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.
: 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.
: 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.
: 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 (
-level < 0.01).
: 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.
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.
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.
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).
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.
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.
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.
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.
We screened three PFBC Italian families for mutations in the SLC20A2, PDGFRB and PDGFB genes.
Phenotypic data were obtained by neurologic examination, CT scan and magnetic resonance imaging. Mutation screening of SLC20A2, PDGFRB and PDGFB was performed by sequencing.
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.
Our data confirm that mutations in SLC20A2 are a major cause of familial idiopathic basal ganglia calcification.
•We screened three PFBC Italian families for mutations in the SLC20A2, PDGFRB and PDGFB genes.•Screening for SLC20A2 gene detected a novel deletion in heterozygous state c.21_21delG.•It consists in a novel frameshift variant p.Leu7Phefs*10 identified in one of these families.•No mutations were detected in the other two families.•Our data confirm that mutations in SLC20A2 are a major cause of IBGC.
•TRACULA is a sensitive tool for detecting white matter changes in ALS.•TRACULA reveals white matter abnormalities in the corticospinal and cingulum tracts.•TRACULA has the potential to be translated ...in clinical practice.
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.
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.
TRACULA showed specific significant alterations of several DTI parameters in the corticospinal tract of the ALS group with respect to controls.
The same finding was detected using the well-known TBSS analysis. Similarly, both methods depicted also additional microstructural changes in the cingulum.
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.
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