Evolution of white matter tract microstructure across the life span

Enduring differential patterns of neuronal loss and myelination along 6-month pulsatile gonadotropin-releasing hormone therapy in individuals with Down syndrome

Despite major progress in understanding the impact of the triplicated chromosome 21 on the brain and behaviour in Down syndrome, our knowledge of the underlying neurobiology in humans is still limited. We sought to address some of the pertinent questions about the drivers of brain structure differences and their associations with cognitive function in Down syndrome. To this aim, in a pilot magnetic resonance imaging (MRI) study, we monitored brain anatomy in individuals with Down syndrome receiving pulsatile gonadotropin-releasing hormone (GnRH) therapy over 6 months in comparison with typically developed age- and sex-matched healthy controls. We analysed cross-sectional (Down syndrome/healthy controls n  = 11/27; Down syndrome-2 females/9 males, age 26.7 ± 5.0 years old; healthy controls-8 females/19 males, age 24.1 ± 2.5 years old) and longitudinal (Down syndrome/healthy controls n  = 8/13; Down syndrome-1 female/7 males, age 26.4 ± 5.3 years old; healthy controls-4 females/9 males, 24.7 ± 2.2 years old) relaxometry and diffusion-weighted MRI data alongside standard cognitive assessment. The statistical tests looked for cross-sectional baseline differences and for differential changes over time between Down syndrome and healthy controls. The post hoc analysis confined to the Down syndrome group, tested for potential time-dependent interactions between individuals' overall cognitive performance and associated brain anatomy changes. The brain MRI statistical analyses covered both grey and white matter regions across the whole brain allowing for investigation of regional volume, macromolecular/myelin and iron content, additionally to diffusion tensor and neurite orientation and dispersion density characterization across major white matter tracts. The cross-sectional analysis showed reduced frontal, temporal and cerebellar volumes in Down syndrome with only the cerebellar differences remaining significant after adjustment for the presence of microcephaly (P family-wise-corrected < 0.05). The volume reductions were paralleled by decreased cortical and subcortical macromolecular/myelin content confined to the cortical motor system, thalamus and basal ganglia (P family-wise-corrected < 0.05). All major white matter tracts showed a ubiquitous mean diffusivity and intracellular volume fraction reduction contrasted with no differences in magnetization transfer saturation metrics (P family-wise-corrected < 0.05). Compared with healthy controls over the same period, Down syndrome individuals under GnRH therapy showed cognitive improvement (Montreal Cognitive Assessment from 11.4 ± 5.5 to 15.1 ± 5.6; P < 0.01) on the background of stability of the observed differential neuroanatomical patterns. Despite the lack of adequate Down syndrome control group, we interpret the obtained cross-sectional and longitudinal findings in young adults as evidence for predominant neurodevelopmental neuronal loss due to dysfunctional neurogenesis without signs for short-term myelin loss.

From Circuits to Lifespan: Translating Mouse and Human Timelines with Neuroimaging-Based Tractography

Animal models are commonly used to investigate developmental processes and disease risk, but humans and model systems (e.g., mice) differ substantially in the pace of development and aging. The timeline of human developmental circuits is well known, but it is unclear how such timelines compare with those in mice. We lack age alignments across the lifespan of mice and humans. Here, we build upon our Translating Time resource, which is a tool that equates corresponding ages during development. We collected 1,125 observations from age-related changes in body, bone, dental, and brain processes to equate corresponding ages across humans, mice, and rats to boost power for comparison across humans and mice. We acquired high-resolution diffusion MR scans of mouse brains (n = 16) of either sex at sequential stages of postnatal development [postnatal day (P)3, 4, 12, 21, 60] to track brain circuit maturation (e.g., olfactory association, transcallosal pathways). We found heterogeneity in white matter pathway growth. Corpus callosum growth largely ceases days after birth, while the olfactory association pathway grows through P60. We found that a P3-4, mouse equates to a human at roughly GW24 and a P60 mouse equates to a human in teenage years. Therefore, white matter pathway maturation is extended in mice as it is in humans, but there are species-specific adaptations. For example, olfactory-related wiring is protracted in mice, which is linked to their reliance on olfaction. Our findings underscore the importance of translational tools to map common and species-specific biological processes from model systems to humans.

Sleep Health and White Matter Integrity in the UK Biobank

Many people experience impaired sleep health, yet knowledge about its neurobiological correlates is limited. As previous studies have found associations between white matter integrity and several sleep traits, white matter integrity could be causally implicated in poor sleep health. However, these studies were often limited by small sample sizes. In this study, we examine associations between multiple indices of white matter integrity and sleep health in 29,114 UK Biobank participants. Late chronotype, daytime sleepiness, insomnia symptoms and, most extensively, long sleep duration were independently associated with diffusion MRI markers of reduced white matter integrity. Previous findings showing an association between insomnia symptoms and decreased fractional anisotropy (FA) in the anterior internal capsule could not be replicated. To our knowledge, the current analysis is the first study to find an association between long sleep duration and impaired microstructural white matter integrity. Previous assumptions concerning the role of white matter integrity for insomnia are challenged.

Subject-Specific Mapping of Excess Manganese Accumulation in the Brain of Welders Using Magnetic Resonance Imaging Relaxometry

Chronic overexposure to manganese (Mn) can occur in occupational settings, such as welding, leading to increased Mn levels in the brain. Excess brain Mn accumulation may result in neurotoxicity, which is characterized by Parkinsonian-like symptoms including motor and cognitive dysfunctions. In this work, we demonstrate a novel methodology for personalized diagnosis and spatial characterization of abnormal Magnetic Resonance Imaging R1 (R1 = 1/T1) relaxation rates arising from excessive Mn accumulation in welders' brains. Utilizing voxel-wise population-derived norms based on a frequency age-matched non-exposed group (n = 25), we demonstrate the ability to conduct subject-specific assessments and mapping of Mn exposure using MRI relaxometry. Our results show elevated R1 in multiple brain regions in individual welders, but also extreme between-subject variability in Mn accumulation, debasing the concept that high exposures correlate with uniformly high Mn deposition in the brain. Consequently, the presented personalized methodology serves as a counterpart to group-based comparison, which allows for understanding the level of individual exposure and the toxicokinetics of Mn accumulation. This work lays a foundation for improved occupational health assessments and preventive measures against neurotoxic metal exposure.

Polygenic scores for autism are associated with reduced neurite density in adults and children from the general population

Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.

Artificial Intelligence for Neuroimaging in Pediatric Cancer

BACKGROUND/OBJECTIVES

Artificial intelligence (AI) is transforming neuroimaging by enhancing diagnostic precision and treatment planning. However, its applications in pediatric cancer neuroimaging remain limited. This review assesses the current state, potential applications, and challenges of AI in pediatric neuroimaging for cancer, emphasizing the unique needs of the pediatric population.

METHODS

A comprehensive literature review was conducted, focusing on AI's impact on pediatric neuroimaging through accelerated image acquisition, reduced radiation, and improved tumor detection. Key methods include convolutional neural networks for tumor segmentation, radiomics for tumor characterization, and several tools for functional imaging. Challenges such as limited pediatric datasets, developmental variability, ethical concerns, and the need for explainable models were analyzed.

