Multimodal population brain imaging in the UK Biobank prospective epidemiological study

Simultaneous multislice diffusion imaging using navigator-free multishot spiral acquisitions

PURPOSE

This work aims to raise a novel design for navigator-free multiband (MB) multishot uniform-density spiral (UDS) acquisition and reconstruction, and to demonstrate its utility for high-efficiency, high-resolution diffusion imaging.

THEORY AND METHODS

Our design focuses on the acquisition and reconstruction of navigator-free MB multishot UDS diffusion imaging. For acquisition, radiofrequency-pulse encoding was used to achieve controlled aliasing in parallel imaging in MB imaging. For reconstruction, a new algorithm named slice-projection onto convex sets-enhanced inherent correction of phase errors (slice-POCS-ICE) was proposed to simultaneously estimate diffusion-weighted images and intershot phase variations for each slice. The efficacy of the proposed methods was evaluated in both numerical simulation and in vivo experiments.

RESULTS

In both numerical simulation and in vivo experiments, slice-POCS-ICE estimated phase variations more precisely and provided results with better image quality than other methods. The intershot phase variations and MB slice aliasing artifacts were simultaneously resolved using the proposed slice-POCS-ICE algorithm.

CONCLUSION

The proposed navigator-free MB multishot UDS acquisition and reconstruction method is an effective solution for high-efficiency, high-resolution diffusion imaging.

Mediation Analyses Link Cardiometabolic Factors and Liver Fat With White Matter Hyperintensities and Cognitive Performance: A UK Biobank Study

Background

Liver fat is associated with cardiometabolic disease, cerebrovascular disease, and dementia. Cerebrovascular disease, most often cerebral small vessel disease, identified by magnetic resonance imaging as white matter hyperintensities (WMHs) often contributes to dementia. However, liver fat's role in the relationship between cardiometabolic risk, WMHs, and cognitive performance is unclear.

Methods

In the UK Biobank cohort (N = 32,461, 52.6% female; mean age 64.2 ± 7.7 years; n = 23,354 in the cognitive performance subsample), we used linear regression to investigate associations between cardiometabolic factors measured at baseline and liver fat, WMHs, and cognitive performance measured at follow-up, which was 9.3 ± 2.0 years later on average. We used structural equation modeling to investigate whether liver fat mediated associations between cardiometabolic factors and WMHs and whether WMHs mediated associations between liver fat and cognitive performance.

Results

Nearly all cardiometabolic factors were significantly associated with liver fat (|r| range = 0.03-0.41, p = 3.4 × 10-8 to 0) and WMHs (|r| = 0.04-0.15, p = 5.8 × 10-13 to 7.0 × 10-159) in regression models. Liver fat was associated with WMHs (r = 0.11, p = 4.3 × 10-82) and cognitive performance (r = -0.03, p = 1.6 × 10-7). Liver fat mediated the associations between cardiometabolic factors and WMHs (|βmediation| = 0.003-0.027, p mediation = 1.9 × 10-8 to 0), and WMHs mediated the associations between liver fat and cognitive performance (βmediation = -0.01, p mediation = 0).

Conclusions

Our findings indicate that liver fat mediates associations between cardiometabolic factors and WMHs and that WMHs mediate the association between liver fat and cognitive performance. This suggests that liver fat may be important for understanding the effects of cardiometabolic factors on cerebrovascular disease and cognitive function. Experimental studies are warranted to determine relevant targets for preventing vascular-driven cognitive impairment.

Determining the atlas correspondence of Desikan-Killiany-Tourville and Glasser MMP1 atlases across magnetic field strengths

BACKGROUND

Over sixty-six brain atlases exist to parcellate the brain based on cytoarchitecture, function, and connectivity. Because atlas choice depends on individual study goals and hypotheses, variability in findings contributes to challenges in replication, validation, and reconciling the results across studies. Our goal was to measure the intersection of three commonly used atlases and create a tool to find regional correspondence between the atlases.

NEW METHOD

This study used three independent samples of anatomical MRI data acquired with different B0 magnetic field strengths: 1.5 Tesla (T), 3 T, and 7 T. The Desikan-Killiany- Tourville (DKT) and Glasser atlases were used to parcellate the brain. Coefficient-of- variation of regional volumes was measured to evaluate regional variability across subjects in each atlas. DKT and Glasser parcellation correspondence was calculated to answer the shared question of what Glasser regions intersect with a DKT region and vice versa and to investigate consistency of the parcellations in relation to each other across a variety of individuals and image resolutions.

RESULTS

We found that regional correspondence was consistent across field strengths for the DKT and Glasser parcellations despite showing population variability in volume, age, and sex, and was validated in the Schaefer400 atlas. Parcellation intersection data along with sample code to calculate specific regional correspondence is available.

COMPARISON WITH EXISTING METHODS

Prior studies have attempted to reconcile multiple atlases, but did not compare voxel- by-voxel on real data.

CONCLUSION

This analysis created a tool for researchers and can aid in comparisons with differing atlas choice and variable field strengths.

Metabolic Dysfunction-Associated Steatotic Liver Disease Is Associated With Accelerated Brain Ageing: A Population-Based Study

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is linked to cognitive decline and dementia risk. We aimed to investigate the association between MASLD and brain ageing and explore the role of low-grade inflammation.

METHODS

Within the UK Biobank, 30 386 chronic neurological disorders-free participants who underwent brain magnetic resonance imaging (MRI) scans were included. Individuals were categorised into no MASLD/related SLD and MASLD/related SLD (including subtypes of MASLD, MASLD with increased alcohol intake [MetALD] and MASLD with other combined aetiology). Brain age was estimated using machine learning by 1079 brain MRI phenotypes. Brain age gap (BAG) was calculated as the difference between brain age and chronological age. Low-grade inflammation (INFLA) was calculated based on white blood cell count, platelet, neutrophil granulocyte to lymphocyte ratio and C-reactive protein. Data were analysed using linear regression and structural equation models.

RESULTS

At baseline, 7360 (24.2%) participants had MASLD/related SLD. Compared to participants with no MASLD/related SLD, those with MASLD/related SLD had significantly larger BAG (β = 0.86, 95% CI = 0.70, 1.02), as well as those with MASLD (β = 0.59, 95% CI = 0.41, 0.77) or MetALD (β = 1.57, 95% CI = 1.31, 1.83). The association between MASLD/related SLD and larger BAG was significant across middle-aged (< 60) and older (≥ 60) adults, males and females, and APOE ɛ4 carriers and non-carriers. INFLA mediated 13.53% of the association between MASLD/related SLD and larger BAG (p < 0.001).

CONCLUSION

MASLD/related SLD, as well as MASLD and MetALD, is associated with accelerated brain ageing, even among middle-aged adults and APOE ɛ4 non-carriers. Low-grade systemic inflammation may partially mediate this association.

Associations Between Walking Pace, APOE-ε4 Genotype, and Brain Health in Middle-Aged to Older Adults

PURPOSE

This study aimed to investigate whether self-reported walking pace (a marker of physical function) and the presence of APOE-ε4 allele interact to modify brain health outcomes.

METHODS

We used data from a prospective cohort study of middle-aged to older adults from the UK Biobank who self-reported walking pace (slow or steady-to-brisk) and who were initially free of dementia ( n = 415,110). Incident all-cause dementia was obtained from hospital and death registry records, and structural brain volumes (right and left hippocampus volumes, total gray matter volume, and volume of white matter hyperintensities) were measured from a subset of participants ( n = 33,113). Cox proportional hazard models and generalized linear models were used to assess associations between exposures and outcomes.

