Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

Gray and White Matter Metrics Demonstrate Distinct and Complementary Prediction of Differences in Cognitive Performance in Children: Findings from ABCD (N = 11,876)

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either gray or white matter metrics in humans, leaving open the key question as to whether gray or white matter microstructure plays distinct or complementary roles supporting cognitive performance. To compare the role of gray and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with gray and white matter measures. Specifically, we compared how gray matter (volume, cortical thickness, and surface area) and white matter measures (volume, fractional anisotropy, and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study; 5,680 female, 6,196 male) at 10 years old. We found that gray and white matter metrics bring partly nonoverlapping information to predict cognitive performance. The models with only gray or white matter explained respectively 15.4 and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in, we additionally found that different metrics within gray and white matter had different predictive power and that the tracts/regions that were most predictive of cognitive performance differed across metrics. These results show that studies focusing on a single metric in either gray or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

Aging, cingulate cortex, and cognition: insights from structural MRI, emotional recognition, and theory of mind

The cingulate cortex is a limbic structure involved in multiple functions, including emotional processing, pain, cognition, memory, and spatial orientation. The main goal of this structural Magnetic Resonance Imaging (MRI) study was to investigate whether age affects the cingulate cortex uniformly across its anteroposterior dimensions and determine if the effects of age differ based on sex, hemisphere, and regional cingulate anatomy, in a large cohort of healthy individuals across the adult lifespan. The second objective aimed to explore whether the decline in emotional recognition accuracy and Theory of Mind (ToM) is linked to the potential age-related reductions in the pregenual anterior cingulate (ACC) and anterior midcingulate (MCC) cortices. We recruited 126 healthy participants (18-85 years) for this study. MRI datasets were acquired on a 4.7 T system. The cingulate cortex was manually segmented into the pregenual ACC, anterior MCC, posterior MCC, and posterior cingulate cortex (PCC). We observed negative relationships between the presence and length of the superior cingulate gyrus and bilateral volumes of pregenual ACC and anterior MCC. Age showed negative effects on the volume of all cingulate cortical subregions bilaterally except for the right anterior MCC. Most of the associations between age and the cingulate subregional volumes were linear. We did not find a significant effect of sex on cingulate cortical volumes. However, stronger effects of age were observed in men compared to women. This study also demonstrated that performance on an emotional recognition task was linked to pregenual ACC volume, whist the ToM capabilities were related to the size of pregenual ACC and anterior MCC. These results suggest that the cingulate cortex contributes to emotional recognition ability and ToM across the adult lifespan.

Developmental order, fibre caliber, and vascularization predict tract-wise declines: Testing retrogenesis and physiological predictions in white matter aging

To understand the consistently observed spatial distribution of white-matter (WM) aging, developmentally driven theories of retrogenesis have gained traction, positing that the order WM development predicts declines. Regions that develop first are often 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 the "gains-predict-loss" model. The validity of such theories remains uncertain, in part due to lack of clarity on the definition of developmental order. Our recent findings also suggest that WM degeneration may vary by physiological parameters such as perfusion. Furthermore, it is informative to link perfusion to fibre metabolic need, which varies with fibre size. Here we address the question of whether WM degeneration is determined by development trajectory or physiological state across both microstructural and perfusion measures using data drawn from the Human Connectome Project in Aging (HCP-A). Our results indicate that developmental order of tract myelination provides the strongest support for the retrogenesis hypothesis, with the last to complete myelination the first to decline. Moreover, higher mean axon diameter and lower macrovascular density are associated with lower degrees of WM degeneration across measures. Tract perfusion, in turn also tends to be higher and the arterial transit time longer for tracts that appear first. These findings suggest that WM degeneration in different tracts may be governed by their developmental trajectories and physiology, and ultimately influenced by each tract's metabolic demand.

Independent and combined effects of methamphetamine use disorders and APOEε4 allele on cognitive performance and brain morphometry

AIMS

Prior studies showed that methamphetamine (METH) users had greater than normal age-related brain atrophy; whether having the apolipoprotein E (APOE)-ε4 allele may be a contributory factor has not been evaluated. We aimed to determine the independent and combined effects of chronic heavy METH use and having at least one copy of the APOE-ε4 allele (APOE-ε4+) on brain morphometry and cognition, especially in relation to aging.

METHODS

We compared brain morphometry and cognitive performance in 77 individuals with chronic heavy METH use (26 APOE-ε4+, 51 APOE-ε4-) and 226 Non-METH users (66 APOE-ε4+, 160 APOE-ε4-), using a 2 × 2 design (two-way analysis of co-variance). Vertex-wise cortical volumes, thickness and seven subcortical volumes, were automatically measured using FreeSurfer. Linear regression between regional brain measures, and cognitive scores that showed group differences were evaluated. Group differences in age-related decline in brain and cognitive measures were also explored.