RESULTS

AI has shown significant potential to improve imaging quality, reduce scan times, and enhance diagnostic accuracy in pediatric neuroimaging, resulting in improved accuracy in tumor segmentation and outcome prediction for treatment. However, progress is hindered by the scarcity of pediatric datasets, issues with data sharing, and the ethical implications of applying AI in vulnerable populations.

CONCLUSIONS

To overcome current limitations, future research should focus on building robust pediatric datasets, fostering multi-institutional collaborations for data sharing, and developing interpretable AI models that align with clinical practice and ethical standards. These efforts are essential in harnessing the full potential of AI in pediatric neuroimaging and improving outcomes for children with cancer.

Different Grey Matter Microstructural Patterns in Cognitively Healthy Versus Typical Ageing Healthy Versus Typical Brain Ageing

Ageing is a complex phenomenon affecting a wide range of coexisting biological processes. The homogeneity of the studied population is an essential parameter for valid interpretations of outcomes. The presented study capitalises on the MRI data available in the Human Connectome Project-Aging (HCP-A) and, within individuals over 55 years of age who passed the HCP-A section criteria, compares a subgroup of 37 apparently neurocognitively healthy individuals selected based on stringent criteria with 37 age and sex-matched individuals still representative of typical ageing but who did not pass the stringent definition of neurocognitively healthy. Specifically, structural scans, diffusion weighted imaging and T1w/T2w ratio were utilised. Furthermore, data of 26 HCP-A participants older than 90 years as notional 'super-agers' were analysed. The relationship of age and several microstructural MRI metrics (T1w/T2w ratio, mean diffusivity, intracellular volume fraction and free water volume fraction) differed significantly between typical and healthy ageing cohort in areas highly relevant for ageing such as hippocampus, prefrontal and temporal cortex and cerebellum. However, the trajectories of the healthy ageing population did not show substantially better overlap with the findings in people older than 90 than those of the typical population. Therefore, caution must be exercised in the choice of adequate study group characteristics relevant for respective ageing-related hypotheses. Contrary to typical ageing group, the healthy ageing cohort may show generally stable levels of several MRI metrics of interest.

Hearing loss and its relation to longitudinal changes in white matter microstructure in older adults: The Rotterdam Study

Hearing loss is considered a potentially modifiable risk factor for dementia. The sensory deprivation theory postulates that hearing loss adversely affects cognition in older adults through structural brain changes, but longitudinal studies are scarce. To find evidence for a possible detrimental effect of hearing loss on white matter microstructure, we carried out a longitudinal study in the population-based Rotterdam Study. A total of 1877 participants with a median age at baseline of 56.4 years (IQR: [52.2-60.0]) underwent audiometry and had longitudinal diffusion imaging data available with a mean follow-up of 4.0 years. A lower level of hearing acuity was associated with worse white matter microstructure in the left uncinate fasciculus and superior longitudinal fasciculus at baseline. Poorer hearing acuity was also associated with faster microstructural deterioration over time in the left superior longitudinal fasciculus. The strongest effects were observed for low-frequency hearing thresholds, while the high-frequency thresholds showed the weakest associations. These results suggest that hearing loss may contribute to the age-related decline in brain structure, consistent with the sensory deprivation theory.

Frontotemporal structure preservation underlies the protective effect of lifetime intellectual cognitive reserve on cognition in the elderly

BACKGROUND

Cognitive decline with age has heterogeneous, which might be related to the accumulation of protective factors called cognitive reserve, especially intellectual engagement factors over the life course. However, how lifetime intellectual cognitive reserve (LICR) protects cognitive function in the elderly remains unclear. We aimed to examine the relationship between LICR and cognition and the mild cognitive impairment (MCI) risk, as well as the neural mechanism of LICR on cognition.

METHODS

A total of 5126 participants completed extensive neuropsychological tests, with LICR indicator encompassing early education, midlife occupational complexity, and mental leisure activities after retirement. Confirmatory factor analysis was performed to derive LICR score and cognitive function scores, then the hierarchical regression analysis was used to explore the relationship between LICR and cognitive functions and the risk of MCI. We further explored the macro- and micro-structural preservation underly LICR in 1117 participants. Multiple regressions and tract-based spatial statistics were used to explore the relationship between LICR and gray matter volume and white matter microstructure (FA value). Finally, using the mediation model to explore the relationship of "LICR-brain-cognition".

RESULT

The new LICR index, which was more protective than its single indexes, could protect widespread cognitive functions and was associated with a reduction in MCI risk (Odds Ratio, 0.52; 95% CI, 0.47-0.57). For the structure basis of LICR, the higher LICR score was associated with the greater gray matter volume in right fusiform gyrus (t = 4.62, FDR corrected, p < 0.05) and left orbital superior frontal gyrus (t = 4.56, FDR corrected, p < 0.05), and the higher FA values in the frontotemporal related white matter fiber tracts. Furthermore, the right fusiform gyrus partially mediated the relationship between LICR and executive processing ability (β = 0.01, p = 0.02) and general cognitive ability (β = 0.01, p = 0.03).

CONCLUSIONS

The new comprehensive cognitive reserve index could promote the temporal macro-structural preservation and thus contribute to maintain better cognitive function. These findings highlight the importance of intellectual CR accumulation over the life course in successful cognitive aging and MCI prevention, thereby contributing to improve the quality of life in the elderly.

Testing retrogenesis and physiological explanations for tract-wise white matter aging: links to developmental order, fibre calibre, and vascularization

To understand the consistently observed spatial distribution of white-matter (WM) aging, developmentally driven theories termed "retrogenesis" have gained traction, positing that the order of WM tract development predicts the order of declines. Regions that develop first are expected to deteriorate the last, i.e. "last-in-first-out". Alternatively, regions which develop most rapidly may also decline most rapidly in aging, or "gains-predict-loss". The validity of such theories remains uncertain, in part due to lack of clarity on the definition of developmental order. Importantly, our recent findings suggest that WM aging is also associated with physiological parameters such as perfusion, which may be linked to fibre metabolic need, which in turn varies with fibre size. Here we address the extent to which the degree of WM aging is determined by development trajectory (i.e. retrogenesis) and/or by physiological state. We obtained microstructural and perfusion measures using data from the Human Connectome Project in Aging (HCP-A), complemented by a meta-analysis involving maps of fibre calibre and macrovascular volume. Our results suggest that (1) while tracts that appear last or finish myelinating first in development display the slowest aging, the pattern of aging is not fully explained by retrogenesis; in fact, time courses of tract emergence and myelination give rise to opposite associations with WM decline; (2) tracts that appear earlier also have higher mean axon calibre and are also associated with lower degrees of WM microstructural aging; (3) such tracts also tend to exhibit relatively sustained CBF with a higher rate of lengthening of the arterial transit times (ATT), suggestive of collateral blood supply. These findings were also sex dependent in a tract-specific manner. Future work will investigate whether these are ultimately influenced by each tract's metabolic demand and the role of macrovascular collateral flow.