RESULTS

Slow walking pace and the presence of APOE-ε4 allele were associated with increased dementia risk (HR = 1.79 [95% CI = 1.66-1.93], P < 0.001; HR = 3.06 [2.90-3.23], P < 0.001, respectively), and there was an interaction between these associations, indicating that the association of walking pace with dementia risk is modified by APOE-ε4 status (reference group: HR Steady-Brisk/APOE-ε4- = 1; HR Slow/APOE-ε4- = 2.03 [1.84-2.25], P < 0.001; HR Steady-Brisk/APOE-ε4+ = 3.21 [3.02-3.41], P < 0.001; HR Slow/APOE-ε4+ = 4.99 [4.48-5.58], P < 0.001). Slow self-reported walking pace was associated with worse brain volume outcomes, and these associations were not modified by APOE-ε4 genotype.

CONCLUSIONS

These results suggest walking pace and APOE-ε4 status independently influence brain volume outcomes, but both factors independently and jointly contribute to increased dementia risk. Individuals with both risk factors (slow walking pace and APOE-ε4 allele) show the strongest associations with dementia risk.

Large-scale proteomic analyses of incident Alzheimer's disease reveal new pathophysiological insights and potential therapeutic targets

Pathophysiological evolutions in early-stage Alzheimer's disease (AD) are not well understood. We used data of 2923 Olink plasma proteins from 51,296 non-demented middle-aged adults. During a follow-up of 15 years, 689 incident AD cases occurred. Cox-proportional hazard models were applied to identify AD-associated proteins in different time intervals. Through linking to protein categories, changing sequences of protein z-scores can reflect pathophysiological evolutions. Mendelian randomization using blood protein quantitative loci data provided causal evidence for potentially druggable proteins. We identified 48 AD-related proteins, with CEND1, GFAP, NEFL, and SYT1 being top hits in both near-term (HR:1.15-1.77; P:9.11 × 10-65-2.78 × 10-6) and long-term AD risk (HR:1.20-1.54; P:2.43 × 10-21-3.95 × 10-6). These four proteins increased 15 years before AD diagnosis and progressively escalated, indicating early and sustained dysfunction in synapse and neurons. Proteins related to extracellular matrix organization, apoptosis, innate immunity, coagulation, and lipid homeostasis showed early disturbances, followed by malfunctions in metabolism, adaptive immunity, and final synaptic and neuronal loss. Combining CEND1, GFAP, NEFL, and SYT1 with demographics generated desirable predictions for 10-year (AUC = 0.901) and over-10-year AD (AUC = 0.864), comparable to full model. Mendelian randomization supports potential genetic link between CEND1, SYT1, and AD as outcome. Our findings highlight the importance of exploring the pathophysiological evolutions in early stages of AD, which is essential for the development of early biomarkers and precision therapeutics.

Can fundus features tell us something about 3D eye shape?

PURPOSE

To determine whether imaging features derived from fundus photographs contain 3D eye shape information beyond that available from spherical equivalent refraction (SER).

METHODS

We analysed 99 eyes of 68 normal adults in the UK Biobank. An ellipsoid was fitted to the entire volume of each posterior eye (vitreous chamber without the lens)-segmented from magnetic resonance imaging of the brain. Asphericity was computed based on the semidiameters of the ellipsoid's axes to describe posterior eye shape along the horizontal (temporal-nasal) and vertical (superior-inferior) meridians, while volume was calculated as the total number of foreground voxels. Mixed-effects linear regression models were used to test the association of SER with asphericity and volume, controlling for age and sex. Then, the association between various fundus features and asphericity was tested-both before and after controlling for SER, age and sex.

RESULTS

Posterior eyes were generally oblate (asphericity > 0), but the degree of oblateness reduced as SER decreased, with the shape tending towards prolateness in high myopia. Neither sex nor age influenced asphericity. However, males had larger posterior eyes on average (this difference disappeared after height was additionally controlled for). Optic disc (OD) orientation, OD-fovea angle, vessel tortuosity, vessel fractal dimension and central retinal arteriolar or venular equivalent (CRAE or CRVE) showed significant univariable associations with asphericity along at least one meridian. After controlling for SER, age and sex, a more negative OD-fovea angle (larger OD-fovea angular separation) remained significantly associated with reduced horizontal oblateness (p = 0.01). Similarly, decreasing CRAE (narrower arterioles) remained significantly associated with reduced oblateness along both the horizontal (p = 0.04) and vertical (p < 0.01) meridians.

CONCLUSIONS

Variations in OD-fovea angle and CRAE are associated with differences in ocular asphericity-even in eyes with similar SER-suggesting that fundus imaging provides eye shape information beyond what is available from refractive error alone.

Encoding 3D information in 2D feature maps for brain CT-Angiography

We propose learnable 3D pooling (L3P), a CNN module designed to compress 3D information into 2D feature maps using anisotropic convolutions and unidirectional max pooling. Specifically, we used L3P followed by a 2D network to generate predictions from 3D brain CT-Angiography (CTA) in the context of large vessel occlusion (LVO). To further demonstrate its versatility, we extended its application to 3D brain MRI analysis for brain age prediction. First, we designed an experiment to classify the LVO-affected hemisphere (left or right), projecting the input CTA into the sagittal plane, which allowed to assess the ability of L3P to encode the 3D location where the location information was in the 3D-to-2D compression axis. Second, we evaluated the use of L3P on LVO detection as a binary classification (presence or absence). We compared the L3P models performance to that of 2D and stroke-specific 3D models. L3P models achieved results equivalent to stroke-specific 3D models while requiring fewer parameters and resources and provided better results than 2D models using maximum intensity projection images as input. The generalizability of L3P approach was evaluated on the LVO-affected hemisphere detection using data from a single site for training/validation and data from 36 other sites for testing, achieving an AUC of 0.83 on the test set. L3P also performed comparably or better than a fully 3D network on a brain age prediction task with a separate T1 MRI dataset, demonstrating its versatility across different tasks and imaging modalities. Additionally, L3P models generated more interpretable feature maps.

Characterization of brain morphology associated with metabolic dysfunction-associated steatotic liver disease in the UK Biobank

BACKGROUND

Emerging evidence has linked metabolic dysfunction-associated steatotic liver disease (MASLD) with accelerated cognitive decline and dementia. We aimed to investigate the associations of MASLD with volumes of total brain tissue and subcortical grey matter, and white matter microstructures in the UK Biobank.

METHODS

This cross-sectional study included 29,195 individuals (aged 45-82 years) from the UK Biobank who undertook a magnetic resonance imaging (MRI) sub-study between 2014 and 2022. The brain MRI covers three modalities (T1, T2 FLAIR, and diffusion). Volumes of grey matter, subcortical grey matter structures, and regional cortex were derived from T1-weighted images. Fractional anisotropy (FA) and mean diffusivity (MD) were derived from diffusion tensor imaging (DTI) to assess global and tract-specific microstructure. MASLD was defined as the MRI-derived proton density fat fraction (MRI-PDFF) ≥5% and the presence of at least one cardiometabolic criterion. Data were analysed using multiple linear regression models.

RESULTS

MASLD was significantly associated with smaller volumes of total grey matter and subcortical grey matter (p < 0.05) and reduced Alzheimer's disease (AD)-signature cortical thickness (multivariable-adjusted β = -0.04; 95% confidence interval [CI]: -0.07, -0.01). Having MASLD was associated with higher total white matter hyperintensity (WMH) volume (multivariable-adjusted β = 0.12; 95% CI: 0.10, 0.15). For white matter microstructure, MASLD was associated with increased global FA (multivariable-adjusted β = 0.05; 95% CI: 0.03, 0.08) and reduced global MD (multivariable-adjusted β = -0.04; 95% CI: -0.07, -0.01).

CONCLUSIONS

Brain morphology associated with MASLD is characterized by smaller subcortical grey matter volume and higher coherence but lower magnitudes of white matter microstructure.

