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

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 T1weighted 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 BACKGROUND The 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, 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]. Furthermore, after accounting for total intracranial volume development, men tend to have a peak higher percentage of WM whereas women have a higher percentage of GM [18]. Several studies have reported sex-specific developmental differences in the relations between cerebral structure and function [35], yet conversely some have observed no significant interactions of sex and cognitive measures when age was not included [29]. However, many of these studies have failed to find these relationships in large sample sizes whereby (N>100). The use of imaging techniques to identify structural components and functionality of the brain is still a new field of research. There are three types of neuro-imaging techniques that have been frequently used in order to assess the suspected relationship between WM and processing speed: Magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and functional magnetic resonance imaging (fMRI). MRI is a technique that began to see heavy diagnostic usage within the last two decades. With the ability to determine clear structural brain images, MRI scans have opened up a wide range of diagnostic and research opportunities which have included mapping anatomical regions [5]. The relationship between structure and function has been reported through methods such as identifying physical damages to regions and their respective effects, or in some cases the gradual growth or deterioration [14]. For the purposes of this thesis, MRI was singularly used. DTI is a technique that records fractional anisotropy (FA) -or the diffusion of water molecules along the main direction of axon bundles and their myelin. This water diffusion is directionally predisposed to following the axon bundles, which reveals the microstructural components of the WM. Structural images of the WM tracts along targeted areas of the brain can be visually recorded in both 2 and 3-dimensional images. Although a powerful tool of WM visualization, DTI suffers from the drawbacks of frequent artifacts, partial volume mapping, and the inability to cope with non-Gaussian diffusion [2]. DTI is not used for total volumetric determination and is therefore commonly coupled with preexisting MRI-determined WM volumes, whereby there have been reports of protracted WM development over adolescence. [1]. Some studies have used DTI to assess how physical damages such as lesions affect the liquid diffusion, as well as WM maturation over periods such as adolescence [1]. It has been reported in some cases that damage to white matter tracts may underlie age-related cognitive decline [10, 34] Nevertheless, many of these studies have used a sample of either elderly or adult participants in determining processing speed [26,34]; in the cases of children and adolescents included, the sample remained relatively small (N<100). The present study looks to build upon the suggested relationships between brain matter structure and function, and therefore the identification of FA through DTI and fMRI in adolescence are particularly relevant. fMRI is frequently coupled with DTI or MRI in order to observe a possible relationship between different brain regions and their activity. In one study, fMRI and DTI was used to test the hypothesis that white matter integrity, as indexed from the FA, was a mechanism of slowing in episodic memory retrieval [6, 14, 34]. Although positive correlations between maturation and increased brain activity have been reported, cases of fMRI studies had a sample of participants primarily above the age of adolescence; these were in periods of slowing WM growth that is conversely reported in younger counterparts [4, 11, 2729]. However, there have been several studies on WM structure in relation to processing speed within young participants as well -there are some reports of reduced FA observed in children with autism [4]. Differential effects of regional variations in diffusion properties on memory and functional speed have also been reported, as well as some effects on processing speed with regards to physical damages like lesions on WM tracts [8, 10, 32]. These studies suggest that, among other factors, the structure of WM is significant in relationship to the functionality of the brain and its processes. The present study looks to identify the possible extent to which the amount of the structure – volume in this case – may relate to the processing speed of adolescence in a larger sample. Although it has been suggested that damage and inhibition of structures has adverse effects, conversely how might a higher amount of volume contribute to cognitive speed? Much of the research in the above cases using DTI and fMRI depended heavily on the structural WM determination from MRI [4, 31, 32]. A large sample size and varying participant ages was a limiting factor; the goal of the present study was to report on a large sample of only adolescents (ages 11-17 at scan date used) WM volume. Based on previous evidence [6, 25, 28, 32], mature WM tracts have been reported to greater processing speed. Therefore, it is hypothesized that in this developmental period, processing speed will be positively related to a great amount of WM as most likely related by an older age. It is also expected that a negative relationship be found with lower volumes of WM -immature, younger ages -and processing speed.