Asymmetry of the Frontal Aslant Tract Depends on Handedness

BACKGROUND AND PURPOSE

The human brain displays structural and functional disparities between its hemispheres, with such asymmetry extending to the frontal aslant tract. This plays a role in a variety of cognitive functions, including speech production, language processing, and executive functions. However, the factors influencing the laterality of the frontal aslant tract remain incompletely understood. Handedness is hypothesized to impact frontal aslant tract laterality, given its involvement in both language and motor control. In this study, we aimed to investigate the relationship between handedness and frontal aslant tract lateralization, providing insight into this aspect of brain organization.

MATERIALS AND METHODS

The Automated Tractography Pipeline was used to generate the frontal aslant tract for both right and left hemispheres in a cohort of 720 subjects sourced from the publicly available Human Connectome Project in Aging database. Subsequently, macrostructural and microstructural parameters of the right and left frontal aslant tract were extracted for each individual in the study population. The Edinburgh Handedness Inventory scores were used for the classification of handedness, and a comparative analysis across various handedness groups was performed.

RESULTS

An age-related decline in both macrostructural parameters and microstructural integrity was noted within the studied population. The frontal aslant tract demonstrated a greater volume and larger diameter in male subjects compared with female participants. Additionally, a left-side laterality of the frontal aslant tract was observed within the general population. In the right-handed group, the volume (P < .001), length (P < .001), and diameter (P = .004) of the left frontal aslant tract were found to be higher than those of the right frontal aslant tract. Conversely, in the left-handed group, the volume (P = .040) and diameter (P = .032) of the left frontal aslant tract were lower than those of the right frontal aslant tract. Furthermore, in the right-handed group, the volume and diameter of the frontal aslant tract showed left-sided lateralization, while in the left-handed group, a right-sided lateralization was evident.

CONCLUSIONS

The laterality of the frontal aslant tract appears to differ with handedness. This finding highlights the complex interaction between brain lateralization and handedness, emphasizing the importance of considering handedness as a factor in evaluating brain structure and function.

Prediction of H3K27M Alteration Status in Brainstem Glioma Using Multi-Shell Diffusion MRI Metrics

BACKGROUND

Multi-shell diffusion characteristics may help characterize brainstem gliomas (BSGs) and predict H3K27M status.

PURPOSE

To identify the diffusion characteristics of BSG patients and investigate the predictive values of various diffusion metrics for H3K27M status in BSG.

STUDY TYPE

Prospective.

POPULATION

Eighty-four BSG patients (median age 10.5 years [IQR 6.8-30.0 years]) were included, of whom 56 were pediatric and 28 were adult patients.

FIELD STRENGTH/SEQUENCE

3 T, multi-shell diffusion imaging.

ASSESSMENT

Diffusion kurtosis imaging and neurite orientation dispersion and density imaging analyses were performed. Age, gender, and diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity, radial diffusivity (RD), mean kurtosis (MK), axial kurtosis (AK), radial kurtosis, intracellular volume fraction (ICVF), orientation dispersion index, and isotropic volume fraction (ISOVF), were compared between H3K27M-altered and wildtype BSG patients.

STATISTICAL TESTS

Chi-square test, Mann-Whitney U test, multivariate analysis of variance (MANOVA), step-wise multivariable logistic regression. P-values <0.05 were considered significant.

RESULTS

82.4% pediatric and 57.1% adult patients carried H3K27M alteration. In the whole group, the H3K27M-altered BSGs demonstrated higher FA, AK and lower RD, ISOVF. The combination of age and median ISOVF showed fair performance for H3K27M prediction (AUC = 0.78). In the pediatric group, H3K27M-altered BSGs showed higher FA, AK, MK, ICVF and lower RD, MD, ISOVF. The combinations of median ISOVF, 5th percentile of FA, median MK and median MD showed excellent predictive power (AUC = 0.91). In the adult group, H3K27M-altered BSGs showed higher ICVF and lower RD, MD. The 75th percentile of RD demonstrated fair performance for H3K27M status prediction (AUC = 0.75).

DATA CONCLUSION

Different alteration patterns of diffusion measures were identified between H3K27M-altered and wildtype BSGs, which collectively had fair to excellent predictive value for H3K27M alteration status, especially in pediatric patients.

EVIDENCE LEVEL

2 TECHNICAL EFFICACY: Stage 3.

Superagers Resist Typical Age-Related White Matter Structural Changes

Superagers are elderly individuals with the memory ability of people 30 years younger and provide evidence that age-related cognitive decline is not inevitable. In a sample of 64 superagers (mean age, 81.9; 59% women) and 55 typical older adults (mean age, 82.4; 64% women) from the Vallecas Project, we studied, cross-sectionally and longitudinally over 5 years with yearly follow-ups, the global cerebral white matter status as well as region-specific white matter microstructure assessment derived from diffusivity measures. Superagers and typical older adults showed no difference in global white matter health (total white matter volume, Fazekas score, and lesions volume) cross-sectionally or longitudinally. However, analyses of diffusion parameters revealed the better white matter microstructure in superagers than in typical older adults. Cross-sectional differences showed higher fractional anisotropy (FA) in superagers mostly in frontal fibers and lower mean diffusivity (MD) in most white matter tracts, expressed as an anteroposterior gradient with greater group differences in anterior tracts. FA decrease over time is slower in superagers than in typical older adults in all white matter tracts assessed, which is mirrored by MD increases over time being slower in superagers than in typical older adults in all white matter tracts except for the corticospinal tract, the uncinate fasciculus, and the forceps minor. The better preservation of white matter microstructure in superagers relative to typical older adults supports resistance to age-related brain structural changes as a mechanism underpinning the remarkable memory capacity of superagers, while their regional aging pattern is in line with the last-in-first-out hypothesis.

Age-related Differences in Response Inhibition Are Mediated by Frontoparietal White Matter but Not Functional Activity

Healthy older adults often exhibit lower performance but increased functional recruitment of the frontoparietal control network during cognitive control tasks. According to the cortical disconnection hypothesis, age-related changes in the microstructural integrity of white matter may disrupt inter-regional neuronal communication, which in turn can impair behavioral performance. Here, we use fMRI and diffusion-weighted imaging to determine whether age-related differences in white matter microstructure contribute to frontoparietal over-recruitment and behavioral performance during a response inhibition (go/no-go) task in an adult life span sample (n = 145). Older and female participants were slower (go RTs) than younger and male participants, respectively. However, participants across all ages were equally accurate on the no-go trials, suggesting some participants may slow down on go trials to achieve high accuracy on no-go trials. Across the life span, functional recruitment of the frontoparietal network within the left and right hemispheres did not vary as a function of age, nor was it related to white matter fractional anisotropy (FA). In fact, only frontal FA and go RTs jointly mediated the association between age and no-go accuracy. Our results therefore suggest that frontal white matter cortical "disconnection" is an underlying driver of age-related differences in cognitive control, and white matter FA may not fully explain functional task-related activation in the frontoparietal network during the go/no-go task. Our findings add to the literature by demonstrating that white matter may be more important for certain cognitive processes in aging than task-related functional activation.

Brain elastography in aging relates to fluid/solid trendlines

The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.

The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood

Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.