Effects of obesity on aging brain and cognitive decline: A cohort study from the UK Biobank

Objective

To investigate the impact of obesity on brain structure and cognition using large neuroimaging and genetic data.

Methods

Associations between body mass index (BMI), gray matter volume (GMV), whiter matter hyper-intensities (WMH), and fluid intelligence score (FIS) were estimated in 30283 participants from the UK Biobank. Longitudinal data analysis was conducted. Genome-wide association studies were applied to explore the genetic loci associations among BMI, GMV, WMH, and FIS. Mendelian Randomization analyses were applied to further estimate the effects of obesity on changes in the brain and cognition.

Results

The observational analysis revealed that BMI was negatively associated with GMV (r = -0.15, p < 1 × 10-24) and positively associated with WMH (r = 0.08, p < 1 × 10-16). The change in BMI was negatively associated with the change in GMV (r = -0.04, p < 5 × 10-5). Genetic overlap was observed among BMI, GMV, and FIS at SBK1 (rs2726032), SGF29 (rs17707300), TUFM (rs3088215), AKAP6 (rs1051695), IL27 (rs4788084), and SPI1 (rs3740689 and rs935914). The MR analysis provided evidence that higher BMI was associated with lower GMV (β=-1119.12, p = 5.77 ×10-6), higher WMH (β=42.76, p = 6.37 ×10-4), and lower FIS (β=-0.081, p = 1.92 ×10-23).

Conclusions

The phenotypic and genetic association between obesity and aging brain and cognitive decline suggested that weight control could be a promising strategy for slowing the aging brain.

Combining cross-sectional and longitudinal genomic approaches to identify determinants of cognitive and physical decline

Large-scale genomic studies focusing on the genetic contribution to human aging have mostly relied on cross-sectional data. With the release of longitudinally curated aging phenotypes by the UK Biobank (UKBB), it is now possible to study aging over time at genome-wide scale. In this work, we evaluated the suitability of competing models of change in realistic simulation settings, performed genome-wide association scans on simulation-validated measures of age-related deweekcline, and followed up with LD-score regression and Mendelian Randomization (MR) analyses. Focusing on global cognitive and physical function, we observed marked differences between baseline function (θ) and accelerated decline (Δ). Both outcomes showed distinct heritability levels (e.g., 31.38%h θ 2 versus 3.15%h Δ 2 for physical function) and different associated loci (e.g., DUSP6 specific to physical Δ). Further, we found little commonalities across the two dimensions of aging-while cognitive decline was largely driven by Alzheimer's disease liability (standardized MR-effect, γ = 0.17), physical decline was mostly impacted by telomere length (γ = -0.05) and bone mineral density (γ = -0.05). Our work highlights the utility of longitudinal genomic efforts to scrutinize age-dependent genetic and environmental effects on physical and cognitive outcomes. Careful modelling and attention to participation characteristics are, however, crucial for valid inference.

Cardiovascular risk and obesity impact loss of grey matter volume earlier in males than females

BACKGROUND

It remains imperative to discover the time course that cardiovascular risk factors influence neurodegeneration in males and females and decipher whether the apolipoprotein (APOE) genotype mediates this relationship. Here we perform a large-scale evaluation of the influence of cardiovascular risk and obesity on brain volume in males and females in different age groups.

METHODS

34 425 participants between the ages of 45 and 82 years were recruited from the UK Biobank database https://www.ukbiobank.ac.uk. T1-weighted structural MR images (n=34 425) were downloaded locally for all participants, and voxel-based morphometry was performed to characterise the volumetric changes of the whole brain. The influence of Framingham cardiovascular risk (general cardiovascular risk), abdominal subcutaneous adipose tissue, and visceral adipose tissue volume (obesity) on cortical grey matter volume across different decades of life was evaluated with voxel-wise analysis.

RESULTS

In males, cardiovascular risk and obesity demonstrated the greatest influence on lower grey matter volume between 55-64 years of age. Female participants showed the greatest effect on lower grey matter volume between 65-74 years of age. Associations remained significant in APOE ε4 carriers and APOE ε4 non-carriers when evaluated separately.

CONCLUSIONS

The strongest influence of cardiovascular risk and obesity on reduced brain volume was between 55-64 years of age in males, whereas women were most susceptible to the detrimental effects of cardiovascular risk a decade later between 65-74 years of age. Here we elucidate the timing that targeting cardiovascular risk factors and obesity should be implemented in males and females to prevent neurodegeneration and Alzheimer's disease development.

Enhanced structural brain connectivity analyses using high diffusion-weighting strengths

Tractography is a unique modality for the in vivo measurement of structural connectivity, crucial for understanding brain networks and neurological conditions. With increasing b-value, the diffusion-weighting signal becomes primarily sensitive to the intra-axonal signal. However, it remains unclear how tractography is affected by this observation. Here, using open-source datasets, we showed that at high b-values, DWI reduces the uncertainty in estimating fiber orientations. Specifically, we found the ratio of biologically-meaningful longer-range connections increases, accompanied with downstream impact of redistribution of connectome and network metrics. However, when going beyond b = 6000 s/mm2, the loss of SNR imposed a penalty. Lastly, we showed that the data reaches satisfactory reproducibility with b-values above 1200 s/mm2. Overall, the results suggest that using b-values above 2500 s/mm2 is essential for more accurate connectome reconstruction by reducing uncertainty in fiber orientation estimation, supporting the use of higher b-value protocols in standard diffusion MRI scans and pipelines.

White matter connections within the central sulcus subserving the somato-cognitive action network

The somato-cognitive action network (SCAN) consists of three nodes interspersed within Penfield's motor effector regions. The configuration of the somato-cognitive action network nodes resembles the one of the 'plis de passage' of the central sulcus: small gyri bridging the precentral and postcentral gyri. Thus, we hypothesize that these may provide a structural substrate of the somato-cognitive action network. Using microdissections of 16 human hemispheres, we consistently identified a chain of three distinct plis de passage with increased underlying white matter in locations analogous to the somato-cognitive action network nodes. We mapped localizations of plis de passage into standard stereotactic space to seed functional MRI connectivity across 9000 resting-state functional MRI scans, which demonstrated the connectivity of these sites with the somato-cognitive action network. Intraoperative recordings during direct electrical central sulcus stimulation further identified inter-effector regions corresponding to plis de passage locations. This work provides a critical step towards an improved understanding of the somato-cognitive action network in both structural and functional terms. Furthermore, our work has the potential to guide the development of refined motor cortex stimulation techniques for treating brain disorders and operative resective techniques for complex surgery of the motor cortex.

Deciphering the influence of socioeconomic status on brain structure: insights from Mendelian randomization

Socioeconomic status (SES) influences physical and mental health, however its relation with brain structure is less well documented. Here, we examine the role of SES on brain structure using Mendelian randomisation. First, we conduct a multivariate genome-wide association study of SES using educational attainment, household income, occupational prestige, and area-based social deprivation, with an effective sample size of N = 947,466. We identify 554 loci associated with SES and distil these loci into those that are common across those four traits. Second, using an independent sample of ~35,000 we provide evidence to suggest that SES is protective against white matter hyperintensities as a proportion of intracranial volume (WMHicv). Third, we find that differences in SES still afford a protective effect against WMHicv, independent of that made by cognitive ability. Our results suggest that SES is a modifiable risk factor, causal in the maintenance of cognitive ability in older-age.

