The aim of this study was the morphological and further chemical characterisation of neurons immunoreactive for leu-enkephalin (leuENK). Ten wholemounts of small and large intestinal segments from nine patients were immunohistochemically triple-stained for leuENK/neurofilament 200 (NF)/substance P (SP). Based on their simultaneous NF-reactivity and 3D reconstruction of single NF-reactive cells, 97.5% of leuENK-positive neurons displayed the appearance of stubby neurons: small somata; short, stubby dendrites and one axon. Of these leuENK-reactive stubby neurons, 91.3% did not display co-reactivity for SP whereas 8.7% were SP-co-reactive. As to their axonal projection pattern, 50.4% of the recorded leuENK stubby neurons had axons running orally whereas in 29.4% they ran anally; the directions of the remaining 20.2% could not be determined. No axons were seen to enter into secondary strands of the myenteric plexus. Somal area measurements revealed clearly smaller somata of leuENK-reactive stubby neurons (between 259+/-47 microm(2) and 487+/-113 microm(2)) than those of putative sensory type II neurons (between 700+/-217 microm(2) and 1,164+/-396 microm(2)). The ratio dendritic field area per somal area of leuENK-reactive stubby neurons was between 2.0 and 2.8 reflecting their short dendrites. Additionally, we estimated the proportion of leuENK-positive neurons in comparison to the putative whole myenteric neuron population in four leuENK/anti-Hu doublestained wholemounts. This proportion ranged between 5.9% and 8.3%. We suggest leuENK-reactive stubby neurons to be muscle motor neurons and/or ascending interneurons. Furthermore, we explain why we do not use the term "Dogiel type I neurons" for this population.

White matter signal abnormality (WMSA) is often present in the MRIs of older persons with mobility impairment. We examined the relationship between impaired mobility and the progressive accrual of WMSA. Mobility was assessed with the Short Physical Performance Battery (SPPB) and quantitative measures of gait and balance. Fourteen subjects had baseline and follow-up MRI scans performed 20 months apart. WMSA was detected and quantified using automated computer algorithms. In the control subjects, WMSA volume increased by 0.02+/-0.05% ICCV (percent intracranial cavity volume)/year while the WMSA of mobility impaired subjects increased five-times faster (0.10+/-0.10 ICCV/year, p=0.03). WMSA volume was related to some of the mobility measures and was sensitive to change which was not true of the other MRI variables. The study demonstrates the sensitivity of longitudinal automated volumetric analysis of WMSA to differentiate differences in the accrual rate of WMSA in groups selected on the basis of mobility. Based on these results, we propose that a subset of subjects with mobility impairment have accelerated, disease related WMSA accrual, thus explaining the rapid progression of mobility impairment in some older persons without apparent cause. This study demonstrates that quantitative MRI and performance measures can provide valuable insight into the rate of progression and pathophysiologic abnormalities underlying mobility impairment.

OBJECTIVE: Cognitive impairments and academic failure are commonly reported in survivors of preterm birth. Recent studies suggest an important role for the cerebellum in the development of cognitive and social functions. The objective of this study was to examine the impact of prematurity itself, as well as prematurity-related brain injuries, on early postnatal cerebellar growth with quantitative MRI. METHODS: Advanced 3-dimensional volumetric MRI was performed and cerebellar volumes were obtained by manual outlining in preterm (

OBJECTIVE: The purpose of this study was to investigate abnormalities in the surface complexity of the prefrontal cortex and in the hemispheric asymmetry of cortical complexity in first-episode patients with schizophrenia.

During neurosurgical procedures the objective of the neurosurgeon is to achieve the resection of as much diseased tissue as possible while achieving the preservation of healthy brain tissue. The restricted capacity of the conventional operating room to enable the surgeon to visualize critical healthy brain structures and tumor margin has lead, over the past decade, to the development of sophisticated intraoperative imaging techniques to enhance visualization. However, both rigid motion due to patient placement and nonrigid deformations occurring as a consequence of the surgical intervention disrupt the correspondence between preoperative data used to plan surgery and the intraoperative configuration of the patient’s brain. Similar challenges are faced in other interventional therapies, such as in cryoablation of the liver, or biopsy of the prostate. We have developed algorithms to model the motion of key anatomical structures and system implementations that enable us to estimate the deformation of the critical anatomy from sequences of volumetric images and to prepare updated fused visualizations of preoperative and intraoperative images at a rate compatible with surgical decision making. This paper reviews the experience at Brigham and Women’s Hospital through the process of developing and applying novel algorithms for capturing intraoperative deformations in support of image guided therapy.

We present a computational framework for image-based analysis and interpretation of statistical differences in anatomical shape between populations. Applications of such analysis include understanding developmental and anatomical aspects of disorders when comparing patients versus normal controls, studying morphological changes caused by aging, or even differences in normal anatomy, for example, differences between genders. Once a quantitative description of organ shape is extracted from input images, the problem of identifying differences between the two groups can be reduced to one of the classical questions in machine learning of constructing a classifier function for assigning new examples to one of the two groups while making as few misclassifications as possible. The resulting classifier must be interpreted in terms of shape differences between the two groups back in the image domain. We demonstrate a novel approach to such interpretation that allows us to argue about the identified shape differences in anatomically meaningful terms of organ deformation. Given a classifier function in the feature space, we derive a deformation that corresponds to the differences between the two classes while ignoring shape variability within each class. Based on this approach, we present a system for statistical shape analysis using distance transforms for shape representation and the support vector machines learning algorithm for the optimal classifier estimation and demonstrate it on artificially generated data sets, as well as real medical studies.