MRI examination included grey matter morphometry and whole brain structural connectivity analysis. Compared to infection recovered controls, patients had thinner cortex in a specific cluster centred on the left posterior superior temporal gyrus. In addition, lower fractional anisotropy (FA) and higher radial diffusivity (RD) were observed in widespread areas of the patients' cerebral white matter relative to these controls. Correlations between cognitive status and brain abnormalities revealed a relationship between altered connectivity of white matter regions and impairments of episodic memory, overall cognitive function, attention and verbal fluency. This study shows that patients with neurological Long COVID suffer brain changes, especially in several white matter areas, and these are associated with impairments of specific cognitive functions.

Age-atypical brain functional networks in autism spectrum disorder: a normative modeling approach.

BACKGROUND: Despite extensive research into the neural basis of autism spectrum disorder (ASD), the presence of substantial biological and clinical heterogeneity among diagnosed individuals remains a major barrier. Commonly used case‒control designs assume homogeneity among subjects, which limits their ability to identify biological heterogeneity, while normative modeling pinpoints deviations from typical functional network development at individual level. METHODS: Using a world-wide multi-site database known as Autism Brain Imaging Data Exchange, we analyzed individuals with ASD and typically developed (TD) controls (total n = 1218) aged 5-40 years, generating individualized whole-brain network functional connectivity (FC) maps of age-related atypicality in ASD. We then used local polynomial regression to estimate a networkwise normative model of development and explored correlations between ASD symptoms and brain networks. RESULTS: We identified a subset exhibiting highly atypical individual-level FC, exceeding 2 standard deviation from the normative value. We also identified clinically relevant networks (mainly default mode network) at cohort level, since the outlier rates decreased with age in TD participants, but increased in those with autism. Moreover, deviations were linked to severity of repetitive behaviors and social communication symptoms. CONCLUSIONS: Individuals with ASD exhibit distinct, highly individualized trajectories of brain functional network development. In addition, distinct developmental trajectories were observed among ASD and TD individuals, suggesting that it may be challenging to identify true differences in network characteristics by comparing young children with ASD to their TD peers. This study enhances understanding of the biological heterogeneity of the disorder and can inform precision medicine.

Genetic control of MRI contrast using the manganese transporter Zip14.

PURPOSE: Gene-expression reporter systems, such as green fluorescent protein, have been instrumental to understanding biological processes in living organisms at organ system, tissue, cell, and molecular scales. More than 30 years of work on developing MRI-visible gene-expression reporter systems has resulted in a variety of clever application-specific methods. However, these techniques have not yet been widely adopted, so a general-purpose expression reporter is still required. Here, we demonstrate that the manganese ion transporter Zip14 is an in vivo MRI-visible, flexible, and robust gene-expression reporter to meet this need. METHODS: Plasmid constructs consisting of a cell type-specific promoter, gene coding for human Zip14, and a histology-visible tag were packaged into adeno-associated viruses. These viruses were intracranially injected into the mouse brain. Serial in vivo MRI was performed using a vendor-supplied 3D-MPRAGE sequence. No additional contrast agents were administered. Animals were sacrificed after the last imaging timepoint for immunohistological validation. RESULTS: Neuron-specific overexpression of Zip14 produced substantial and long-lasting changes in MRI contrast. Using appropriate viruses enabled both anterograde and retrograde neural tracing. Expression of Zip14 in astrocytes also enabled MRI of glia populations in the living mammalian brain. CONCLUSIONS: The flexibility of this system as an MRI-visible gene-expression reporter will enable many applications of serial, high-resolution imaging of gene expression for basic science and therapy development.

suMRak: a multi-tool solution for preclinical brain MRI data analysis.

Introduction: Magnetic resonance imaging (MRI) is invaluable for understanding brain disorders, but data complexity poses a challenge in experimental research. In this study, we introduce suMRak, a MATLAB application designed for efficient preclinical brain MRI analysis. SuMRak integrates brain segmentation, volumetry, image registration, and parameter map generation into a unified interface, thereby reducing the number of separate tools that researchers may require for straightforward data handling. Methods and implementation: All functionalities of suMRak are implemented using the MATLAB App Designer and the MATLAB-integrated Python engine. A total of six helper applications were developed alongside the main suMRak interface to allow for a cohesive and streamlined workflow. The brain segmentation strategy was validated by comparing suMRak against manual segmentation and ITK-SNAP, a popular open-source application for biomedical image segmentation. Results: When compared with the manual segmentation of coronal mouse brain slices, suMRak achieved a high Sørensen-Dice similarity coefficient (0.98 ± 0.01), approaching manual accuracy. Additionally, suMRak exhibited significant improvement (p = 0.03) when compared to ITK-SNAP, particularly for caudally located brain slices. Furthermore, suMRak was capable of effectively analyzing preclinical MRI data obtained in our own studies. Most notably, the results of brain perfusion map registration to T2-weighted images were shown, improving the topographic connection to anatomical areas and enabling further data analysis to better account for the inherent spatial distortions of echoplanar imaging. Discussion: SuMRak offers efficient MRI data processing of preclinical brain images, enabling researchers' consistency and precision. Notably, the accelerated brain segmentation, achieved through K-means clustering and morphological operations, significantly reduces processing time and allows for easier handling of larger datasets.