The Genomic Intersection of Oligodendrocyte Dynamics in Schizophrenia and Aging Unravels Novel Pathological Mechanisms and Therapeutic Potentials

Schizophrenia is a significant worldwide health concern, affecting over 20 million individuals and contributing to a potential reduction in life expectancy by up to 14.5 years. Despite its profound impact, the precise pathological mechanisms underlying schizophrenia continue to remain enigmatic, with previous research yielding diverse and occasionally conflicting findings. Nonetheless, one consistently observed phenomenon in brain imaging studies of schizophrenia patients is the disruption of white matter, the bundles of myelinated axons that provide connectivity and rapid signalling between brain regions. Myelin is produced by specialised glial cells known as oligodendrocytes, which have been shown to be disrupted in post-mortem analyses of schizophrenia patients. Oligodendrocytes are generated throughout life by a major population of oligodendrocyte progenitor cells (OPC), which are essential for white matter health and plasticity. Notably, a decline in a specific subpopulation of OPC has been identified as a principal factor in oligodendrocyte disruption and white matter loss in the aging brain, suggesting this may also be a factor in schizophrenia. In this review, we analysed genomic databases to pinpoint intersections between aging and schizophrenia and identify shared mechanisms of white matter disruption and cognitive dysfunction.

Polygenic scores for autism are associated with neurite density in adults and children from the general population

Genetic variants linked to autism are thought to change cognition and behaviour by altering the structure and function of the brain. Although a substantial body of literature has identified structural brain differences in autism, it is unknown whether autism-associated common genetic variants are linked to changes in cortical macro- and micro-structure. We investigated this using neuroimaging and genetic data from adults (UK Biobank, N = 31,748) and children (ABCD, N = 4,928). Using polygenic scores and genetic correlations we observe a robust negative association between common variants for autism and a magnetic resonance imaging derived phenotype for neurite density (intracellular volume fraction) in the general population. This result is consistent across both children and adults, in both the cortex and in white matter tracts, and confirmed using polygenic scores and genetic correlations. There were no sex differences in this association. Mendelian randomisation analyses provide no evidence for a causal relationship between autism and intracellular volume fraction, although this should be revisited using better powered instruments. Overall, this study provides evidence for shared common variant genetics between autism and cortical neurite density.

An open relaxation-diffusion MRI dataset in neurosurgical studies

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset's quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

Impact of corpus callosum integrity on functional interhemispheric connectivity and cognition in healthy subjects

To examine the corpus callosum's (CC) integrity in terms of fractional anisotropy (FA) and how it affects resting-state hemispheric connectivity (rs-IHC) and cognitive function in healthy individuals. Sixty-eight healthy individuals were recruited for the study. The global FA (gFA) and FA values of each CC tract (forceps minor, body, tapetum, and forceps major) were evaluated using diffusion-weighted imaging (DWI) sequences. The homotopic functional connectivity technique was used to quantify the effects of FA in the CC tracts on bilateral functional connectivity, including the confounding effect of gFA. Brain regions with higher or lower rs-IHC were identified using the threshold-free cluster enhancement family-wise error-corrected p-value of 0.05. The null hypothesis was rejected if the p-value was ≤ 0.05 for the nonparametric partial correlation technique. Several clusters of increased rs-IHC were identified in relation to the FA of individual CC tracts, each with a unique topographic distribution and extension. Only forceps minor FA values correlated with cognitive scores. The integrity of CC influences rs-IHC differently in healthy subjects. Specifically, forceps minor anisotropy impacts rs-IHC and cognition more than other CC tracts do.

Age-related differences in structural and resting-state functional brain network organization across the adult lifespan: A cross-sectional study

We investigated age-related trends in the topology and hierarchical organization of brain structural and functional networks using diffusion-weighted imaging and resting-state fMRI data from a large cohort of healthy aging adults. At the cross-modal level, we explored age-related patterns in the RC involvement of different functional subsystems using a high-resolution functional parcellation. We further assessed age-related differences in the structure-function coupling as well as the network vulnerability to damage to rich club connectivity. Regardless of age, the structural and functional brain networks exhibited a rich club organization and small-world topology. In older individuals, we observed reduced integration and segregation within the frontal-occipital regions and the cerebellum along the brain's medial axis. Additionally, functional brain networks displayed decreased integration and increased segregation in the prefrontal, centrotemporal, and occipital regions, and the cerebellum. In older subjects, structural networks also exhibited decreased within-network and increased between-network RC connectivity. Furthermore, both within-network and between-network RC connectivity decreased in functional networks with age. An age-related decline in structure-function coupling was observed within sensory-motor, cognitive, and subcortical networks. The structural network exhibited greater vulnerability to damage to RC connectivity within the language-auditory, visual, and subcortical networks. Similarly, for functional networks, increased vulnerability was observed with damage to RC connectivity in the cerebellum, language-auditory, and sensory-motor networks. Overall, the network vulnerability decreased significantly in subjects older than 70 in both networks. Our findings underscore significant age-related differences in both brain functional and structural RC connectivity, with distinct patterns observed across the adult lifespan.

Evaluation of tractography-based myelin-weighted connectivity across the lifespan

Introduction

Recent studies showed that the myelin of the brain changes in the life span, and demyelination contributes to the loss of brain plasticity during normal aging. Diffusion-weighted magnetic resonance imaging (dMRI) allows studying brain connectivity in vivo by mapping axons in white matter with tractography algorithms. However, dMRI does not provide insight into myelin; thus, combining tractography with myelin-sensitive maps is necessary to investigate myelin-weighted brain connectivity. Tractometry is designated for this purpose, but it suffers from some serious limitations. Our study assessed the effectiveness of the recently proposed Myelin Streamlines Decomposition (MySD) method in estimating myelin-weighted connectomes and its capacity to detect changes in myelin network architecture during the process of normal aging. This approach opens up new possibilities compared to traditional Tractometry.

Methods

In a group of 85 healthy controls aged between 18 and 68 years, we estimated myelin-weighted connectomes using Tractometry and MySD, and compared their modulation with age by means of three well-known global network metrics.

Results

Following the literature, our results show that myelin development continues until brain maturation (40 years old), after which degeneration begins. In particular, mean connectivity strength and efficiency show an increasing trend up to 40 years, after which the process reverses. Both Tractometry and MySD are sensitive to these changes, but MySD turned out to be more accurate.

Conclusion

After regressing the known predictors, MySD results in lower residual error, indicating that MySD provides more accurate estimates of myelin-weighted connectivity than Tractometry.

Development of white matter fiber covariance networks supports executive function in youth

During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition.

Comparing neuromotor functions in 45- and 65-year-old adults with 18-year-old adolescents

Aim

This cross-sectional analysis investigates how neuromotor functions of two independent cohorts of approximately 45- and 65-year-old individuals are different from 18-year-old adolescents using the Zurich Neuromotor Assessment-2 (ZNA-2).

Methods

A total of 186 individuals of the Zurich Longitudinal Studies (ZLS) born in the 1950s (mean age 65.1 years, SD = 1.2 year, range of ages 59.0-67.5 years, n = 151, 82 males) and 1970s (mean age 43.6 years, SD = 1.3 year, range of ages 40.8-46.6 years, n = 35, 16 males) were tested with the ZNA-2 on 14 motor tasks combined in 5 motor components: fine motor, pure motor, balance, gross motor, and associated movements. Motor performance measures were converted into standard deviation scores (SDSs) using the normative data for 18-year-old individuals as reference.

