Gray matter density loss in essential tremor: a lobule by lobule analysis of the cerebellum

Background

The pathophysiological basis for essential tremor (ET) remains unclear, although evidence increasingly links it to a disordered and perhaps degenerative cerebellum. Prior imaging studies have treated the cerebellum en bloc. Our hypothesis was that regional differences in cerebellar gray matter (GM) density may better distinguish ET cases from controls.

Forty-seven ET cases and 36 control subjects were imaged using magnetic resonance imaging (MRI). The cerebellum was segmented into 34 lobes using a Spatially Unbiased Infra-Tentorial Template (SUIT) atlas within the Statistical Parametric Mapping (SPM) analysis package. Age, gender and Montreal Cognitive Assessment (MoCA) scores were regressed out from the statistical models to isolate group effects. ET cases were further stratified into phenotypically-defined subgroups. The Benjamini-Hochberg False Discovery Rate procedure (BH FDR) (α = 0.1) was used to correct for multiple comparisons.

Results

When all ET cases and controls were compared, none of the regions met the BH FDR criteria for significance. When compared with controls, ET cases with head or jaw tremor (n = 27) had significant changes in GM density in nine cerebellar lobules, with a majority in the left cerebellar region, and each meeting the BH FDR criteria. Likewise, ET cases with voice tremor (n = 22) exhibited significant changes in 11 lobules in both left and right regions and the vermis. These analyses, in sum, indicated decreases in GM density in lobules I-IV, V, VI, VII and VIII as well as the vermis. ET cases with severe tremor (n = 20) did not show regions of change that survived the BH FDR procedure when compared to controls.

Conclusions

We showed that ET cases with various forms of cranial tremor differed from controls with respect to cerebellar GM density, with evidence of GM reduction across multiple cerebellar regions. Additional work, using a lobule-by-lobule approach, is needed to confirm these results and precisely map the regional differences in ET cases, subgroups of ET cases, and controls.

Variations in human pulmonary fissures and lobes: a study conducted in nepalese cadavers

The fissures of lungs are embryologically separating the bronchopulmonary segments, which later on persist in interlobar planes of fully developed lung. Fifty lungs (23 right side and 27 left side), obtained during routine dissection and preserved in formalin constituted the material for present study. In them, variations in fissures and lobes of lung were observed and compared with the previous studies. Seven right sided and 14 left sided lungs showed incomplete oblique fissure. Incomplete horizontal fissure of right lung was observed in eight lungs while it was completely missing in three specimens. A right lung with “lobe of the azygos vein” separated by a supernumerary fissure in medial surface was found. One of the right lung had both superior accessory fissure and inferior accessory fissure and four other right lungs and one left lung presented only with inferior accessory fissure. A vertical notch was found in middle lobe of one right lung. Eight left lungs exhibited with left minor fissure among them two lungs had lingula appearing as a separate lobe. Knowledge of variations in fissures and lobes is of interest to all medical professionals to exactly interpret radiographs, computed tomography scans, to diagnose, plan and modify a surgical procedure depending on the merit of the case and also in certain classical clinical cases pertaining to lung pathologies.

Sleep/wake movement velocities, trajectories and micro-arousals during maturation in rats

Background

Sleep is regulated by two main processes. The circadian process provides a 24-h rhythm and the homeostatic process reflects sleep pressure, which increases in the course of wakefulness and decreases during sleep. Both of these processes undergo major changes during development. For example, sleep homeostasis, measured by means of electroencephalogram (EEG) slow-wave activity (SWA, EEG power between 0.5 and 4.5 Hz), peaks around puberty and decreases during adolescence. In humans and rats these changes have been related to cortical maturation. We aimed to explore whether additional parameters as state dynamic (dynamic of sleep/wake behavior) parameters of movement velocity, trajectories and micro-arousals provide markers of rat maturation. The state dynamics reflect the stability of sleep within a specific sleep stage. We applied a state space technique (SST), a quantitative and unbiased method, based on frequency band ratios of the EEG to analyze the development of different sleep/wake states and state dynamics between vigilance states. EEG of recording electrodes at the frontal and parietal lobe were analyzed using conventional scoring criteria and SST.

Results

We found that movement velocity, trajectories between sleep states and micro-arousals changed as an inverse U-shaped curve across maturation. At all ages, movement velocity over the frontal lobe is higher compared to the parietal lobe, while the number of trajectories and micro-arousals are reduced. Furthermore, we showed that SWA correlates negatively with movement velocity and the number of micro-arousals. The velocity in the parietal lobe correlates positively with the number of micro-arousals. As for SWA, trajectories seem primarily to depend on sleep homeostasis regulatory mechanisms while the movement velocity seems to be modulated by other sleep regulators like the circadian rhythms.

Conclusions

New insights in sleep/wake state dynamics are established with the SST, because trajectories, micro-arousals and velocities are not evident by traditional scoring methods. These dynamic measures may represent new indicators for changes in sleep regulatory processes across maturation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12868-017-0343-6) contains supplementary material, which is available to authorized users.

Estimating uncertainty in MRF-based image segmentation: A perfect-MCMC approach

Typical methods for image segmentation, or labeling, formulate and solve an optimization problem to produce a single optimal solution. For applications in clinical decision support relying on automated medical image segmentation, it is also desirable for methods to inform about (i) the uncertainty in label assignments or object boundaries or (ii) alternate close-to-optimal solutions. However, typical methods fail to do so. To estimate uncertainty, while some Bayesian methods rely on simplified prior models and approximate variational inference schemes, others rely on sampling segmentations from the associated posterior model using (i) traditional Markov chain Monte Carlo (MCMC) methods based on Gibbs sampling or (ii) approximate perturbation models. However, in such typical approaches, in practice, the resulting inference or generated sample set are approximations that deviate significantly from those indicated by the true posterior. To estimate uncertainty, we propose the modern paradigm of perfect MCMC sampling to sample multi-label segmentations from generic Bayesian Markov random field (MRF) models, in finite time for exact inference. Furthermore, for exact sampling in generic Bayesian MRFs, we extend the theory underlying Fill's algorithm to generic MRF models by proposing a novel bounding-chain algorithm. On several classic problems in medical image analysis, and several modeling and inference schemes, results on simulated data and clinical brain magnetic resonance images show that our uncertainty estimates gain accuracy over several state-of-the-art inference methods.