Biology subjects

Maximov, I. I.

Publications and source records attributed to Maximov, I. I..

14 recordsLinked to original sources

bioRxiv2024

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) organization 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 complemen-tary 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 inves-tigate how tract-based and whole WM skeleton parameters from different single- and multi-compartment 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). Multi-compartment dMRI approaches provided the strongest WM associations with age, and unique insights into brain asymmetry. Kurto-sis was most indicative of PP and BMI. WM-based sex classifications and polygenic score associations for common psychiatric disorders and Alzheimers disease were similar across diffusion approaches. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life. Multi-compartment dMRI approaches, and particularly the examined Bayesian approach, provide additional information to conventional approaches in such examinations.

neuroscience↗

bioRxiv2024

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 ageing 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 (Vidal-Pineiro et al. 2021). Here, we advance these findings by using 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. In concordance with the original findings, we find insufficient evidence that BA reflects the rate of brain ageing. However, modality-specific differences in brain ages reflected the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.

neuroscience↗

bioRxiv2024

FreeSurfer version-shuffling can boost brain age predictions

Abstract / Key pointsO_LIThe influence of FreeSurfer version-dependent variability in reconstructed cortical features on brain age predictions is average small when varying training and test splits from the same data. C_LIO_LIFreeSurfer version differences can lead to some variability in brain age dependent on the choice of algorithm and individual differences in brain morphometry, highlighting the advantage of repeated random train-test splitting. C_LIO_LIShuffling of differently processed FreeSurfer data dependent on the FreeSurfer version increases performance and generalizability of the brain age prediction model. C_LI

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bioRxiv2023

Brain asymmetries from midlife to old adulthood and hemispheric brain age

The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N =48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.

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bioRxiv2023

Brain age predictions in longitudinal data reveal the importance of scan quality and field strength

IntroductionBrain age, the estimation of a persons age from magnetic resonance imaging (MRI) parameters, has been used as a general indicator of health. The marker requires however further validation for application in clinical contexts. Here, we show how brain age predictions perform for for the same individual at various time points and validate our findings with age-matched healthy controls. MethodsWe used densly sampled T1-weighted MRI data from four individuals (from two datasets) to observe how brain age corresponds to age and is influenced by acquision and quality parameters. For validation, we used two cross-sectional datasets. Brain age was predicted by a pre-trained deep learning model. ResultsWe find small within-subject correlations between age and brain age. We also find evidence for the influence of field strength on brain age which replicated in the cross-sectional validation data, and inconclusive effects of scan quality. ConclusionThe absence of maturation effects for the age range in the presented sample, brain age model-bias (including training age distribution and field strength) and model error are potential reasons for small relationships between age and brain age in longitudinal data. Future brain age models should account for differences in field strength and intra-individual differences.

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bioRxiv2022

Brain-wide associations between white matter and agehighlight the role of fornix microstructure in brain age

Unveiling the details of white matter (WM) maturation throughout ageing is a fundamental question for understanding the ageing brain. In an extensive comparison of brain age predictions and age- associations of WM features from different diffusion approaches, we analysed UK Biobank diffusion Magnetic Resonance Imaging (dMRI) data across midlife and older age (N = 35,749, 44.6 to 82.8 years of age). Conventional and advanced dMRI approaches were consistent in predicting brain age. WM-age associations indicate a steady microstructure degeneration with increasing age from midlife to older ages. Brain age was estimated best when combining diffusion approaches, showing different aspects of WM contributing to brain age. Fornix was found as the central region for brain age predictions across diffusion approaches in complement to forceps minor as another important region. These regions exhibited a general pattern of positive associations with age for intra axonal water fractions, axial, radial diffusivities and negative relationships with age for mean diffusivities, fractional anisotropy, kurtosis. We encourage the application of multiple dMRI approaches for detailed insights into WM, and the further investigation of fornix and forceps as potential biomarkers of brain age and ageing.

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bioRxiv2022

A new cortical parcellation based on systematic review of primate anatomical tracing studies on corticostriatal projections

Corticostriatal projections form the input level of a circuitry that connects the cerebral cortex, basal ganglia, and thalamus. Three distinct, functional subcircuits exist according to the tripartite model: Sensorimotor cortices projecting mainly to the dorsolateral striatum; associative cortices projecting to the dorsomedial striatum and limbic cortices projecting to the ventral striatum. However, there is to date no atlas that allows researchers to label cortical projection areas belonging to each of these subcircuits separately. To address this research gap, the aim of this study was threefold: First, to systematically review anatomical tracing studies that focused on corticostriatal projections in non-human primates, and to classify their findings according to the tripartite model. Second, to develop an atlas of the human cerebral cortex based on this classification. Third, to test the hypothesis that labels in this atlas show structural connectivity with specific striatal subregions in humans using diffusion-based tractography in a sample of 24 healthy participants. In total, 98 studies met the inclusion criteria for our systematic review. Information about projections from the cortex to the striatum was systematically extracted by Brodmann area, and cortical areas were classified by their dominant efferent projections. Taking known homological and functional similarities and differences between non-human primate and human cortical regions into account, a new human corticostriatal projection (CSP) atlas was developed. Using human diffusion-based tractography analyses, we found that the limbic and sensorimotor atlas labels showed preferential structural connectivity with the ventral and dorsolateral striatum, respectively. However, the pattern of structural connectivity for the associative label showed the greatest degree of overlap with other labels. We provide this new atlas as a freely available tool for neuroimaging studies, where it allows for the first-time delineation of anatomically informed regions-of-interest to study functional subcircuits within the corticostriatal circuitry. This tool will enable specific investigations of subcircuits involved in the pathogenesis of neuropsychiatric illness such as schizophrenia and bipolar disorders. Highlights- Systematic review of anatomical projections from the cerebral cortex to the striatum in non-human primates. - Development of a novel cortical atlas for use in neuroimaging studies focusing on the corticostriatal brain circuitry. - Tractography in human diffusion-weighted imaging data to test if associative, limbic, and sensorimotor cortical atlas labels show preferential connectivity to regions within the striatum.