Results

The motor performance of the 45-year-old individuals was remarkably similar to that of the 18-year-olds (SDS from -0.22 to 0.25) apart from associated movements (-0.49 SDS). The 65-year-olds showed lower performance than the 18-year-olds in all components of the ZNA-2, with the smallest difference observed for associated movements (-0.67 SDS) and the largest for gross motor skills (-2.29 SDS). Higher body mass index (BMI) was associated with better performance on gross motor skills for 45-year-olds but with worse performance for 65-year-olds. More educational years had positive effects on gross motor skills for both ages.

Interpretation

With the exception of associated movements, neuromotor functions as measured with the ZNA-2 are very similar in 45- and 18-year-olds. In contrast, at age 65 years, all neuromotor components show significantly lower function than the norm population at 18 years. Some evidence was found for the last-in-first-out hypothesis: the functions that developed later during adolescence, associated movements and gross motor skills, were the most vulnerable to age-related decline.

Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping

Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.

Single-subject electroencephalography measurement of interhemispheric transfer time for the in-vivo estimation of axonal morphology

Assessing axonal morphology in vivo opens new avenues for the combined study of brain structure and function. A novel approach has recently been introduced to estimate the morphology of axonal fibers from the combination of magnetic resonance imaging (MRI) data and electroencephalography (EEG) measures of the interhemispheric transfer time (IHTT). In the original study, the IHTT measures were computed from EEG data averaged across a group, leading to bias of the axonal morphology estimates. Here, we seek to estimate axonal morphology from individual measures of IHTT, obtained from EEG data acquired in a visual evoked potential experiment. Subject-specific IHTTs are computed in a data-driven framework with minimal a priori constraints, based on the maximal peak of neural responses to visual stimuli within periods of statistically significant evoked activity in the inverse solution space. The subject-specific IHTT estimates ranged from 8 to 29 ms except for one participant and the between-session variability was comparable to between-subject variability. The mean radius of the axonal radius distribution, computed from the IHTT estimates and the MRI data, ranged from 0 to 1.09 μm across subjects. The change in axonal g-ratio with axonal radius ranged from 0.62 to 0.81 μm-α . The single-subject measurement of the IHTT yields estimates of axonal morphology that are consistent with histological values. However, improvement of the repeatability of the IHTT estimates is required to improve the specificity of the single-subject axonal morphology estimates.

White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan

Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.

White matter microstructural integrity continues to develop from adolescence to young adulthood in mice and humans: Same phenotype, different mechanism

As direct evaluation of a mouse model of human neurodevelopment, adolescent and young adult mice and humans underwent MR diffusion tensor imaging to quantify age-related differences in microstructural integrity of brain white matter fibers. Fractional anisotropy (FA) was greater in older than younger mice and humans. Despite the cross-species commonality, the underlying developmental mechanism differed: whereas evidence for greater axonal extension contributed to higher FA in older mice, evidence for continuing myelination contributed to higher FA in human adolescent development. These differences occurred in the context of species distinctions in overall brain growth: whereas the continued growth of the brain and skull in the murine model can accommodate volume expansion into adulthood, human white matter volume and myelination continue growth into adulthood within a fixed intracranial volume. Appreciation of the similarities and differences in developmental mechanism can enhance the utility of animal models of brain white matter structure, function, and response to exogenous manipulation.

Metabolic syndrome and adiposity: Risk factors for decreased myelin in cognitively healthy adults

Metabolic syndrome (MetS) is a cluster of conditions that affects ∼25% of the global population, including excess adiposity, hyperglycemia, dyslipidemia, and elevated blood pressure. MetS is one of major risk factors not only for chronic diseases, but also for dementia and cognitive dysfunction, although the underlying mechanisms remain poorly understood. White matter is of particular interest in the context of MetS due to the metabolic vulnerability of myelin maintenance, and the accumulating evidence for the importance of the white matter in the pathophysiology of dementia. Therefore, we investigated the associations of MetS risk score and adiposity (combined body mass index and waist circumference) with myelin water fraction measured with myelin water imaging. In 90 cognitively and neurologically healthy adults (20-79 years), we found that both high MetS risk score and adiposity were correlated with lower myelin water fraction in late-myelinating prefrontal and associative fibers, controlling for age, sex, race, ethnicity, education and income. Our findings call for randomized clinical trials to establish causality between MetS, adiposity, and myelin content, and to explore the potential of weight loss and visceral adiposity reduction as means to support maintenance of myelin integrity throughout adulthood, which could open new avenues for prevention or treatment of cognitive decline and dementia.

Learning Spectral Fractional Anisotropy and Mean Diffusivity Features as Neuroimaging Biomarkers for Tracking White Matter Integrity Changes in Myotonic Dystrophy Type 1 Patients using Deep Convolutional Neural Networks

Myotonic dystrophy type 1 (DM1) is a genetic neuromuscular progressive multisystem disease that results in a broad spectrum of clinical central nervous system (CNS) involvement, including problems with memory, attention, executive functioning, and social cognition. Fractional anisotropy and mean diffusivity along-tract data calculated using diffusion tensor imaging techniques play a vital role in assessing white matter microstructural changes associated with neurodegeneration caused by DM1. In this work, a novel spectrogram-based deep learning method is proposed to characterize white matter network alterations in DM1 with the goal of building a deep learning model as neuroimaging biomarkers of DM1. The proposed method is evaluated on fractional anisotropies and mean diffusivities along-tract data calculated for 25 major white matter tracts of 46 DM1 patients and 96 unaffected controls. The evaluation data consists of a total of 7100 spectrogram images. The model achieved 91% accuracy in identifying DM1, a significant improvement compared to previous methods.Clinical relevance- Clinical care of DM1 is particularly challenging due to DM1 multisystem involvement and the disease variability. Patients with DM1 often experience neurological and psychological symptoms, such as excessive sleepiness and apathy, that greatly impact their quality of life. Some of DM1 CNS symptoms may be responsive to treatment. The goal of this research is to gain a deeper understanding of the impact of DM1 on the CNS and to develop a deep learning model that can serve as a biomarker for the disease, with the potential to be used in future clinical trials as an outcome measure.

New horizons in emotional well-being and brain aging: Potential lessons from cross-species research

Emotional wellbeing (EWB) is a multi-faceted concept of immediate relevance to human health. NIH recently initiated a series of research networks to advance understanding of EWB. Our network (NEW Brain Aging) focuses on mechanistic understanding of EWB in relation to brain aging. Here, by synthesizing the literature on emotional processing and the underlying brain circuit mechanisms in human and non-human animals, we propose a reactivity and reappraisal model for understanding EWB and its age-related changes. This model emphasizes the dynamic interactions between affective stimuli, behavioral/physiological responses, brain emotional states, and subjective feelings. It also aims to integrate the unique emotional processes involved in explaining EWB in aging humans with the emerging mechanistic insight of topologically conserved emotional brain networks from cross-species studies. We also highlight the research opportunities and challenges in EWB and brain aging research and the potential application of the model in addressing these issues.

