Carotid Artery Dissection Induced Acute Tongue Swelling in a Cocaine User.

Carotid artery dissection represents a well-recognized cause of hypoglossal nerve paralysis even if it is less known the cause of acute tongue swelling. We report a 42-year old men who presented to our observation with acute tongue swelling and atrophy of left side of tongue from a hypoglossal nerve injury. A magnetic resonance imaging revealed a denervation of the left half of the tongue from a hypoglossal nerve injury due to left extracranial internal carotid artery (ICA) dissection, without evidence of ischemic stroke. The urine toxicology screen test revealed a positivity for cocaine. This case report suggest to perform in young patient a toxicological drug screening test in presence of ICA dissection with hypoglossal nerve injury and an acute tongue swelling. However clinical data must be performed to validate this observation and to analyze the negative effect of cocaine use.

Exoskeleton-Robot Assisted Therapy in Stroke Patients: A Lesion Mapping Study.

Background: Technology-supported rehabilitation is emerging as a solution to support therapists in providing a high-intensity, repetitive and task-specific treatment, aimed at improving stroke recovery. End-effector robotic devices are known to positively affect the recovery of arm functions, however there is a lack of evidence regarding exoskeletons. This paper evaluates the impact of cerebral lesion load on the response to a validated robotic-assisted rehabilitation protocol. Methods: Fourteen hemiparetic patients were assessed in a within-subject design (age 66.9 ± 11.3 years; 10 men and 4 women). Patients, in post-acute phase, underwent 7 weeks of bilateral arm training assisted by an exoskeleton robot combined with a conventional treatment (consisting of simple physical activity together with occupational therapy). Clinical and neuroimaging evaluations were performed immediately before and after rehabilitation treatments. Fugl-Meyer (FM) and Motricity Index (MI) were selected to measure primary outcomes, i.e., motor function and strength. Functional independance measure (FIM) and Barthel Index were selected to measure secondary outcomes, i.e., daily living activities. Voxel-based lesion symptom mapping (VLSM) was used to determine the degree of cerebral lesions associated with motor recovery. Results: Robot-assisted rehabilitation was effective in improving upper limb motor function recovery, considering both primary and secondary outcomes. VLSM detected that lesion load in the superior region of the corona radiata, internal capsule and putamen were significantly associated with recovery of the upper limb as defined by the FM scores (p-level < 0.01). Conclusions: The probability of functional recovery from stroke by means of exoskeleton robotic rehabilitation relies on the integrity of specific subcortical regions involved in the primary motor pathway. This is consistent with previous evidence obtained with conventional neurorehabilitation approaches.

The corticospinal tract profile in amyotrophic lateral sclerosis.

This work evaluates the potential in diagnostic application of a new advanced neuroimaging method, which delineates the profile of tissue properties along the corticospinal tract (CST) in amyotrophic lateral sclerosis (ALS), by means of diffusion tensor imaging (DTI). Twenty-four ALS patients and twenty-four demographically matched healthy subjects were enrolled in this study. The Automated Fiber Quantification (AFQ), a tool for the automatic reconstruction of white matter tract profiles, based on a deterministic tractography algorithm to automatically identify the CST and quantify its diffusion properties, was used. At a group level, the highest non-overlapping DTI-related differences were detected in the cerebral peduncle, posterior limb of the internal capsule, and primary motor cortex. Fractional anisotropy (FA) decrease and mean diffusivity (MD) and radial diffusivity (RD) increases were detected when comparing ALS patients to controls. The machine learning approach used to assess the clinical utility of this DTI tool revealed that, by combining all DTI metrics measured along tract between the cerebral peduncle and the corona radiata, a mean 5-fold cross validation accuracy of 80% was reached in discriminating ALS from controls. Our study provides a useful new neuroimaging tool to characterize ALS-related neurodegenerative processes by means of CST profile. We demonstrated that specific microstructural changes in the upper part of the brainstem might be considered as a valid biomarker. With further validations this method has the potential to be considered a promising step toward the diagnostic utility of DTI measures in ALS. Hum Brain Mapp 38:727-739, 2017. © 2016 Wiley Periodicals, Inc.

Biomarkers of Eating Disorders Using Support Vector Machine Analysis of Structural Neuroimaging Data: Preliminary Results.

Presently, there are no valid biomarkers to identify individuals with eating disorders (ED). The aim of this work was to assess the feasibility of a machine learning method for extracting reliable neuroimaging features allowing individual categorization of patients with ED. Support Vector Machine (SVM) technique, combined with a pattern recognition method, was employed utilizing structural magnetic resonance images. Seventeen females with ED (six with diagnosis of anorexia nervosa and 11 with bulimia nervosa) were compared against 17 body mass index-matched healthy controls (HC). Machine learning allowed individual diagnosis of ED versus HC with an Accuracy ≥ 0.80. Voxel-based pattern recognition analysis demonstrated that voxels influencing the classification Accuracy involved the occipital cortex, the posterior cerebellar lobule, precuneus, sensorimotor/premotor cortices, and the medial prefrontal cortex, all critical regions known to be strongly involved in the pathophysiological mechanisms of ED. Although these findings should be considered preliminary given the small size investigated, SVM analysis highlights the role of well-known brain regions as possible biomarkers to distinguish ED from HC at an individual level, thus encouraging the translational implementation of this new multivariate approach in the clinical practice.

