Investigating Brain Structure Using Voxel-Based Methods with Magnetic Resonance Imaging

The number of people suffering from neurodegenerative diseases, such as Alzheimer`s disease, increased dramatically over the past centuries and is expected to increase even further within the next years. Based on predictions of the World Health Organization and Alzheimer`s Disease International, 115 million people will suffer from dementia by the year 2050. An additionally increase in other age related neurodegenerative diseases is also forecasted. Quite naturally, neurodegenerative diseases became a focus of attention of governments and health insurances, trying to control the uprising financial burden. Early detection and treatment of neurodegenerative diseases could be an important component in containing this problem. In particular, researchers focused on automatic methods to analyze patients’ imaging data. One way to detect structural changes in magnetic resonance images (MRI) is the voxel-based method approach. It was specifically implemented for various imaging modalities, e.g. T1-weighted images or diffusion tensor imaging (DTI). Voxel-based morphometry (VBM), a method specifically designed to analyze T1-weighted images, has become very popular over the last decade. Investigations using VBM revealed numerous structural brain changes related to, e.g. neurodegeneration, learning induced structural changes or aging. Although voxel-based methods are designed to be robust and reliable structural change detection methods, it is known that they can be influenced by physical and physiological factors. Dehydration, for example, can affect the volume of brain structures and possibly induce a confound in morphometric studies. Therefore, three-dimensional T1-weighted images were acquired of six young and healthy subjects during different states of hydration. Measurements during normal hydration, hyperhydration, and dehydration made it possible to assess consequential volume changes in gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF). The datasets were analyzed using VBM, FreeSurfer and SIENA. A significant decrease of GM and WM volume, associated with dehydration, was found in various brain regions. The most prominent effects were found in temporal and parietal areas, in the left inferior orbito-frontal region, and in the extra-nuclear region. Moreover, we found consistent increases in CSF, an expansion around 6% of the ventricular system affecting both lateral ventricles, i.e. the third and fourth ventricle. Similar degrees of shrinkage in WM volume and increase of the ventricular system have been reported in studies of Alzheimer’s disease during disease progression and in its prestage mild cognitive impairment. Based on these findings, a potential confound in GM and WM or CSF studies due to the subjects’ hydration state cannot be excluded and should be appropriately addressed. These results underline the sensitivity of VBM and might also concern other voxel-based methods, such as Tract-Based Spatial Statistics (TBSS). TBSS was specifically designed for WM analyses and its sensitivity might be helpful for revealing the spatial relation of structural WM changes and related blood serum biomarkers. Two common brain related biomarkers are the glial protein S100B, a plasticity inducing neuro- and gliotrophin, and neuron-specific enolase (NSE), a marker for neuronal damage. However, the spatial specificity of these biomarkers for brain region has not been investigated in vivo until now. Therefore, we acquired two MRI parameters – T1- weighted and DTI - sensitive to changes in GM and WM, and obtained serum S100B and NSE levels of 41 healthy subjects. Additionally, the gene expression of S100B on the whole brain level in a male cohort of three subjects from the Allen Brain Database was analyzed. Furthermore, a female post mortal brain was investigated using double immunofluorescence labeling with oligodendrocyte markers. It could be shown that S100B is specifically related to white matter structures, namely the corpus callosum, anterior forceps and superior longitudinal fasciculus in female subjects. This effect was observed in fractional anisotropy and radial diffusivity – the latest an indicator of myelin changes. Histological data confirmed a co-localization of S100B with oligodendrocyte markers in the human corpus callosum. S100B was most abundantly expressed in the corpus callosum according to the whole genome Allen Human Brain Atlas. In addition, NSE was related to gray matter structures, namely the amygdala. This effect was detected across sexes. The data demonstrates a very high S100B expression in white matter tracts, in particular in human corpus callosum. This was the first in vivo study validating the specificity of the glial marker S100B for the human brain, and supporting the assumption that radial diffusivity represents a myelin marker. The results open a new perspective for future studies investigating major neuropsychiatric disorders. All above mentioned studies are mainly dependent on the sensitivity and accuracy of soft and hardware parameters. In particular, technical developments have improved acquisition accuracy in the field of MRI. Interestingly, very little is known about the confounding effects of variations due to hardware parameters and their possible impact on reliability and sensitivity of VBM. Recent studies have shown that different acquisition parameters may influence VBM results. Therefore age-related GM changes were investigated with VBM in 36 healthy volunteers grouped into 12 young, 12 middle-aged and 12 elderly subject. Six T1-weighted datasets were acquired per subject with a 12-channel matrix coil, as well as a 32-channel array, MP-RAGE and MP2RAGE, and with isotropic resolutions of 0.8 and 1 mm. DARTEL-VBM was applied on all images and GM, WM and CSF segments were statistically analyzed.. Paired t-tests and statistical interaction tests revealed significant effects of acquisition parameters on the estimated gray-matter-density (GMD) in various cortical and subcortical brain regions. MP2RAGE seemed slightly less prone to false positive results when comparing data acquired with different RF coils and yielded superior segmentation of deep GM structures. With the 12-channel coil, MP-RAGE was superior in detecting age-related changes, especially in cortical structures. Most differences between both sequences became insignificant with the 32-channel coil, indicating that the MP2RAGE images benefited more from the improved signal-to-noise ratio and improved parallel-imaging reconstruction). A possible explanation might be an overestimation of the GM compartment on the MP-RAGE images. In view of substantial effects obtained for all parameters, careful standardization of the acquisition protocol is advocated. While the current investigation focused on aging effects, similar results are expected for other VBM studies, like on plasticity or neurodegenerative diseases. This work has shown that voxel-based methods are sensitive to subtle structural brain changes, independent of imaging modality and scanning parameters. In particular, the studies investigated and discussed the analysis of T1- and diffusion weighted images with VBM and TBSS in the context of dehydration, blood serum sensitive biomarkers and aging were discussed. The major goal of these studies was the investigation of the sensitivity of voxel-based methods. In conclusion, sensitivity and accuracy of voxelbased methods is already high, but it can be increased significantly, using optimal hardand software parameters. It is of note, though, that these optimizations and the concomitant increase of detection sensitivity could also introduce additional confounding factors in the imaging data and interfere with the latter preprocessing and statistical computations. To avoid an interference e.g. originating from physiological parameters, a very careful selection and monitoring of biological parameters of each volunteer throughout the whole study is recommended. A potential impact of scanning parameters can be minimized by strict adherence to the imaging protocol for each study subjectwithin a study. A general increase in detection sensitivity due to optimized parameters selection in hard- and/or can not be concluded by the above mentioned studies. Although the present work addressed some of those issues, the topic of optimal selection of parameters for morphometric studies is still very complex and controversial and has to be individually decided. Further investigations are needed to define more general scanning and preprocessing standards to increase detection sensitivity without the concomitant amplification of confounding factors