Search

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., Competing Interests: Declaration of Competing Interest Authors report no conflicts of interest., (Copyright © 2023. Published by Elsevier Inc.)

The sources of inter- and intra-individual variability in age-related cognitive decline remain poorly understood. We examined the association between 20-year trajectories of cognitive decline and multimodal brain structure and morphology in older age. We used the Whitehall II Study, an extensively characterised cohort with 3T brain magnetic resonance images acquired at older age (mean age = 69.52 ± 4.9) and 5 repeated cognitive performance assessments between mid-life (mean age = 53.2 ±4.9 years) and late-life (mean age = 67.7 ± 4.9). Using non-negative matrix factorization, we identified 10 brain components integrating cortical thickness, surface area, fractional anisotropy, and mean and radial diffusivities. We observed two latent variables describing distinct brain-cognition associations. The first describes variations in 5 structural components associated with low mid-life performance across multiple cognitive domains, decline in reasoning, but maintenance of fluency abilities. The second describes variations in 6 structural components associated with low mid-life performance in fluency and memory, but retention of multiple abilities. Expression of latent variables predicts future cognition 3.2 years later (mean age = 70.87 ± 4.9). This data-driven approach highlights brain-cognition relationships wherein individuals degrees of cognitive decline and maintenance across diverse cognitive functions are both positively and negatively associated with markers of cortical structure., (Copyright © 2022. Published by Elsevier Inc.)

The striatum is a major subcortical connection hub that has been heavily implicated in a wide array of motor and cognitive functions. Here, we developed a normative multimodal, data-driven microstructural parcellation of the striatum using non-negative matrix factorization (NMF) based on multiple magnetic resonance imaging-based metrics (mean diffusivity, fractional anisotropy, and the ratio between T1- and T2-weighted structural scans) from the Human Connectome Project Young Adult dataset (n = 329 unrelated participants, age range: 22-35, F/M: 185/144). We further explored the biological and functional relationships of this parcellation by relating our findings to motor and cognitive performance in tasks known to involve the striatum as well as demographics. We identified 5 spatially distinct striatal components for each hemisphere. We also show the gain in component stability when using multimodal versus unimodal metrics. Our findings suggest distinct microstructural patterns in the human striatum that are largely symmetric and that relate mostly to age and sex. Our work also highlights the putative functional relevance of these striatal components to different designations based on a Neurosynth meta-analysis., (Copyright © 2021. Published by Elsevier Inc.)

Many magnetic resonance imaging (MRI) measures are being studied longitudinally to explore topics such as biomarker detection and clinical staging. A pertinent concern to longitudinal work is MRI scanner upgrades. When upgrades occur during the course of a longitudinal MRI neuroimaging investigation, there may be an impact on the compatibility of pre- and post-upgrade measures. Similarly, subject motion is another issue that may be detrimental to MRI work and embedding volumetric navigators (vNavs) within acquisition sequences has emerged as a technique that allows for prospective motion correction. Our research group recently underwent an upgrade from a Siemens MAGNETOM 3T Tim Trio system to a Siemens MAGNETOM 3T Prisma Fit system. The goals of the current work were to: 1) investigate the impact of this upgrade on commonly used structural imaging measures and proton magnetic resonance spectroscopy indices ("Prisma Upgrade protocol") and 2) examine structural imaging measures in a sequence with vNavs alongside a standard acquisition sequence ("vNav protocol"). While high reliability was observed for most of the investigated MRI outputs, suboptimal reliability was observed for certain indices. Across the scanner upgrade, increases in frontal, temporal, and cingulate cortical thickness (CT) and thalamus volume, along with decreases in parietal CT and amygdala, globus pallidus, hippocampus, and striatum volumes, were observed. No significant impact of the upgrade was found in 1 H-MRS analyses. Further, CT estimates were found to be larger in MPRAGE acquisitions compared to vNav-MPRAGE acquisitions mainly within temporal areas, while the opposite was found mostly in parietal brain regions. The results from this work should be considered in longitudinal study designs and comparable prospective motion correction investigations are warranted in cases of marked head movement., Competing Interests: Declaration of Competing Interest Authors report no conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

The hippocampus has been extensively studied in various neuropsychiatric disorders throughout the lifespan. However, inconsistent results have been reported with respect to which subfield volumes are most related to age. Here, we investigate whether these discrepancies may be explained by experimental design differences that exist between studies. Multiple datasets were used to collect 1690 magnetic resonance scans from healthy individuals aged 18-95 years old. Standard T1-weighted (T1w; MPRAGE sequence, 1 mm 3 voxels), high-resolution T2-weighted (T2w; SPACE sequence, 0.64 mm 3 voxels) and slab T2-weighted (Slab; 2D turbo spin echo, 0.4 × 0.4 × 2 mm 3 voxels) images were included. The MAGeT Brain algorithm was used for segmentation of the hippocampal grey matter (GM) subfields and peri-hippocampal white matter (WM) subregions. Linear mixed-effect models and Akaike information criterion were used to examine linear, second or third order natural splines relationship between hippocampal volumes and age. We demonstrated that stratum radiatum/lacunosum/moleculare and fornix subregions expressed the highest relative volumetric decrease, while the cornus ammonis 1 presented a relative volumetric preservation of its volume with age. We also found that volumes extracted from slab images demonstrated different age-related relationships compared to volumes extracted from T1w and T2w images. The current work suggests that although T1w, T2w and slab derived subfield volumetric outputs are largely homologous, modality choice plays a meaningful role in the volumetric estimation of the hippocampal subfields., (Copyright © 2021. Published by Elsevier Inc.)

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses., Competing Interests: Declaration of competing interest Authors report no conflicts of interest., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Twin study designs have been previously used to investigate the heritability of neuroanatomical measures, such as regional cortical volumes. Volume can be fractionated into surface area and cortical thickness, where both measures are considered to have independent genetic and environmental bases. Region of interest (ROI) and vertex-wise approaches have been used to calculate heritability of cortical thickness and surface area in twin studies. In our study, we estimate heritability using the Human Connectome Project magnetic resonance imaging dataset composed of healthy young twin and non-twin siblings (mean age of 29, sample size of 757). Both ROI and vertex-wise methods were used to compare regional heritability of cortical thickness and surface area. Heritability estimates were controlled for age, sex, and total ipsilateral surface area or mean cortical thickness. In both approaches, heritability estimates of cortical thickness and surface area were lower when accounting for average ipsilateral cortical thickness and total surface area respectively. When comparing both approaches at a regional level, the vertex-wise approach showed higher surface area and lower cortical thickness heritability estimates compared to the ROI approach. The calcarine fissure had the highest surface area heritability estimate (ROI: 44%, vertex-wise: 50%) and posterior cingulate gyrus had the highest cortical thickness heritability (ROI: 50%, vertex-wise 40%). We also observed that limitations in image processing and variability in spatial averaging errors based on regional size may make obtaining true estimates of cortical thickness and surface area challenging in smaller regions. It is important to identify which approach is best suited to estimate heritability based on the research hypothesis and the size of the regions being investigated., (Copyright © 2018 Elsevier Inc. All rights reserved.)