Precision Estimates of Longitudinal Brain Aging Capture Unexpected Individual Differences in One Year: Summary: A novel brain imaging method boosts precision to reveal variable brain aging trajectories

Longitudinal studies are required to measure individual differences in human brain aging, but they are difficult to estimate over short intervals because of measurement error. Using cluster scanning, an approach that reduces error by densely repeating rapid structural scans, we assessed brain aging in individuals across three longitudinal timepoints spaced across one year. Cluster scanning substantially improved the precision of individualized estimates, revealing previously undetectable individual differences in brain change. In just one year, expected differences in the rates of brain aging between younger and older individuals were evident, as were differences between cognitively unimpaired and impaired individuals. Each person's brain change trajectory was compared to modeled normative expectations from a large cohort of age-matched UK Biobank participants. Cognitively unimpaired older individuals variably revealed relative brain maintenance, unexpectedly rapid decline, and asymmetrical changes. These atypical brain aging trajectories were found across structures and verified in independent within-individual test and retest data. Cluster scanning promises to advance our understanding of the marked heterogeneity in brain aging by affording better short-term tracking of individual variability in structural change.

Parsing clinical and neurobiological sources of heterogeneity in depression

BACKGROUND

Patients with depression vary from one-another in their clinical and neuroimaging presentation, yet the relationship between clinical and neuroimaging sources of variation is poorly understood. Determining sources of heterogeneity in depression is important to gain insights into its diverse and complex neural etiology. This study aims to test if depression heterogeneity is characterized by subgroups that differ both clinically and neurobiologically and/or whether multiple neuroimaging profiles give rise to the same clinical presentation.

METHODS

This study utilizes population-based data from the UK Biobank over multiple imaging sites. Clinically dissociated groups were selected to isolate clinical characteristics of depression (symptoms of anhedonia, depressed mood, and somatic disturbance; severity indices of lifetime chronicity and acute impairment; and late onset). Residual neuroimaging heterogeneity within each group was assessed using neuroimaging driven clustering.

RESULTS

The clinically dissociated subgroups had significantly larger neuroimaging normative deviations than a comparison heterogeneous group and had distinct neuroimaging profiles from each other. Imaging driven clustering within each clinically dissociated group identified two stable subtypes within the acute impairment group that differed significantly in cognitive ability, despite identical clinical profiles.

CONCLUSIONS

The study identified distinct neuroimaging profiles related to particular clinical depression features that may explain inconsistencies in the literature and sub-clusters within the acute impairment group with cognitive differences that were only differentiable by neuroimaging. Our results provide evidence that multiple neuroimaging profiles may give rise to the same clinical presentation, emphasizing the presence of complex interactions between clinical and neuroimaging sources of heterogeneity.

Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI

Age-related white matter (WM) microstructure maturation and decline occur throughout the human lifespan, complementing the process of gray matter development and degeneration. Here, we create normative lifespan reference curves for global and regional WM microstructure by harmonizing diffusion MRI (dMRI)-derived data from ten public datasets (N = 40,898 subjects; age: 3-95 years; 47.6% male). We tested three harmonization methods on regional diffusion tensor imaging (DTI) based fractional anisotropy (FA), a metric of WM microstructure, extracted using the ENIGMA-DTI pipeline. ComBat-GAM harmonization provided multi-study trajectories most consistent with known WM maturation peaks. Lifespan FA reference curves were validated with test-retest data and used to assess the effect of the ApoE4 risk factor for dementia in WM across the lifespan. We found significant associations between ApoE4 and FA in WM regions associated with neurodegenerative disease even in healthy individuals across the lifespan, with regional age-by-genotype interactions. Our lifespan reference curves and tools to harmonize new dMRI data to the curves are publicly available as eHarmonize ( https://github.com/ahzhu/eharmonize ).

High-Order Graphical Topology Analysis of Brain Functional Connectivity Networks Using fMRI

The brain connectivity network can be represented as a graph to reveal its intrinsic topological properties. While classical graph theory provides a powerful framework for examining brain connectivity patterns, it often focuses on low-order graphical indicators and pays less attention to high-order topological metrics, which are crucial to the comprehensive understanding of brain topology. In this paper, we capture high-order topological features via a graphical topology analysis framework for brain connectivity networks derived from functional Magnetic Resonance Imaging (fMRI). Several high-order metrics are examined across varying sparsity levels of binary graphs to trace the evolution of brain networks. Topological phase transitions are primarily investigated that reflect brain criticality, and a novel indicator called "redundant energy" is proposed to measure the chaos level of the brain. Extensive experiments on diverse datasets from healthy controls validate the reproducibility and generalizability of our framework. The results demonstrate that around critical points, classical graph theoretical indicators change sharply, driven by crucial brain regions that have high node curvatures. Further investigations on fMRI of subjects with and without Parkinson's disease uncover significant alterations in high-order topological features which are further associated with the severity of the disease. This study provides a fresh perspective on studying topological architectures of the brain, with the potential to expand our comprehension on brain function in both healthy and diseased states.

Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age

Background: A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. This study aimed to investigate the associations between accelerometer-measured PA and brain age. Methods: A total of 16,972 eligible participants with both valid T 1-weighted neuroimaging and accelerometer data from the UK Biobank was included. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM). Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders. Results: The brain age estimated by LightGBM achieved an appreciable performance (r = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (r = 0.90, MAE = 3.03). We found that LPA (F = 2.47, P = 0.04), MPA (F = 6.49, P < 1 × 10-300), VPA (F = 4.92, P = 2.58 × 10-5), and MVPA (F = 6.45, P < 1 × 10-300) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Furthermore, mediation analysis suggested that BAG partially mediated the associations between PA and cognitive functions as well as brain-related disorders. Conclusions: Our study revealed a U-shaped association between accelerometer-measured PA and BAG, highlighting that advanced brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.

Comparison of different neurite density metrics with brain asymmetry evaluation

The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.

Reduced neurovascular coupling is associated with increased cardiovascular risk without established cerebrovascular disease: A cross-sectional analysis in UK Biobank

Mid-life vascular risk factors predict late-life cerebrovascular diseases and poor global brain health. Although endothelial dysfunction is hypothesized to contribute to this process, evidence of impaired neurovascular function in early stages remains limited. In this cross-sectional study of 31,934 middle-aged individuals from UK Biobank without established cerebrovascular disease, the overall 10-year risk of cardiovascular events was associated with reduced neurovascular coupling (p < 2 × 10-16) during a visual task with functional MRI, including in participants with no clinically apparent brain injury on MRI. Diabetes, smoking, waist-hip ratio, and hypertension were each strongly associated with decreased neurovascular coupling with the strongest relationships for diabetes and smoking, whilst in older adults there was an inverted U-shaped relationship with DBP, peaking at 70-80 mmHg DBP. These findings indicate that mid-life vascular risk factors are associated with impaired cerebral endothelial-dependent neurovascular function in the absence of overt brain injury. Neurovascular dysfunction, measured by neurovascular coupling, may play a role in the development of late-life cerebrovascular disease, underscoring the need for further longitudinal studies to explore its potential as a mediator of long-term cerebrovascular risk.

Regular use of opioids and dementia, cognitive measures, and neuroimaging outcomes among UK Biobank participants with chronic non-cancer pain

INTRODUCTION

We investigated the association between regular opioid use and incident dementia, neuroimaging outcomes, and cognitive measures.

METHODS

Cox regression was used to assess the association between opioid use and incident dementia among197,673 UK Biobank participants with chronic non-cancer pain. Linear and logistic regression were applied to explore the associations between opioid use and dementia-related neuroimaging and cognitive function outcomes.

RESULTS

Regular opioid use was associated with a 20% higher risk of all-cause dementia and a 49% higher risk of vascular dementia (VD) compared with those not using analgesics. Moreover, those using strong opioids had a 72% higher risk of all-cause dementia and a 155% higher risk of VD. Strong opioid use was also linked to reductions in hippocampal, white matter, and total brain volumes. Lastly, regular opioid use was associated with lower fluid intelligence.

DISCUSSION

A higher risk of dementia was observed among participants regularly using opioids, escalating with opioid strength.