RESULTS

Regardless of APOE-ε4 genotype, METH users had lower Motor Z-scores (P = 0.005), thinner right lateral-orbitofrontal cortices (P < 0.001), smaller left pars-triangularis gyrus volumes (P = 0.004), but larger pallida, hippocampi and amygdalae (P = 0.004-0.006) than nonusers. Across groups, APOE-ε4+ METH users had the smallest volumes of superior frontal cortical gyri bilaterally, and of the smallest volume in left rostral-middle frontal gyri (all P-values <0.001). Smaller right superior-frontal gyrus predicted poorer motor function only in APOE-ε4+ participants (interaction-P < 0.001). Cortical volumes and thickness declined with age similarly across all participants; however, APOE-ε4-carriers showed thinner right inferior parietal cortices than noncarriers at younger age (interaction-P < 0.001).

CONCLUSIONS

Chronic heavy use and having at least one copy of the APOE-ε4 allele may have synergistic effects on brain atrophy, particularly in frontal cortices, which may contribute to their poorer cognitive function. However, the enlarged subcortical volumes in METH users replicated prior studies, and are likely due to METH-mediated neuroinflammation.

Grey and white matter metrics demonstrate distinct and complementary prediction of differences in cognitive performance in children: Findings from ABCD (N= 11 876)

Individual differences in cognitive performance in childhood are a key predictor of significant life outcomes such as educational attainment and mental health. Differences in cognitive ability are governed in part by variations in brain structure. However, studies commonly focus on either grey or white matter metrics in humans, leaving open the key question as to whether grey or white matter microstructure play distinct or complementary roles supporting cognitive performance. To compare the role of grey and white matter in supporting cognitive performance, we used regularized structural equation models to predict cognitive performance with grey and white matter measures. Specifically, we compared how grey matter (volume, cortical thickness and surface area) and white matter measures (volume, fractional anisotropy and mean diffusivity) predicted individual differences in cognitive performance. The models were tested in 11,876 children (ABCD Study, 5680 female; 6196 male) at 10 years old. We found that grey and white matter metrics bring partly non-overlapping information to predict cognitive performance. The models with only grey or white matter explained respectively 15.4% and 12.4% of the variance in cognitive performance, while the combined model explained 19.0%. Zooming in we additionally found that different metrics within grey and white matter had different predictive power, and that the tracts/regions that were most predictive of cognitive performance differed across metric. These results show that studies focusing on a single metric in either grey or white matter to study the link between brain structure and cognitive performance are missing a key part of the equation.

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.

Association between circadian sleep regulation and cortical gyrification in young and older adults

The circadian system orchestrates sleep timing and structure and is altered with increasing age. Sleep propensity, and particularly REM sleep is under strong circadian control and has been suggested to play an important role in brain plasticity. In this exploratory study, we assessed whether surface-based brain morphometry indices are associated with circadian sleep regulation and whether this link changes with age. Twenty-nine healthy older (55-82 years; 16 men) and 28 young participants (20-32 years; 13 men) underwent both structural magnetic resonance imaging and a 40-h multiple nap protocol to extract sleep parameters over day and night time. Cortical thickness and gyrification indices were estimated from T1-weighted images acquired during a classical waking day. We observed that REM sleep was significantly modulated over the 24-h cycle in both age groups, with older adults exhibiting an overall reduction in REM sleep modulation compared to young individuals. Interestingly, when taking into account the observed overall age-related reduction in REM sleep throughout the circadian cycle, higher day-night differences in REM sleep were associated with increased cortical gyrification in the right inferior frontal and paracentral regions in older adults. Our results suggest that a more distinctive allocation of REM sleep over the 24-h cycle is associated with regional cortical gyrification in aging, and thereby point towards a protective role of circadian REM sleep regulation for age-related changes in brain organization.

Structural and microstructural thalamocortical network disruption in sporadic behavioural variant frontotemporal dementia

BACKGROUND

Using multi-block methods we combined multimodal neuroimaging metrics of thalamic morphology, thalamic white matter tract diffusion metrics, and cortical thickness to examine changes in behavioural variant frontotemporal dementia. (bvFTD).