Atypical developmental trajectories of white matter microstructure in prenatal alcohol exposure: Preliminary evidence from neurite orientation dispersion and density imaging

Introduction

Fetal alcohol spectrum disorder (FASD), a life-long condition resulting from prenatal alcohol exposure (PAE), is associated with structural brain anomalies and neurobehavioral differences. Evidence from longitudinal neuroimaging suggest trajectories of white matter microstructure maturation are atypical in PAE. We aimed to further characterize longitudinal trajectories of developmental white matter microstructure change in children and adolescents with PAE compared to typically-developing Controls using diffusion-weighted Neurite Orientation Dispersion and Density Imaging (NODDI).

Materials and methods

Participants: Youth with PAE (n = 34) and typically-developing Controls (n = 31) ages 8-17 years at enrollment. Participants underwent formal evaluation of growth and facial dysmorphology. Participants also completed two study visits (17 months apart on average), both of which involved cognitive testing and an MRI scan (data collected on a Siemens Prisma 3 T scanner). Age-related changes in the orientation dispersion index (ODI) and the neurite density index (NDI) were examined across five corpus callosum (CC) regions defined by tractography.

Results

While linear trajectories suggested similar overall microstructural integrity in PAE and Controls, analyses of symmetrized percent change (SPC) indicated group differences in the timing and magnitude of age-related increases in ODI (indexing the bending and fanning of axons) in the central region of the CC, with PAE participants demonstrating atypically steep increases in dispersion with age compared to Controls. Participants with PAE also demonstrated greater increases in ODI in the mid posterior CC (trend-level group difference). In addition, SPC in ODI and NDI was differentially correlated with executive function performance for PAE participants and Controls, suggesting an atypical relationship between white matter microstructure maturation and cognitive function in PAE.

Discussion

Preliminary findings suggest subtle atypicality in the timing and magnitude of age-related white matter microstructure maturation in PAE compared to typically-developing Controls. These findings add to the existing literature on neurodevelopmental trajectories in PAE and suggest that advanced biophysical diffusion modeling (NODDI) may be sensitive to biologically-meaningful microstructural changes in the CC that are disrupted by PAE. Findings of atypical brain maturation-behavior relationships in PAE highlight the need for further study. Further longitudinal research aimed at characterizing white matter neurodevelopmental trajectories in PAE will be important.

Symmetric data-driven fusion of diffusion tensor MRI: Age differences in white matter

In the past 20 years, white matter (WM) microstructure has been studied predominantly using diffusion tensor imaging (DTI). Decreases in fractional anisotropy (FA) and increases in mean (MD) and radial diffusivity (RD) have been consistently reported in healthy aging and neurodegenerative diseases. To date, DTI parameters have been studied individually (e.g., only FA) and separately (i.e., without using the joint information across them). This approach gives limited insights into WM pathology, increases the number of multiple comparisons, and yields inconsistent correlations with cognition. To take full advantage of the information in a DTI dataset, we present the first application of symmetric fusion to study healthy aging WM. This data-driven approach allows simultaneous examination of age differences in all four DTI parameters. We used multiset canonical correlation analysis with joint independent component analysis (mCCA + jICA) in cognitively healthy adults (age 20-33, n = 51 and age 60-79, n = 170). Four-way mCCA + jICA yielded one high-stability modality-shared component with co-variant patterns of age differences in RD and AD in the corpus callosum, internal capsule, and prefrontal WM. The mixing coefficients (or loading parameters) showed correlations with processing speed and fluid abilities that were not detected by unimodal analyses. In sum, mCCA + jICA allows data-driven identification of cognitively relevant multimodal components within the WM. The presented method should be further extended to clinical samples and other MR techniques (e.g., myelin water imaging) to test the potential of mCCA+jICA to discriminate between different WM disease etiologies and improve the diagnostic classification of WM diseases.

Temporal trajectories of normal myelination and axonal development assessed by quantitative macromolecular and diffusion MRI: Ultrastructural and immunochemical validation in a rabbit model

INTRODUCTION

Quantitative and non-invasive measures of brain myelination and maturation during development are of great importance to both clinical and translational research communities. While the metrics derived from diffusion tensor imaging, are sensitive to developmental changes and some pathologies, they remain difficult to relate to the actual microstructure of the brain tissue. The advent of advanced model-based microstructural metrics requires histological validation. The purpose of the study was to validate novel, model-based MRI techniques, such as macromolecular proton fraction mapping (MPF) and neurite orientation and dispersion indexing (NODDI), against histologically derived indexes of myelination and microstructural maturation at various stages of development.

METHODS

New Zealand White rabbit kits underwent serial in-vivo MRI examination at postnatal days 1, 5, 11, 18, and 25, and as adults. Multi-shell, diffusion-weighted experiments were processed to fit NODDI model to obtain estimates, intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Macromolecular proton fraction (MPF) maps were obtained from three source (MT-, PD-, and T1-weighted) images. After MRI sessions, a subset of animals was euthanized and regional samples of gray and white matter were taken for western blot analysis, to determine myelin basic protein (MBP), and electron microscopy, to estimate axonal, myelin fractions and g-ratio.

RESULTS

MPF of white matter regions showed a period of fast growth between P5 and P11 in the internal capsule, with a later onset in the corpus callosum. This MPF trajectory was in agreement with levels of myelination in the corresponding brain region, as assessed by western blot and electron microscopy. In the cortex, the greatest increase of MPF occurred between P18 and P26. In contrast, myelin, according to MBP western blot, saw the largest hike between P5 and P11 in the sensorimotor cortex and between P11 and P18 in the frontal cortex, which then seemingly plateaued after P11 and P18 respectively. G-ratio by MRI markers decreased with age in the white matter. However, electron microscopy suggest a relatively stable g-ratio throughout development.

CONCLUSION

Developmental trajectories of MPF accurately reflected regional differences of myelination rate in different cortical regions and white matter tracts. MRI-derived estimation of g-ratio was inaccurate during early development, likely due to the overestimation of axonal volume fraction by NODDI due to the presence of a large proportion of unmyelinated axons.

Topography of associations between cardiovascular risk factors and myelin loss in the ageing human brain

Our knowledge of the mechanisms underlying the vulnerability of the brain's white matter microstructure to cardiovascular risk factors (CVRFs) is still limited. We used a quantitative magnetic resonance imaging (MRI) protocol in a single centre setting to investigate the cross-sectional association between CVRFs and brain tissue properties of white matter tracts in a large community-dwelling cohort (n = 1104, age range 46-87 years). Arterial hypertension was associated with lower myelin and axonal density MRI indices, paralleled by higher extracellular water content. Obesity showed similar associations, though with myelin difference only in male participants. Associations between CVRFs and white matter microstructure were observed predominantly in limbic and prefrontal tracts. Additional genetic, lifestyle and psychiatric factors did not modulate these results, but moderate-to-vigorous physical activity was linked to higher myelin content independently of CVRFs. Our findings complement previously described CVRF-related changes in brain water diffusion properties pointing towards myelin loss and neuroinflammation rather than neurodegeneration.

Submillimeter T1 atlas for subject-specific abnormality detection at 7T

PURPOSE

Studies at 3T have shown that T₁ relaxometry enables characterization of brain tissues at the single-subject level by comparing individual physical properties to a normative atlas. In this work, an atlas of normative T₁ values at 7T is introduced with 0.6 mm isotropic resolution and its clinical potential is explored in comparison to 3T.