HIGHLIGHTS

Regular opioid use was associated with an increased risk of all-cause dementia and VD. Those using strong opioids had a much higher risk of all-cause dementia and VD. Strong opioid use was also associated with worse neuroimaging outcomes. Regular opioid use was also associated with lower fluid intelligence.

Associations of metabolic syndrome with risks of dementia and cognitive impairment: A systematic review and meta-analysis

BackgroundPrevious studies have linked metabolic syndrome (MetS) to dementia risk.ObjectiveWe conducted a systematic review and meta-analysis to assess the association between MetS and dementia as well as cognitive impairment, with additional focus on individual MetS components.MethodsWe systematically searched the PubMed, Embase, and Cochrane Library databases from inception through July 2024. We used random-effects models to calculate relative risks (RRs) and odds ratios (ORs) with 95% confidence intervals (CIs). Publication bias was evaluated using the Egger's test, while potential sources of heterogeneity were investigated through meta-regression, subgroup, and sensitivity analyses.ResultsOur analysis included 21 studies with a total of 411,810 participants. MetS was associated with increased risks of all-cause dementia (RR = 1.33, 95% CI = 1.03-1.71, I² = 85.8%) and vascular dementia (RR = 2.07, 95% CI = 1.32-3.24, I² = 10.1%), but not Alzheimer's disease (RR = 1.10, 95% CI = 0.64-1.91, I² = 81.8%). Regarding cognitive impairment, longitudinal studies showed an increased risk (OR = 1.38, 95% CI = 1.24-1.53, I² = 3.3%), with similar findings in cross-sectional studies (OR = 1.65, 95% CI = 1.19-2.28, I² = 85.3%).ConclusionsThis study found that MetS is significantly associated with increased risks of dementia and cognitive impairment, with each component potentially being a modifiable factor. These findings may help guide clinicians in recommending lifestyle interventions to prevent cognitive decline and promote brain health.

Versatile MRI acquisition and processing protocol for population-based neuroimaging

Neuroimaging has an essential role in studies of brain health and of cerebrovascular and neurodegenerative diseases, requiring the availability of versatile magnetic resonance imaging (MRI) acquisition and processing protocols. We designed and developed a multipurpose high-resolution MRI protocol for large-scale and long-term population neuroimaging studies that includes structural, diffusion-weighted and functional MRI modalities. This modular protocol takes almost 1 h of scan time and is, apart from a concluding abdominal scan, entirely dedicated to the brain. The protocol links the acquisition of an extensive set of MRI contrasts directly to the corresponding fully automated data processing pipelines and to the required quality assurance of the MRI data and of the image-derived phenotypes. Since its successful implementation in the population-based Rhineland Study (ongoing, currently more than 11,000 participants, target participant number of 20,000), the proposed MRI protocol has proved suitable for epidemiological and clinical cross-sectional and longitudinal studies, including multisite studies. The approach requires expertise in magnetic resonance image acquisition, in computer science for the data management and the execution of processing pipelines, and in brain anatomy for the quality assessment of the MRI data. The protocol takes ~1 h of MRI acquisition and ~20 h of data processing to complete for a single dataset, but parallelization over multiple datasets using high-performance computing resources reduces the processing time. By making the protocol, MRI sequences and pipelines available, we aim to contribute to better comparability, interoperability and reusability of large-scale neuroimaging data.

Chronic pain is associated with greater brain entropy in the prefrontal cortex

Chronic pain is a debilitating clinical condition and a severe public health issue that demands to be addressed. Neuroimaging-based techniques have been widely adopted to investigate the neural underpinnings of chronic pain. Despite the efforts the complex nature of pain experience as well as the heterogeneity of chronic pain have made the identification of neuroimaging-based biomarkers extremely challenging. In this study, resting-state fMRI-based brain entropy, a measure reflecting the "irregularity" of brain activity, was adopted as a biomarker of chronic pain by comparing individuals with chronic pain and healthy controls in a sample of middle-to-old-age participants (n > 30,000) drawn from the UK Biobank database. Abnormal brain entropy is associated with altered brain dynamics and may serve as a potential marker of disrupted pain processing in individuals with chronic pain. Compared to healthy controls, individuals with chronic pain exhibited increased brain entropy in a broad set of regions including the frontal, temporal, and occipital lobes, as well as the cerebellum. In addition, individuals with a more distributed chronic pain showed increased brain entropy in occipital lobes. When examining distinct types of chronic pain individually, only participants with headache and pain all over the body showed brain entropy differences compared to a matched sample of healthy controls. PERSPECTIVE: This article investigates the neural substrates of chronic pain using brain entropy, a measure of the randomness and irregularity of brain activity. This measure could potentially aid in the assessment and treatment of chronic pain.

Tobacco Smoking Functional Networks: A Whole-Brain Connectome Analysis in 24 539 Individuals

INTRODUCTION

Nicotine addiction, a multifaceted neuropsychiatric disorder, profoundly impacts brain functions through interactions with neural pathways. Despite its significance, the impact of tobacco smoking on the whole-brain functional connectome remains largely unexplored.

AIMS AND METHODS

We conducted a whole-brain analysis on 24 539 adults aged 40 and above from the United Kingdom Biobank cohort. Subjects were categorized into individuals who use nicotine and those who do not use nicotine based on current and chronic tobacco smoking information. Functional connectivity was assessed using resting-state functional magnetic resonance imaging. We employed a network analysis method to assess the systematic effects of tobacco smoking on brain connectome by identifying subnetworks that show nicotine-use-related differences.

RESULTS

Our analyses revealed two nicotine-use-related subnetworks with distinct network structure (permutation p < .001). In the first network, there is a significant decrease in resting-state functional connectivity (rsFC) between the basal ganglia regions (eg, nucleus accumbens) and 73% of the remaining brain regions, emphasizing the central hub role of basal ganglia in addictive smoking behaviors. Additionally, a data-driven subnetwork, mainly involving regions from frontal and occipital lobes, showed reduced rsFC among individuals who use nicotine.

CONCLUSIONS

The results suggest significant alterations in the communication and coordination among the basal ganglia and the broader network of brain regions. The observed changes in rsFC indicate a widespread disruption in the connectivity patterns associated with nicotine use.

IMPLICATIONS

This study identifies rsFC subnetworks related to chronic nicotine use through whole-brain connectome analysis. The findings confirm that widespread alterations in rsFC are centered around hub nodes within the basal ganglia, including bilateral nucleus accumbens, putamen, caudate, and globus pallidus. In addition, our analysis found a clique-forming subnetwork vulnerable to tobacco smoking consisting of regions from the visual, dorsal/ventral attention, and frontoparietal networks.

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes

Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI framework for parametric spherical deconvolution of diffusion MRI data of the brain. We demonstrate its utility for estimating white matter fibre orientations, mapping uncertainty of voxel-based estimates and performing probabilistic tractography by spatially propagating fibre orientation uncertainty. We conduct an extensive comparison against established Bayesian methods based on Markov-Chain Monte-Carlo (MCMC) and find that: a) in-silico training can lead to calibrated SBI networks with accurate parameter estimates and uncertainty mapping for both single- and multi-shell diffusion MRI, b) SBI allows amortised inference of the posterior distribution of model parameters given unseen observations, which is orders of magnitude faster than MCMC, c) SBI-based tractography yields reconstructions that have a high level of agreement with their MCMC-based counterparts, equal to or higher than scan-rescan reproducibility of estimates. We further demonstrate how SBI design considerations (such as dealing with noise, defining priors and handling model selection) can affect performance, allowing us to identify optimal practices. Taken together, our results show that SBI provides a powerful alternative to classical Bayesian inference approaches for fast and accurate model estimation and uncertainty mapping in MRI.

