Diffeomorphic sulcal shape analysis on the cortex

DeepSSM: A blueprint for image-to-shape deep learning models

Statistical shape modeling (SSM) characterizes anatomical variations in a population of shapes generated from medical images. Statistical analysis of shapes requires consistent shape representation across samples in shape cohort. Establishing this representation entails a processing pipeline that includes anatomy segmentation, image re-sampling, shape-based registration, and non-linear, iterative optimization. These shape representations are then used to extract low-dimensional shape descriptors that are anatomically relevant to facilitate subsequent statistical analyses in different applications. However, the current process of obtaining these shape descriptors from imaging data relies on human and computational resources, requiring domain expertise for segmenting anatomies of interest. Moreover, this same taxing pipeline needs to be repeated to infer shape descriptors for new image data using a pre-trained/existing shape model. Here, we propose DeepSSM, a deep learning-based framework for learning the functional mapping from images to low-dimensional shape descriptors and their associated shape representations, thereby inferring statistical representation of anatomy directly from 3D images. Once trained using an existing shape model, DeepSSM circumvents the heavy and manual pre-processing and segmentation required by classical models and significantly improves the computational time, making it a viable solution for fully end-to-end shape modeling applications. In addition, we introduce a model-based data-augmentation strategy to address data scarcity, a typical scenario in shape modeling applications. Finally, this paper presents and analyzes two different architectural variants of DeepSSM with different loss functions using three medical datasets and their downstream clinical application. Experiments showcase that DeepSSM performs comparably or better to the state-of-the-art SSM both quantitatively and on application-driven downstream tasks. Therefore, DeepSSM aims to provide a comprehensive blueprint for deep learning-based image-to-shape models.

Automatic landmark detection and registration of brain cortical surfaces via quasi-conformal geometry and convolutional neural networks

In medical imaging, surface registration is extensively used for performing systematic comparisons between anatomical structures, with a prime example being the highly convoluted brain cortical surfaces. To obtain a meaningful registration, a common approach is to identify prominent features on the surfaces and establish a low-distortion mapping between them with the feature correspondence encoded as landmark constraints. Prior registration works have primarily focused on using manually labeled landmarks and solving highly nonlinear optimization problems, which are time-consuming and hence hinder practical applications. In this work, we propose a novel framework for the automatic landmark detection and registration of brain cortical surfaces using quasi-conformal geometry and convolutional neural networks. We first develop a landmark detection network (LD-Net) that allows for the automatic extraction of landmark curves given two prescribed starting and ending points based on the surface geometry. We then utilize the detected landmarks and quasi-conformal theory for achieving the surface registration. Specifically, we develop a coefficient prediction network (CP-Net) for predicting the Beltrami coefficients associated with the desired landmark-based registration and a mapping network called the disk Beltrami solver network (DBS-Net) for generating quasi-conformal mappings from the predicted Beltrami coefficients, with the bijectivity guaranteed by quasi-conformal theory. Experimental results are presented to demonstrate the effectiveness of our proposed framework. Altogether, our work paves a new way for surface-based morphometry and medical shape analysis.

Landmark-guided region-based spatial normalization for functional magnetic resonance imaging

As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential preprocessing step becomes extremely important. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g., aging populations). To address this shortcoming, we propose a novel spatial normalization technique that takes advantage of the existing surface‐based human brain parcellation to automatically identify and match regional landmarks. To simplify the nonlinear whole brain registration, the identified landmarks of each region and its counterpart are registered independently with topology‐preserving deformation. Next, the regional warping fields are combined by an inverse distance weighted interpolation technique to have a global warping field for the whole brain. To ensure that the final warping field is topology‐preserving, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed solution using both simulated and real (structural and functional) human brain images. Our evaluation shows that our solution can enhance structural correspondence compared to the existing methods. Such improvement also increases the sensitivity and specificity of the functional imaging studies, reducing the required number of subjects and subsequent study costs. We conclude that our proposed solution can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts which is now common in clinical and aging studies.

