A Surface-based Approach for Classification of 3D Neuroanatomic Structures

We present a new framework for 3D surface object classification that combines a powerful shape description method with suitable pattern classification techniques. Spherical harmonic parameterization and normalization techniques are used to describe a surface shape and derive a dual high dimensional landmark representation. A point distribution model is applied to reduce the dimensionality. Fisher\u27s linear discriminants and support vector machines are used for classification. Several feature selection schemes are proposed for learning better classifiers. After showing the effectiveness of this framework using simulated shape data, we apply it to real hippocampal data in schizophrenia and perform extensive experimental studies by examining different combinations of techniques. We achieve best leave-one-out cross-validation accuracies of 93% (whole set, N=56) and 90% (right-handed males, N=39), respectively, which are competitive with the best results in previous studies using different techniques on similar types of data. Furthermore, to help medical diagnosis in practice, we employ a threshold-free receiver operating characteristic (ROC) approach as an alternative evaluation of classification results as well as propose a new method for visualizing discriminative patterns

A four-dimensional probabilistic atlas of the human brain

The authors describe the development of a four-dimensional atlas and reference system that includes both macroscopic and microscopic information on structure and function of the human brain in persons between the ages of 18 and 90 years. Given the presumed large but previously unquantified degree of structural and functional variance among normal persons in the human population, the basis for this atlas and reference system is probabilistic. Through the efforts of the International Consortium for Brain Mapping (ICBM), 7,000 subjects will be included in the initial phase of database and atlas development. For each subject, detailed demographic, clinical, behavioral, and imaging information is being collected. In addition, 5,800 subjects will contribute DNA for the purpose of determining genotype-phenotype-behavioral correlations. The process of developing the strategies, algorithms, data collection methods, validation approaches, database structures, and distribution of results is described in this report. Examples of applications of the approach are described for the normal brain in both adults and children as well as in patients with schizophrenia. This project should provide new insights into the relationship between microscopic and macroscopic structure and function in the human brain and should have important implications in basic neuroscience, clinical diagnostics, and cerebral disorders

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline that identifies specific neuropathologies-amyloid plaques and cerebral amyloid angiopathy-in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotate > 70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieve strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualize morphology distributions at high resolution. Resulting network-derived amyloid beta (Aβ)-burden scores correlate well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrates that networks learn patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability suggests a route to neuropathologic deep phenotyping

Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder

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

abstract: 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.The final version of this article, as published in NeuroImage: Clinical, can be viewed online at: http://www.sciencedirect.com/science/article/pii/S2213158216302649?via%3Dihu

Multi-atlas segmentation of subcortical brain structures via the AutoSeg software pipeline

pre-printAutomated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has show its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple altases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population

The Quantitative Genetics of Neurodevelopment: A Magnetic Resonance Imaging Study of Childhood and Adolescence

Understanding the causes of individual differences in brain structure may give clues about the etiology of cognition, personality, and psychopathology, and also may identify endophenotypes for molecular genetic studies on brain development. We performed a comprehensive statistical genetic study of anatomic neuroimaging data from a large pediatric sample (N=600+) of twins and family members from the Child Psychiatry Branch at the NIMH. These analyses included variance decomposition of structural volumetric endophenotypes at several levels of resolution, voxel-level analysis of cortical thickness, assessment of gene by age interaction, several multivariate genetic analyses, and a search for genetically-mediated brain-behavioral relationships. These analyses found strong evidence for a genetic role in the generation of individual differences in brain volumes, with the exception of the cerebellum and the lateral ventricles. Subsequent multivariate analyses demonstrated that most of the genetic variance in large volumes shares a common source. More subtle analyses suggest that although this global genetic factor is the principal determinant of neuroanatomic variability, genetic factors also mediate regional variability in cortical thickness and are different for gray and white matter volumes. Models using graph theory show that brain structure follows small-world architectural rules, and that these relationships are genetically-determined. Structural homologues appeared to be strongly related genetically, which was further confirmed using novel methods for semi-multivariate quantitative genetic analysis at the voxel level. Studies on interactions with age were mixed. We found evidence of gene by age interaction on frontal and temporal lobar volumes, indicating that the role of genetic factors on these structures is dynamic during childhood. Analyses on cortical thickness at a finer scale, however, showed that environmental factors are more important in childhood, and environmental changes were responsible for most of the changes in heritability over this age range. When assessing the relationship between brain and behavior, we found weak negative genetic correlations and positive environmental correlations between IQ and cortical thickness, which appear to partially cancel each other out. More complex models allowing for age interactions suggest that high and low IQ groups have different patterns of gene by age interactions in concordance with prior literature on cortical phenotypes