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The habenula consists of a pair of small epithalamic nuclei located adjacent to the dorsomedial thalamus. Despite increasing interest in imaging the habenula due to its critical role in mediating subcortical reward circuitry, in vivo neuroimaging research targeting the human habenula has been limited by its small size and low anatomical contrast. In this work, we have developed an objective semi-automated habenula segmentation scheme consisting of histogram-based thresholding, region growing, geometric constraints, and partial volume estimation steps. This segmentation scheme was designed around in vivo 3 T myelin-sensitive images, generated by taking the ratio of high-resolution T1w over T2w images. Due to the high myelin content of the habenula, the contrast-to-noise ratio with the thalamus in the in vivo 3 T myelin-sensitive images was significantly higher than the T1w or T2w images alone. In addition, in vivo 7 T myelin-sensitive images (T1w over T2*w ratio images) and ex vivo proton density-weighted images, along with histological evidence from the literature, strongly corroborated the in vivo 3 T habenula myelin contrast used in the proposed segmentation scheme. The proposed segmentation scheme represents a step toward a scalable approach for objective segmentation of the habenula suitable for both morphological evaluation and habenula seed region selection in functional and diffusion MRI applications. [ABSTRACT FROM AUTHOR]

Object: The aim of this study was to demonstrate a new automatic brain segmentation method in magnetic resonance imaging (MRI). Materials and methods: The signal of a spoiled gradient-recalled echo (SPGR) sequence acquired with multiple flip angles was used to map T1, and a subsequent fit of a multi-compartment model yielded parametric maps of partial volume estimates of the different compartments. The performance of the proposed method was assessed through simulations as well as in-vivo experiments in five healthy volunteers. Results: Simulations indicated that the proposed method was capable of producing robust segmentation maps with good reliability. Mean bias was below 3 % for all tissue types, and the corresponding similarity index (Dice's coefficient) was over 95 % (SNR = 100). In-vivo experiments yielded realistic segmentation maps, with comparable quality to results obtained with an established segmentation method. Relative whole-brain cerebrospinal fluid, grey matter, and white matter volumes were (mean ± SE) respectively 6.8 ± 0.5, 47.3 ± 1.1, and 45.9 ± 1.3 % for the proposed method, and 7.5 ± 0.6, 46.2 ± 1.2, and 46.3 ± 0.9 % for the reference method. Conclusion: The proposed approach is promising for brain segmentation and partial volume estimation. The straightforward implementation of the method is attractive, and protocols that already rely on SPGR-based T1 mapping may employ this method without additional scans. [ABSTRACT FROM AUTHOR]

The hippocampus has been known to be an important structure as a biomarker for Alzheimer’s disease (AD) and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. In this study, an automated hippocampal segmentation method based on a graph-cuts algorithm combined with atlas-based segmentation and morphological opening was proposed. First of all, the atlas-based segmentation was applied to define initial hippocampal region for a priori information on graph-cuts. The definition of initial seeds was further elaborated by incorporating estimation of partial volume probabilities at each voxel. Finally, morphological opening was applied to reduce false positive of the result processed by graph-cuts. In the experiments with twenty-seven healthy normal subjects, the proposed method showed more reliable results (similarity index=0.81±0.03) than the conventional atlas-based segmentation method (0.72±0.04). Also as for segmentation accuracy which is measured in terms of the ratios of false positive and false negative, the proposed method (precision=0.76±0.04, recall=0.86±0.05) produced lower ratios than the conventional methods (0.73±0.05, 0.72±0.06) demonstrating its plausibility for accurate, robust and reliable segmentation of hippocampus. [ABSTRACT FROM AUTHOR]

Abstract: The degree of white matter (WM) myelination is rather inhomogeneous across the brain. White matter appears differently across the cortical lobes in MR images acquired during early postnatal development. Specifically at 1-year of age, the gray/white matter contrast of MR T1 and T2 weighted images in prefrontal and temporal lobes is reduced as compared to the rest of the brain, and thus, tissue segmentation results commonly show lower accuracy in these lobes. In this novel work, we propose the use of spatial intensity growth maps (IGM) for T1 and T2 weighted images to compensate for local appearance inhomogeneity. The IGM captures expected intensity changes from 1 to 2years of age, as appearance homogeneity is greatly improved by the age of 24months. The IGM was computed as the coefficient of a voxel-wise linear regression model between corresponding intensities at 1 and 2years. The proposed IGM method revealed low regression values of 1–10% in GM and CSF regions, as well as in WM regions at maturation stage of myelination at 1year. However, in the prefrontal and temporal lobes we observed regression values of 20–25%, indicating that the IGM appropriately captures the expected large intensity change in these lobes mainly due to myelination. The IGM is applied to cross-sectional MRI datasets of 1-year-old subjects via registration, correction and tissue segmentation of the IGM-corrected dataset. We validated our approach in a small leave-one-out study of images with known, manual ‘ground truth’ segmentations. [Copyright &y& Elsevier]