Nonparametric Bayesian Regression and Classification on Manifolds, With Applications to 3D Cochlear Shapes

Advanced shape analysis studies such as regression and classification need to be performed on curved manifolds, where often, there is a lack of standard statistical formulations. To overcome these limitations, we introduce a novel machine-learning method on the shape space of curves that avoids direct inference on infinite-dimensional spaces and instead performs Bayesian inference with spherical Gaussian processes decomposition. As an application, we study the shape of the cochlear spiral-shaped cavity within the petrous part of the temporal bone. This problem is particularly challenging due to the relationship between shape and gender, especially in children. Experimental results for both synthetic and real data show improved performance compared to state-of-the-art methods.

Deep Learning of Warping Functions for Shape Analysis

Rate-invariant or reparameterization-invariant matching between functions and shapes of curves, respectively, is an important problem in computer vision and medical imaging. Often, the computational cost of matching using approaches such as dynamic time warping or dynamic programming is prohibitive for large datasets. Here, we propose a deep neural-network-based approach for learning the warping functions from training data consisting of a large number of optimal matches, and use it to predict optimal diffeomorphic warping functions. Results show prediction performance on a synthetic dataset of bump functions and two-dimensional curves from the ETH-80 dataset as well as a significant reduction in computational cost.

A Cooperative Autoencoder for Population-Based Regularization of CNN Image Registration

Spatial transformations are enablers in a variety of medical image analysis applications that entail aligning images to a common coordinate systems. Population analysis of such transformations is expected to capture the underlying image and shape variations, and hence these transformations are required to produce anatomically feasible correspondences. This is usually enforced through some smoothness-based generic metric or regularization of the deformation field. Alternatively, population-based regularization has been shown to produce anatomically accurate correspondences in cases where anatomically unaware (i.e., data independent) regularization fail. Recently, deep networks have been used to generate spatial transformations in an unsupervised manner, and, once trained, these networks are computationally faster and as accurate as conventional, optimization-based registration methods. However, the deformation fields produced by these networks require smoothness penalties, just as the conventional registration methods, and ignores population-level statistics of the transformations. Here, we propose a novel neural network architecture that simultaneously learns and uses the population-level statistics of the spatial transformations to regularize the neural networks for unsupervised image registration. This regularization is in the form of a bottleneck autoencoder, which learns and adapts to the population of transformations required to align input images by encoding the transformations to a low dimensional manifold. The proposed architecture produces deformation fields that describe the population-level features and associated correspondences in an anatomically relevant manner and are statistically compact relative to the state-of-the-art approaches while maintaining computational efficiency. We demonstrate the efficacy of the proposed architecture on synthetic data sets, as well as 2D and 3D medical data.

Skeletal Shape Correspondence Through Entropy

We present a novel approach for improving the shape statistics of medical image objects by generating correspondence of skeletal points. Each object's interior is modeled by an s-rep, i.e., by a sampled, folded, two-sided skeletal sheet with spoke vectors proceeding from the skeletal sheet to the boundary. The skeleton is divided into three parts: the up side, the down side, and the fold curve. The spokes on each part are treated separately and, using spoke interpolation, are shifted along that skeleton in each training sample so as to tighten the probability distribution on those spokes' geometric properties while sampling the object interior regularly. As with the surface/boundary-based correspondence method of Cates et al., entropy is used to measure both the probability distribution tightness and the sampling regularity, here of the spokes' geometric properties. Evaluation on synthetic and real world lateral ventricle and hippocampus data sets demonstrate improvement in the performance of statistics using the resulting probability distributions. This improvement is greater than that achieved by an entropy-based correspondence method on the boundary points.