METHODS

T₁ maps were acquired in two separate healthy cohorts scanned at 3T and 7T. Using transfer learning, a template-based brain segmentation algorithm was adapted to ultra-high field imaging data. After segmenting brain tissues, volumes were normalized into a common space, and an atlas of normative T₁ values was established by modeling the T₁ inter-subject variability. A method for single-subject comparisons restricted to white matter and subcortical structures was developed by computing Z-scores. The comparison was applied to eight patients scanned at both field strengths for proof of concept.

RESULTS

The proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results.

CONCLUSION

A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.

Morphometric dis-similarity between cortical and subcortical areas underlies cognitive function and psychiatric symptomatology: a preadolescence study from ABCD

Preadolescence is a critical period characterized by dramatic morphological changes and accelerated cortico-subcortical development. Moreover, the coordinated development of cortical and subcortical regions underlies the emerging cognitive functions during this period. Deviations in this maturational coordination may underlie various psychiatric disorders that begin during preadolescence, but to date these deviations remain largely uncharted. We constructed a comprehensive whole-brain morphometric similarity network (MSN) from 17 neuroimaging modalities in a large preadolescence sample (N = 8908) from Adolescent Brain Cognitive Development (ABCD) study and investigated its association with 10 cognitive subscales and 27 psychiatric subscales or diagnoses. Based on the MSNs, each brain was clustered into five modules with distinct cytoarchitecture and evolutionary relevance. While morphometric correlation was positive within modules, it was negative between modules, especially between isocortical and paralimbic/subcortical modules; this developmental dissimilarity was genetically linked to synapse and neurogenesis. The cortico-subcortical dissimilarity becomes more pronounced longitudinally in healthy children, reflecting developmental differentiation of segregated cytoarchitectonic areas. Higher cortico-subcortical dissimilarity (between the isocortical and paralimbic/subcortical modules) were related to better cognitive performance. In comparison, children with poor modular differentiation between cortex and subcortex displayed higher burden of externalizing and internalizing symptoms. These results highlighted cortical-subcortical morphometric dissimilarity as a dynamic maturational marker of cognitive and psychiatric status during the preadolescent stage and provided insights into brain development.

Development of White Matter Fiber Covariance Networks Supports Executive Function in Youth

The white matter architecture of the human brain undergoes substantial development throughout childhood and adolescence, allowing for more efficient signaling between brain regions that support executive function. Increasingly, the field understands grey matter development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. While white matter development also appears asynchronous, previous studies have largely relied on anatomical atlases to characterize white matter tracts, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Here, we leveraged advances in diffusion modeling and unsupervised machine learning to delineate white matter fiber covariance networks comprised of structurally similar areas of white matter in a cross-sectional sample of 939 youth aged 8-22 years. We then evaluated associations between fiber covariance network structural properties with both age and executive function using generalized additive models. The identified fiber covariance networks aligned with the known architecture of white matter while simultaneously capturing novel spatial patterns of coordinated maturation. Fiber covariance networks showed heterochronous increases in fiber density and cross section that generally followed hierarchically organized temporal patterns of cortical development, with the greatest increases in unimodal sensorimotor networks and the most prolonged increases in superior and anterior transmodal networks. Notably, we found that executive function was associated with structural features of limbic and association networks. Taken together, this study delineates data-driven patterns of white matter network development that support cognition and align with major axes of brain maturation.

Frontostriatal and limbic contributions to cognitive decline in Parkinson's disease

BACKGROUND AND PURPOSE

The circuitry underlying heterogenous cognitive profiles in Parkinson's disease (PD) remains unclear. The purpose of this study is to investigate whether structural changes in frontostriatal and limbic pathways contribute to different cognitive trajectories in PD.

METHODS

We obtained clinical and multimodal MRI data from 120 control and 122 PD subjects without dementia or severe motor disability. T1/T2-weighted images estimated volume, and diffusion imaging evaluated fractional anisotropy (FA) of frontostriatal (striatum and frontostriatal white matter [FSWM]) and limbic (hippocampus and fornix) structures. Montreal Cognitive Assessment (MoCA) gauged total and domain-specific (attention/executive and memory) cognitive function. Linear mixed-effects models were used to compare MRI and cognitive progression over 4.5 years between controls and PD and evaluate associations between baseline MRI and cognitive changes in PD.

RESULTS

At baseline, control and PD groups were comparable, except PD participants had smaller striatal volume (p < 0.001). Longitudinally, PD showed faster decline in hippocampal volume, FSWM FA, and fornix FA (ps < .016), but not striatal volume (p = .218). Total and domain-specific MoCA scores declined faster in PD (ps < .030). In PD, lower baseline hippocampal volume (p = .005) and fornix FA (p = .032), but not striatal volume (p = .662) or FSWM FA (p = .143), were associated with faster total MoCA decline. Baseline frontostriatal metrics of striatal volume and FSWM FA were associated with faster attention/executive decline (p < .038), whereas lower baseline hippocampal volume was associated with faster memory decline (p = .005).

CONCLUSION

In PD, frontostriatal structural metrics are associated with attention/executive tasks, whereas limbic changes correlated with faster global cognitive decline, particularly in memory tasks.

Patterns of risk-Using machine learning and structural neuroimaging to identify pedophilic offenders

Background

Child sexual abuse (CSA) has become a focal point for lawmakers, law enforcement, and mental health professionals. With high prevalence rates around the world and far-reaching, often chronic, individual, and societal implications, CSA and its leading risk factor, pedophilia, have been well investigated. This has led to a wide range of clinical tools and actuarial instruments for diagnosis and risk assessment regarding CSA. However, the neurobiological underpinnings of pedosexual behavior, specifically regarding hands-on pedophilic offenders (PO), remain elusive. Such biomarkers for PO individuals could potentially improve the early detection of high-risk PO individuals and enhance efforts to prevent future CSA.

Aim

To use machine learning and MRI data to identify PO individuals.

Methods

From a single-center male cohort of 14 PO individuals and 15 matched healthy control (HC) individuals, we acquired diffusion tensor imaging data (anisotropy, diffusivity, and fiber tracking) in literature-based regions of interest (prefrontal cortex, anterior cingulate cortex, amygdala, and corpus callosum). We trained a linear support vector machine to discriminate between PO and HC individuals using these WM microstructure data. Post hoc, we investigated the PO model decision scores with respect to sociodemographic (age, education, and IQ) and forensic characteristics (psychopathy, sexual deviance, and future risk of sexual violence) in the PO subpopulation. We assessed model specificity in an external cohort of 53 HC individuals.

Results

The classifier discriminated PO from HC individuals with a balanced accuracy of 75.5% (sensitivity = 64.3%, specificity = 86.7%, P ₅₀₀₀ = 0.018) and an out-of-sample specificity to correctly identify HC individuals of 94.3%. The predictive brain pattern contained bilateral fractional anisotropy in the anterior cingulate cortex, diffusivity in the left amygdala, and structural prefrontal cortex-amygdala connectivity in both hemispheres. This brain pattern was associated with the number of previous child victims, the current stance on sexuality, and the professionally assessed risk of future sexual violent reoffending.

