Surface-based morphometry of the corpus callosum in young children of ages 1-5.

The corpus callosum (CC) is a large white matter fiber bundle in the brain and is involved in various cognitive, sensory, and motor processes. While implicated in various developmental and psychiatric disorders, much is yet to be uncovered about the normal development of this structure, especially in young children. Additionally, while sexual dimorphism has been reported in prior literature, observations have not necessarily been consistent. In this study, we use morphometric measures including surface tensor-based morphometry (TBM) to investigate local changes in the shape of the CC in children between the ages of 12 and 60 months, in intervals of 12 months. We also analyze sex differences in each of these age groups. We observed larger significant clusters in the earlier ages between 12 v 24 m and between 48 v 60 m and localized differences in the anterior region of the body of the CC. Sex differences were most pronounced in the 12 m group. This study adds to the growing literature of work aiming to understand the developing brain and emphasizes the utility of surface TBM as a useful tool for analyzing regional differences in neuroanatomical morphometry.

White matter tract changes in pediatric posterior fossa brain tumor survivors after surgery and chemotherapy.

Background: Survivors of pediatric posterior fossa brain tumors are susceptible to the adverse effects of treatment as they grow into adulthood. While the exact neurobiological mechanisms of these outcomes are not yet understood, the effects of treatment on white matter (WM) tracts in the brain can be visualized using diffusion tensor (DT) imaging. We investigated these WM microstructural differences using the statistical method tract-specific analysis (TSA). We applied TSA to the DT images of 25 children with a history of posterior fossa tumor (15 treated with surgery, 10 treated with surgery and chemotherapy) along with 21 healthy controls. Between these 3 groups, we examined differences in the most used DTI metric, fractional anisotropy (FA), in 11 major brain WM tracts. Results: Lower FA was found in the splenium of the corpus callosum (CC), the bilateral corticospinal tract (CST), the right inferior frontal occipital fasciculus (IFOF) and the left uncinate fasciculus (UF) in children with brain tumors as compared to healthy controls. Lower FA, an indicator of microstructural damage to WM, was observed in 4 of the 11 WM tracts examined in both groups of children with a history of posterior fossa tumor, with an additional tract unique to children who received surgery and chemotherapy (left UF). Conclusions: Our findings indicate that a history of tumor in the posterior fossa and surgical resection may have effects on the WM in other parts of the brain.

Developmental changes of the central sulcus morphology in young children.

The human brain grows rapidly in early childhood, reaching 95% of its final volume by age 6. Understanding brain growth in childhood is important both to answer neuroscience questions about anatomical changes in development, and as a comparison metric for neurological disorders. Metrics for neuroanatomical development including cortical measures pertaining to the sulci can be instrumental in early diagnosis, monitoring, and intervention for neurological diseases. In this paper, we examine the development of the central sulcus in children aged 12-60 months from structural magnetic resonance images. The central sulcus is one of the earliest sulci to develop at the fetal stage and is implicated in diseases such as Attention Deficit Hyperactive Disorder and Williams syndrome. We investigate the relationship between the changes in the depth of the central sulcus with respect to age. In our results, we observed a pattern of depth present early on, that had been previously observed in adults. Results also reveal the presence of a rightward depth asymmetry at 12 months of age at a location related to orofacial movements. That asymmetry disappears gradually, mostly between 12 and 24 months, and we suggest that it is related to the development of language skills.

Neurocranium thickness mapping in early childhood.

The neurocranium changes rapidly in early childhood to accommodate the growing brain. Developmental disorders and environmental factors such as sleep position may lead to abnormal neurocranial maturation. Therefore, it is important to understand how this structure develops, in order to provide a baseline for early detection of anomalies. However, its anatomy has not yet been well studied in early childhood due to the lack of available imaging databases. In hospitals, CT is typically used to image the neurocranium when a pathology is suspected, but the presence of ionizing radiation makes it harder to construct databases of healthy subjects. In this study, instead, we use a dataset of MRI data from healthy normal children in the age range of 6 months to 36 months to study the development of the neurocranium. After extracting its outline from the MRI data, we used a conformal geometry-based analysis pipeline to detect local thickness growth throughout this age span. These changes will help us understand cranial bone development with respect to the brain, as well as detect abnormal variations, which will in turn inform better treatment strategies for implicated disorders.

Structural neuroimaging.

Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.

Image Postprocessing Adoption Trends in Clinical Medical Imaging.

There is an increased consensus in the medical and research community about the huge benefits quantitative imaging can bring to radiology. According to the Organisation for Economic Co-operation and Development, approximately 80 million CT and 34 million MRI scans are performed yearly in the United States alone, and the vast majority are currently evaluated through visual inspection. However, quantitative imaging can greatly reduce the burden on radiologists, by diminishing read time and improving diagnostic accuracy. This research was funded by the National Science Foundation's I-Corps and Beat-the-Odds programs to interview more than 350 medical imaging professionals (clinicians, radiologists, policymakers, companies) around the world and determine current needs and trends in the use of postprocessing tools. Here the authors present a summary of these interviews for the adult and pediatric realms. The results indicate that clinical quantitative image analysis is increasingly popular and that we are at the cusp of a revolution in the field in terms of adoption.

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.

Central sulcus depth and sulcal profile differences between congenitally blind and sighted individuals.