A Riemannian Framework for Linear and Quadratic Discriminant Analysis on the Tangent Space of Shapes

We present a Riemannian framework for linear and quadratic discriminant classification on the tangent plane of the shape space of curves. The shape space is infinite dimensional and is constructed out of square root velocity functions of curves. We introduce the notion of mean and covariance of shape-valued random variables and samples from a tangent space to the pre-shapes (invariant to translation and scaling) and then extend it to the full shape space (rotational invariance). The shape observations from the population are approximated by coefficients of a Fourier basis of the tangent space. The algorithms for linear and quadratic discriminant analysis are then defined using reduced dimensional features obtained by projecting the original shape observations on to the truncated Fourier basis. We show classification results on synthetic data and shapes of cortical sulci, corpus callosum curves, as well as facial midline curve profiles from patients with fetal alcohol syndrome (FAS).

Relationships Between Altered Functional Magnetic Resonance Imaging Activation and Cortical Thickness in Patients With Euthymic Bipolar I Disorder

BACKGROUND

Performance during cognitive control functional magnetic resonance imaging (fMRI) tasks are associated with frontal lobe hypoactivation in patients with bipolar disorder, even while euthymic. Here, we study the structural underpinnings for this functional abnormality simultaneously with brain activation data.

METHODS

In a sample of ninety adults (45 with inter-episode Bipolar I disorder and 45 healthy controls), we explored whether abnormal functional activation patterns in bipolar euthymic subjects during a Go-NoGo fMRI task are associated with regional deficits in cortical gray matter thickness in the same regions. Cross-sectional differences in fMRI activation were used to form a-priori hypotheses for region-of-interest cortical gray matter thickness analyses. fMRI BOLD to structural magnetic resonance imaging (sMRI) thickness correlations were conducted across the sample and within patients and controls separately.

RESULTS

During response inhibition (NoGo minus Go), bipolar subjects showed significant hypoactivation and reduced thickness in the inferior frontal cortex (IFC), superior frontal gyrus and cingulate compared to controls. Cingulate hypoactivation corresponded with reduced regional thickness. A significant activation by disease state interaction was observed with thickness in left prefrontal areas.

CONCLUSIONS

Reduced cingulate fMRI activation is associated with reduced cortical thickness. In the left frontal lobe, a thinner cortex was associated with increased fMRI activation in patients, but showed a reverse trend in controls. These findings suggest that reduced activation in the IFC and cingulate during a response inhibition task may have an underlying structural etiology, which may explain task-related functional hypoactivation that persists even when patients are euthymic.

Conformal invariants for multiply connected surfaces: Application to landmark curve-based brain morphometry analysis

Landmark curves were widely adopted in neuroimaging research for surface correspondence computation and quantified morphometry analysis. However, most of the landmark based morphometry studies only focused on landmark curve shape difference. Here we propose to compute a set of conformal invariant-based shape indices, which are associated with the landmark curve induced boundary lengths in the hyperbolic parameter domain. Such shape indices may be used to identify which surfaces are conformally equivalent and further quantitatively measure surface deformation. With the surface Ricci flow method, we can conformally map a multiply connected surface to the Poincaré disk. Our algorithm provides a stable method to compute the shape index values in the 2D (Poincaré Disk) parameter domain. The proposed shape indices are succinct, intrinsic and informative. Experimental results with synthetic data and 3D MRI data demonstrate that our method is invariant under isometric transformations and able to detect brain surface abnormalities. We also applied the new shape indices to analyze brain morphometry abnormalities associated with Alzheimer' s disease (AD). We studied the baseline MRI scans of a set of healthy control and AD patients from the Alzheimer' s Disease Neuroimaging Initiative (ADNI: 30 healthy control subjects vs. 30 AD patients). Although the lengths of the landmarks in Euclidean space, cortical surface area, and volume features did not differ between the two groups, our conformal invariant based shape indices revealed significant differences by Hotelling' s T² test. The novel conformal invariant shape indices may offer a new sensitive biomarker and enrich our brain imaging analysis toolset for studying diagnosis and prognosis of AD.