Abstract: Accurate reconstruction of the inner and outer cortical surfaces of the human cerebrum is a critical objective for a wide variety of neuroimaging analysis purposes, including visualization, morphometry, and brain mapping. The Anatomic Segmentation using Proximity (ASP) algorithm, previously developed by our group, provides a topology-preserving cortical surface deformation method that has been extensively used for the aforementioned purposes. However, constraints in the algorithm to ensure topology preservation occasionally produce incorrect thickness measurements due to a restriction in the range of allowable distances between the gray and white matter surfaces. This problem is particularly prominent in pediatric brain images with tightly folded gyri. This paper presents a novel method for improving the conventional ASP algorithm by making use of partial volume information through probabilistic classification in order to allow for topology preservation across a less restricted range of cortical thickness values. The new algorithm also corrects the classification of the insular cortex by masking out subcortical tissues. For 70 pediatric brains, validation experiments for the modified algorithm, Constrained Laplacian ASP (CLASP), were performed by three methods: (i) volume matching between surface-masked gray matter (GM) and conventional tissue-classified GM, (ii) surface matching between simulated and CLASP-extracted surfaces, and (iii) repeatability of the surface reconstruction among 16 MRI scans of the same subject. In the volume-based evaluation, the volume enclosed by the CLASP WM and GM surfaces matched the classified GM volume 13% more accurately than using conventional ASP. In the surface-based evaluation, using synthesized thick cortex, the average difference between simulated and extracted surfaces was 4.6 ± 1.4 mm for conventional ASP and 0.5 ± 0.4 mm for CLASP. In a repeatability study, CLASP produced a 30% lower RMS error for the GM surface and a 8% lower RMS error for the WM surface compared with ASP. [Copyright &y& Elsevier]

Little is known about the neural correlates of delusional infestation (DI), the delusional belief to be infested with pathogens. So far, evidence comes mainly from case reports and case series. We investigated brain morphology in 16 DI patients and 16 healthy controls using structural magnetic resonance imaging and a multivariate data analysis technique, i.e. source-based morphometry (SBM). In addition, we explored differences in brain structure in patient subgroups based on disease aetiology. SBM revealed two patterns exhibiting significantly (p<0.05, Bonferroni-corrected) lower grey and higher white matter volume in DI patients compared to controls. Lower grey matter volume was found in medial prefrontal cortex, anterior cingulate cortex, medial temporal lobe structures (parahippocampus and hippocampus), sensorimotor cortices, bilateral insula and thalamus and inferior parietal regions. Higher white matter volume was found in medial and middle frontal and temporal cortices, left insula and lentiform nucleus. Grey matter volume was abnormal in both "psychiatric" (primary DI and DI associated with an affective disorder) and "organic" DI (DI due to a medical condition). In contrast, aberrant white matter volume was only confirmed for the "organic" DI patient subgroup. These results suggest prefrontal, temporal, parietal, insular, thalamic and striatal dysfunction underlying DI. Moreover, the data suggest that aetiologically distinct presentations of DI share similar patterns of abnormal grey matter volume, whereas aberrant white matter volume appears to be restricted to organic cases., (© 2013.)

Little is known about the neural basis of delusional infestation (DI), the delusional belief to be infested with pathogens. Case series and the response to anti-dopaminergic medication indicate disruptions in dopaminergic neurotransmission in the striatum (caudate, putamen), but did not allow for population-based inference. Here, we report the first whole-brain structural neuroimaging study to investigate gray and white matter abnormalities in DI compared to controls. In this study, we used structural magnetic resonance imaging and voxel-based morphometry to investigate gray and white matter volume in 16 DI patients and 16 matched healthy controls. Lower gray matter volume in DI patients compared to controls was found in left medial, lateral and right superior frontal cortices, left anterior cingulate cortex, bilateral insula, left thalamus, right striatal areas and in lateral and medial temporal cortical regions (p<0.05, cluster-corrected). Higher white matter volume in DI patients compared to controls was found in right middle cingulate, left frontal opercular and bilateral striatal regions (p<0.05, cluster-corrected). This study shows that structural changes in prefrontal, temporal, insular, cingulate and striatal brain regions are associated with DI, supporting a neurobiological model of disrupted prefrontal control over somato-sensory representations., (Copyright © 2013 Elsevier Inc. All rights reserved.)