Validation of the brain health index in the European Prevention of Alzheimer's Dementia cohort.

Background: Brain Health Index (BHI) assimilates various MRI sequences, giving a quantitative measure of brain health. To date, BHI validation has been cross-sectional and limited to selected populations. Further large-scale validation and assessment of temporal change is required to understand its clinical utility. Aim: Assess 1) relationships between variables associated with cognitive decline and BHI 2) associations between BHI and measures of cognition and 3) longitudinal changes in BHI and relationship with cognitive function. Methods: BHI computation involved Gaussian mixture-model cluster analysis of T1, T2, T2*, and T2 FLAIR MRI data from participants within the European Prevention of Alzheimer's Dementia (EPAD) cohort. Group differences (gender- and health-based) were evaluated using independent samples Welch's t-tests. Relationships between BHI, age and cognitive tests used linear regression. Longitudinal analysis (12/24 months) utilised mixed linear regression models to examine BHI changes, and paired BHI/cognition associations. Results: Data from N = 1496 predominantly Caucasian participants (50-88 years old, 43.32% male) were used. BHI scores were lower in those with diabetes (p < 0.001, d = 0.419), hypertension (p < 0.001, d = 0.375), hypercholesterolemia (p < 0.001, d = 0.193) and stroke (p < 0.05, d = 0.512). APOE was not significantly related to BHI scores. After correction for age, cross-sectional BHI scores were significantly associated with all measures of cognitive function in males, but only the Four Mountains Test (4MT) in females. Longitudinal change in BHI and cognition were not consistently related. Conclusions: BHI is a valid marker of cognitive decline and relatively stable over 1-2 year follow-up periods. Further work should assess temporal changes over a longer duration and determine relationships between BHI and cognition in more diverse populations.

The association between neuroimaging data and presence of lateropulsion after stroke.

BACKGROUND: Post-stroke lateropulsion is prevalent and has been associated with varied lesion locations, but existing imaging studies are limited by small participant cohorts. Evidence to guide lateropulsion rehabilitation is also limited. Improved understanding of lesion localization associated with lateropulsion post-stroke may inform more targeted intervention approaches. OBJECTIVES: This study investigated the associations between stroke neuroimaging data and presence of lateropulsion at inpatient rehabilitation admission. METHODS: This prospective, observational study included participants aged ≥65 years, admitted for inpatient stroke rehabilitation. Using routinely collected clinical neuroimaging data, stroke type, location, and volume were reported, and their association with lateropulsion presence (Four-Point Pusher Score - 4PPS) at admission was explored. RESULTS: Of 144 included participants, 82 (56.9%) had lateropulsion (4PPS ≥1). Lateropulsion presence was univariately associated with hemorrhagic stroke (p = 0.002), frontal cortical involvement (OR = 2.17, 95%CI 1.02-6.46), and white matter involvement (OR = 2.45, 95%CI 1.24-4.85), particularly frontal white matter (p = 0.021). Lesions involving the posterior limb of the internal capsule (OR = 2.88, 95% CI 1.14-7.27) and those involving the entire thalamus (OR = 1.0, p = 0.03) were associated with lateropulsion presence. When stratified by stroke type, no specific location was significantly associated with lateropulsion presence in hemorrhagic strokes. Among participants with ischemic stroke, involvement of the pre-central gyrus (OR = 2.45, 95%CI 1.05-5.76), post-central gyrus (OR = 2.76, 95%CI 1.15-6.60), inferior parietal cortex (OR = 3.95, 95%CI 1.43-10.90), and supramarginal gyrus (OR = 3.73, 95%CI 1.25-11.13) were associated with lateropulsion presence. The stroke laterality and size were not significantly associated with lateropulsion presence. CONCLUSIONS: The findings indicate a role of network disconnection in the post-stroke lateropulsion presence. Future, larger-cohort lesion-network mapping studies are recommended.

Never-Ending Learning for Explainable Brain Computing.

