PSACNN: Pulse Sequence Adaptive Fast Whole Brain Segmentation

With the advent of convolutional neural networks~(CNN), supervised learning methods are increasingly being used for whole brain segmentation. However, a large, manually annotated training dataset of labeled brain images required to train such supervised methods is frequently difficult to obtain or create. In addition, existing training datasets are generally acquired with a homogeneous magnetic resonance imaging~(MRI) acquisition protocol. CNNs trained on such datasets are unable to generalize on test data with different acquisition protocols. Modern neuroimaging studies and clinical trials are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is very difficult to standardize the gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input acquisition. Our approach relies on building approximate forward models of pulse sequences that produce a typical test image. For a given pulse sequence, we use its forward model to generate plausible, synthetic training examples that appear as if they were acquired in a scanner with that pulse sequence. Sampling over a wide variety of pulse sequences results in a wide variety of augmented training examples that help build an image contrast invariant model. Our method trains a single CNN that can segment input MRI images with acquisition parameters as disparate as $T_1$-weighted and $T_2$-weighted contrasts with only $T_1$-weighted training data. The segmentations generated are highly accurate with state-of-the-art results~(overall Dice overlap$=0.94$), with a fast run time~($\approx$ 45 seconds), and consistent across a wide range of acquisition protocols.Comment: Typo in author name corrected. Greves -> Grev

Correction of inter-scanner and within-subject variance in structural MRI based automated diagnosing

Neurological Motor Disorder

Comprehensive Evaluation of Healthy Volunteers Using Multi-Modality Brain Injury Assessments: An Exploratory, Observational Study

Introduction: Even though mild traumatic brain injury is common and can result in persistent symptoms, traditional measurement tools can be insensitive in detecting functional deficits after injury. Some newer assessments do not have well-established norms, and little is known about how these measures perform over time or how cross-domain assessments correlate with one another. We conducted an exploratory study to measure the distribution, stability, and correlation of results from assessments used in mild traumatic brain injury in healthy, community-dwelling adults.Materials and Methods: In this prospective cohort study, healthy adult men and women without a history of brain injury underwent a comprehensive brain injury evaluation that included self-report questionnaires and neurological, electroencephalography, sleep, audiology/vestibular, autonomic, visual, neuroimaging, and laboratory testing. Most testing was performed at 3 intervals over 6 months.Results: The study enrolled 83 participants, and 75 were included in the primary analysis. Mean age was 38 years, 58 were male, and 53 were civilians. Participants did not endorse symptoms of post-concussive syndrome, PTSD, or depression. Abnormal neurological examination findings were rare, and 6 had generalized slowing on electroencephalography. Actigraphy and sleep diary showed good sleep maintenance efficiency, but 21 reported poor sleep quality. Heart rate variability was most stable over time in the sleep segment. Dynavision performance was normal, but 41 participants had abnormal ocular torsion. On eye tracking, circular, horizontal ramp, and reading tasks were more likely to be abnormal than other tasks. Most participants had normal hearing, videonystagmography, and rotational chair testing, but computerized dynamic posturography was abnormal in up to 21% of participants. Twenty-two participants had greater than expected white matter changes for age by MRI. Most abnormal findings were dispersed across the population, though a few participants had clusters of abnormalities.Conclusions: Despite our efforts to enroll normal, healthy volunteers, abnormalities on some measures were surprisingly common.Trial Registration: This study was registered at www.clinicaltrials.gov, trial identifier NCT01925963

Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder

This thesis deals with the development of novel machine learning applications to automatically detect brain disorders based on magnetic resonance imaging (MRI) data, with a particular focus on Alzheimer’s disease and the autism spectrum disorder. Machine learning approaches are used extensively in neuroimaging studies of brain disorders to investigate abnormalities in various brain regions. However, there are many technical challenges in the analysis of neuroimaging data, for example, high dimensionality, the limited amount of data, and high variance in that data due to many confounding factors. These limitations make the development of appropriate computational approaches more challenging. To deal with these existing challenges, we target multiple machine learning approaches, including supervised and semi-supervised learning, domain adaptation, and dimensionality reduction methods.In the current study, we aim to construct effective biomarkers with sufficient sensitivity and specificity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.In addition, we investigate various data-driven feature selection and classification methods for whole brain, voxel-based classification analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems

The effects of the 15q11.2 BP1-BP2 copy number variant on white matter microstructure

Altered white matter structure has been consistently reported in neurodevelopmental disorders. A key question is whether genetic risk variants that are associated with neurodevelopmental disorders, are also associated with changes in white matter. The 15q11.2 BP1-BP2 copy number variant (CNV) is emerging as a recognised syndrome and has been associated with several neurodevelopmental disorders, including autism spectrum disorders (ASD) and schizophrenia. The cytoplasmic FMR1 interacting protein 1 (CYFIP1), a gene in this region, is involved in two distinct complexes, known to regulate actin cytoskeleton dynamics and protein translation - mechanisms that are crucial in white matter dynamics. This thesis describes a translational project combining a diverse set of multidisciplinary experiments to investigate the effects of the 15q11.2 BP1BP2 CNV on white matter microstructure. In Chapters 3 and 4, using diffusion tensor imaging (DTI) methods, I demonstrate a link between 15q11.2 BP1-BP2 CNV dosage and altered white matter microstructure in human carriers, where bidirectional CNV dosage leads to opposite changes in white matter measures. In Chapters 5, 6 and 7, using a novel Cyfip1 haploinsufficiency rat model to model the low dosage of CYFIP1 in 15q11.2 BP1-BP2 deletion carriers, I investigate how this gene could contribute to the phenotype seen in Chapters 3 and 4. Combining DTI, histology and in vitro methods, I report that Cyfip1 haploinsufficiency leads to thinning of the myelin sheath in the corpus callosum, and suggest that these changes are caused by abnormal mechanisms involving myelin basic protein distribution in mature oligodendrocytes. In conclusion, these results show that variations at the 15q11.2 BP1-BP2 chromosomal region lead to white matter abnormalities, and suggest that Cyfip1 influences myelination in the central nervous system in a rat model, providing an insight into a possible contribution made by low dosage of CYFIP1 to 15q11.2 BP1-BP2 deletion associated phenotypes