White Matter Development in Adolescence: A DTI Study

Asato, Miya R.; Terwilliger, Robert; Woo, John; Luna, Beatríz

doi:10.1093/cercor/bhp282

Cited by 450 publications

(488 citation statements)

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“…They are also consistent with activation studies using functional MRI, which have reported greater interhemispheric activation in females on a language task, in which they excelled (44), and greater focal intrahemispheric activation in males on a spatial task, in which they excelled (45). With respect to development, DTI studies (23,24) have shown higher FA and lower MD in the CC in females during midadolescence, confirming a similar trend in our data. Although FA and MD provide measures of WM integrity, connectomic studies like ours are required to complete the picture of connection-wise systems.…”

Section: Discussionsupporting

confidence: 92%

“…Examination of DTI-based scalar measures (16) of fractional anisotropy (FA) and mean diffusivity (MD) has demonstrated diverse outcomes that include increased FA and decreased MD in males in major WM regions (17)(18)(19), higher CC-specific FA in females (20,21), and lower axial and radial diffusivity measures (22) in males. Throughout the developmental period, females displayed higher FA and lower MD in the midadolescent age (12-14 y) (23), and this result was established on a larger sample size (114 subjects) as well (24). On the other hand, sex differences on the entire age range (childhood to old age) demonstrated higher FA and lower MD in males (19,25,26).…”

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confidence: 77%

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Sex differences in the structural connectome of the human brain

Ingalhalikar

Smith

Parker

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

998

621

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Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes. diffusion imaging | gender differences S ex differences are of enduring scientific and societal interest because of their prominence in the behavior of humans and nonhuman species (1). Behavioral differences may stem from complementary roles in procreation and social structure; examples include enhanced motor and spatial skills and greater proclivity for physical aggression in males and enhanced verbally mediated memory and social cognition in females (2, 3). With the advent of neuroimaging, multiple studies have found sex differences in the brain (4) that could underlie the behavioral differences. Males have larger crania, proportionate to their larger body size, and a higher percentage of white matter (WM), which contains myelinated axonal fibers, and cerebrospinal fluid (5), whereas women demonstrate a higher percentage of gray matter after correcting for intracranial volume effect (6). Sex differences in the relative size and shape of specific brain structures have also been reported (7), including the hippocampus, amygdala (8, 9), and corpus callosum (CC) (10). Furthermore, developmental differences in tissue growth suggest that there is an anatomical sex difference during maturation (11,12), although links to observed behavioral differences have not been established.Recent studies have used diffusion tensor imaging (DTI) to characterize WM architecture and underlying fiber tracts by exploiting the anisotropic water diffusion in WM (13-15). Examination of DTI-based scalar measures (16) of fractional anisotropy (FA) and mean diffusivity (MD) has demonstrated diverse outcomes that include increased FA and decreased MD in males in major WM regions (17-19), higher CC-specific...

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Section: Discussionsupporting

confidence: 92%

mentioning

confidence: 77%

Sex differences in the structural connectome of the human brain

Ingalhalikar

Smith

Parker

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

998

621

View full text Add to dashboard Cite

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“…Our findings regarding functional connectivity may also be explained by neural maturation. As diffusion becomes more restricted with age in frontal pathways (Asato, Terwilliger, Woo, & Luna, 2010; Liston et al., 2006), functional connectivity patterns may become more consistent across different cognitive tasks, as suggested by the current study. In order to further explore the relationship between age‐related morphological reorganization and neural recruitment, future studies should directly compare measures of cortical activity and connectivity with indices of anatomical neural maturation (e.g., cortical thinning and axonal myelination).…”

Section: Discussionmentioning

confidence: 99%

Neural activity patterns between different executive tasks are more similar in adulthood than in adolescence

et al. 2018

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BackgroundAdolescence is a time of ongoing neural maturation and cognitive development, especially regarding executive functions. In the current study, age‐related differences in the neural correlates of different executive functions were tracked by comparing three age groups consisting of adolescents and young adults.MethodsBrain activity was measured with functional magnetic resonance imaging (fMRI) from 167 human participants (13‐ to 14‐year‐old middle adolescents, 16‐ to 17‐year‐old late adolescents and 20‐ to 24‐year‐old young adults; 80 female, 87 male) while they performed attention and working memory tasks. The tasks were designed to tap into four putative sub‐processes of executive function: division of attention, inhibition of distractors, working memory, and attention switching.ResultsBehaviorally, our results demonstrated superior task performance in older participants across all task types. When brain activity was examined, young adult participants demonstrated a greater degree of overlap between brain regions recruited by the different executive tasks than adolescent participants. Similarly, functional connectivity between frontoparietal cortical regions was less task specific in the young adult participants than in adolescent participants.ConclusionsTogether, these results demonstrate that the similarity between different executive processes in terms of both neural recruitment and functional connectivity increases with age from middle adolescence to early adulthood, possibly contributing to age‐related behavioral improvements in executive functioning. These developmental changes in brain recruitment may reflect a more homogenous morphological organization between process‐specific neural networks, increased reliance on a more domain‐general network involved in executive processing, or developmental changes in cognitive strategy.