Exploring the nature of human intelligence and behavior is a longstanding pursuit in cognitive neuroscience, driven by the accumulation of knowledge, information, and data across various studies. However, achieving a unified and transparent interpretation of findings presents formidable challenges. In response, an explainable brain computing framework is proposed that employs the never-ending learning paradigm, integrating evidence combination and fusion computing within a Knowledge-Information-Data (KID) architecture. The framework supports continuous brain cognition investigation, utilizing joint knowledge-driven forward inference and data-driven reverse inference, bolstered by the pre-trained language modeling techniques and the human-in-the-loop mechanisms. In particular, it incorporates internal evidence learning through multi-task functional neuroimaging analyses and external evidence learning via topic modeling of published neuroimaging studies, all of which involve human interactions at different stages. Based on two case studies, the intricate uncertainty surrounding brain localization in human reasoning is revealed. The present study also highlights the potential of systematization to advance explainable brain computing, offering a finer-grained understanding of brain activity patterns related to human intelligence.

Population Neuroscience: Principles and Advances.

In population neuroscience, three disciplines come together to advance our knowledge of factors that shape the human brain: neuroscience, genetics, and epidemiology (Paus, Human Brain Mapping 31:891-903, 2010). Here, I will come back to some of the background material reviewed in more detail in our previous book (Paus, Population Neuroscience, 2013), followed by a brief overview of current advances and challenges faced by this integrative approach.

Clinical and neuroimaging characterization of the first frontotemporal dementia family carrying the MAPT p.K298E mutation.

We present an in-depth clinical and neuroimaging analysis of a family carrying the MAPT K298E mutation associated with frontotemporal dementia (FTD). Initial identification of this mutation in a single clinical case led to a comprehensive investigation involving four affected siblings allowing to elucidate the mutation's phenotypic expression.A 60-year-old male presented with significant behavioral changes and progressed rapidly, exhibiting speech difficulties and cognitive decline. Neuroimaging via FDG-PET revealed asymmetrical frontotemporal hypometabolism. Three siblings subsequently showed varied but consistent clinical manifestations, including abnormal behavior, speech impairments, memory deficits, and motor symptoms correlating with asymmetric frontotemporal atrophy observed in MRI scans.Based on the genotype-phenotype correlation, we propose that the p.K298E mutation results in early-onset behavioral variant FTD, accompanied by a various constellation of speech and motor impairment.This detailed characterization expands the understanding of the p.K298E mutation's clinical and neuroimaging features, underlining its role in the pathogenesis of FTD. Further research is crucial to comprehensively delineate the clinical and epidemiological implications of the MAPT p.K298E mutation.

Clinical and neuroanatomical predictors of post-stroke fatigue.

INTRODUCTION: Post-stroke fatigue (PSF) has been described as early exhaustion with tiredness that develops during physical or mental activity and generally does not improve with rest. There are inconsistent findings on the relationship between the characteristics of the ischemic brain lesion and PSF. However, some studies suggest that specific neuroanatomical and neuroplastic changes could explain post-stroke fatigue. The aim was to evaluate the severity of PSF in relation to the location and the size of the ischemic lesion in acute stroke patients to establish possible predictors of PSF. PATIENTS AND METHODS: We performed a prospective observational study to establish potential early predictors of long-term PSF, which was assessed using the Fatigue Assessment Scale six months after ischemic stroke. After segmenting brain infarcts on Diffusion-Weighted Imaging (DWI) images, we studied the association with PSF using Voxel-Based Lesion-Symptom Mapping (VLSM). RESULTS: Out of 104 patients, 61 (59 %) reported PSF. Female sex and history of diabetes mellitus were associated with a greater risk of developing PSF. The association of PSF with female sex was confirmed in a replication cohort of 50 patients. The ischemic lesion volume was not associated with PSF, and VBLSM analysis did not identify any specific brain area significantly associated with PSF. CONCLUSIONS: PSF is frequent in stroke patients, especially women, even after six months. The absence of neuroanatomical correlates of PSF suggests that it is a multifactorial process with biological, psychological, and social risk factors that require further study.

Bridging the gap: improving correspondence between low-field and high-field magnetic resonance images in young people.

Background: Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in low-resource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods: T1- and T2-weighted structural MR images were obtained from a low-field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9-26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results: Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60-0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85-0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion: Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems.

A Human Brain Map of Mitochondrial Respiratory Capacity and Diversity.

