Surface mapping of gastric motor functions using MRI: a comparative study between humans and rats.

The stomach's ability to store, mix, propel, and empty its content requires highly coordinated motor functions. However, current diagnostic tools cannot simultaneously assess these motor processes. This study aimed to use magnetic resonance imaging (MRI) to map multifaceted gastric motor functions, including accommodation, tonic and peristaltic contractions, and emptying, through a single noninvasive experiment for both humans and rats. Ten humans and 10 Sprague-Dawley rats consumed MRI-visible semisolid meals and underwent MRI scans. We used a surface model to analyze MRI data, capturing the deformation of the stomach wall on ingestion or during digestion. We inferred muscle activity, mapped motor processes, parcellated the stomach into functional regions, and revealed cross-species distinctions. In humans, both the fundus and antrum distended postmeal, followed by sustained tonic contractions to regulate intragastric pressure. Peristaltic contractions initiated from the distal fundus, including three concurrent wavefronts oscillating at 3.3 cycles/min and traveling at 1.7 to 2.9 mm/s. These motor functions facilitated linear gastric emptying with a 61-min half-time. In contrast, rats exhibited peristalsis from the midcorpus, showing two wavefronts oscillating at 5.0 cycles/min and traveling at 0.4 to 0.9 mm/s. For both species, motility features allowed functional parcellation of the stomach along a midcorpus division. This study maps region- and species-specific gastric motor functions. We demonstrate the value of MRI with surface modeling in understanding gastric physiology and its potential to become a new standard for clinical and preclinical investigations of gastric disorders at both individual and group levels.NEW & NOTEWORTHY A novel MRI technique can visualize how the stomach accommodates, mixes, and propels food for digestion in humans and animals alike. Digital models of gastric MRI reveal the functional maps, organization, and distinction of the stomach across individuals and species. This technique holds the unique potential to advance basic and clinical studies of functional gastric disorders.

Fast three-dimensional image generation for healthy brain aging using diffeomorphic registration.

Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject-specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state-of-the-art deep learning-based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three-dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1-weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies-3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state-of-the-art results. The assumption of linear brain decay was accurate in these cohorts (R2  ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning-based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points.

MDReg-Net: Multi-resolution diffeomorphic image registration using fully convolutional networks with deep self-supervision.

We present a diffeomorphic image registration algorithm to learn spatial transformations between pairs of images to be registered using fully convolutional networks (FCNs) under a self-supervised learning setting. Particularly, a deep neural network is trained to estimate diffeomorphic spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and warped moving images, similar to those adopted in conventional image registration algorithms. The network is implemented in a multi-resolution image registration framework to optimize and learn spatial transformations at different image resolutions jointly and incrementally with deep self-supervision in order to better handle large deformation between images. A spatial Gaussian smoothing kernel is integrated with the FCNs to yield sufficiently smooth deformation fields for diffeomorphic image registration. The spatial transformations learned at coarser resolutions are utilized to warp the moving image, which is subsequently used as input to the network for learning incremental transformations at finer resolutions. This procedure proceeds recursively to the full image resolution and the accumulated transformations serve as the final transformation to warp the moving image at the finest resolution. Experimental results for registering high-resolution 3D structural brain magnetic resonance (MR) images have demonstrated that image registration networks trained by our method obtain robust, diffeomorphic image registration results within seconds with improved accuracy compared with state-of-the-art image registration algorithms.

Early Pleistocene hominin teeth from Gongwangling of Lantian, Central China.

