Microstructural Changes of the Human Brain from Early to Mid-Adulthood

Tian, Lihong; Ma, Lin

doi:10.3389/fnhum.2017.00393

Cited by 21 publications

(22 citation statements)

References 72 publications

(124 reference statements)

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“…For example, we did not observe decreases in FA in several regions, including the SCC, fornix (cres/stria terminals), bilateral RLIC, PCR, sagittal stratum (SS), external capsule (EC), and right SLF and SFO. Consistent with the results from other studies, the SCC changes were not observed; fornix (cres/stria terminals) alterations not in studies; RLIC, SS, EC, and right SLF changes not in study; PCR not in study; bilateral CGH and right SLF and SFO not in study . Notably, FA in the superior cerebellar peduncle (SCP) increased with age in a previous study, which was not replicated in the present study.…”

Section: Discussionsupporting

confidence: 84%

“…The fornix and cingulum, which connect limbic structures involved in the Papez circuit, were also affected by age . Tian and Ma reported obvious age‐related alterations in regions including the GCC, bilateral CST, and left SLF . Furthermore, the frontal and GCC changes in FA and fiber density were observed in a study, and growth of projection and association tracts' orientation dispersion index (ODI) was examined .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Age‐related changes in fiber tracts in healthy adult brains: A generalized q‐sampling and connectometry study

Liu

Gao

Zhang

et al. 2018

Magnetic Resonance Imaging

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Section: Discussionsupporting

confidence: 84%

Section: Discussionmentioning

confidence: 99%

Age‐related changes in fiber tracts in healthy adult brains: A generalized q‐sampling and connectometry study

Liu

Gao

Zhang

et al. 2018

Magnetic Resonance Imaging

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“…7 of the mentioned report). Note that two recent studies (46)(47) have reported opposite age effects for AD (decrease) and RD (increase) with stable MD during the 18 to 55 year age period; however, as both studies used simple linear modeling due to small sample sizes, no age at extreme value could be observed. Rather, our findings are compatible with the AD-RD variation heterochrony that has been noticed earlier during childhood and adolescence at the individual tract level with stronger decrease for RD than for AD (44)(30).…”

Section: Discussionmentioning

confidence: 92%

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

Beaudet

Tsuchida

Petit

et al. 2020

Preprint

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Parameters of water diffusion in white matter derived from diffusion-weighted imaging (DWI), such as fractional anisotropy (FA), mean, axial, and radial diffusivity (MD, AD and RD), and more recently, peak width of skeletonized mean diffusivity (PSMD), have been proposed as potential markers of normal and pathological brain ageing. However, their relative evolution over the entire adult lifespan in healthy individuals remains partly unknown during early and late adulthood, and particularly for the PSMD index. Here, we gathered and meta-analyzed cross-sectional diffusion tensor imaging (DTI) data from 10 population-based cohort studies in order to establish the time course of white matter water diffusion phenotypes from post-adolescence to late adulthood. DTI data were obtained from a total of 20,005 individuals aged 18.1 to 92.6 years and analyzed with the same pipeline for computing DTI metrics. For each individual MD, AD, RD, and FA mean values were computed over their FA volume skeleton, PSMD being calculated as the 90% peak width of the MD values distribution across the FA skeleton. Mean values of each DTI metric were found to strongly vary across cohorts, most likely due to major differences in DWI acquisition protocols as well as pre-processing and DTI model fitting. However, age effects on each DTI metric were found to be highly consistent across cohorts. RD, MD and AD variations with age exhibited the same U-shape pattern, first slowly decreasing during post-adolescence until the age of 30, 40 and 50, respectively, then progressively increasing until late life. FA showed a reverse profile, initially increasing then continuously decreasing, slowly until the 70’s, then sharply declining thereafter. By contrast, PSMD constantly increased, first slowly until the 60’s, then more sharply. These results demonstrate that, in the general population, age affects PSMD in a manner different from that of other DTI metrics. The constant increase in PSMD throughout the entire adult life, including during post-adolescence, indicates that PSMD could be an early marker of the ageing process.

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“…Mapping out the underlying microstructure of the brain is essential for many tasks in neuroscience [1,2,3]. In studies of disease [4,5], aging [6], or development [7], for instance, a rich description of the microstructure is first needed before being able to compare brains across different conditions. Detailed maps of brain structure have also led to important discoveries regarding relationships between structure and function [8,9], and provide a necessary sign post when targeting specific brain regions for subsequent studies.…”

Section: Introductionmentioning

confidence: 99%

A Deep Feature Learning Approach for Mapping the Brain’s Microarchitecture and Organization

Balwani

Dyer

2020

Preprint

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Models of neural architecture and organization are critical for the study of disease, aging, and development. Unfortunately, automating the process of building maps of microarchitectural differences both within and across brains still remains a challenge. In this paper, we present a way to build data-driven representations of brain structure using deep learning. With this model we can build meaningful representations of brain structure within an area, learn how different areas are related to one another anatomically, and use this model to discover new regions of interest within a sample that share similar characteristics in terms of their anatomical composition. We start by training a deep convolutional neural network to predict the brain area that it is in, using only small snapshots of its immediate surroundings. By requiring that the network learn to discriminate brain areas from these local views, it learns a rich representation of the underlying anatomical features that allow it to distinguish different brain areas. Once we have the trained network, we open up the black box, extract features from its last hidden layer, and then factorize them. After forming a low-dimensional factorization of the network's representations, we find that the learned factors and their embeddings can be used to further resolve biologically meaningful subdivisions within brain regions (e.g., laminar divisions and barrels in somatosensory cortex). These findings speak to the potential use of neural networks to learn meaningful features for modeling neural architecture, and discovering new patterns in brain anatomy directly from images.Much of our study of the brain and it's organization, which dates back to Cajal's beautiful renderings of neural architecture [10], still relies on human reasoning to define regions-ofinterest (ROIs). This is done by examining different parts of an image, either in terms of their anatomical compositions or patterning, and then building an atlas or model of how the architecture changes across different brain regions. Moving forward however, given the everincreasing sizes of new neuroimaging datasets, we need automated solutions that can find characteristics of brain structure, discover architectural patterns or primitives that are characteristic of a brain area, and also provide good ways to discover substructures (like cortical lamina) within a known brain area.Unfortunately, when attempting to translate expert human knowledge into an automated method for region discovery, it is often unclear how to define the necessary image properties to extract. Moreover, extracting these features can be difficult to automate, or they may not be robust to minor changes in data (e.g., illumination conditions, background intensity, or noise) [11,12]. Features therefore typically need to be hand-crafted for each individual dataset and application, and problems with generalization are only further exacerbated by biological variability, as well as process variations introduced during sample preparation, imaging, or post...

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Microstructural Changes of the Human Brain from Early to Mid-Adulthood

Cited by 21 publications

References 72 publications

Age‐related changes in fiber tracts in healthy adult brains: A generalized q‐sampling and connectometry study

Age‐related changes in fiber tracts in healthy adult brains: A generalized q‐sampling and connectometry study

Age-related changes of Peak width Skeletonized Mean Diffusivity (PSMD) across the adult life span: a multi-cohort study

A Deep Feature Learning Approach for Mapping the Brain’s Microarchitecture and Organization

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