METHOD

Twenty-three patients with sporadic bvFTD and 24 healthy controls underwent structural and diffusion MRI scans. Clinical severity was assessed using the Clinical Dementia Rating scale and behavioural severity using the Frontal Behaviour Inventory by patient caregivers. Thalamic volumes were manually segmented. Anterior and posterior thalamic radiation fractional anisotropy and mean diffusivity were extracted using Tract-Based Spatial Statistics. Finally, cortical thickness was assessed using Freesurfer. We used shape analyses, diffusion measures, and cortical thickness as features in sparse multi-block partial least squares (PLS) discriminatory analyses to classify participants within bvFTD or healthy control groups. Sparsity was tuned with five-fold cross-validation repeated 10 times. Final model fit was assessed using permutation testing. Additionally, sparse multi-block PLS was used to examine associations between imaging features and measures of dementia severity.

RESULTS

Bilateral anterior-dorsal thalamic atrophy, reduction in mean diffusivity of thalamic projections, and frontotemporal cortical thinning, were the main features predicting bvFTD group membership. The model had a sensitivity of 96%, specificity of 68%, and was statistically significant using permutation testing (p = 0.012). For measures of dementia severity, we found similar involvement of regional thalamic and cortical areas as in discrimination analyses, although more extensive thalamo-cortical white matter metric changes.

CONCLUSIONS

Using multimodal neuroimaging, we demonstrate combined structural network dysfunction of anterior cortical regions, cortical-thalamic projections, and anterior thalamic regions in sporadic bvFTD.

A cross-sectional and longitudinal study of human brain development: The integration of cortical thickness, surface area, gyrification index, and cortical curvature into a unified analytical framework

Brain maturation studies typically examine relationships linking a single morphometric feature with cognition, behavior, age, or other demographic characteristics. However, the coordinated spatiotemporal arrangement of morphological features across development and their associations with behavior are unclear. Here, we examine covariation across multiple cortical features (cortical thickness [CT], surface area [SA], local gyrification index [GI], and mean curvature [MC]) using magnetic resonance images from the NIMH developmental cohort (ages 5-25). Neuroanatomical covariance was examined using non-negative matrix factorization (NMF), which decomposes covariance resulting in a parts-based representation. Cross-sectionally, we identified six components of covariation which demonstrate differential contributions of CT, GI, and SA in hetero- vs. unimodal areas. Using this technique to examine covariance in rates of change to identify longitudinal sources of covariance highlighted preserved SA in unimodal areas and changes in CT and GI in heteromodal areas. Using behavioral partial least squares (PLS), we identified a single latent variable (LV) that recapitulated patterns of reduced CT, GI, and SA related to older age, with limited contributions of IQ and SES. Longitudinally, PLS revealed three LVs that demonstrated a nuanced developmental pattern that highlighted a higher rate of maturational change in SA and CT in higher IQ and SES females. Finally, we situated the components in the changing architecture of cortical gradients. This novel characterization of brain maturation provides an important understanding of the interdependencies between morphological measures, their coordinated development, and their relationship to biological sex, cognitive ability, and the resources of the local environment.

Physiologically intense FDG uptake of distal spinal cord on total-body PET/CT

OBJECTIVE

Physiologically mild-to-moderate FDG uptake of the spinal cord was reported. However, we noticed intense FDG uptake of distal spinal cord in several patients without definite spinal cord lesions on total-body PET/CT. Thus, this study aimed to investigate the frequency, pattern, intensity, and associations of FDG uptake in such cases on total-body PET/CT.

METHODS

The clinical characteristics of age, gender, body mass index (BMI), lower extremity symptom, diabetes, and fasting blood glucose level, and total-body FDG PET/CT metabolic parameters of maximum standard uptake value (SUVmax), SUVmax of lean body mass (SUVlbm), and SUVmax of body surface area (SUVbsa), were retrospectively analyzed in 527 patients without definite spinal cord lesions. Intense FDG uptake was defined as greater than liver glucometabolism on visual analysis, and T5 cord was selected as cord background.

RESULTS

Intense FDG uptake of distal spinal cord was observed in 87 out of 527 patients (16.5%) and involved with 2-3 vertebral segments including T11-T12 in 33 cases (38.0%), T12-L1 in 29 (33.3%), and T11-L1 in 25 (28.7%). No lesions were demonstrated on follow-up physical examinations, MRI or contrast-enhanced CT in these 87 cases with intense FDG accumulation in the distal spinal cord. The median SUVmax, SUVlbm, and SUVbsa of distal spinal cord with intense FDG uptake were 3.8 (2.7-5.5), 2.9 (2.2-4.3), and 1.0 (0.7-1.6), respectively. Significant differences in SUVmax, SUVlbm, and SUVbsa of distal cord and cord background were found between the groups with and without intense FDG uptake (P < 0.05). Moreover, significant differences in ratios of distal spinal cord-to-cord background, to mediastinal blood pool, and to liver were observed between two groups (P < 0.05). Intense FDG uptake of distal cord was associated with age, diabetic status, and blood glucose level.