Mitochondrial oxidative phosphorylation (OxPhos) powers brain activity1,2, and mitochondrial defects are linked to neurodegenerative and neuropsychiatric disorders3,4, underscoring the need to define the brain's molecular energetic landscape5-10. To bridge the cognitive neuroscience and cell biology scale gap, we developed a physical voxelization approach to partition a frozen human coronal hemisphere section into 703 voxels comparable to neuroimaging resolution (3×3×3 mm). In each cortical and subcortical brain voxel, we profiled mitochondrial phenotypes including OxPhos enzyme activities, mitochondrial DNA and volume density, and mitochondria-specific respiratory capacity. We show that the human brain contains a diversity of mitochondrial phenotypes driven by both topology and cell types. Compared to white matter, grey matter contains >50% more mitochondria. We show that the more abundant grey matter mitochondria also are biochemically optimized for energy transformation, particularly among recently evolved cortical brain regions. Scaling these data to the whole brain, we created a backward linear regression model integrating several neuroimaging modalities11, thereby generating a brain-wide map of mitochondrial distribution and specialization that predicts mitochondrial characteristics in an independent brain region of the same donor brain. This new approach and the resulting MitoBrainMap of mitochondrial phenotypes provide a foundation for exploring the molecular energetic landscape that enables normal brain functions, relating it to neuroimaging data, and defining the subcellular basis for regionalized brain processes relevant to neuropsychiatric and neurodegenerative disorders.

On the variability of dynamic functional connectivity assessment methods.

BACKGROUND: Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods. METHODS: We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity. RESULTS: Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages. CONCLUSIONS: Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience.

Introduction: Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited. Methods: T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images. Results: Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR. Discussion: The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.

Cortical microinfarcts in adults with Down syndrome assessed with 3T-MRI.

BACKGROUND: Cortical microinfarcts (CMI) were attributed to cerebrovascular disease and cerebral amyloid angiopathy (CAA). CAA is frequent in Down syndrome (DS) while hypertension is rare, yet no studies have assessed CMI in DS. METHODS: We included 195 adults with DS, 63 with symptomatic sporadic Alzheimer's disease (AD), and 106 controls with 3T magnetic resonance imaging. We assessed CMI prevalence in each group and CMI association with age, AD clinical continuum, vascular risk factors, vascular neuroimaging findings, amyloid/tau/neurodegeneration biomarkers, and cognition in DS. RESULTS: CMI prevalence was 11.8% in DS, 4.7% in controls, and 17.5% in sporadic AD. In DS, CMI increased in prevalence with age and the AD clinical continuum, was clustered in the parietal lobes, and was associated with lacunes and cortico-subcortical infarcts, but not hemorrhagic lesions. DISCUSSION: In DS, CMI are posteriorly distributed and related to ischemic but not hemorrhagic findings suggesting they might be associated with a specific ischemic CAA phenotype. HIGHLIGHTS: This is the first study to assess cortical microinfarcts (assessed with 3T magnetic resonance imaging) in adults with Down syndrome (DS). We studied the prevalence of cortical microinfarcts in DS and its relationship with age, the Alzheimer's disease (AD) clinical continuum, vascular risk factors, vascular neuroimaging findings, amyloid/tau/neurodegeneration biomarkers, and cognition. The prevalence of cortical microinfarcts was 11.8% in DS and increased with age and along the AD clinical continuum. Cortical microinfarcts were clustered in the parietal lobes, and were associated with lacunes and cortico-subcortical infarcts, but not hemorrhagic lesions. In DS, cortical microinfarcts are posteriorly distributed and related to ischemic but not hemorrhagic findings suggesting they might be associated with a specific ischemic phenotype of cerebral amyloid angiopathy.

Environmental and sociocultural factors are associated with pain-related brain structure among diverse individuals with chronic musculoskeletal pain: intersectional considerations.

Chronic musculoskeletal pain including knee osteoarthritis (OA) is a leading cause of disability worldwide. Previous research indicates ethnic-race groups differ in the pain and functional limitations experienced with knee OA. However, when socioenvironmental factors are included in analyses, group differences in pain and function wane. Pain-related brain structures are another area where ethnic-race group differences have been observed. Environmental and sociocultural factors e.g., income, education, experiences of discrimination, and social support influence brain structures. We investigate if environmental and sociocultural factors reduce previously observed ethnic-race group differences in pain-related brain structures. Data were analyzed from 147 self-identified non-Hispanic black (NHB) and non-Hispanic white (NHW), middle and older aged adults with knee pain in the past month. Information collected included health and pain history, environmental and sociocultural resources, and brain imaging. The NHB adults were younger and reported lower income and education compared to their NHW peers. In hierarchical multiple regression models, sociocultural and environmental factors explained 6-37% of the variance in pain-related brain regions. Self-identified ethnicity-race provided an additional 4-13% of explanatory value in the amygdala, hippocampus, insula, bilateral primary somatosensory cortex, and thalamus. In the rostral/caudal anterior cingulate and dorsolateral prefrontal cortex, self-identified ethnicity-race was not a predictor after accounting for environmental, sociocultural, and demographic factors. Findings help to disentangle and identify some of the factors contributing to ethnic-race group disparities in pain-related brain structures. Numerous arrays of environmental and sociocultural factors remain to be investigated. Further, the differing sociodemographic representation of our NHB and NHW participants highlights the role for intersectional considerations in future research.

Computational limits to the legibility of the imaged human brain.

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.