Electroconvulsive therapy and structural neuroplasticity in neocortical, limbic and paralimbic cortex

Electroconvulsive therapy (ECT) is a highly effective and rapidly acting treatment for severe depression. To understand the biological bases of therapeutic response, we examined variations in cortical thickness from magnetic resonance imaging (MRI) data in 29 patients scanned at three time points during an ECT treatment index series and in 29 controls at two time points. Changes in thickness across time and with symptom improvement were evaluated at high spatial resolution across the cortex and within discrete cortical regions of interest. Patients showed increased thickness over the course of ECT in the bilateral anterior cingulate cortex (ACC), inferior and superior temporal, parahippocampal, entorhinal and fusiform cortex and in distributed prefrontal areas. No changes across time occurred in controls. In temporal and fusiform regions showing significant ECT effects, thickness differed between patients and controls at baseline and change in thickness related to therapeutic response in patients. In the ACC, these relationships occurred in treatment responders only, and thickness measured soon after treatment initiation predicted the overall ECT response. ECT leads to widespread neuroplasticity in neocortical, limbic and paralimbic regions and changes relate to the extent of antidepressant response. Variations in ACC thickness, which discriminate treatment responders and predict response early in the course of ECT, may represent a biomarker of overall clinical outcome. Because post-mortem studies show focal reductions in glial density and neuronal size in patients with severe depression, ECT-related increases in thickness may be attributable to neuroplastic processes affecting the size and/or density of neurons and glia and their connections.

Major Superficial White Matter Abnormalities in Huntington's Disease

BACKGROUND

The late myelinating superficial white matter at the juncture of the cortical gray and white matter comprising the intracortical myelin and short-range association fibers has not received attention in Huntington's disease. It is an area of the brain that is late myelinating and is sensitive to both normal aging and neurodegenerative disease effects. Therefore, it may be sensitive to Huntington's disease processes.

METHODS

Structural MRI data from 25 Pre-symptomatic subjects, 24 Huntington's disease patients and 49 healthy controls was run through a cortical pattern-matching program. The surface corresponding to the white matter directly below the cortical gray matter was then extracted. Individual subject's Diffusion Tensor Imaging (DTI) data was aligned to their structural MRI data. Diffusivity values along the white matter surface were then sampled at each vertex point. DTI measures with high spatial resolution across the superficial white matter surface were then analyzed with the General Linear Model to test for the effects of disease.

RESULTS

There was an overall increase in the axial and radial diffusivity across much of the superficial white matter (p < 0.001) in Pre-symptomatic subjects compared to controls. In Huntington's disease patients increased diffusivity covered essentially the whole brain (p < 0.001). Changes are correlated with genotype (CAG repeat number) and disease burden (p < 0.001).

CONCLUSIONS

This study showed broad abnormalities in superficial white matter even before symptoms are present in Huntington's disease. Since, the superficial white matter has a unique microstructure and function these abnormalities suggest it plays an important role in the disease.

The superficial white matter in Alzheimer's disease

White matter abnormalities have been shown in the large deep fibers of Alzheimer's disease patients. However, the late myelinating superficial white matter comprised of intracortical myelin and short-range association fibers has not received much attention. To investigate this area, we extracted a surface corresponding to the superficial white matter beneath the cortex and then applied a cortical pattern-matching approach which allowed us to register and subsequently sample diffusivity along thousands of points at the interface between the gray matter and white matter in 44 patients with Alzheimer's disease (Age: 71.02 ± 5.84, 16M/28F) and 47 healthy controls (Age 69.23 ± 4.45, 19M/28F). In patients we found an overall increase in the axial and radial diffusivity across most of the superficial white matter (P < 0.001) with increases in diffusivity of more than 20% in the bilateral parahippocampal regions and the temporal and frontal lobes. Furthermore, diffusivity correlated with the cognitive deficits measured by the Mini-Mental State Examination scores (P < 0.001). The superficial white matter has a unique microstructure and is critical for the integration of multimodal information during brain maturation and aging. Here we show that there are major abnormalities in patients and the deterioration of these fibers relates to clinical symptoms in Alzheimer's disease.

