Sinchai Tsao

Neuroimaging Changes in the Brain in Contact versus Noncontact Sport Athletes Using Diffusion Tensor Imaging

OBJECTIVE Traumatic brain injury in contact sports has significant impact on short-term neurologic and neurosurgical function as well as longer-term cognitive disability. In this study, we aim to demonstrate that contact sport participants exhibit differences in diffusion tensor imaging (DTI) caused by repeated physical impacts on the brain. We also aim to determine that impact incurred by the contact sports athletes during the season may result in the differences between the pre- and postseason DTI scans. METHODS DTI data were collected from 10 contact-sport (mean age 20.4 ± 1.36 years) and 13 age-matched noncontact-sport (mean age 19.5 ± 1.03 years) male athletes on a 3-Tesla magnetic resonance imaging scanner. A single-shot, echo-planar imaging sequence with b-value of 1000 s/mm(2) and 25 gradient directions was used. Eight of the athletes were again scanned after the end of the season. The b0 nondiffusion-weighted image was averaged five times. Voxel-wise, two-sample t tests were run for all group comparisons, and in each case, the positive false-discovery rate was computed to assess the whole-map, multiple-comparison corrected significance. RESULTS There were significant differences in the fractional anisotropy values in the inferior fronto-occipital fasciculus, parts of the superior and posterior coronal radiate, and the splenium of the corpus callosum (CC) as well as smaller clusters in the genu and parts of the body of the CC. In addition, the external capsule also shows some difference between the contact and noncontact athlete brains. In addition, the preseason and postseason showed differences in these regions, however, the postseason P-values show significance in more areas of the CC. CONCLUSIONS There are significant DTI changes in the CC, the external capsule, the inferior fronto-occipital fasciculus, as well as regions such as the superior/posterior corona radiata the preseason contact versus the noncontact control athletes were compared and also when the postseason contact athletes with the control athletes were compared.

Mapping of ApoE4 Related White Matter Damage using Diffusion MRI.

ApoliopoproteinE Ɛ4 (ApoE-Ɛ4) polymorphism is the most well known genetic risk factor for developing Alzheimers Disease. The exact mechanism through which ApoE 4 increases AD risk is not fully known, but may be related to decreased clearance and increased oligomerization of Aβ. By making measurements of white matter integrity via diffusion MR and correlating the metrics in a voxel-based statistical analysis with ApoE-Ɛ4 genotype (whilst controlling for vascular risk factor, gender, cognitive status and age) we are able to identify changes in white matter associated with carrying an ApoE Ɛ4 allele. We found potentially significant regions (Puncorrected < 0:05) near the hippocampus and the posterior cingulum that were independent of voxels that correlated with age or clinical dementia rating (CDR) status suggesting that ApoE may affect cognitive decline via a pathway in dependent of normal aging and acute insults that can be measured by CDR and Framingham Coronary Risk Score (FCRS).

A T1 and DTI fused 3D corpus callosum analysis in MCI subjects with high and low cardiovascular risk profile

Understanding the extent to which vascular disease and its risk factors are associated with prodromal dementia, notably Alzheimers disease (AD), may enhance predictive accuracy as well as guide early interventions. One promising avenue to determine this relationship consists of looking for reliable and sensitive in-vivo imaging methods capable of characterizing the subtle brain alterations before the clinical manifestations. However, little is known from the imaging perspective about how risk factors such as vascular disease influence AD progression. Here, for the first time, we apply an innovative T1 and DTI fusion analysis of 3D corpus callosum (CC) on mild cognitive impairment (MCI) populations with different levels of vascular profile, aiming to de-couple the vascular factor in the prodromal AD stage. Our new fusion method successfully increases the detection power for differentiating MCI subjects with high from low vascular risk profiles, as well as from healthy controls. MCI subjects with high and low vascular risk profiles showed differed alteration patterns in the anterior CC, which may help to elucidate the inter-wired relationship between MCI and vascular risk factors.

Evaluating the predictive power of multivariate tensor-based morphometry in Alzheimer's disease progression via convex fused sparse group Lasso

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end we combine a predictive multi-task machine learning method1 with novel MR-based multivariate morphometric surface map of the hippocampus2 to predict future cognitive scores of patients. Previous work by Zhou et al.1 has shown that a multi-task learning framework that performs prediction of all future time points (or tasks) simultaneously can be used to encode both sparsity as well as temporal smoothness. They showed that this can be used in predicting cognitive outcomes of Alzheimers Disease Neuroimaging Initiative (ADNI) subjects based on FreeSurfer-based baseline MRI features, MMSE score demographic information and ApoE status. Whilst volumetric information may hold generalized information on brain status, we hypothesized that hippocampus specific information may be more useful in predictive modeling of AD. To this end, we applied Shi et al.2s recently developed multivariate tensor-based (mTBM) parametric surface analysis method to extract features from the hippocampal surface. We show that by combining the power of the multi-task framework with the sensitivity of mTBM features of the hippocampus surface, we are able to improve significantly improve predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

3D pre- versus post-season comparisons of surface and relative pose of the corpus callosum in contact sport athletes

Mild traumatic brain injury (MTBI) or concussive injury affects 1.7 million Americans annually, of which 300,000 are due to recreational activities and contact sports, such as football, rugby, and boxing[1]. Finding the neuroanatomical correlates of brain TBI non-invasively and precisely is crucial for diagnosis and prognosis. Several studies have shown the in influence of traumatic brain injury (TBI) on the integrity of brain WM [2-4]. The vast majority of these works focus on athletes with diagnosed concussions. However, in contact sports, athletes are subjected to repeated hits to the head throughout the season, and we hypothesize that these have an influence on white matter integrity. In particular, the corpus callosum (CC), as a small structure connecting the brain hemispheres, may be particularly affected by torques generated by collisions, even in the absence of full blown concussions. Here, we use a combined surface-based morphometry and relative pose analyses, applying on the point distribution model (PDM) of the CC, to investigate TBI related brain structural changes between 9 pre-season and 9 post-season contact sport athlete MRIs. All the data are fed into surface based morphometry analysis and relative pose analysis. The former looks at surface area and thickness changes between the two groups, while the latter consists of detecting the relative translation, rotation and scale between them.