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“…As an insulator on certain axons, myelin increases the speed of signal impulses via saltatory conduction. WM functions to connect areas of GM and sends these signals to a variety of regions including the brainstem and limbic system [1]. These signals are used as the nervous system's communication, and can be translated into cognitive functioning processes or motor processes [1,7].…”

Section: Introductionmentioning

confidence: 99%

“…However, increases in GM volumes peak well before an individual's 20's, whereas increases in WM volume peaks in middle age [18,31]. In general, WM has been determined to increase significantly during adolescence [1,15,18,21,25,26,28] yet decreases in later adolescent stages [13,15,26,29]. There is also evidence to suggest that WM develops differently between males and females; men have a parallel rate of growth in both GM and WM to cranial volume, whereas women increase in WM at a slower rate in relation to cranial volume [3,9,11,13,35].…”

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confidence: 99%

Determining the Relationship between White Matter Volume and Processing Speed in Adolescence

Laxamana

2016

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Neuroimaging techniques have recently been used to research and identify relationships between structure and function. Because adolescence is a critical period for development of the brain, the present study's goal was to identify the possible relationships between white matter (WM) volume and processing speed during this period. White matter was of particular interest as it consists of the myelinated axons of some nerve cells which are essential for nervous system functioning. The WM volume of 157 adolescents (ages 12-17) were determined from T1-weighted images via FMRIB's Automated Segmentation Tool (FAST), whereby the brain was separated into WM, gray matter (GM), and cerebral spinal fluid (CSF). A composite score for processing speed was indicated by a z-normalized mean score from the Wechsler Abbreviated Scale of Intelligence (WASI) block design test, Paced Auditory Serial Addition Test (PASAT), Delis-Kaplan Executive Function System (D-KEFS) which included raw scores of Color-Word Conditions 1-4, verbal and spatial working memory 2-back tasks, and the hit reaction time score from the Continuous Performance Test (CPT). Results suggested that there was a small positive relationship between WM volume and age at the time of the scan, which agrees with several reported literatures. Processing speed was defined as a more negative value below the mean; the initial hypothesis was further supported as a negative relationship between WM volume and age to processing speed in adolescence observed. Although the correlation and significance was relatively low between the variables, the sample suggests that as an adolescent's WM volume or age increased or decreased, processing speed was reflected as marginally quicker or slower. INTRODUCTION AND BACKGROUNDThe human brain is primarily composed of grey matter (GM) and white matter (WM). GM consists of neuronal cell bodies, neuropil, glial cells, synapses, and capillaries, while WM is primarily composed of glial cells and myelinated axons. The GM's color represents the neuronal cell bodies, while WM's color is due to the myelin sheath. As an insulator on certain axons, myelin increases the speed of signal impulses via saltatory conduction. WM functions to connect areas of GM and sends these signals to a variety of regions including the brainstem and limbic system [1]. These signals are used as the nervous system's communication, and can be translated into cognitive functioning processes or motor processes [1,7]. The speed of the signal transmitted is related to the thickness and degree of myelination, and damage to aforementioned regions results in a reduction in functional processing speed [30]. Diseases that damage WM or myelin, such as Alzheimer's, multiple sclerosis (MS), or other factors like lesions also result in reduced function or ability to send these signals [6,10,32].In the developmental period of adolescence, both GM and WM undergo varying structural maturation [16,18,23,31]. However, increases in GM volumes peak well before an individual's 20's, wherea...

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White Matter Development in Adolescence: A DTI Study

Cited by 450 publications

References 108 publications

Sex differences in the structural connectome of the human brain

Sex differences in the structural connectome of the human brain

Neural activity patterns between different executive tasks are more similar in adulthood than in adolescence

Determining the Relationship between White Matter Volume and Processing Speed in Adolescence

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