Structural Plasticity of the Hippocampus and Amygdala Induced by Electroconvulsive Therapy in Major Depression

BACKGROUND

Electroconvulsive therapy (ECT) elicits a rapid and robust clinical response in patients with refractory depression. Neuroimaging measurements of structural plasticity relating to and predictive of ECT response may point to the mechanisms underlying rapid antidepressant effects and establish biomarkers to inform other treatments. Here, we determine the effects of diagnosis and of ECT on global and local variations of hippocampal and amygdala structures in major depression and predictors of ECT-related clinical response.

METHODS

Longitudinal changes in hippocampal and amygdala structures were examined in patients with major depression (N = 43, scanned three times: prior to ECT, after the second ECT session, and within 1 week of completing the ECT treatment series), referred for ECT as part of their standard clinical care. Cross-sectional comparisons with demographically similar controls (N = 32, scanned twice) established effects of diagnosis.

RESULTS

Patients showed smaller hippocampal volumes than controls at baseline (p < .04). Both the hippocampal and the amygdala volumes increased with ECT (p < .001) and in relation to symptom improvement (p < .01). Hippocampal volume at baseline predicted subsequent clinical response (p < .05). Shape analysis revealed pronounced morphometric changes in the anterior hippocampus and basolateral and centromedial amygdala. All structural measurements remained stable across time in controls.

CONCLUSIONS

ECT-induced neuroplasticity in the hippocampus and amygdala relates to improved clinical response and is pronounced in regions with prominent connections to ventromedial prefrontal cortex and other limbic structures. Smaller hippocampal volumes at baseline predict a more robust clinical response. Neurotrophic processes including neurogenesis shown in preclinical studies may underlie these structural changes.

Robust estimation of group-wise cortical correspondence with an application to macaque and human neuroimaging studies

We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.

Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1 -weighted images were processed using three different surface-based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT-cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT-cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples.

Ictal depth EEG and MRI structural evidence for two different epileptogenic networks in mesial temporal lobe epilepsy

Hypersynchronous (HYP) and low voltage fast (LVF) activity are two separate ictal depth EEG onsets patterns often recorded in presurgical patients with MTLE. Evidence suggests the mechanisms generating HYP and LVF onset seizures are distinct, including differential involvement of hippocampal and extra-hippocampal sites. Yet the extent of extra-hippocampal structural alterations, which could support these two common seizures, is not known. In the current study, preoperative MRI from 24 patients with HYP or LVF onset seizures were analyzed to determine changes in cortical thickness and relate structural changes to spatiotemporal properties of the ictal EEG. Overall, onset and initial ipsilateral spread of HYP onset seizures involved mesial temporal structures, whereas LVF onset seizures involved mesial and lateral temporal as well as orbitofrontal cortex. MRI analysis found reduced cortical thickness correlated with longer duration of epilepsy. However, in patients with HYP onsets, the most affected areas were on the medial surface of each hemisphere, including parahippocampal regions and cingulate gyrus, whereas in patients with LVF onsets, the lateral surface of the anterior temporal lobe and orbitofrontal cortex showed the greatest effect. Most patients with HYP onset seizures were seizure-free after resective surgery, while a higher proportion of patients with LVF onset seizures had only worthwhile improvement. Our findings confirm the view that recurrent seizures cause progressive changes in cortical thickness, and provide information concerning the structural basis of two different epileptogenic networks responsible for MTLE. One, identified by HYP ictal onsets, chiefly involves hippocampus and is associated with excellent outcome after standardized anteromedial temporal resection, while the other also involves lateral temporal and orbitofrontal cortex and a seizure-free surgical outcome occurs less after this procedure. These results suggest that a more extensive tailored resection may be required for patients with the second type of MTLE.