CONCLUSIONS

Intense FDG uptake of distal spinal cord on total-body PET/CT may be physiological, more common in younger age, patients without diabetes, or lower fasting blood glucose.

White matter damage due to pulsatile versus steady blood pressure differs by vascular territory: A cross-sectional analysis of the UK Biobank cohort study

Small vessel disease is associated with age, mean blood pressure (MAP) and blood pressure pulsatility (PP). We used data from the UK Biobank cohort study to determine the relative importance of MAP versus PP driving white matter injury within individual white matter tracts, particularly in the anterior and posterior vascular territory. The associations between blood pressure and diffusion indices in 27 major tracts were analysed using unadjusted and fully-adjusted general linear models and mixed-effect linear models. Blood pressure and neuroimaging data were available for 37,041 participants (mean age 64+/-7.5 years, 53% female). In unadjusted analyses, MAP and PP were similarly associated with diffusion indices in the anterior circulation. In the posterior circulation, the associations were weaker, particularly for MAP. In fully-adjusted analyses, MAP remained associated with all diffusion indices in the anterior circulation, independently of age. In the posterior circulation, the effect of MAP became protective. PP remained associated with greater mean diffusivity and extracellular free water diffusion in the anterior circulation and all diffusion indices in the posterior circulation. There was a significant interaction between PP and age. This implies discordant mechanisms for chronic white matter injury in different brain regions and potentially in the associated stroke risks.

Deciphering the Network Effects of Deep Brain Stimulation in Parkinson's Disease

INTRODUCTION

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an established therapy for Parkinson's disease (PD). However, a more detailed characterization of the targeted network and its grey matter (GM) terminals that drive the clinical outcome is needed. In this direction, the use of MRI after DBS surgery is now possible due to recent advances in hardware, opening a window for the clarification of the association between the affected tissue, including white matter fiber pathways and modulated GM regions, and the DBS-related clinical outcome. Therefore, we present a computational framework for reconstruction of targeted networks on postoperative MRI.

METHODS

We used a combination of preoperative whole-brain T1-weighted (T1w) and diffusion-weighted MRI data for morphometric integrity assessment and postoperative T1w MRI for electrode reconstruction and network reconstruction in 15 idiopathic PD patients. Within this framework, we made use of DBS lead artifact intensity profiles on postoperative MRI to determine DBS locations used as seeds for probabilistic tractography to cortical and subcortical targets within the motor circuitry. Lastly, we evaluated the relationship between brain microstructural characteristics of DBS-targeted brain network terminals and postoperative clinical outcomes.

RESULTS

The proposed framework showed robust performance for identifying the DBS electrode positions. Connectivity profiles between the primary motor cortex (M1), supplementary motor area (SMA), and DBS locations were strongly associated with the stimulation intensity needed for the optimal clinical outcome. Local diffusion properties of the modulated pathways were related to DBS outcomes. STN-DBS motor symptom improvement was highly associated with cortical thickness in the middle frontal and superior frontal cortices, but not with subcortical volumetry.

CONCLUSION

These data suggest that STN-DBS outcomes largely rely on the modulatory interference from cortical areas, particularly M1 and SMA, to DBS locations.

Sex Differences in Cortical Morphometry and White Matter Microstructure During Brain Aging and Their Relationships to Cognition

Changes in brain structure are associated with aging, and accompanied by the gradual deterioration of cognitive functions, which manifests differently in males and females. Here, we quantify the age-related spatial aging patterns of brain gray and white matter structures, their volume reduction rate, their relationships with specific cognitive functions, as well as differences between males and females in a cross-sectional nondementia dataset. We found that both males and females showed extensive age-related decreases in the volumes of most gray matter and white matter regions. Females have larger regions where the volume decreases with age and a greater slope (females: 0.199%, males: 0.183%) of volume decrease in gray matter. For white matter, no significant sex differences were found in age-related regions, and the slope of volume decrease. More significant associations were identified between brain structures and cognition in males during aging than females. This study explored the age-related regional variations in gray matter and white matter, as well as the sex differences in a nondemented elderly population. This study helps to further understand the aging of the brain structure and sex differences in the aging of brain structures and provides new evidence for the aging of nondemented individuals.

The interplay between gray matter and white matter neurodegeneration in subjective cognitive decline

Aims: To investigate the interplay between gray matter (GM) and white matter (WM) neurodegeneration in subjective cognitive decline (SCD), including thickness across the whole cortical mantle, hippocampal volume, and integrity across the whole WM.

