Multidimensional Alternating Kernel Method for cortical layer segmentation in 3D reconstructed histology.

The neocortex of the brain can be divided into six layers each with a distinct cell composition and connectivity pattern. Recently, sensory deprivation, including congenital deafness, has been shown to alter cortical structure (e.g. the cortical thickness) of the feline auditory cortex with variable and inconsistent results. Thus, understanding these complex changes will require further study of the constituent cortical layers in three-dimensional space. Further progress crucially depends on the use of objective computational techniques that can reliably characterize spatial properties of the complex cortical structure. Here a method for cortical laminar segmentation is derived and applied to the three-dimensional cortical areas reconstructed from a series of histological sections from four feline brains. In this approach, the Alternating Kernel Method was extended to fit a multi-variate Gaussian mixture model to a feature space consisting of both staining intensity and a biologically plausible equivolumetric depth map. This research method•Extends the Alternating Kernel Method to multi-dimensional feature spaces.•Uses it to segment the cortical layers in reconstructed histology volume. Segmentation features include staining intensity and a biologically plausible equivolumetric depth map.•Validates results in auditory cortical areas of feline brains, two with normal hearing and two with congenital deafness.

Normal and equivolumetric coordinate systems for cortical areas.

We describe coordinate systems adapted for the space between two surfaces, such as those delineating the highly folded cortex in mammalian brains. These systems are estimated in order to satisfy geometric priors, including streamline normality or equivolumetric conditions on layers. We give a precise mathematical formulation of these problems, and present numerical simulations based on diffeomorphic registration methods, comparing them with recent approaches. Our method involves•Diffeomorphic registration of inner and outer folded folded surfaces.•Followed by equivolumetric reparametrization of layers to yield coordinate system.

A 7 Tesla Amygdalar-Hippocampal Shape Analysis of Lithium Response in Bipolar Disorder.

Research to discover clinically useful predictors of lithium response in patients with bipolar disorder has largely found them to be elusive. We demonstrate here that detailed neuroimaging may have the potential to fill this important gap in mood disorder therapeutics. Lithium treatment and bipolar disorder have both been shown to affect anatomy of the hippocampi and amygdalae but there is no consensus on the nature of their effects. We aimed to investigate structural surface anatomy changes in amygdala and hippocampus correlated with treatment response in bipolar disorder. Patients with bipolar disorder (N = 14) underwent lithium treatment, were classified by response status at acute and long-term time points, and scanned with 7 Tesla structural MRI. Large Deformation Diffeomorphic Metric Mapping was applied to detect local differences in hippocampal and amygdalar anatomy between lithium responders and non-responders. Anatomy was also compared to 21 healthy comparison participants. A patch of the ventral surface of the left hippocampus was found to be significantly atrophied in non-responders as compared to responders at the acute time point and was associated at a trend-level with long-term response status. We did not detect an association between response status and surface anatomy of the right hippocampus or amygdala. To the best of our knowledge, this is the first shape analysis of hippocampus and amygdala in bipolar disorder using 7 Tesla MRI. These results can inform future work investigating possible neuroimaging predictors of lithium response in bipolar disorder.

Visualising the topography of the acoustic radiation in clinical diffusion tensor imaging scans.

PURPOSE: It has long been thought that the acoustic radiation (AR) white matter fibre tract from the medial geniculate body of the thalamus to the Heschl's gyrus cannot be reconstructed via single-fibre analysis of clinical diffusion tensor imaging (DTI) scans. A recently developed single-fibre probabilistic method suggests otherwise. The method uses dynamic programming (DP) to compute the most probable paths between two regions of interest. This study aims to observe the ability of single-fibre probabilistic analysis via DP to visualise the AR in clinical DTI scans from legacy pilot cohorts of subjects with normal hearing (NH) and profound hearing loss (HL). METHODS: Single-fibre probabilistic analysis via DP was applied to reconstruct 3D models of the AR in the two cohorts. DTI and T1 data at 1.5 T for subjects with NH (n = 11) and HL (n = 5), as well as 3 T for NH (n = 1) and HL (n = 1), were used. RESULTS: The topographical features of AR previously observed in post-mortem and multi-fibre analyses can be visualised in DTI scans of 16 subjects and 2 atlases with a success rate of 100%. Relative to MNI coordinates, there was no significant difference in the varifold distances between the topography of the tracts in the 1.5 T cohort. CONCLUSION: The AR can be visualised in clinical 1.5 T and 3 T DTI scans using single-fibre probabilistic analysis via DP, hence, the potential for DP to visualise the AR in medical and pre-surgical applications in pathologies such as vestibular schwannoma, multiple sclerosis, thalamic tumours and stroke as well as hearing loss.

Motor cortical thickness is related to effort-based decision-making in humans.

Although motor cortex is integral in driving physical exertion, how its inherent properties influence decisions to exert is unknown. In this study, we examined how anatomical properties of motor cortex are related to participants' subjective valuations of effort and their decisions to exert effort. We used computational modeling to characterize participants' subjective valuation of physical effort during an effort-based decision-making task in which they made choices about exerting different levels of hand-grip exertion. We also acquired structural MRI data from these participants and extracted anatomical measures of each individual's hand knob, the region of motor cortex recruited during hand-grip exertion. We found that individual participants' cortical thickness of hand knob was associated with their effort-based decisions regarding hand exertion. These data provide evidence that the anatomy of an individual's motor cortex is an important factor in decisions to engage in physical activity.NEW & NOTEWORTHY How effortful a task feels is an integral aspect of human decision-making that influences choices to engage in physical activity. We show that properties of motor cortex (the brain region responsible for physical exertion) are related to assessments of effort and decisions to exert. These findings provide a link between the anatomical properties of motor cortex and the cognitive function of effort-based choice.

Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

Adaptive postprocessing techniques for myocardial tissue tracking with displacement-encoded MR imaging.

UNLABELLED: The purpose of this study was to prospectively assess the effects of two adaptive postprocessing techniques on the evaluation of myocardial function with displacement-encoded magnetic resonance (MR) imaging, including sensitivity for abnormal wall motion, with two-dimensional echocardiography as the reference standard. Sixteen patients (11 men, five women; age range, 26-74 years) and 12 volunteers (six men, six women; age range, 29-53 years) underwent breath-hold MR imaging. Institutional review board approval and informed consent were obtained. Adaptive phase-unwrapping and spatial filtering techniques were compared with conventional phase-unwrapping and spatial filtering techniques. Use of the adaptive techniques led to a reduced rate of failure with the phase-unwrapping technique from 18.9% to 0.6% (P < .001), resulted in lower variability of segmental strain measurements among healthy volunteers (P < .001 to P = .02), and increased the sensitivity of quantitative detection of abnormal segments in patients from 82.5% to 87.7% (P = .034). The adaptive techniques improved the semiautomated postprocessing of displacement-encoded cardiac images and increased the sensitivity of detection of abnormal wall motion in patients. SUPPLEMENTAL MATERIAL: http://radiology.rsnajnls.org/cgi/content/full/246/1/229/DC1.