Joint Morphometry of Fiber Tracts and Gray Matter Structures Using Double Diffeomorphisms

This work proposes an atlas construction method to jointly analyse the relative position and shape of fiber tracts and gray matter structures. It is based on a double diffeomorphism which is a composition of two diffeomorphisms. The first diffeomorphism acts only on the white matter keeping fixed the gray matter of the atlas. The resulting white matter, together with the gray matter, are then deformed by the second diffeomorphism. The two diffeomorphisms are related and jointly optimised. In this way, the, first diffeomorphisms explain the variability in structural connectivity within the population, namely both changes in the connected areas of the gray matter and in the geometry of the pathway of the tracts. The second diffeomorphisms put into correspondence the homologous anatomical structures across subjects. Fiber bundles are approximated with weighted prototypes using the metric of weighted currents. The atlas, the covariance matrix of deformation parameters and the noise variance of each structure are automatically estimated using a Bayesian approach. This method is applied to patients with Tourette syndrome and controls showing a variability in the structural connectivity of the left cortico-putamen circuit.

Pydpiper: a flexible toolkit for constructing novel registration pipelines

Using neuroimaging technologies to elucidate the relationship between genotype and phenotype and brain and behavior will be a key contribution to biomedical research in the twenty-first century. Among the many methods for analyzing neuroimaging data, image registration deserves particular attention due to its wide range of applications. Finding strategies to register together many images and analyze the differences between them can be a challenge, particularly given that different experimental designs require different registration strategies. Moreover, writing software that can handle different types of image registration pipelines in a flexible, reusable and extensible way can be challenging. In response to this challenge, we have created Pydpiper, a neuroimaging registration toolkit written in Python. Pydpiper is an open-source, freely available software package that provides multiple modules for various image registration applications. Pydpiper offers five key innovations. Specifically: (1) a robust file handling class that allows access to outputs from all stages of registration at any point in the pipeline; (2) the ability of the framework to eliminate duplicate stages; (3) reusable, easy to subclass modules; (4) a development toolkit written for non-developers; (5) four complete applications that run complex image registration pipelines “out-of-the-box.” In this paper, we will discuss both the general Pydpiper framework and the various ways in which component modules can be pieced together to easily create new registration pipelines. This will include a discussion of the core principles motivating code development and a comparison of Pydpiper with other available toolkits. We also provide a comprehensive, line-by-line example to orient users with limited programming knowledge and highlight some of the most useful features of Pydpiper. In addition, we will present the four current applications of the code.

Superficial white matter: effects of age, sex, and hemisphere

Structural and diffusion imaging studies demonstrate effects of age, sex, and asymmetry in many brain structures. However, few studies have addressed how individual differences might influence the structural integrity of the superficial white matter (SWM), comprised of short-range association (U-fibers), and intracortical axons. This study thus applied a sophisticated computational analysis approach to structural and diffusion imaging data obtained from healthy individuals selected from the International Consortium for Brain Mapping (ICBM) database across a wide adult age range (n=65, age: 18-74 years, all Caucasian). Fractional anisotropy (FA), radial diffusivity (RD), and axial diffusivity (AD) were sampled and compared at thousands of spatially matched SWM locations and within regions-of-interest to examine global and local variations in SWM integrity across age, sex, and hemisphere. Results showed age-related reductions in FA that were more pronounced in the frontal SWM than in the posterior and ventral brain regions, whereas increases in RD and AD were observed across large areas of the SWM. FA was significantly greater in left temporoparietal regions in men and in the posterior callosum in women. Prominent leftward FA and rightward AD and RD asymmetries were observed in the temporal, parietal, and frontal regions. Results extend previous findings restricted to the deep white matter pathways to demonstrate regional changes in the SWM microstructure relating to processes of demyelination and/or to the number, coherence, or integrity of axons with increasing age. SWM fiber organization/coherence appears greater in the left hemisphere regions spanning language and other networks, while more localized sex effects could possibly reflect sex-specific advantages in information strategies.

