Niharika Gajawelli

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.

A T1 and DTI fused 3D corpus callosum analysis in pre- vs. post-season contact sports players

Sports related traumatic brain injury (TBI) is a worldwide public health issue, and damage to the corpus callosum (CC) has been considered as an important indicator of TBI. However, contact sports players suffer repeated hits to the head during the course of a season even in the absence of diagnosed concussion, and less is known about their effect on callosal anatomy. In addition, T1-weighted and diffusion tensor brain magnetic resonance images (DTI) have been analyzed separately, but a joint analysis of both types of data may increase statistical power and give a more complete understanding of anatomical correlates of subclinical concussions in these athletes. Here, for the first time, we fuse T1 surface-based morphometry and a new DTI analysis on 3D surface representations of the CCs into a single statistical analysis on these subjects. Our new combined method successfully increases detection power in detecting differences between pre- vs. post-season contact sports players. Alterations are found in the ventral genu, isthmus, and splenium of CC. Our findings may inform future health assessments in contact sports players. The new method here is also the first truly multimodal diffusion and T1-weighted analysis of the CC, and may be useful to detect anatomical changes in the corpus callosum in other multimodal datasets.

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.

Putamen development in children 12 to 21 months old

We studied the developmental trajectory of the putamen in 13-21 months old children using multivariate surface tensor-based morphometry. Our results indicate surface changes between 12 and 15 months’ age groups in the middle superior part the left putamen. The growth of the left putamen at earlier ages slows down after 15 months. The most important surface changes were detected in the right putamen between 18 and 21 months and were located in the anterior part of the structure. Our results demonstrate the heterochronic growth of the right and left putamen related to different functional subregions within putamen. Our results are compatible with previous studies devoted to total putamen volume changes during normal development.

Characterization of the central sulcus in the brain in early childhood

Characterization of the developing brain during early childhood is of interest for both neuroscience and medicine, and in particular, is key to understanding what goes wrong in neurodevelopmental disorders. In particular, the cortex grows rapidly in the first 3 years of life, and creating a normative atlas can provide a comparison tool to diagnose disorders at an early stage, thereby empowering early interventional therapies. Zooming in on specific sulci may provide additional targeted information, and notably, an understanding of central sulcus growth can provide important insight on the development of laterality. However, there currently do not exist any atlases of specific changes in sulci as the brain grows. In this pilot study, we explore regional differences in the depth of the central sulcus between two and three year old infants using brain magnetic resonance images.

EdgeRunner: a novel shape-based pipeline for tumours analysis and characterisation

Abstract Characterisation of tumours on imaging is usually performed qualitatively by visual analysis by radiologists. However, incorporating quantitative imaging features would potentially increase diagnostic accuracy and would improve patient care and management. The goal is to eventually be able to distinguish certain quantifiable features, which along with qualitative ones could differentiate between benign and malignant tumours. This may obviate the need for invasive procedures such as a biopsy, and may also allow for earlier detection as well as better follow-up. The shape of a tumour is often qualitatively described to help differentiate between tumor types. A smooth round outline is generally thought to be indicative of a benign or slowly growing lesion. A malignant neoplasm, on the other hand, tends to have disorganised growth and has a lobulated or spiculated margin. In this manuscript, we introduce a new computed tomography-based pipeline for tumour analysis and characterisation. This method yields an easily interpretable histogram-based index of lobularity. The EdgeRunner Pipeline performs equally well in studies of individual subjects, or in population-based assessments. Importantly, the methods can be easily translated to clinical use.

Central sulcus development in early childhood

Mapping out the development of the brain in early childhood is a critical part of understanding neurological disorders. The brain grows rapidly in early life, reaching 95% of the final volume by age 6. A normative atlas containing structural parameters that indicate development would be a powerful tool in understanding the progression of neurological diseases. Although some studies have begun exploring cortical development in pediatric imaging, sulci have not been examined extensively. Here, we study the changes in the Central Sulcus (CS), which is one of the earliest sulci to develop from the fetal stage, at early developmental age 1–3 years old using high resolution magnetic resonance images. Parameterization of the central sulcus was performed and results show us that the CS change corresponds to the development of the mouth and tongue regions.