Multimodal population study reveals the neurobiological underpinnings of chronotype

The rapid shifts in society have altered human behavioural patterns, with increased evening activities, increased screen time and changed sleep schedules. As an explicit manifestation of circadian rhythms, chronotype is closely intertwined with physical and mental health. Night owls often exhibit unhealthier lifestyle habits, are more susceptible to mood disorders and have poorer physical fitness compared with early risers. Although individual differences in chronotype yield varying consequences, their neurobiological underpinnings remain elusive. Here we conducted a pattern-learning analysis with three brain-imaging modalities (grey matter volume, white-matter integrity and functional connectivity) and capitalized on 976 phenotypes in 27,030 UK Biobank participants. The resulting multilevel analysis reveals convergence on the basal ganglia, limbic system, hippocampus and cerebellum. The pattern derived from modelling actigraphy wearables data of daily movement further highlighted these key brain features. Overall, our population-level study comprehensively investigates chronotype, emphasizing its close connections with habit formation, reward processing and emotional regulation.

White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life

Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.

Volumetric Changes in Cerebellar Transverse Zones: Age and Sex Effects in Health and Neurological Disorders

Cerebellar volumetric changes are intricately linked to aging, with distinct patterns across its transverse zones, the functional subdivisions characterized by unique cytoarchitectural and connectivity profiles. Despite research efforts, the cerebellar aging process in health and neurological disorders remains poorly understood. In this study, we investigated the effects of age and sex on total cerebellum, transverse zone, and lobule volumes using MRI data from over 45,000 participants compiled from six neuroimaging datasets. We also propose a framework for estimating cerebellum age as an indicator of cerebellar health. Significant age-dependent volume reductions were observed across transverse zones, with the central zone (CZ; lobules VI and VII) exhibiting the steepest decline in both health and neurological disorders. This finding highlights the CZ's vulnerability to aging and its critical role in cognitive and emotional processing. We also found prominent sex differences in age-dependent volumetric changes. Males exhibited smaller total intracranial volume (TIV)-adjusted cerebellum volume and faster age-dependent volume reduction than females in both health and mild cognitive impairment (MCI), Alzheimer disease (AD), and Parkinson disease (PD). In contrast, females with schizophrenia (SZ) and cocaine use disorder (CUD) revealed faster age-dependent cerebellar volume reduction than males. Patients with MCI, AD, and PD experienced more pronounced atrophy in the posterior (PZ) and nodular (NZ) zones compared to age-matched healthy controls, while SZ patients were characterized by a more prominent reduction in CZ. In CUD, a non-significant volume decline was observed in all zones compared to the controls. Moreover, our framework for estimating cerebellum age revealed a notable difference in cerebellar aging between healthy individuals and neurological patients. Finally, we charted age-dependent changes in cerebellar volume in healthy individuals, focusing on transverse zones capturing the functional subdivisions. These findings underscore the potential of cerebellar volumetric analysis as a biomarker for early detection and monitoring of neurodegenerative and neuropsychiatric disorders. Our novel approach complements and enhances MRI-based analyses, providing essential insights into the pathogenesis of aging, neurodegeneration, and chronic neuropsychiatric conditions.

Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change

The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect aging processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing, and future brain changes across different tissue types from macro- to microstructure remains controversial. Here, we use multimodal imaging data of 39,325 UK Biobank participants, aged 44-82 years at baseline and 2,520 follow-ups within 1.12-6.90 years to examine BA changes and their relationship to anatomical brain changes. We find insufficient evidence to conclude that BA reflects the rate of brain aging. However, modality-specific differences in brain ages reflect the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.

A genetically informed brain atlas for enhancing brain imaging genomics

Brain imaging genomics has manifested considerable potential in illuminating the genetic determinants of human brain structure and function. This has propelled us to develop the GIANT (Genetically Informed brAiN aTlas) that accounts for genetic and neuroanatomical variations simultaneously. Integrating voxel-wise heritability and spatial proximity, GIANT clusters brain voxels into genetically informed regions, while retaining fundamental anatomical knowledge. Compared to conventional (non-genetics) brain atlases, GIANT exhibits smaller intra-region variations and larger inter-region variations in terms of voxel-wise heritability. As a result, GIANT yields increased regional SNP heritability, enhanced polygenicity, and its polygenic risk score explains more brain volumetric variation than traditional neuroanatomical brain atlases. We provide extensive validation to GIANT and demonstrate its neuroanatomical validity, confirming its generalizability across populations with diverse genetic ancestries and various brain conditions. Furthermore, we present a comprehensive genetic architecture of the GIANT regions, covering their functional annotation at the molecular levels, their associations with other complex traits/diseases, and the genetic and phenotypic correlations among GIANT-defined imaging endophenotypes. In summary, GIANT constitutes a brain atlas that captures the complexity of genetic and neuroanatomical heterogeneity, thereby enhancing the discovery power and applicability of imaging genomics investigations in biomedical science.

A multi-site, multi-modal travelling-heads resource for brain MRI harmonisation

Despite its great potential for studying the living brain, magnetic resonance imaging (MRI) can be often limited by nuisance non-biological factors, such as hardware/software differences between scanners, which can interfere with biological variability. This lack of standardisation or harmonisation between scanners hinders reproducibility and quantifiability of MRI. Towards addressing this challenge, we present one of the most comprehensive MRI harmonisation resources, based on a travelling heads paradigm; healthy volunteers scanned repeatedly across different scanners. The Oxford-Nottingham Harmonisation (ON-Harmony) resource offers data from 20 participants each scanned on six different 3 T MRI scanners from three major vendors (GE/Philips/Siemens) across five imaging sites. Each scanning session includes five imaging modalities (T1w/T2w/dMRI/rfMRI/SWI) with protocols aligned to the UK Biobank, while for about half of the participants five within-scanner repeats are additionally acquired. The 165 multi-modal scanning sessions allow mapping of different pools of variability (biological, between-scanner, within-scanner) for hundreds of MRI-derived measures. We describe the breadth of information contained in the publicly-available data and showcase their reuse potential for evaluating efficacy of harmonisation approaches.

The human brainstem's red nucleus was upgraded to support goal-directed action

The red nucleus, a large brainstem structure, coordinates limb movement for locomotion in quadrupedal animals. In humans, its pattern of anatomical connectivity differs from that of quadrupeds, suggesting a different purpose. Here, we apply our most advanced resting-state functional connectivity based precision functional mapping in highly sampled individuals (n = 5), resting-state functional connectivity in large group-averaged datasets (combined n ~ 45,000), and task based analysis of reward, motor, and action related contrasts from group-averaged datasets (n > 1000) and meta-analyses (n > 14,000 studies) to precisely examine red nucleus function. Notably, red nucleus functional connectivity with motor-effector networks (somatomotor hand, foot, and mouth) is minimal. Instead, connectivity is strongest to the action-mode and salience networks, which are important for action/cognitive control and reward/motivated behavior. Consistent with this, the red nucleus responds to motor planning more than to actual movement, while also responding to rewards. Our results suggest the human red nucleus implements goal-directed behavior by integrating behavioral valence and action plans instead of serving a pure motor-effector function.

Imaging the cerebral vasculature at different scales: translational tools to investigate the neurovascular interfaces

The improvements in imaging technology opened up the possibility to investigate the structure and function of cerebral vasculature and the neurovascular unit with unprecedented precision and gaining deep insights not only on the morphology of the vessels but also regarding their function and regulation related to the cerebral activity. In this review, we will dissect the different imaging capabilities regarding the cerebrovascular tree, the neurovascular unit, the haemodynamic response function, and thus, the vascular-neuronal coupling. We will discuss both clinical and preclinical setting, with a final discussion on the current scenery in cerebrovascular imaging where magnetic resonance imaging and multimodal microscopy emerge as the most potent and versatile tools, respectively, in the clinical and preclinical context.

