Evidence for a novel subcortical mechanism for posterior cingulate cortex atrophy in HIV peripheral neuropathy.

We previously reported that neuropathic pain was associated with smaller posterior cingulate cortical (PCC) volumes, suggesting that a smaller/dysfunctional PCC may contribute to development of pain via impaired mind wandering. A gap in our previous report was lack of evidence for a mechanism for the genesis of PCC atrophy in HIV peripheral neuropathy. Here we investigate if volumetric differences in the subcortex for those with neuropathic paresthesia may contribute to smaller PCC volumes, potentially through deafferentation of ascending white matter tracts resulting from peripheral nerve damage in HIV neuropathy. Since neuropathic pain and paresthesia are highly correlated, statistical decomposition was used to separate pain and paresthesia symptoms to determine which regions of brain atrophy are associated with both pain and paresthesia and which are associated separately with pain or paresthesia. HIV+ individuals (N = 233) with and without paresthesia in a multisite study underwent structural brain magnetic resonance imaging. Voxel-based morphometry and a segmentation/registration tool were used to investigate regional brain volume changes associated with paresthesia. Analysis of decomposed variables found that smaller midbrain and thalamus volumes were associated with paresthesia rather than pain. However, atrophy in the PCC was related to both pain and paresthesia. Peak thalamic atrophy (p = 0.004; MNI x = - 14, y = - 24, z = - 2) for more severe paresthesia was in a region with reciprocal connections with the PCC. This provides initial evidence that smaller PCC volumes in HIV peripheral neuropathy are related to ascending white matter deafferentation caused by small fiber damage observed in HIV peripheral neuropathy.

A combined post-mortem magnetic resonance imaging and quantitative histological study of multiple sclerosis pathology.

Multiple sclerosis is a chronic inflammatory neurological condition characterized by focal and diffuse neurodegeneration and demyelination throughout the central nervous system. Factors influencing the progression of pathology are poorly understood. One hypothesis is that anatomical connectivity influences the spread of neurodegeneration. This predicts that measures of neurodegeneration will correlate most strongly between interconnected structures. However, such patterns have been difficult to quantify through post-mortem neuropathology or in vivo scanning alone. In this study, we used the complementary approaches of whole brain post-mortem magnetic resonance imaging and quantitative histology to assess patterns of multiple sclerosis pathology. Two thalamo-cortical projection systems were considered based on their distinct neuroanatomy and their documented involvement in multiple sclerosis: lateral geniculate nucleus to primary visual cortex and mediodorsal nucleus of the thalamus to prefrontal cortex. Within the anatomically distinct thalamo-cortical projection systems, magnetic resonance imaging derived cortical thickness was correlated significantly with both a measure of myelination in the connected tract and a measure of connected thalamic nucleus cell density. Such correlations did not exist between these markers of neurodegeneration across different thalamo-cortical systems. Magnetic resonance imaging lesion analysis depicted clearly demarcated subcortical lesions impinging on the white matter tracts of interest; however, quantitation of the extent of lesion-tract overlap failed to demonstrate any appreciable association with the severity of markers of diffuse pathology within each thalamo-cortical projection system. Diffusion-weighted magnetic resonance imaging metrics in both white matter tracts were correlated significantly with a histologically derived measure of tract myelination. These data demonstrate for the first time the relevance of functional anatomical connectivity to the spread of multiple sclerosis pathology in a 'tract-specific' pattern. Furthermore, the persisting relationship between metrics from post-mortem diffusion-weighted magnetic resonance imaging and histological measures from fixed tissue further validates the potential of imaging for future neuropathological studies.

Target identification for stereotactic thalamotomy using diffusion tractography.

BACKGROUND: Stereotactic targets for thalamotomy are usually derived from population-based coordinates. Individual anatomy is used only to scale the coordinates based on the location of some internal guide points. While on conventional MR imaging the thalamic nuclei are indistinguishable, recently it has become possible to identify individual thalamic nuclei using different connectivity profiles, as defined by MR diffusion tractography. METHODOLOGY AND PRINCIPAL FINDINGS: Here we investigated the inter-individual variation of the location of target nuclei for thalamotomy: the putative ventralis oralis posterior (Vop) and the ventral intermedius (Vim) nucleus as defined by probabilistic tractography. We showed that the mean inter-individual distance of the peak Vop location is 7.33 mm and 7.42 mm for Vim. The mean overlap between individual Vop nuclei was 40.2% and it was 31.8% for Vim nuclei. As a proof of concept, we also present a patient who underwent Vop thalamotomy for untreatable tremor caused by traumatic brain injury and another patient who underwent Vim thalamotomy for essential tremor. The probabilistic tractography indicated that the successful tremor control was achieved with lesions in the Vop and Vim respectively. CONCLUSIONS: Our data call attention to the need for a better appreciation of the individual anatomy when planning stereotactic functional neurosurgery.