Methods: We included 225 cognitively unimpaired individuals from a community-based cohort. Subjective cognitive complaints were assessed through 9 questions covering amnestic and non-amnestic cognitive domains. In our cohort, 123 individuals endorsed from one to six subjective cognitive complaints (i.e. they fulfilled the diagnostic criteria for SCD), while 102 individuals reported zero complaints. GM neurodegeneration was assessed through measures of cortical thickness across the whole mantle and hippocampal volume. WM neurodegeneration was assessed through measures of mean diffusivity (MD) across the whole WM skeleton. Mediation analysis and multiple linear regression were conducted to investigate the interplay between the measures of GM and WM neurodegeneration.

Results: A higher number of complaints was associated with reduced hippocampal volume, cortical thinning in several frontal and temporal areas and the insula, and higher MD across the WM skeleton, with a tendency to spare the occipital lobe. SCD-related cortical thinning and increased MD were associated with each other and jointly contributed to complaints, but the contribution of cortical thinning to the number of complaints was stronger.

Conclusions: Neurodegeneration processes affecting the GM and WM seem to be associated with each other in SCD and include brain areas other than those typically targeted by Alzheimer’s disease. Our findings suggest that SCD may be a sensitive behavioral marker of heterogeneous brain pathologies in individuals recruited from the community.

Contributions of Brain Function and Structure to Three Different Domains of Cognitive Control in Normal Aging

Cognitive control involves the flexible allocation of mental resources during goal-directed behavior and comprises three correlated but distinct domains-inhibition, shifting, and working memory. The work of Don Stuss and others has demonstrated that frontal and parietal cortices are crucial to cognitive control, particularly in normal aging, which is characterized by reduced control mechanisms. However, the structure-function relationships specific to each domain and subsequent impact on performance are not well understood. In the current study, we examined both age and individual differences in functional activity associated with core domains of cognitive control in relation to fronto-parietal structure and task performance. Participants (n = 140, aged 20-86 years) completed three fMRI tasks: go/no-go (inhibition), task switching (shifting), and n-back (working memory), in addition to structural and diffusion imaging. All three tasks engaged a common set of fronto-parietal regions; however, the contributions of age, brain structure, and task performance to functional activity were unique to each domain. Aging was associated with differences in functional activity for all tasks, largely in regions outside common fronto-parietal control regions. Shifting and inhibition showed greater contributions of structure to overall decreases in brain activity, suggesting that more intact fronto-parietal structure may serve as a scaffold for efficient functional response. Working memory showed no contribution of structure to functional activity but had strong effects of age and task performance. Together, these results provide a comprehensive and novel examination of the joint contributions of aging, performance, and brain structure to functional activity across multiple domains of cognitive control.

The effect of vascular health factors on white matter microstructure mediates age-related differences in executive function performance

Even within healthy aging, vascular risk factors can detrimentally influence cognition, with executive functions (EF) particularly vulnerable. Fronto-parietal white matter (WM) connectivity in part, supports EF and may be particularly sensitive to vascular risk. Here, we utilized structural equation modeling in 184 healthy adults (aged 20-94 years of age) to test the hypotheses that: 1) fronto-parietal WM microstructure mediates age effects on EF; 2) higher blood pressure (BP) and white matter hyperintensity (WMH) burden influences this association. All participants underwent comprehensive cognitive and neuropsychological testing including tests of processing speed, executive function (with a focus on tasks that require switching and inhibition) and completed an MRI scanning session that included FLAIR imaging for semi-automated quantification of white matter hyperintensity burden and diffusion-weighted imaging for tractography. Structural equation models were specified with age (as a continuous variable) and blood pressure predicting within-tract WMH burden and fractional anisotropy predicting executive function and processing speed. Results indicated that fronto-parietal white matter of the genu of the corpus collosum, superior longitudinal fasciculus, and the inferior frontal occipital fasciculus (but not cortico-spinal tract) mediated the association between age and EF. Additionally, increased systolic blood pressure and white matter hyperintensity burden within these white matter tracts contribute to worsening white matter health and are important factors underlying age-brain-behavior associations. These findings suggest that aging brings about increases in both BP and WMH burden, which may be involved in the degradation of white matter connectivity and in turn, negatively impact executive functions as we age.