Conclusion

Aberrant white matter microstructure in the prefronto-temporo-limbic circuit could be a potential neurobiological correlate for PO individuals at high-risk of reoffending with CSA. Although preliminary and exploratory at this point, our findings highlight the general potential of MRI-based biomarkers and particularly WM microstructure patterns for future CSA risk assessment and preventive efforts.

Neurite Orientation Dispersion and Density Imaging in Psychiatric Disorders: A Systematic Literature Review and a Technical Note

While major psychiatric disorders lack signature diagnostic neuropathologies akin to dementias, classic postmortem studies have established microstructural involvement, i.e., cellular changes in neurons and glia, as a key pathophysiological finding. Advanced magnetic resonance imaging techniques allow mapping of cellular tissue architecture and microstructural abnormalities in vivo, which holds promise for advancing our understanding of the pathophysiology underlying psychiatric disorders. Here, we performed a systematic review of case-control studies using neurite orientation dispersion and density imaging (NODDI) to assess brain microstructure in psychiatric disorders and a selective review of technical considerations in NODDI. Of the 584 potentially relevant articles, 18 studies met the criteria to be included in this systematic review. We found a general theme of abnormal gray and white matter microstructure across the diagnostic spectrum. We also noted significant variability in patterns of neurite density and fiber orientation within and across diagnostic groups, as well as associations between brain microstructure and phenotypical variables. NODDI has been successfully used to detect subtle microstructure abnormalities in patients with psychiatric disorders. Given that NODDI indices may provide a more direct link to pathophysiological processes, this method may not only contribute to advancing our mechanistic understanding of disease processes, it may also be well positioned for next-generation biomarker development studies.

Frontal grey matter microstructure is associated with sleep slow waves characteristics in late midlife

STUDY OBJECTIVES

The ability to generate slow waves (SW) during non-rapid eye movement (NREM) sleep decreases as early as the 5th decade of life, predominantly over frontal regions. This decrease may concern prominently SW characterized by a fast switch from hyperpolarized to depolarized, or down-to-up, state. Yet, the relationship between these fast and slow switcher SW and cerebral microstructure in ageing is not established.

METHODS

We recorded habitual sleep under EEG in 99 healthy late midlife individuals (mean age = 59.3 ± 5.3 years; 68 women) and extracted SW parameters (density, amplitude, frequency) for all SW as well as according to their switcher type (slow vs. fast). We further used neurite orientation dispersion and density imaging (NODDI) to assess microstructural integrity over a frontal grey matter region of interest (ROI).

RESULTS

In statistical models adjusted for age, sex, and sleep duration, we found that a lower SW density, particularly for fast switcher SW, was associated with a reduced orientation dispersion of neurites in the frontal ROI (p = 0.018, R2β* = 0.06). In addition, overall SW frequency was positively associated with neurite density (p = 0.03, R2β* = 0.05). By contrast, we found no significant relationships between SW amplitude and NODDI metrics.

CONCLUSIONS

Our findings suggest that the complexity of neurite organization contributes specifically to the rate of fast switcher SW occurrence in healthy middle-aged individuals, corroborating slow and fast switcher SW as distinct types of SW. They further suggest that the density of frontal neurites plays a key role for neural synchronization during sleep.

TRIAL REGISTRATION NUMBER

EudraCT 2016-001436-35.

Surface-based tracking for short association fibre tractography

It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.

An accelerated degeneration of white matter microstructure and networks in the nondemented old-old

The nondemented old-old over the age of 80 comprise a rapidly increasing population group; they can be regarded as exemplars of successful aging. However, our current understanding of successful aging in advanced age and its neural underpinnings is limited. In this study, we measured the microstructural and network-based topological properties of brain white matter using diffusion-weighted imaging scans of 419 community-dwelling nondemented older participants. The participants were further divided into 230 young-old (between 72 and 79, mean = 76.25 ± 2.00) and 219 old-old (between 80 and 92, mean = 83.98 ± 2.97). Results showed that white matter connectivity in microstructure and brain networks significantly declined with increased age and that the declined rates were faster in the old-old compared with young-old. Mediation models indicated that cognitive decline was in part through the age effect on the white matter connectivity in the old-old but not in the young-old. Machine learning predictive models further supported the crucial role of declines in white matter connectivity as a neural substrate of cognitive aging in the nondemented older population. Our findings shed new light on white matter connectivity in the nondemented aging brains and may contribute to uncovering the neural substrates of successful brain aging.

Bridging cognition and action: executive functioning mediates the relationship between white matter fiber density and complex motor abilities in older adults

Aging may be associated with motor decline that is attributed to deteriorating white matter microstructure of the corpus callosum (CC), among other brain-related factors. Similar to motor functioning, executive functioning (EF) typically declines during aging, with age-associated changes in EF likewise being linked to altered white matter connectivity in the CC. Given that both motor and executive functions rely on white matter connectivity via the CC, and that bimanual control is thought to rely on EF, the question arises whether EF can at least party account for the proposed link between CC-connectivity and motor control in older adults. To address this, diffusion magnetic resonance imaging data were obtained from 84 older adults. A fiber-specific approach was used to obtain fiber density (FD), fiber cross-section (FC), and a combination of both metrics in eight transcallosal white matter tracts. Motor control was assessed using a bimanual coordination task. EF was determined by a domain-general latent EF-factor extracted from multiple EF tasks, based on a comprehensive test battery. FD of transcallosal prefrontal fibers was associated with cognitive and motor performance. EF partly accounted for the relationship between FD of prefrontal transcallosal pathways and motor control. Our results underscore the multidimensional interrelations between callosal white matter connectivity (especially in prefrontal brain regions), EF across multiple domains, and motor control in the older population. They also highlight the importance of considering EF when investigating brain-motor behavior associations in older adults.

CYP2C19 expression modulates affective functioning and hippocampal subiculum volume-a large single-center community-dwelling cohort study

Given controversial findings of reduced depressive symptom severity and increased hippocampus volume in CYP2C19 poor metabolizers, we sought to provide empirical evidence from a large-scale single-center longitudinal cohort in the community-dwelling adult population-Colaus|PsyCoLaus in Lausanne, Switzerland (n = 4152). We looked for CYP2C19 genotype-related behavioral and brain anatomy patterns using a comprehensive set of psychometry, water diffusion- and relaxometry-based magnetic resonance imaging (MRI) data (BrainLaus, n = 1187). Our statistical models tested for differential associations between poor metabolizer and other metabolizer status with imaging-derived indices of brain volume and tissue properties that explain individuals' current and lifetime mood characteristics. The observed association between CYP2C19 genotype and lifetime affective status showing higher functioning scores in poor metabolizers, was mainly driven by female participants (ß = 3.9, p = 0.010). There was no difference in total hippocampus volume between poor metabolizer and other metabolizer, though there was higher subiculum volume in the right hippocampus of poor metabolizers (ß = 0.03, p_FDRcorrected = 0.036). Our study supports the notion of association between mood phenotype and CYP2C19 genotype, however, finds no evidence for concomitant hippocampus volume differences, with the exception of the right subiculum.

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

Purpose

We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.

Theory

The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.

Methods

MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.

Results

The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm^−α.

Conclusion

The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.