A new SLC20A2 mutation identified in southern Italy family with primary familial brain calcification.

BACKGROUND: Primary familial brain calcification (PFBC) is a rare neurodegenerative disease characterized by bilateral calcifications mostly located in the basal ganglia and in the thalami, cerebellum and subcortical white matter. Clinical manifestations of this disease include a large spectrum of movement disorders and neuropsychiatric disturbances. PFBC is genetically heterogeneous and typically transmitted in an autosomal dominant fashion. Three causative genes have been reported: SLC20A2, PDGFRB and PDGFB. OBJECTIVE: We screened three PFBC Italian families for mutations in the SLC20A2, PDGFRB and PDGFB genes. METHODS: Phenotypic data were obtained by neurologic examination, CT scan and magnetic resonance imaging. Mutation screening of SLC20A2, PDGFRB and PDGFB was performed by sequencing. RESULTS: We identified a new heterozygous deletion c.21_21delG (p.L7Ffs*10) in SLC20A2 gene in one of these families. No mutations were detected in the other two families. CONCLUSIONS: Our data confirm that mutations in SLC20A2 are a major cause of familial idiopathic basal ganglia calcification.

Automatic Detection of White Matter Hyperintensities in Healthy Aging and Pathology Using Magnetic Resonance Imaging: A Review.

White matter hyperintensities (WMH) are commonly seen in the brain of healthy elderly subjects and patients with several neurological and vascular disorders. A truly reliable and fully automated method for quantitative assessment of WMH on magnetic resonance imaging (MRI) has not yet been identified. In this paper, we review and compare the large number of automated approaches proposed for segmentation of WMH in the elderly and in patients with vascular risk factors. We conclude that, in order to avoid artifacts and exclude the several sources of bias that may influence the analysis, an optimal method should comprise a careful preprocessing of the images, be based on multimodal, complementary data, take into account spatial information about the lesions and correct for false positives. All these features should not exclude computational leanness and adaptability to available data.

The relationship between regional microstructural abnormalities of the corpus callosum and physical and cognitive disability in relapsing-remitting multiple sclerosis.

Significant corpus callosum (CC) involvement has been found in relapsing-remitting multiple sclerosis (RRMS), even if conventional magnetic resonance imaging measures have shown poor correlation with clinical disability measures. In this work, we tested the potential of multimodal imaging of the entire CC to explain physical and cognitive disability in 47 patients with RRMS. Values of thickness, fractional anisotropy (FA) and mean diffusivity (MD) were extracted from 50 regions of interest (ROIs) sampled along the bundle. The relationships between clinical, neuropsychological and imaging variables were assessed by using Spearman's correlation. Multiple linear regression analysis was employed in order to identify the relative importance of imaging metrics in modeling different clinical variables. Regional fiber composition of the CC differentially explained the response variables (Expanded Disability Status Scale [EDSS], cognitive impairment). Increases in EDSS were explained by reductions in CC thickness and MD. Cognitive impairment was mainly explained by FA reductions in the genu and splenium. Regional CC imaging properties differentially explained disability within RRMS patients revealing strong, distinct patterns of correlation with clinical and cognitive status of patients affected by this specific clinical phenotype.

Effect of aging on magnetic resonance measures differentiating progressive supranuclear palsy from Parkinson's disease.

Imaging measurements, such as the ratio of the midsagittal areas of the midbrain and pons (midbrain/pons) and the Magnetic Resonance Parkinsonism Index (MRPI), have been proposed to differentiate progressive supranuclear palsy (PSP) from Parkinson's disease (PD). However, abnormal midbrain/pons values suggestive of PSP have also been reported in elderly individuals and in patients with PD. We investigated the effect of aging on single or combined imaging measurements of the brainstem. We calculated the midbrain/pons and the MRPI (the ratio of the midsagittal areas of the pons and the midbrain multiplied by the ratio of the middle cerebellar peduncle and superior cerebellar peduncle widths) in 152 patients affected by PD, 25 patients with PSP, and a group of 81 age-matched and sex-matched healthy controls using a 3-Tesla magnetic resonance imaging scanner. In healthy controls, aging was negatively correlated with midsagittal area of the midbrain and midbrain/pons values. In patients with PD, in addition to the effect of aging, the disease status further influenced the midbrain/pons values (R(2) = 0.23; P < 0.001). In both groups, MRPI values were not influenced either by aging or by disease status. No effect of aging on either midbrain/pons or MRPI values was shown in the patients with PSP. Our findings indicated that the MRPI was not significantly influenced by aging or disease-related changes occurring in PD; whereas, in contrast, the midbrain/pons was influenced. Therefore, the MRPI appears to be a more reliable imaging measurement compared with midbrain/pons values for differentiating PSP from PD and controls in an elderly population.