We used BrainVisa software in an exploratory analysis measuring the depth and sulcal profile of the central sulci of congenitally blind and sighted individuals. We found the greatest differences between the groups at locations on the central sulcus corresponding with the pli de passage fronto-parietal moyen (PPFM), suggesting a cortical reorganization of the primary sensorimotor area of the hand within the central sulcus. This may be in response to the congenitally blind individuals' mastery of Braille or general increase of hand use in everyday life.

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

INTRODUCTION: Prediction of Alzheimer's 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. METHODS: Previous work has shown that a multi-task learning framework that performs prediction of all future time points simultaneously (cFSGL) can be used to encode both sparsity as well as temporal smoothness. The authors showed that this method is able to predict cognitive outcomes of ADNI subjects using 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 a multivariate tensor-based parametric surface analysis method (mTBM) to extract features from the hippocampal surfaces. RESULTS: We combined mTBM features with traditional surface features such as middle axis distance, the Jacobian determinant as well as 2 of the Jacobian principal eigenvalues to yield 7 normalized hippocampal surface maps of 300 points each. By combining these 7 × 300 = 2100 features together with the previous ~350 features, we illustrate how this type of sparsifying method can be applied to an entire surface map of the hippocampus that yields a feature space that is 2 orders of magnitude larger than what was previously attempted. CONCLUSIONS: By combining the power of the cFSGL multi-task machine learning framework with the addition of AD sensitive mTBM feature maps of the hippocampus surface, we are able to improve the predictive performance of ADAS cognitive scores 6, 12, 24, 36 and 48 months from baseline.

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 Alzheimer's 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.

Cranial thickness changes in early childhood.

The neurocranium changes rapidly in early childhood to accommodate the developing brain. However, developmental disorders may cause abnormal growth of the neurocranium, the most common one being craniosynostosis, affecting about 1 in 2000 children. It is important to understand how the brain and neurocranium develop together to understand the role of the neurocranium in neurodevelopmental outcomes. However, the neurocranium is not as well studied as the human brain in early childhood, due to a lack of imaging data. CT is typically employed to investigate the cranium, but, due to ionizing radiation, may only be used for clinical cases. However, the neurocranium is also visible on magnetic resonance imaging (MRI). Here, we used a large dataset of MRI images from healthy children in the age range of 1 to 2 years old and extracted the neurocranium. A conformal geometry based analysis pipeline is implemented to determine a set of statistical atlases of the neurocranium. A growth model of the neurocranium will help us understand cranial bone and suture development with respect to the brain, which will in turn inform better treatment strategies for neurocranial disorders.

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.

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 in TBI, and may be useful to detect anatomical changes in the corpus callosum in other multimodal datasets.

Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values.

Advances in diffusion-weighted magnetic resonance imaging (DW-MRI) have led to many alternative diffusion sampling strategies and analysis methodologies. A common objective among methods is estimation of white matter fiber orientations within each voxel, as doing so permits in-vivo fiber-tracking and the ability to study brain connectivity and networks. Knowledge of how DW-MRI sampling schemes affect fiber estimation accuracy, tractography and the ability to recover complex white-matter pathways, differences between results due to choice of analysis method, and which method(s) perform optimally for specific data sets, all remain important problems, especially as tractography-based studies become common. In this work, we begin to address these concerns by developing sets of simulated diffusion-weighted brain images which we then use to quantitatively evaluate the performance of six DW-MRI analysis methods in terms of estimated fiber orientation accuracy, false-positive (spurious) and false-negative (missing) fiber rates, and fiber-tracking. The analysis methods studied are: 1) a two-compartment "ball and stick" model (BSM) (Behrens et al., 2003); 2) a non-negativity constrained spherical deconvolution (CSD) approach (Tournier et al., 2007); 3) analytical q-ball imaging (QBI) (Descoteaux et al., 2007); 4) q-ball imaging with Funk-Radon and Cosine Transform (FRACT) (Haldar and Leahy, 2013); 5) q-ball imaging within constant solid angle (CSA) (Aganj et al., 2010); and 6) a generalized Fourier transform approach known as generalized q-sampling imaging (GQI) (Yeh et al., 2010). We investigate these methods using 20, 30, 40, 60, 90 and 120 evenly distributed q-space samples of a single shell, and focus on a signal-to-noise ratio (SNR = 18) and diffusion-weighting (b = 1000 s/mm(2)) common to clinical studies. We found that the BSM and CSD methods consistently yielded the least fiber orientation error and simultaneously greatest detection rate of fibers. Fiber detection rate was found to be the most distinguishing characteristic between the methods, and a significant factor for complete recovery of tractography through complex white-matter pathways. For example, while all methods recovered similar tractography of prominent white matter pathways of limited fiber crossing, CSD (which had the highest fiber detection rate, especially for voxels containing three fibers) recovered the greatest number of fibers and largest fraction of correct tractography for complex three-fiber crossing regions. The synthetic data sets, ground-truth, and tools for quantitative evaluation are publically available on the NITRC website as the project "Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations" at http://www.nitrc.org/projects/sim_dwi_brain.

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.

Evaluating the Predictive Power of Multivariate Tensor-based Morphometry in Alzheimers 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.

Mapping of ApoE4 Related White Matter Damage using Diffusion MRI.

ApoliopoproteinE ε4 (ApoE-ε4) polymorphism is the most well known genetic risk factor for developing Alzheimers Disease. The exact mechanism through which ApoE ε4 increases AD risk is not fully known, but may be related to decreased clearance and increased oligomerization of Aß. By making measurements of white matter integrity via diffusion MR and correlating the metrics in a voxel-based statistical analysis with ApoE-ε4 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 ε4 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).

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 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.

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.