The fossil hominin individual from Gongwangling of Lantian, Central China, represents one of the earliest members attributed to Homo erectus in East Asia. Recent paleomagnetic analyses have yielded an age of 1.63 Ma for the Gongwangling hominin. The fossils from this site are critical to characterize the morphological features of early hominins in East Asia and to understand their relationships with other earlier and later members of the genus Homo. However, most morphological details of the Gongwangling cranium were obliterated due to postmortem erosion and deformation. Here we used high-resolution microcomputed tomography and three-dimensional virtual imaging techniques to extract the teeth and reconstruct the worn/damaged areas, describe the external morphology, measure crown diameters, record nonmetric traits of the crown and root, and investigate the shape of the enamel-dentine junction using geometric morphometrics. We compared the data obtained from the six teeth of the Gongwangling hominin with African early Homo, African and Georgian Homo erectus s.l., Asian Homo erectus, Homo antecessor, pre-Neanderthals, Neanderthals, and modern humans. Our results show that the Gongwangling specimens display affinities with other specimens attributed to H. erectus s.l. The highly divergent and noncoalesced three-root system in the Gongwangling specimens is comparable to that in the Early Pleistocene members of H. erectus s.l., and differs from Middle Pleistocene representatives of the species. The enamel-dentine junction shape of the Gongwangling molars prefigures the Asian H. erectus pattern later found in East Asian Middle Pleistocene H. erectus. The morphological comparisons between East Asian Early Pleistocene (e.g., Gongwangling, Meipu, and Quyuan River Mouth) and Middle Pleistocene H. erectus (e.g., Zhoukoudian, Hexian, and Yiyuan) suggest a potential temporal trend within this species in East Asia.

Direct cortical thickness estimation using deep learning-based anatomy segmentation and cortex parcellation.

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are an important biomarker to study neurodegenerative and neurological disorders. Diffeomorphic registration-based cortical thickness (DiReCT) is a known technique to derive such measures from non-surface-based volumetric tissue maps. ANTs provides an open-source method for estimating cortical thickness, derived by applying DiReCT to an atlas-based segmentation. In this paper, we propose DL+DiReCT, a method using high-quality deep learning-based neuroanatomy segmentations followed by DiReCT, yielding accurate and reliable cortical thickness measures in a short time. We evaluate the methods on two independent datasets and compare the results against surface-based measures from FreeSurfer. Good correlation of DL+DiReCT with FreeSurfer was observed (r = .887) for global mean cortical thickness compared to ANTs versus FreeSurfer (r = .608). Experiments suggest that both DiReCT-based methods had higher sensitivity to changes in cortical thickness than Freesurfer. However, while ANTs showed low scan-rescan robustness, DL+DiReCT showed similar robustness to Freesurfer. Effect-sizes for group-wise differences of healthy controls compared to individuals with dementia were highest with the deep learning-based segmentation. DL+DiReCT is a promising combination of a deep learning-based method with a traditional registration technique to detect subtle changes in cortical thickness.

Symplectomorphic registration with phase space regularization by entropy spectrum pathways.

PURPOSE: The ability to register image data to a common coordinate system is a critical feature of virtually all imaging studies. However, in spite of the abundance of literature on the subject and the existence of several variants of registration algorithms, their practical utility remains problematic, as commonly acknowledged even by developers of these methods. METHODS: A new registration method is presented that utilizes a Hamiltonian formalism and constructs registration as a sequence of symplectomorphic maps in conjunction with a novel phase space regularization. For validation of the framework a panel of deformations expressed in analytical form is developed that includes deformations based on known physical processes in MRI and reproduces various distortions and artifacts typically present in images collected using these different MRI modalities. RESULTS: The method is demonstrated on the three different magnetic resonance imaging (MRI) modalities by mapping between high resolution anatomical (HRA) volumes, medium resolution diffusion weighted MRI (DW-MRI) and HRA volumes, and low resolution functional MRI (fMRI) and HRA volumes. CONCLUSIONS: The method has shown an excellent performance and the panel of deformations was instrumental to quantify its repeatability and reproducibility in comparison to several available alternative approaches.

Coupling brain-tumor biophysical models and diffeomorphic image registration.