Age-related decline in the brain: a longitudinal study on inter-individual variability of cortical thickness, area, volume, and cognition

Magnetic Resonance Imaging (MRI) studies have shown that cortical volume declines with age. Although volume is a multiplicative measure consisting of thickness and area, few studies have focused on both its components. Information on decline variability and associations between person-specific changes of different brain metrics, brain regions, and cognition is sparse. In addition, the estimates have often been biased by the measurement error, because three repeated measures are minimally required to separate the measurement error from person-specific changes. With a sample size of N = 231, five repeated measures, and an observational time span of seven years, this study explores the associations between changes of different brain metrics, brain regions, and cognitive abilities in aging. Person-specific changes were obtained by latent growth curve models using Bayesian estimation. Our data indicate that both thickness and area are important contributors to volumetric changes. In most brain regions, area clearly declined on average over the years, while thickness showed only little decline. However, there was also substantial variation around the average slope in thickness and area. The correlation pattern of changes in thickness between brain regions was strong and largely homogenous. The pattern for changes in area was similar but weaker, indicating that factors affecting area may be more region-specific. Changes in thickness and volume were substantially correlated with changes in cognition. In some brain regions, changes in area were also related to changes in cognition. Overall, studying the associations between the trajectories of brain regions in different brain metrics provides insights into the regional heterogeneity of structural changes. SIGNIFICANCE STATEMENT: Many studies have described volumetric brain changes in aging. Few studies have focused on both its individual components: area and thickness. Longitudinal studies with three or more time points are highly needed, because they provide more precise average change estimates and, more importantly, allow us to quantify the associations between changes in the different brain metrics, brain regions, and other variables (e.g. cognitive abilities). Studying these associations is important because they can provide information regarding possible underlying factors of these changes. Our study, with a large sample size, five repeated measures, and an observational time span of seven years, provides new insights about the associations between person-specific changes in thickness, area, volume, and cognitive abilities.

Age affects white matter microstructure and episodic memory across the older adult lifespan

Diffusion imaging studies have observed age-related degradation of white matter that contributes to cognitive deficits separately in younger-old (ages 65-89) and oldest-old (ages 90+) adults. But it remains unclear whether these age effects are magnified in advanced age groups, which may reflect disease-related pathology. Here, we tested whether age-related differences in white matter microstructure followed linear or nonlinear patterns across the entire older adult lifespan (65-98 years), these patterns were influenced by oldest-old adults at increased risk of dementia (cognitive impairment no dementia, CIND), and they explained age effects on episodic memory. Results revealed nonlinear microstructure declines across fiber classes (medial temporal, callosal, association, projection and/or thalamic) that were largest for medial temporal fibers. These patterns remained after excluding oldest-old participants with CIND, indicating that aging of white matter microstructure cannot solely be explained by pathology associated with early cognitive impairment. Moreover, finding that the effect of age on episodic memory was mediated by medial temporal fiber microstructure suggests it is essential for facilitating memory-related neural signals across the older adult lifespan.

Tract Specificity of Age Effects on Diffusion Tensor Imaging Measures of White Matter Health

Well-established literature indicates that older adults have poorer cerebral white matter integrity, as measured through diffusion tensor imaging (DTI). Age differences in DTI have been observed widely across white matter, although some tracts appear more sensitive to the effects of aging than others. Factors like APOE ε4 status and sex may contribute to individual differences in white matter integrity that also selectively impact certain tracts, and could influence DTI changes in aging. The present study explored the degree to which age, APOE ε4, and sex exerted global vs. tract specific effects on DTI metrics in cognitively healthy late middle-aged to older adults. Data from 49 older adults (ages 54–92) at two time-points separated by approximately 2.7 years were collected. DTI metrics, including fractional anisotropy (FA) and mean diffusivity (MD), were extracted from nine white matter tracts and global white matter. Results showed that across timepoints, FA and MD increased globally, with no tract-specific changes observed. Baseline age had a global influence on both measures, with increasing age associated with lower FA and higher MD. After controlling for global white matter FA, age additionally predicted FA for the genu, callosum body, inferior fronto-occipital fasciculus (IFOF), and both anterior and posterior cingulum. Females exhibited lower global FA on average compared to males. In contrast, MD was selectively elevated in the anterior cingulum and superior longitudinal fasciculus (SLF), for females compared to males. APOE ε4 status was not predictive of either measure. In summary, these results indicate that age and sex are associated with both global and tract-specific alterations to DTI metrics among a healthy older adult cohort. Older women have poorer white matter integrity compared to older men, perhaps related to menopause-induced metabolic changes. While age-related alterations to white matter integrity are global, there is substantial variation in the degree to which tracts are impacted, possibly as a consequence of tract anatomical variability. The present study highlights the importance of accounting for global sources of variation in DTI metrics when attempting to investigate individual differences (due to age, sex, or other factors) in specific white matter tracts.