Application of fMRI to obesity research: differences in reward pathway activation measured with fMRI BOLD during visual presentation of high and low calorie foods

The factors behind the neural mechanisms that motivate food choice and obesity are not well known. Furthermore, it is not known when these neural mechanisms develop and how they are influenced by both genetic and environmental factors. This study uses fMRI together with clinical data to shed light on the aforementioned questions by investigating how appetite-related activation in the brain changes with low versus high caloric foods in pre-pubescent girls. Previous studies have shown that obese adults have less striatal D2 receptors and thus reduced Dopamine (DA) signaling leading to the reward-deficit theory of obesity. However, overeating in itself reduces D2 receptor density, D2 sensitivity and thus reward sensitivity. The results of this study will show how early these neural mechanisms develop and what effect the drastic endocrinological changes during puberty has on these mechanisms. Our preliminary results showed increased activations in the Putamen, Insula, Thalamus and Hippocampus when looking at activations where High Calorie > Low Calorie. When comparing High Calorie > Control and Low Calorie > Control, the High > Control test showed increased significant activation in the frontal lobe. The Low > Control also yielded significant activation in the Left and Right Fusiform Gyrus, which did not appear in the High > Control test. These results indicate that the reward pathway activations previously shown in post-puberty and adults are present in pre-pubescent teens. These results may suggest that some of the preferential neural mechanisms of reward are already present pre-puberty.

Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry

Prediction of Alzheimers disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel MR‐based multivariate morphometric surface map of the hippocampus (mTBM) to predict future cognitive scores of patients.

TractRender: a new generalized 3D medical image visualization and output platform

Diffusion MRI allows us not only voxelized diffusion characteristics but also the potential to delineate neuronal fiber path through tractography. There is a dearth of flexible open source tractography software programs for visualizing these complicated 3D structures. Moreover, rendering these structures using various shading, lighting, and representations will result in vastly different graphical feel. In addition, the ability to output these objects in various formats increases the utility of this platform. We have created TractRender that leverages openGL features through Matlab, allowing for maximum ease of use but still maintain the flexibility of custom scene rendering.

The power of hybrid / fusion imaging metrics in future PACS systems: a case study into the white matter hyperintensity prenumbra using FLAIR and diffusion MR

Most white matter related neurological disease exhibit a large number of White Matter Hyperintensities (WMHs) on FLAIR MRI images. However, these lesions are not well understood. At the same time, Diffusion MRI has been gaining popularity as a powerful method of characterizing White Matter (WM) integrity. This work aims to study the behavior of the diffusion signal within the WMH voxels. The goal is to develop hybrid MR metrics that leverage information from multiple MR acquisitions to solve clinical problems. In our case, we are trying to address the WMH penumbra (as defined by Maillard et al 20112) where WMH delineates a foci that is more widespread than the actual damage area presumably due to acute inflammation. Our results show that diffusion MR metrics may be able to better delineate tissue that is inflamed versus scar tissue but may be less specific to lesions than FLAIR. Therefore, a hybrid metric that encodes information from both FLAIR and Diffusion MR may yield new and novel imaging information about the progression of white matter disease progression. We hope that this work also demonstrates how future PACS systems could have image fusion capabilities that would be able to leverage information from multiple imaging series to yield new and novel imaging contrast.

Novel MRI methodology to detect human whole-brain connectivity changes after ingestion of fructose or glucose

A novel MRI protocol has been developed to investigate the differential effects of glucose or fructose consumption on whole-brain functional brain connectivity. A previous study has reported a decrease in the fMRI blood oxygen level dependent (BOLD) signal of the hypothalamus following glucose ingestion, but due to technical limitations, was restricted to a single slice covering the hypothalamus, and thus unable to detect whole-brain connectivity. In another previous study, a protocol was devised to acquire whole-brain fMRI data following food intake, but only after restricting image acquisition to an MR sampling or repetition time (TR) of 20s, making the protocol unsuitable to detect functional connectivity above 0.025Hz. We have successfully implemented a continuous 36-min, 40 contiguous slices, whole-brain BOLD acquisition protocol on a 3T scanner with TR=4.5s to ensure detection of up to 0.1Hz frequencies for whole-brain functional connectivity analysis. Human data were acquired first with ingestion of water only, followed by a glucose or fructose drink within the scanner, without interrupting the scanning. Whole-brain connectivity was analyzed using standard correlation methodology in the 0.01-0.1 Hz range. The correlation coefficient differences between fructose and glucose ingestion among targeted regions were converted to t-scores using the water-only correlation coefficients as a null condition. Results show a dramatic increase in the hypothalamic connectivity to the hippocampus, amygdala, insula, caudate and the nucleus accumben for fructose over glucose. As these regions are known to be key components of the feeding and reward brain circuits, these results suggest a preference for fructose ingestion.