We present SIBIA (Scalable Integrated Biophysics-based Image Analysis), a framework for joint image registration and biophysical inversion and we apply it to analyze MR images of glioblastomas (primary brain tumors). We have two applications in mind. The first one is normal-to-abnormal image registration in the presence of tumor-induced topology differences. The second one is biophysical inversion based on single-time patient data. The underlying optimization problem is highly non-linear and non-convex and has not been solved before with a gradient-based approach. Given the segmentation of a normal brain MRI and the segmentation of a cancer patient MRI, we determine tumor growth parameters and a registration map so that if we "grow a tumor" (using our tumor model) in the normal brain and then register it to the patient image, then the registration mismatch is as small as possible. This "coupled problem" two-way couples the biophysical inversion and the registration problem. In the image registration step we solve a large-deformation diffeomorphic registration problem parameterized by an Eulerian velocity field. In the biophysical inversion step we estimate parameters in a reaction-diffusion tumor growth model that is formulated as a partial differential equation (PDE). In SIBIA, we couple these two sub-components in an iterative manner. We first presented the components of SIBIA in "Gholami et al., Framework for Scalable Biophysics-based Image Analysis, IEEE/ACM Proceedings of the SC2017", in which we derived parallel distributed memory algorithms and software modules for the decoupled registration and biophysical inverse problems. In this paper, our contributions are the introduction of a PDE-constrained optimization formulation of the coupled problem, and the derivation of a Picard iterative solution scheme. We perform extensive tests to experimentally assess the performance of our method on synthetic and clinical datasets. We demonstrate the convergence of the SIBIA optimization solver in different usage scenarios. We demonstrate that using SIBIA, we can accurately solve the coupled problem in three dimensions (2563 resolution) in a few minutes using 11 dual-x86 nodes.

The same modality medical image registration with large deformation and clinical application based on adaptive diffeomorphic multi-resolution demons.

BACKGROUND: Diffeomorphic demons can not only guarantee smooth and reversible deformation, but also avoid unreasonable deformation. However, the number of iterations which has great influence on the registration result needs to be set manually. METHODS: This study proposed a novel method to exploit the adaptive diffeomorphic multi-resolution demons algorithm to the non-rigid registration of the same modality medical images with large deformation. Firstly an optimized non-rigid registration framework and the diffeomorphism strategy were used, and then a similarity energy function based on the grey value was designed as registration metric, lastly termination condition was set based on the variation of this metric and iterations can be stopped adaptively. Quantitative analyses based on the registration evaluation indexes were conducted to prove the validity of this method. RESULTS: Registration result of synthetic image and the same modality MRI and CT image was compared with those obtained by other demons algorithms. Quantitative analyses demonstrated the proposed method's superiority. Medical image with large deformation was produced by rotational distortion and extrusion transform, and the same modality image registration with large deformation was performed successfully. Quantitative analyses showed that the registration evaluation indexes remained stable with an increase in transform strength. This method can be also applied to pulmonary medical image registration with large deformation successfully, and it showed the clinical application value. The influence of different driving forces and parameters on the registration result was analysed, and the result demonstrated that the proposed method is effective and robust. CONCLUSIONS: This method can solve the non-rigid registration problem of the same modality medical image with large deformation showing promise for diagnostic pulmonary imaging applications.

Evaluation of five diffeomorphic image registration algorithms for mouse brain magnetic resonance microscopy.

The development of genetically engineered mouse models for neuronal diseases and behavioural disorders have generated a growing need for small animal imaging. High-resolution magnetic resonance microscopy (MRM) provides powerful capabilities for noninvasive studies of mouse brains, while avoiding some limits associated with the histological procedures. Quantitative comparison of structural images is a critical step in brain imaging analysis, which highly relies on the performance of image registration techniques. Nowadays, there is a mushrooming growth of human brain registration algorithms, while fine-tuning of those algorithms for mouse brain MRMs is rarely addressed. Because of their topology preservation property and outstanding performance in human studies, diffeomorphic transformations have become popular in computational anatomy. In this study, we specially tuned five diffeomorphic image registration algorithms [DARTEL, geodesic shooting, diffeo-demons, SyN (Greedy-SyN and geodesic-SyN)] for mouse brain MRMs and evaluated their performance using three measures [volume overlap percentage (VOP), residual intensity error (RIE) and surface concordance ratio (SCR)]. Geodesic-SyN performed significantly better than the other methods according to all three different measures. These findings are important for the studies on structural brain changes that may occur in wild-type and transgenic mouse brains.

Alterations in white matter volume and its correlation with neuropsychological scales in patients with Alzheimer's disease: a DARTEL-based voxel-based morphometry study.