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bioRxiv2021

Evidence for widespread alterations in cortical microstructure after 32 hours of sleep deprivation

Cortical microstructure is influenced by circadian rhythm and sleep deprivation, yet the precise underpinnings of these effects remain unclear. The ratio between T1-weighted and T2-weighted magnetic resonance images (T1w/T2w ratio) has been linked to myelin levels and dendrite density and may offer novel insight into the intracortical microstructure of the sleep deprived brain. Here, we examined intracortical T1w/T2w ratio in 41 healthy young adults (26 women) before and after 32 hours of either sleep deprivation (n = 18) or a normal sleep-wake cycle (n = 23). Linear models revealed significant group differences in T1w/T2w ratio change after 32 hours in four clusters, including bilateral effects in the insular, cingulate, and superior temporal cortices, comprising regions involved in attentional, auditory and pain processing. Across clusters, the sleep deprived group showed an increased T1w/T2w ratio, while the normal sleep-wake group exhibited a reduced ratio. These changes were not explained by in-scanner head movement, and 95% of the effects across clusters remained significant after adjusting for cortical thickness and hydration. Compared with a normal sleep-wake cycle, 32 hours of sleep deprivation yields intracortical T1w/T2w ratio increases. While the intracortical changes detected by this study could reflect alterations in myelin or dendritic density, or both, histological analyses are needed to clarify the precise underlying cortical processes.

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bioRxiv2020

Women with a history of previous childbirths show less evident white matter brain ageing

Maternal brain adaptations occur in response to pregnancy, but little is known about how parity impacts white matter (WM) and WM ageing trajectories later in life. Utilising global and regional brain-age prediction based on multi-shell diffusion MRI data, we investigated the association between previous childbirths and WM brain age in 8,895 women in the UK Biobank cohort (age range = 54 - 81 years). The results showed that number of previous childbirths was negatively associated with WM brain age, potentially indicating a protective effect of parity on brain WM later in life. Both global WM and grey matter brain age estimates showed unique contributions to the association with previous childbirths, suggesting partly independent processes. Corpus callosum contributed uniquely to the global WM association with previous childbirths, and showed a stronger relationship relative to several other tracts. While our findings demonstrate a link between reproductive history and brain WM characteristics later in life, longitudinal studies are required to establish causality and determine how parity may influence womens WM trajectories across the lifespan.

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bioRxiv2020

Sleep and sleep deprivation differentially alter white matter microstructure: a mixed model design utilising advanced diffusion modelling

Sleep deprivation influences several critical functions, yet how it affects human brain white matter (WM) is not well understood. The aim of the present work was to investigate the effect of 32 hours of sleep deprivation on WM microstructure compared to changes observed in a normal sleep-wake cycle (SWC). To this end, we utilised diffusion weighted imaging (DWI) including the diffusion tensor model, diffusion kurtosis imaging and the spherical mean technique, a novel biophysical diffusion model. 46 healthy adults (23 sleep deprived vs 23 with normal SWC) underwent DWI across 4 time points (morning, evening, next day morning and next day afternoon, after a total of 32 hours). Linear mixed models revealed significant group x time interaction effects, indicating that sleep deprivation and normal SWC differentially affect WM microstructure. Voxel-wise comparisons showed that these effects spanned large, bilateral WM regions. These findings provide important insight into how sleep deprivation affects the human brain.