Fully automated segmentation of the pons and midbrain using human T1 MR brain images.

PURPOSE: This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called labs: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction. METHODS: This method was first tested on morphological T1-weighted MRIs of 30 healthy subjects. Its reliability was further confirmed by including neurological patients (with Alzheimer's Disease) from the ADNI repository, in whom the presence of volumetric loss within the brainstem had been previously described. Segmentation accuracies were evaluated against expert-drawn manual delineation. To evaluate the quality of LABS segmentation we used volumetric, spatial overlap and distance-based metrics. RESULTS: The comparison between the quantitative measurements provided by LABS against manual segmentations revealed excellent results in healthy controls when considering either the midbrain (DICE measures higher that 0.9; Volume ratio around 1 and Hausdorff distance around 3) or the pons (DICE measures around 0.93; Volume ratio ranging 1.024-1.05 and Hausdorff distance around 2). Similar performances were detected for AD patients considering segmentation of the pons (DICE measures higher that 0.93; Volume ratio ranging from 0.97-0.98 and Hausdorff distance ranging 1.07-1.33), while LABS performed lower for the midbrain (DICE measures ranging 0.86-0.88; Volume ratio around 0.95 and Hausdorff distance ranging 1.71-2.15). CONCLUSIONS: Our study represents the first attempt to validate a new fully automated method for in vivo segmentation of two anatomically complex brainstem subregions. We retain that our method might represent a useful tool for future applications in clinical practice.

Tractography in amyotrophic lateral sclerosis using a novel probabilistic tool: a study with tract-based reconstruction compared to voxel-based approach.

BACKGROUND: Diffusion tensor imaging (DTI) is one of the most sensitive MRI tools for detecting subtle cerebral white matter abnormalities in amyotrophic lateral sclerosis (ALS). Nowadays a plethora of DTI tools have been proposed, but very few methods have been translated into clinical practice. NEW METHOD: The aim of this study is to validate the objective measurement of fiber tracts as provided by a new unbiased and automated tractography reconstruction tool named as TRActs Constrained by UnderLying Anatomy (TRACULA). The reliability of this tract-based approach was evaluated on a dataset of 14 patients with definite ALS compared with 14 age/sex-matched healthy controls. To further corroborate these measurements, we used a well-known voxelwise approach, called tract-based spatial statistics (TBSS), on the same dataset. RESULTS: TRACULA showed specific significant alterations of several DTI parameters in the corticospinal tract of the ALS group with respect to controls. COMPARISON WITH EXISTING METHOD: The same finding was detected using the well-known TBSS analysis. Similarly, both methods depicted also additional microstructural changes in the cingulum. CONCLUSIONS: DTI tractography metrics provided by TRACULA perfectly agree with those previously reported in several post-mortem and DTI studies, thus demonstrating the accuracy of this method in characterizing the microstructural changes occurring in ALS. With further validation (i.e. considering the heterogeneity of other clinical phenotypes), this method has the potential to become useful for clinical practice providing objective measurements that might aid radiologists in the interpretation of MR images and improve diagnostic accuracy of ALS.

Cerebellar-parietal dysfunctions in multiple sclerosis patients with cerebellar signs.

Consistent findings have shown that the cerebellum is critically implicated in a broad range of cognitive processes including executive functions. Of note, cerebellar symptoms and a number of cognitive deficits have been widely reported in patients with multiple sclerosis (MS). This study investigated for the first time the role of cerebellar symptoms in modulating the neural networks associated with a cognitive task broadly used in MS patients (Paced Visual Serial Addition Test (PVSAT)). Twelve relapsing-remitting (RR) MS patients with prevalent cerebellar signs and symptoms (RR-MSc), 15 RR-MS patients without cerebellar manifestation (RR-MSnc) and 16 matched-healthy controls were examined during functional magnetic resonance imaging (fMRI). We tested whether the RR-MSc patients displayed abnormal activations within "cognitive" cerebellar regions and other areas typically engaged in working memory and tightly connected with the cerebellum. Despite similar behavioral performances during fMRI, RR-MSc patients displayed, relatively to both RR-MSnc patients and controls, significantly greater responses in the left cerebellar Crus I/Lobule VI. RR-MSc patients also displayed reduced functional connectivity between the left cerebellar Crus I and the right superior parietal lobule (FWE<.05). These results demonstrated that the presence of the cerebellar signs drastically impacts on the neurofunctional networks underlying working memory in MS. The altered communication between the cerebellum and a cortical area implicated in short-term buffering and storage of relevant information, offer new insights into the pathophysiological mechanisms of cognition in MS.