A Bayesian model of shape and appearance for subcortical brain segmentation.

Automatic segmentation of subcortical structures in human brain MR images is an important but difficult task due to poor and variable intensity contrast. Clear, well-defined intensity features are absent in many places along typical structure boundaries and so extra information is required to achieve successful segmentation. A method is proposed here that uses manually labelled image data to provide anatomical training information. It utilises the principles of the Active Shape and Appearance Models but places them within a Bayesian framework, allowing probabilistic relationships between shape and intensity to be fully exploited. The model is trained for 15 different subcortical structures using 336 manually-labelled T1-weighted MR images. Using the Bayesian approach, conditional probabilities can be calculated easily and efficiently, avoiding technical problems of ill-conditioned covariance matrices, even with weak priors, and eliminating the need for fitting extra empirical scaling parameters, as is required in standard Active Appearance Models. Furthermore, differences in boundary vertex locations provide a direct, purely local measure of geometric change in structure between groups that, unlike voxel-based morphometry, is not dependent on tissue classification methods or arbitrary smoothing. In this paper the fully-automated segmentation method is presented and assessed both quantitatively, using Leave-One-Out testing on the 336 training images, and qualitatively, using an independent clinical dataset involving Alzheimer's disease. Median Dice overlaps between 0.7 and 0.9 are obtained with this method, which is comparable or better than other automated methods. An implementation of this method, called FIRST, is currently distributed with the freely-available FSL package.

Imaging dopamine receptors in humans with [11C]-(+)-PHNO: dissection of D3 signal and anatomy.

[(11)C]-(+)-PHNO is a D3 preferring PET radioligand which has recently opened the possibility of imaging D3 receptors in the human brain in vivo. This imaging tool allows characterisation of the distribution of D3 receptors in vivo and further investigation of their functional role. The specific [(11)C]-(+)-PHNO signal is a mixture of D3 and D2 components with the relative magnitude of each component determined by the regional receptor densities. An accurate and reproducible delineation of regions of interest (ROI) is therefore important for optimal analysis of human PET data. We present a set of anatomical guidelines for the delineation of D3 relevant ROIs including substantia nigra, hypothalamus, ventral pallidum/substantia innominata, ventral striatum, globus pallidus and thalamus. Delineation of these structures using this approach allowed for high intra- and inter-operator reproducibility. Subsequently we used a selective D3 antagonist to dissect the total [(11)C]-(+)-PHNO signal in each region into its D3 and D2 components and estimated the regional fraction of the D3 signal (f(PHNO)(D3)). In descending order of magnitude the following results for the f(PHNO)(D3) were obtained: hypothalamus=100%, substantia nigra=100%, ventral pallidum/substantia innominata=75%, globus pallidus=65%, thalamus=43%, ventral striatum=26% and precommissural-ventral putamen=6%. An automated approach for the delineation of these anatomical regions of interest was also developed and investigated in terms of its reproducibility and accuracy.

Methods for tractography-driven surface registration of brain structures.

Registration of brain structures should bring anatomically equivalent areas into correspondence which is usually done using information from structural MRI modalities. Correspondence can be improved by using other image modalities that provide complementary data. In this paper we propose and evaluate two novel surface registration algorithms which improve within-surface correspondence in brain structures. Both approaches use a white-matter tract similarity function (derived from probabilistic tractography) to match areas of similar connectivity patterns. The two methods differ in the way the deformation field is calculated and in how the multi-scale registration framework is implemented. We validated both algorithms using artificial and real image examples, in both cases showing high registration consistency and the ability to find differences in thalamic sub-structures between Alzheimer's disease and control subjects. The results suggest differences in thalamic connectivity predominantly in the medial dorsal parts of the left thalamus.

Improved optimization for the robust and accurate linear registration and motion correction of brain images.

Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.