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bioRxiv2020

White matter microstructure across the adult lifespan: A mixed longitudinal and cross-sectional study using advanced diffusion models and brain-age prediction

The macro- and microstructural architecture of human brain white matter undergoes substantial alterations throughout development and ageing. Most of our understanding of the spatial and temporal characteristics of these lifespan adaptations come from magnetic resonance imaging (MRI), including diffusion MRI (dMRI), which enables visualisation and quantification of brain white matter with unprecedented sensitivity and detail. However, with some notable exceptions, previous studies have relied on cross-sectional designs, limited age ranges, and diffusion tensor imaging (DTI) based on conventional single-shell dMRI. In this mixed cross-sectional and longitudinal study (mean interval: 15.2 months) including 702 multi-shell dMRI datasets, we combined complementary dMRI models to investigate age trajectories in healthy individuals aged 18 to 94 years (57.12% women). Using linear mixed effect models and machine learning based brain age prediction, we assessed the age-dependence of diffusion metrics, and compared the age prediction accuracy of six different diffusion models, including diffusion tensor (DTI) and kurtosis imaging (DKI), neurite orientation dispersion and density imaging (NODDI), restriction spectrum imaging (RSI), spherical mean technique multi-compartment (SMT-mc), and white matter tract integrity (WMTI). The results showed that the age slopes for conventional DTI metrics (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity [AD], radial diffusivity [RD]) were largely consistent with previous research, and that the highest performing advanced dMRI models showed comparable age prediction accuracy to conventional DTI. Linear mixed effects models and Wilks theorem analysis showed that the FA fine metric of the RSI model and orientation dispersion (OD) metric of the NODDI model showed the highest sensitivity to age. The results indicate that advanced diffusion models (DKI, NODDI, RSI, SMT mc, WMTI) provide sensitive measures of age-related microstructural changes of white matter in the brain that complement and extend the contribution of conventional DTI.

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bioRxiv2020

Feasibility of axon density metrics for brain asymmetry evaluation in the UK Biobank subsample

Standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, a non-linearity of the general model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reproducible and reliable diffusion approaches and to avoid computational model degeneracy, one needs to devise additional theoretical assumptions allowing a stable numerical implementation. As a result, it is possible to estimate intra-axonal water fraction (AWF) representing one of the important structural parameters. AWF can be treated as an indirect measure of axon density and has a strong potential as useful clinical biomarker. A few diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging, and spherical mean technique, allow one to evaluate AWF in the frame of their theoretical assumptions. In the present study, we considered the compatibility of axon density metrics obtained from different diffusion models and the influence of the diffusion metric on a brain asymmetry estimation in UK Biobank sample consisting of 182 subjects. We found AWF derived from a spherical mean technique is the most statistically representative measure. As a result, we revealed that brain asymmetry indecies derived from intra-axonal water fraction weakly decrease along the lifespan, reducing the left-right hemisphere difference within increased age.

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bioRxiv2019

Brain age prediction reveals aberrant brain white matter in schizophrenia and bipolar disorder: A multi-sample diffusion tensor imaging study

BackgroundSchizophrenia (SZ) and bipolar disorders (BD) share substantial neurodevelopmental components affecting brain maturation and architecture. This necessitates a dynamic lifespan perspective in which brain aberrations are inferred from deviations from expected lifespan trajectories. We applied machine learning to diffusion tensor imaging (DTI) indices of white matter structure and organization to estimate and compare brain age between patients with SZ, BD, and healthy controls across 10 cohorts. MethodsWe trained six cross-validated models using different combinations of DTI data from 927 healthy controls (HC, 18-94 years), and applied the models to the test sets including 648 SZ (18-66 years) patients, 185 BD patients (18-64 years), and 990 HC (17-68 years), estimating brain age for each participant. Group differences were assessed using linear models, accounting for age, sex, and scanner. A meta-analytic framework was applied to assess the heterogeneity and generalizability of the results. Results10-fold cross-validation revealed high accuracy for all models. Compared to controls, the model including all feature sets significantly over-estimated the age of patients with SZ (d=-.29) and BD (d=.18), with similar effects for the other models. The meta-analysis converged on the same findings. Fractional anisotropy (FA) based models showed larger group differences than the models based on other DTI-derived metrics. ConclusionsBrain age prediction based on DTI provides informative and robust proxies for brain white matter integrity. Our results further suggest that white matter aberrations in SZ and BD primarily consist of anatomically distributed deviations from expected lifespan trajectories that generalize across cohorts and scanners.

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bioRxiv2019

Towards an optimised processing pipeline for diffusion MRI data: Effects of artefact corrections on diffusion metrics and their age associations in UK Biobank

Increasing interest in the structural and functional organization of the human brain in health and disease encourages the acquisition of big datasets consisting of multiple neuroimaging modalities accompanied by additional information obtained from health records, cognitive tests, biomarkers and genotypes. Diffusion weighted magnetic resonance imaging data enables a range of promising imaging phenotypes probing structural connections as well as macroanatomical and microstructural properties of the brain. The reliability and biological sensitivity and specificity of diffusion data depend on processing pipeline. A state-of-the-art framework for data processing facilitates crossstudy harmonisation and reduces pipeline-related variability. Using diffusion data from the UK Biobank we provide a comprehensive evaluation of different processing steps that have been suggested to reduce imaging artefacts and improve reliability of diffusion metrics. We consider a general pipeline comprising 7 post-processing blocks: noise correction; Gibbs ringing correction; evaluation of field distortions; susceptibility, eddy-current and motion-induced distortion corrections; bias field correction; spatial smoothing; and final diffusion metric estimations. Based on this evaluation, we suggest an optimised diffusion pipeline for processing of diffusion weighted imaging data.

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