Exploring the Relationship between Gray and White Matter in Healthy Adults: A Hybrid Research of Cortical Reconstruction and Tractography

The gray matter (GM) and white matter (WM) are structurally and functionally related in the human brain. Among the numerous neuroimaging studies, yet only a few have investigated these two structures in the same sample. So, there is limited and inconsistent information about how they are correlated in the brain of healthy adults. In this study, we combined cortical reconstruction with diffusion spectrum imaging (DSI) tractography to investigate the relationship between cortical morphology and microstructural properties of major WM tracts in 163 healthy young adults. The results showed that cortical thickness (CTh) was positively correlated with the coherent tract-wise fractional anisotropy (FA) value, and the correlation was stronger in the dorsal areas than in the ventral areas. For other diffusion parameters, CTh was positively correlated with axial diffusivity (AD) of coherent fibers in the frontal areas and negatively correlated with radial diffusivity (RD) of coherent fibers in the dorsal areas. These findings suggest that the correlation between GM and WM is inhomogeneity and could be interpreted with different mechanisms in different brain regions. We hope our research could provide new insights into the studies of diseases in which the GM and WM are both affected.

Age-related assessment of diffusion parameters in specific brain tracts correlated with cortical thinning

The aging process is associated with many brain structural alterations. These changes are not associated with neuronal loss but can be due to cortical structural changes that may be related to white matter (WM) structural alterations. In this study, we evaluated age-related changes in WM and gray matter (GM) parameters and how they correlate for specific brain tracts in a cohort of 158 healthy individuals, aged between 18 and 83 years old. In the tract-cortical analysis, cortical regions connected by tracts demonstrated similar thinning patterns for the majority of tracts. Additionally, a significant relationship was found between mean cortical thinning rate with fractional anisotropy (FA) and mean diffusivity (MD) alteration rates. For all tracts, age was the main effect controlling diffusion parameter alterations. We found no direct correlations between cortical thickness and FA or MD, except for in the fornix, for which the subcallosal gyrus thickness was significantly correlated to FA and MD (p < 0.05 FDR corrected). Our findings lead to the conclusion that alterations in the WM diffusion parameters are explained by the aging process, also associated with cortical thickness changes. Also, the alteration rates of the structural parameters are correlated to the different brain tracts in the aging process.

Gender-Related and Hemispheric Effects in Cortical Thickness-Based Hemispheric Brain Morphological Network

Objective

The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network.

Methods

Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen–Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K = 0.10‐0.36), asymmetry indexes of these graph metrics were derived.

Results

Hemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K = 0.10–0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p < 0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p < 0.05).

Conclusion

Patterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.

Extensive Evaluation of Morphological Statistical Harmonization for Brain Age Prediction

Characterizing both neurodevelopmental and aging brain structural trajectories is important for understanding normal biological processes and atypical patterns that are related to pathological phenomena. Initiatives to share open access morphological data contributed significantly to the advance in brain structure characterization. Indeed, such initiatives allow large brain morphology multi-site datasets to be shared, which increases the statistical sensitivity of the outcomes. However, using neuroimaging data from multi-site studies requires harmonizing data across the site to avoid bias. In this work we evaluated three different harmonization techniques on the Autism Brain Imaging Data Exchange (ABIDE) dataset for age prediction analysis in two groups of subjects (i.e., controls and autism spectrum disorder). We extracted the morphological features from T1-weighted images of a mixed cohort of 654 subjects acquired from 17 sites to predict the biological age of the subjects using three machine learning regression models. A machine learning framework was developed to quantify the effects of the different harmonization strategies on the final performance of the models and on the set of morphological features that are relevant to the age prediction problem in both the presence and absence of pathology. The results show that, even if two harmonization strategies exhibit similar accuracy of predictive models, a greater mismatch occurs between the sets of most age-related predictive regions for the Autism Spectrum Disorder (ASD) subjects. Thus, we propose to use a stability index to extract meaningful features for a robust clinical validation of the outcomes of multiple harmonization strategies.