Integrated genetic analysis and single cell-RNA sequencing for brain image-derived phenotypes and Parkinson's disease

BACKGROUND

Previous studies have reported Parkinson's disease (PD) patients usually have changes in brain image-derived phenotypes (IDPs). However, the role of genetic factors in their association and biological mechanism remains unclear. We aimed to unveil genetic and biological links between brain IDPs and PD.

METHODS

Using genome-wide association study (GWAS) summary statistics and single-cell RNA sequencing (scRNA-seq) data, we performed a comprehensive analysis between 624 brain IDPs and PD. The genetic correlations and causality were examined by linkage disequilibrium score regression (LDSC), two-sample bidirectional Mendelian randomization (MR) and meta-analysis. Potential shared genes were identified using MAGMA and PLACO. Finally, pathway enrichment using FUMA and Metascape, and scRNA-seq analysis were performed to determine biological mechanisms and gene expression atlas across various cell types in brain tissue.

RESULTS

LDSC revealed that 50 brain IDPs were genetically correlated with PD (P < 0.05), in which 5 IDPs, exhibited putative causality on PD through MR (P < 0.05). For instance, we identified that the increased volume of the right thalamus (IVW: OR = 2.08, 95 % CI: 1.33 to 3.25, PFDR = 0.03) was positively correlated with the risk of PD, which was also supported by replicated MR (IVW: OR = 1.63, 95 % CI: 1.17-2.26, PFDR = 0.02) in FinnGen and meta-analysis (OR = 1.78, 95 % CI: 1.36-2.31, PFDR = 5.00 × 10-4). Additionally, we identified 56 unique pleiotropic genes, such as FAM13A, with notable enrichment in neuronal cells. Biological mechanism analysis revealed these genes were enriched in brain tissues and a variety of pathways such as negative regulation of neuron apoptotic processes.

CONCLUSION

We indicated the shared genetic architecture and biological mechanisms between brain IDPs and PD. These findings might provide insights on the therapeutic intervention and early prediction of PD at the brain imaging level.

Plasma proteomics-based brain aging signature and incident dementia risk

Investigating brain-enriched proteins with machine learning methods may enable a brain-specific understanding of brain aging and provide insights into the molecular mechanisms and pathological pathways of dementia. The study aims to analyze associations of brain-specific plasma proteomic aging signature with risks of incident dementia. In 45,429 dementia-free UK Biobank participants at baseline, we generated a brain-specific biological age using 63 brain-enriched plasma proteins with machine learning methods. The brain age gap was estimated, and Cox proportional hazards models were used to study the association with incident all-cause dementia, Alzheimer's disease (AD), and vascular dementia. Per-unit increment in the brain age gap z-score was associated with significantly higher risks of all-cause dementia (hazard ratio [95% confidence interval], 1.67 [1.56-1.79], P < 0.001), AD (1.85 [1.66-2.08], P < 0.001), and vascular dementia (1.86 [1.55-2.24], P < 0.001), respectively. Notably, 2.1% of the study population exhibited extreme old brain aging defined as brain age gap z-score > 2, correlating with over threefold increased risks of all-cause dementia and vascular dementia (3.42 [2.25-5.20], P < 0.001, and 3.41 [1.05-11.13], P = 0.042, respectively), and fourfold increased risk of AD (4.45 [2.32-8.54], P < 0.001). The associations were stronger among participants with healthier lifestyle factors (all P-interaction < 0.05). These findings were corroborated by magnetic resonance imaging assessments showing that a higher brain age gap aligns global pathophysiology of dementia, including global and regional atrophy in gray matter, and white matter lesions (P < 0.001). The brain-specific proteomic age gap is a powerful biomarker of brain aging, indicative of dementia risk and neurodegeneration.

Maintaining Brain Health: The Impact of Physical Activity and Fitness on the Aging Brain-A UK Biobank Study

The growing prevalence of physical and neurological disorders linked to aging poses significant challenges for society. Many of these disorders are closely linked to changes in brain structure and function, highlighting the importance of identifying protective factors that can preserve brain structure in later life and mitigate age-related decline. Physical activity (PA) is consistently linked to physical health and was found to mitigate age-related disorders. However, its effects on markers of brain aging remain inconclusive, partly due to reliance on underpowered studies and self-reported data. We investigated the effects of accelerometer-measured PA and physical fitness on BrainAGE, a machine-learning-derived marker of brain aging, in a large UK Biobank cohort. Using cortical and subcortical neuroimaging-derived features, a BrainAGE model was trained on 21,442 participants (mean absolute error: 3.75 years) and applied to predict BrainAGE for an independent sample of 10,874 participants. Accelerometer-measured moderate-intensity PA, but not self-reported PA, was associated with decelerated brain aging, indicated by a negative BrainAGE. Further, higher hand grip strength, along with lower body mass index (BMI), diastolic blood pressure (DBP), and resting heart rate, was linked to decelerated aging. These fitness measures impacted BrainAGE independently of PA. Additionally, fitness partially accounted for the relationship between PA and BrainAGE. Specifically, BMI, DBP, and resting heart rate showed a significant mediating effect, while grip strength did not. These findings highlight the interplay between PA and fitness in maintaining brain health and provide valuable insights for neuroscience and preventive health measures.

Real-world datasets for the International Registry for Alzheimer's Disease and Other Dementias (InRAD) and other registries: An international consensus

BACKGROUND

Many dementia and Alzheimer's disease (AD) registries operate at local or national levels without standardization or comprehensive real-world data (RWD) collection. This initiative sought to achieve consensus among experts on priority outcomes and measures for clinical practice in caring for patients with symptomatic AD, particularly in the mild cognitive impairment and mild to moderate dementia stages.

OBJECTIVE

The primary aim was to define a minimum dataset (MDS) and extended dataset (EDS) to collect RWD in the new International Registry for AD and Other Dementias (InRAD) and other AD registries. The MDS and EDS focus on informing routine clinical practice, covering relevant comorbidities and safety, and are designed to be easily integrated into existing data capture systems.

METHODS AND RESULTS

An international steering committee (ISC) of AD clinician experts lead the initiative. The first drafts of the MDS and EDS were developed based on a previous global inter-societal Delphi consensus on outcome measures for AD. Based on the ISC discussions, a survey was devised and sent to a wider stakeholder group. The ISC discussed the survey results, resulting in a consensus MDS and EDS covering: patient profile and demographics; lifestyle and anthropometrics; co-morbidities and diagnostics; imaging; treatment; clinical characterization; safety; discontinuation; laboratory tests; patient and care partner outcomes; and interface functionality.

CONCLUSION

By learning from successful examples in other clinical areas, addressing current limitations, and proactively enhancing data quality and analytical rigor, the InRAD registry will be a foundation to contribute to improving patient care and outcomes in neurodegenerative diseases.

A lightweight generative model for interpretable subject-level prediction

Recent years have seen a growing interest in methods for predicting an unknown variable of interest, such as a subject's diagnosis, from medical images depicting its anatomical-functional effects. Methods based on discriminative modeling excel at making accurate predictions, but are challenged in their ability to explain their decisions in anatomically meaningful terms. In this paper, we propose a simple technique for single-subject prediction that is inherently interpretable. It augments the generative models used in classical human brain mapping techniques, in which the underlying cause-effect relations can be encoded, with a multivariate noise model that captures dominant spatial correlations. Experiments demonstrate that the resulting model can be efficiently inverted to make accurate subject-level predictions, while at the same time offering intuitive visual explanations of its inner workings. The method is easy to use: training is fast for typical training set sizes, and only a single hyperparameter needs to be set by the user. Our code is available at https://github.com/chiara-mauri/Interpretable-subject-level-prediction.