Background Non-invasive imaging markers can be used to diagnose Alzheimer's disease (AD) in its early stages, but an optimized quantification analysis to measure the brain integrity has been less studied. Purpose To evaluate white matter volume change and its correlation with neuropsychological scales in patients with AD using a diffeomorphic anatomical registration through exponentiated lie algebra (DARTEL)-based voxel-based morphometry (VBM). Material and Methods The 21 participants comprised 11 patients with AD and 10 age-matched healthy controls. High-resolution magnetic resonance imaging (MRI) data were processed by VBM analysis based on DARTEL algorithm. Results The patients showed significant white matter volume reductions in the posterior limb of the internal capsule, cerebral peduncle of the midbrain, and parahippocampal gyrus compared to healthy controls. In correlation analysis, the parahippocampal volume was positively correlated with the Korean-mini mental state examination score in AD. Conclusion This study provides an evidence for localized white matter volume deficits in conjunction with cognitive dysfunction in AD. These findings would be helpful to understand the neuroanatomical mechanisms in AD and to robust the diagnostic accuracy for AD.

Comparison of Arterial Spin-labeling Perfusion Images at Different Spatial Normalization Methods Based on Voxel-based Statistical Analysis.

PURPOSE: Spatial normalization is a significant image pre-processing operation in statistical parametric mapping (SPM) analysis. The purpose of this study was to clarify the optimal method of spatial normalization for improving diagnostic accuracy in SPM analysis of arterial spin-labeling (ASL) perfusion images. METHODS: We evaluated the SPM results of five spatial normalization methods obtained by comparing patients with Alzheimer's disease or normal pressure hydrocephalus complicated with dementia and cognitively healthy subjects. We used the following methods: 3DT1-conventional based on spatial normalization using anatomical images; 3DT1-DARTEL based on spatial normalization with DARTEL using anatomical images; 3DT1-conventional template and 3DT1-DARTEL template, created by averaging cognitively healthy subjects spatially normalized using the above methods; and ASL-DARTEL template created by averaging cognitively healthy subjects spatially normalized with DARTEL using ASL images only. RESULTS: Our results showed that ASL-DARTEL template was small compared with the other two templates. Our SPM results obtained with ASL-DARTEL template method were inaccurate. Also, there were no significant differences between 3DT1-conventional and 3DT1-DARTEL template methods. In contrast, the 3DT1-DARTEL method showed higher detection sensitivity, and precise anatomical location. CONCLUSIONS: Our SPM results suggest that we should perform spatial normalization with DARTEL using anatomical images.

DR-TAMAS: Diffeomorphic Registration for Tensor Accurate Alignment of Anatomical Structures.

In this work, we propose DR-TAMAS (Diffeomorphic Registration for Tensor Accurate alignMent of Anatomical Structures), a novel framework for intersubject registration of Diffusion Tensor Imaging (DTI) data sets. This framework is optimized for brain data and its main goal is to achieve an accurate alignment of all brain structures, including white matter (WM), gray matter (GM), and spaces containing cerebrospinal fluid (CSF). Currently most DTI-based spatial normalization algorithms emphasize alignment of anisotropic structures. While some diffusion-derived metrics, such as diffusion anisotropy and tensor eigenvector orientation, are highly informative for proper alignment of WM, other tensor metrics such as the trace or mean diffusivity (MD) are fundamental for a proper alignment of GM and CSF boundaries. Moreover, it is desirable to include information from structural MRI data, e.g., T1-weighted or T2-weighted images, which are usually available together with the diffusion data. The fundamental property of DR-TAMAS is to achieve global anatomical accuracy by incorporating in its cost function the most informative metrics locally. Another important feature of DR-TAMAS is a symmetric time-varying velocity-based transformation model, which enables it to account for potentially large anatomical variability in healthy subjects and patients. The performance of DR-TAMAS is evaluated with several data sets and compared with other widely-used diffeomorphic image registration techniques employing both full tensor information and/or DTI-derived scalar maps. Our results show that the proposed method has excellent overall performance in the entire brain, while being equivalent to the best existing methods in WM.

Connectometry: A statistical approach harnessing the analytical potential of the local connectome.

Here we introduce the concept of the local connectome: the degree of connectivity between adjacent voxels within a white matter fascicle defined by the density of the diffusing spins. While most human structural connectomic analyses can be summarized as finding global connectivity patterns at either end of anatomical pathways, the analysis of local connectomes, termed connectometry, tracks the local connectivity patterns along the fiber pathways themselves in order to identify the subcomponents of the pathways that express significant associations with a study variable. This bottom-up analytical approach is made possible by reconstructing diffusion MRI data into a common stereotaxic space that allows for associating local connectomes across subjects. The substantial associations can then be tracked along the white matter pathways, and statistical inference is obtained using permutation tests on the length of coherent associations and corrected for multiple comparisons. Using two separate samples, with different acquisition parameters, we show how connectometry can capture variability within core white matter pathways in a statistically efficient manner and extract meaningful variability from white matter pathways, complements graph-theoretic connectomic measures, and is more sensitive than region-of-interest approaches.