Brain structural covariance networks in obsessive-compulsive disorder: a graph analysis from the ENIGMA Consortium

Brain structural covariance networks reflect covariation in morphology of different brain areas and are thought to reflect common trajectories in brain development and maturation. Large-scale investigation of structural covariance networks in obsessive-compulsive disorder (OCD) may provide clues to the pathophysiology of this neurodevelopmental disorder. Using T1-weighted MRI scans acquired from 1616 individuals with OCD and 1463 healthy controls across 37 datasets participating in the ENIGMA-OCD Working Group, we calculated intra-individual brain structural covariance networks (using the bilaterally-averaged values of 33 cortical surface areas, 33 cortical thickness values, and six subcortical volumes), in which edge weights were proportional to the similarity between two brain morphological features in terms of deviation from healthy controls (i.e. z-score transformed). Global networks were characterized using measures of network segregation (clustering and modularity), network integration (global efficiency), and their balance (small-worldness), and their community membership was assessed. Hub profiling of regional networks was undertaken using measures of betweenness, closeness, and eigenvector centrality. Individually calculated network measures were integrated across the 37 datasets using a meta-analytical approach. These network measures were summated across the network density range of K = 0.10-0.25 per participant, and were integrated across the 37 datasets using a meta-analytical approach. Compared with healthy controls, at a global level, the structural covariance networks of OCD showed lower clustering (P < 0.0001), lower modularity (P < 0.0001), and lower small-worldness (P = 0.017). Detection of community membership emphasized lower network segregation in OCD compared to healthy controls. At the regional level, there were lower (rank-transformed) centrality values in OCD for volume of caudate nucleus and thalamus, and surface area of paracentral cortex, indicative of altered distribution of brain hubs. Centrality of cingulate and orbito-frontal as well as other brain areas was associated with OCD illness duration, suggesting greater involvement of these brain areas with illness chronicity. In summary, the findings of this study, the largest brain structural covariance study of OCD to date, point to a less segregated organization of structural covariance networks in OCD, and reorganization of brain hubs. The segregation findings suggest a possible signature of altered brain morphometry in OCD, while the hub findings point to OCD-related alterations in trajectories of brain development and maturation, particularly in cingulate and orbitofrontal regions.

Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

White Matter Regions With Low Microstructure in Young Adults Spatially Coincide With White Matter Hyperintensities in Older Adults

Microstructural and macrostructural white matter damage occurs frequently with aging, is associated with negative health outcomes, and can be imaged non-invasively as fractional anisotropy (FA) and white matter hyperintensities (WMH), respectively. The extent to which diminished microstructure precedes or results from macrostructural white matter damage is poorly understood. This study evaluated the hypothesis that white matter areas with normatively lower microstructure in young adults are most susceptible to develop WMH in older adults. Forty-nine younger participants (age = 25.8 ± 2.8 years) underwent diffusion-weighted imaging (DWI), and 557 older participants (age = 73.9 ± 5.7 years) underwent DWI and T2-weighted magnetic resonance imaging (MRI). In older adults, WMH had a mostly periventricular distribution with higher frequency in frontal regions. We found lower FA in areas of frank WMH compared to normal-appearing white matter (NAWM) in older adults. Then, to determine if areas of normatively lower white matter microstructure spatially overlap with areas that frequently develop macrostructural damage in older age, we created a WMH frequency map in which each voxel represented the percentage of older adults with a WMH in that voxel. We found lower normative FA in young adults with regions frequently segmented as WMH in older adults. We conclude that low white matter microstructure is observed in areas of white matter macrostructural damage, but white matter microstructure is also normatively low (i.e., at ages 20-30) in regions with high WMH frequency, prior to white matter macrostructural damage.

Joint contributions of cortical morphometry and white matter microstructure in healthy brain aging: A partial least squares correlation analysis

Cortical atrophy and degraded axonal health have been shown to coincide during normal aging; however, few studies have examined these measures together. To lend insight into both the regional specificity and the relative timecourse of structural degradation of these tissue compartments across the adult lifespan, we analyzed gray matter (GM) morphometry (cortical thickness, surface area, volume) and estimates of white matter (WM) microstructure (fractional anisotropy, mean diffusivity) using traditional univariate and more robust multivariate techniques to examine age associations in 186 healthy adults aged 20–94 years old. Univariate analysis of each tissue type revealed that negative age associations were largest in frontal GM and WM tissue and weaker in temporal, cingulate, and occipital regions, representative of not only an anterior‐to‐posterior gradient, but also a medial‐to‐lateral gradient. Multivariate partial least squares correlation (PLSC) found the greatest covariance between GM and WM was driven by the relationship between WM metrics in the anterior corpus callosum and projections of the genu, anterior cingulum, and fornix; and with GM thickness in parietal and frontal regions. Surface area was far less susceptible to age effects and displayed less covariance with WM metrics, while regional volume covariance patterns largely mirrored those of cortical thickness. Results support a retrogenesis‐like model of aging, revealing a coupled relationship between frontal and parietal GM and the underlying WM, which evidence the most protracted development and the most vulnerability during healthy aging.