Evaluating Traditional, Deep Learning and Subfield Methods for Automatically Segmenting the Hippocampus From MRI

Given the relationship between hippocampal atrophy and cognitive impairment in various pathological conditions, hippocampus segmentation from MRI is an important task in neuroimaging. Manual segmentation, though considered the gold standard, is time-consuming and error-prone, leading to the development of numerous automatic segmentation methods. However, no study has yet independently compared the performance of traditional, deep learning-based and hippocampal subfield segmentation methods within a single investigation. We evaluated 10 automatic hippocampal segmentation methods (FreeSurfer, SynthSeg, FastSurfer, FIRST, e2dhipseg, Hippmapper, Hippodeep, FreeSurfer-Subfields, HippUnfold and HSF) across 3 datasets with manually segmented hippocampus labels. Performance metrics included overlap with manual labels, correlations between manual and automatic volumes, volume similarity, diagnostic group differentiation and systematically located false positives and negatives. Most methods, especially deep learning-based ones that were trained on manual labels, performed well on public datasets but showed more error and variability on clinical data. Many methods tended to over-segment, particularly at the anterior hippocampus border, but were able to distinguish between healthy controls, MCI, and dementia patients based on hippocampal volume. Our findings highlight the challenges in hippocampal segmentation from MRI and the need for more publicly accessible datasets with manual labels across diverse ages and pathological conditions.

ENIGMA-Meditation: Worldwide Consortium for Neuroscientific Investigations of Meditation Practices

Meditation is a family of ancient and contemporary contemplative mind-body practices that can modulate psychological processes, awareness, and mental states. Over the last 40 years, clinical science has manualized meditation practices and designed various meditation interventions that have shown therapeutic efficacy for disorders including depression, pain, addiction, and anxiety. Over the past decade, neuroimaging has been used to examine the neuroscientific basis of meditation practices, effects, states, and outcomes for clinical and nonclinical populations. However, the generalizability and replicability of current neuroscientific models of meditation have not yet been established, because they are largely based on small datasets entrenched with heterogeneity along several domains of meditation (e.g., practice types, meditation experience, clinical disorder targeted), experimental design, and neuroimaging methods (e.g., preprocessing, analysis, task-based, resting-state, structural magnetic resonance imaging). These limitations have precluded a nuanced and rigorous neuroscientific phenotyping of meditation practices and their potential benefits. Here, we present ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis)-Meditation, the first worldwide collaborative consortium for neuroscientific investigations of meditation practices. ENIGMA-Meditation will enable systematic meta- and mega-analyses of globally distributed neuroimaging datasets of meditation using shared, standardized neuroimaging methods and tools to improve statistical power and generalizability. Through this powerful collaborative framework, existing neuroscientific accounts of meditation practices can be extended to generate novel and rigorous neuroscientific insights that account for multidomain heterogeneity. ENIGMA-Meditation will inform neuroscientific mechanisms that underlie therapeutic action of meditation practices on psychological and cognitive attributes, thereby advancing the field of meditation and contemplative neuroscience.

Vulnerability to memory decline in aging - a mega-analysis of structural brain change

Brain atrophy is a key factor behind episodic memory loss in aging, but the nature and ubiquity of this relationship remains poorly understood. This study leveraged 13 longitudinal datasets, including 3,737 cognitively healthy adults (10,343 MRI scans; 13,460 memory assessments), to determine whether brain change-memory change associations are more pronounced with age and genetic risk for Alzheimer's Disease. Both factors are associated with accelerated brain decline, yet it remains unclear whether memory loss is exacerbated beyond what atrophy alone would predict. Additionally, we assessed whether memory decline aligns with a global pattern of atrophy or stems from distinct regional contributions. Our mega-analysis revealed a nonlinear relationship between memory decline and brain atrophy, primarily affecting individuals with above-average brain structural decline. The associations were stronger in the hippocampus but also spread across diverse cortical and subcortical regions. The associations strengthened with age, reaching moderate associations in participants in their eighties. While APOE ε4 carriers exhibited steeper brain and memory loss, genetic risk had no effect on the change-change associations. These findings support the presence of common biological macrostructural substrates underlying memory function in older age which are vulnerable to multiple age-related factors, even in the absence of overt pathological changes.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Genetics impact risk of Alzheimer's disease through mechanisms modulating structural brain morphology in late life

BACKGROUND

Alzheimer's disease (AD)-related neuropathological changes can occur decades before clinical symptoms. We aimed to investigate whether neurodevelopment and/or neurodegeneration affects the risk of AD, through reducing structural brain reserve and/or increasing brain atrophy, respectively.

METHODS

We used bidirectional two-sample Mendelian randomisation to estimate the effects between genetic liability to AD and global and regional cortical thickness, estimated total intracranial volume, volume of subcortical structures and total white matter in 37 680 participants aged 8-81 years across 5 independent cohorts (Adolescent Brain Cognitive Development, Generation R, IMAGEN, Avon Longitudinal Study of Parents and Children and UK Biobank). We also examined the effects of global and regional cortical thickness and subcortical volumes from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium on AD risk in up to 37 741 participants.

RESULTS

Our findings show that AD risk alleles have an age-dependent effect on a range of cortical and subcortical brain measures that starts in mid-life, in non-clinical populations. Evidence for such effects across childhood and young adulthood is weak. Some of the identified structures are not typically implicated in AD, such as those in the striatum (eg, thalamus), with consistent effects from childhood to late adulthood. There was little evidence to suggest brain morphology alters AD risk.

CONCLUSIONS

Genetic liability to AD is likely to affect risk of AD primarily through mechanisms affecting indicators of brain morphology in later life, rather than structural brain reserve. Future studies with repeated measures are required for a better understanding and certainty of the mechanisms at play.

Brain functional connectivity and anatomical features as predictors of cognitive behavioral therapy outcome for anxiety in youths

BACKGROUND

Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have a major impact. This study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms.

METHODS

Two datasets were studied: (A) one consisted of n = 54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n = 15 subjects treated for 8 weeks. Connectome predictive modeling (CPM) was used to predict treatment response, as assessed with the PARS. The main analysis included network edges positively correlated with treatment outcome and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses are also presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r, and mean absolute error (MAE).

RESULTS

The main model showed a MAE of approximately 3.5 (95% CI: [3.1-3.8]) points, an R2 of 0.08 [-0.14-0.26], and an r of 0.38 [0.24-0.511]. When testing this model in the left-out sample (B), the results were similar, with an MAE of 3.4 [2.8-4.7], R2-0.65 [-2.29-0.16], and r of 0.4 [0.24-0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2.

CONCLUSIONS

The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, this study does not support the extensive use of CPM to predict outcomes in pediatric anxiety.

Relative strength variability measures for brain structural connectomes and their relationship with cognitive functioning

In this work, we propose a new class of graph measures for weighted connectivity information in the human brain based on node relative strengths: relative strength variability (RSV), measuring susceptibility to targeted attacks, and hierarchical RSV (hRSV), a first weighted statistical complexity measure for networks. Using six different network weights for structural connectomes from the UK Biobank, we conduct comprehensive analyses to explore relationships between the RSV and hRSV, and (i) other known network measures, (ii) general cognitive function (' g '). Both measures exhibit low correlations with other graph measures across all connectivity weightings indicating that they capture new information of the brain connectome. We found higher g was associated with lower RSV and lower hRSV. That is, higher g was associated with higher resistance to targeted attack and lower statistical complexity. Moreover, the proposed measures had consistently stronger associations with g than other widely used graph measures including clustering coefficient and global efficiency and were incrementally significant for predicting g above other measures for five of the six network weights. Overall, we present a new class of weighted network measures based on variations of relative node strengths which significantly improved prediction of general cognition from traditional weighted structural connectomes.

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genome-wide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.