Spatial-temporal atlas of human fetal brain development during the early second trimester

During the second trimester, the human fetal brain undergoes numerous changes that lead to substantial variation in the neonatal in terms of its morphology and tissue types. As fetal MRI is more and more widely used for studying the human brain development during this period, a spatiotemporal atlas becomes necessary for characterizing the dynamic structural changes. In this study, 34 postmortem human fetal brains with gestational ages ranging from 15 to 22 weeks were scanned using 7.0 T MR. We used automated morphometrics, tensor-based morphometry and surface modeling techniques to analyze the data. Spatiotemporal atlases of each week and the overall atlas covering the whole period with high resolution and contrast were created. These atlases were used for the analysis of age-specific shape changes during this period, including development of the cerebral wall, lateral ventricles, Sylvian fissure, and growth direction based on local surface measurements. Our findings indicate that growth of the subplate zone is especially striking and is the main cause for the lamination pattern changes. Changes in the cortex around Sylvian fissure demonstrate that cortical growth may be one of the mechanisms for gyration. Surface deformation mapping, revealed by local shape analysis, indicates that there is global anterior-posterior growth pattern, with frontal and temporal lobes developing relatively quickly during this period. Our results are valuable for understanding the normal brain development trajectories and anatomical characteristics. These week-by-week fetal brain atlases can be used as reference in in vivo studies, and may facilitate the quantification of fetal brain development across space and time.

Model-driven harmonic parameterization of the cortical surface: HIP-HOP

In the context of inter subject brain surface matching, we present a parameterization of the cortical surface constrained by a model of cortical organization. The parameterization is defined via an harmonic mapping of each hemisphere surface to a rectangular planar domain that integrates a representation of the model. As opposed to previous landmark-based registration methods we do not match folds between individuals but instead optimize the fit between cortical sulci and specific iso-coordinate axis in the model. This strategy overcomes some limitation to sulcus-based registration techniques such as topological variability in sulcal landmarks across subjects. Experiments on 62 subjects with manually traced sulci are presented and compared with the result of the Freesurfer software. The evaluation involves a measure of dispersion of sulci with both angular and area distortions. We show that the model-based strategy can lead to a natural, efficient and very fast (less than 5 min per hemisphere) method for defining inter subjects correspondences. We discuss how this approach also reduces the problems inherent to anatomically defined landmarks and open the way to the investigation of cortical organization through the notion of orientation and alignment of structures across the cortex.

Applying tensor-based morphometry to parametric surfaces can improve MRI-based disease diagnosis

Many methods have been proposed for computer-assisted diagnostic classification. Full tensor information and machine learning with 3D maps derived from brain images may help detect subtle differences or classify subjects into different groups. Here we develop a new approach to apply tensor-based morphometry to parametric surface models for diagnostic classification. We use this approach to identify cortical surface features for use in diagnostic classifiers. First, with holomorphic 1-forms, we compute an efficient and accurate conformal mapping from a multiply connected mesh to the so-called slit domain. Next, the surface parameterization approach provides a natural way to register anatomical surfaces across subjects using a constrained harmonic map. To analyze anatomical differences, we then analyze the full Riemannian surface metric tensors, which retain multivariate information on local surface geometry. As the number of voxels in a 3D image is large, sparse learning is a promising method to select a subset of imaging features and to improve classification accuracy. Focusing on vertices with greatest effect sizes, we train a diagnostic classifier using the surface features selected by an L1-norm based sparse learning method. Stability selection is applied to validate the selected feature sets. We tested the algorithm on MRI-derived cortical surfaces from 42 subjects with genetically confirmed Williams syndrome and 40 age-matched controls, multivariate statistics on the local tensors gave greater effect sizes for detecting group differences relative to other TBM-based statistics including analysis of the Jacobian determinant and the largest eigenvalue of the surface metric. Our method also gave reasonable classification results relative to the Jacobian determinant, the pair of eigenvalues of the Jacobian matrix and volume features. This analysis pipeline may boost the power of morphometry studies, and may assist with image-based classification.