Modulative effects of COMT haplotype on age-related associations with brain morphology.

Catechol-O-methyltransferase (COMT), located on chromosome 22q11.2, encodes an enzyme critical for dopamine flux in the prefrontal cortex. Genetic variants of COMT have been suggested to functionally manipulate prefrontal morphology and function in healthy adults. This study aims to investigate modulative roles of individuals COMT SNPs (rs737865, val158met, rs165599) and its haplotypes in age-related brain morphology using an Asian sample with 174 adults aged from 21 to 80 years. We showed an age-related decline in cortical thickness of the dorsal visual pathway, including the left dorsolateral prefrontal cortex, bilateral angular gyrus, right superior frontal cortex, and age-related shape compression in the basal ganglia as a function of the genotypes of the individual COMT SNPs, especially COMT val158met. Using haplotype trend regression analysis, COMT haplotype probabilities were estimated and further revealed an age-related decline in cortical thickness in the default mode network (DMN), including the posterior cingulate, precuneus, supramarginal and paracentral cortex, and the ventral visual system, including the occipital cortex and left inferior temporal cortex, as a function of the COMT haplotype. Our results provided new evidence on an antagonistic pleiotropic effect in COMT, suggesting that genetically programmed neural benefits in early life may have a potential bearing towards neural susceptibility in later life. Hum Brain Mapp 37:2068-2082, 2016. © 2016 Wiley Periodicals, Inc.

Combining tract- and atlas-based analysis reveals microstructural abnormalities in early Tourette syndrome children.

Tourette syndrome (TS) is a neurological disorder that causes uncontrolled repetitive motor and vocal tics in children. Examining the neural basis of TS churned out different research studies that advanced our understanding of the brain pathways involved in its development. Particularly, growing evidence points to abnormalities within the fronto-striato-thalamic pathways. In this study, we combined Tract-Based Spatial Statistics (TBSS) and Atlas-based regions of interest (ROI) analysis approach, to investigate the microstructural diffusion changes in both deep and superficial white matter (SWM) in TS children. We then characterized the altered microstructure of white matter in 27 TS children in comparison with 27 age- and gender-matched healthy controls. We found that fractional anisotropy (FA) decreases and radial diffusivity (RD) increases in deep white matter (DWM) tracts in cortico-striato-thalamo-cortical (CSTC) circuit as well as SWM. Furthermore, we found that lower FA values and higher RD values in white matter regions are correlated with more severe tics, but not tics duration. Besides, we also found both axial diffusivity and mean diffusivity increase using Atlas-based ROI analysis. Our work may suggest that microstructural diffusion changes in white matter is not only restricted to the gray matter of CSTC circuit but also affects SWM within the primary motor and somatosensory cortex, commissural and association fibers. Hum Brain Mapp 37:1903-1919, 2016. © 2016 Wiley Periodicals, Inc.

Multi-contrast multi-scale surface registration for improved alignment of cortical areas.

The position of cortical areas can be approximately predicted from cortical surface folding patterns. However, there is extensive inter-subject variability in cortical folding patterns, prohibiting a one-to-one mapping of cortical folds in certain areas. In addition, the relationship between cortical area boundaries and the shape of the cortex is variable, and weaker for higher-order cortical areas. Current surface registration techniques align cortical folding patterns using sulcal landmarks or cortical curvature, for instance. The alignment of cortical areas by these techniques is thus inherently limited by the sole use of geometric similarity metrics. Magnetic resonance imaging T1 maps show intra-cortical contrast that reflects myelin content, and thus can be used to improve the alignment of cortical areas. In this article, we present a new symmetric diffeomorphic multi-contrast multi-scale surface registration (MMSR) technique that works with partially inflated surfaces in the level-set framework. MMSR generates a more precise alignment of cortical surface curvature in comparison to two widely recognized surface registration algorithms. The resulting overlap in gyrus labels is comparable to FreeSurfer. Most importantly, MMSR improves the alignment of cortical areas further by including T1 maps. As a first application, we present a group average T1 map at a uniquely high-resolution and multiple cortical depths, which reflects the myeloarchitecture of the cortex. MMSR can also be applied to other MR contrasts, such as functional and connectivity data.

DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions.

We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical "blip-up blip-down distortion symmetry" may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.

Automatic whole brain tract-based analysis using predefined tracts in a diffusion spectrum imaging template and an accurate registration strategy.

Automated tract-based analysis of diffusion MRI is an important tool for investigating tract integrity of the cerebral white matter. Current template-based automatic analyses still lack a comprehensive list of tract atlas and an accurate registration method. In this study, tract-based automatic analysis (TBAA) was developed to meet the demands. Seventy-six major white matter tracts were reconstructed on a high-quality diffusion spectrum imaging (DSI) template, and an advanced two-step registration strategy was proposed by incorporating anatomical information of the gray matter from T1-weighted images in addition to microstructural information of the white matter from diffusion-weighted images. The automatic analysis was achieved by establishing a transformation between the DSI template and DSI dataset of the subject derived from the registration strategy. The tract coordinates in the template were transformed to native space in the individual's DSI dataset, and the microstructural properties of major tract bundles were sampled stepwise along the tract coordinates of the subject's DSI dataset. In a validation study of eight well-known tracts, our results showed that TBAA had high geometric agreement with manual tracts in both deep and superficial parts but significantly smaller measurement variability than manual method in functional difference. Additionally, the feasibility of the method was demonstrated by showing tracts with altered microstructural properties in patients with schizophrenia. Fifteen major tract bundles were found to have significant differences after controlling the family-wise error rate. In conclusion, the proposed TBAA method is potentially useful in brain-wise investigations of white matter tracts, particularly for a large cohort study.

Validity of modulation and optimal settings for advanced voxel-based morphometry.

Voxel-based morphometry (VBM) is a widely-used structural neuroimaging technique for comparing meso- and macroscopic regional brain volumes between patients and controls in vivo, but some of its steps, particularly the modulation, lack an experimental validation. The aims of this study were two-fold: a) to assess the effects of modulation to detect mesoscopic (i.e. between microscopic and macroscopic) abnormalities on published, classic VBM; and b) to suggest a set of potentially optimal settings for detecting mesoscopic abnormalities with new, advanced, high-resolution diffeomorphic VBM normalization algorithms. Sensitivity and false positive rate after modulating or not in classic VBM using different software packages and spatial statistics, and after setting a range of different parameters in advanced VBM (ANTS-SyN), were calculated in 10 VBM comparisons of 32 altered vs. 32 unaltered gray matter images from different healthy controls. Simulated brain abnormalities comprised mesoscopic volume differences mainly due to cortical thinning. In classic VBM, modulation was associated with a substantial decrease of the sensitivity to detect mesoscopic abnormalities (p<0.001). Optimal settings for advanced VBM included the omission of modulation, the use of large smoothing kernels, and the application of voxel-based or threshold-free cluster enhancement (TFCE) spatial statistics. The modulation-related decrease in sensitivity was due to an increase in variance, and it was more severe in higher-resolution normalization algorithms. Findings from this study suggest the use of unmodulated VBM to detect mesoscopic abnormalities such as cortical thinning.

Regionally selective atrophy of subcortical structures in prodromal HD as revealed by statistical shape analysis.

Huntington disease (HD) is a neurodegenerative disorder that involves preferential atrophy in the striatal complex and related subcortical nuclei. In this article, which is based on a dataset extracted from the PREDICT-HD study, we use statistical shape analysis with deformation markers obtained through "Large Deformation Diffeomorphic Metric Mapping" of cortical surfaces to highlight specific atrophy patterns in the caudate, putamen, and globus pallidus, at different prodromal stages of the disease. On the basis of the relation to cortico-basal ganglia circuitry, we propose that statistical shape analysis, along with other structural and functional imaging studies, may help expand our understanding of the brain circuitry affected and other aspects of the neurobiology of HD, and also guide the most effective strategies for intervention.