There are limited data on the relationship between normal cerebrospinal fluid (CSF) opening pressure and bilateral transverse sinus stenosis (BTSS); there are also several conflicting reports about the upper limit of normal CSF opening pressure. To evaluate the influence of BTSS on the upper limit of normal CSF opening pressure, we prospectively recorded lumbar CSF opening pressures in 217 adult patients with neurological symptoms who underwent cerebral magnetic resonance venography (MRV). The CSF opening pressures ranged between 65 and 286 mmH(2)O (mean = 149.3, s.d. = 47.5). The upper limit of opening pressure in patients with both normal appearance of transverse sinuses and unilateral transverse sinus stenosis on MRV (n = 167) was 195 mmH(2)O with a range of 65-195 mmH(2)O. All patients with BTSS were headache sufferers, and the upper limit of opening pressure in patients with BTSS (n = 50) was 286 mmH(2)O with a range of 91-286 mmH(2)O. All patients with opening pressures > 200 mmH(2)O displayed BTSS, whereas only 13% of patients with a pressure < 200 mmH(2)O displayed BTSS. Our findings demonstrate that the upper limit of normal CSF opening pressure is related to BTSS, and they also highlight that headache sufferers with opening pressures > 200 mmH(2)O should be tested for BTSS by MRV.

Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.

In this study, we used an automated segmentation of regions of interest and co-registration to diffusion tensor imaging (DTI) images to investigate whether microstructural abnormalities occur in gray structures of the frontal-subcortical circuits in patients with amyotrophic lateral sclerosis (ALS). Twenty-four patients with probable or definite sporadic ALS and 22 healthy controls were enrolled in the study. Thirteen out of 24 ALS patients and all of the control subjects underwent a detailed neuropsychological evaluation. DTI was performed to measure mean diffusivity (MD) and fractional anisotropy in the frontal cortex, caudate, putamen, globus pallidus, thalamus, amygdala and hippocampus. MD values of ALS patients were significantly higher in the frontal cortex (P = 0.023), caudate (P = 0.01), thalamus (P = 0.019), amygdala (P = 0.012) and hippocampus (P = 0.002) compared to controls. MD of these structures significantly correlated to a variable degree with neurological disability and neuropsychological dysfunctions. The increased MD values in several cortical and subcortical gray structures and their correlations with neuropsychological variables substantiate a multisystemic degeneration in ALS and suggest that dysfunctions of frontal-subcortical circuits could play a pivotal role in frontal impairment and behavioral symptoms in ALS patients.

Introduction The presence of cognitive dysfunctions in Multiple Sclerosis (MS) has been well described as one of the most common co-morbidities [1-3], with a prevalence ranging from 45% to 65% [4]. The most affected cognitive domains in MS are memory, visuospatial perception, executive functions, attention and information processing speed [5]. Although the cerebellum has been traditionally associated with motor control, recently a growing body of clinical and experimental evidences has suggested that it may be also involved in non-motor functions [6-8]. In fact, it has been shown that cerebellar abnormalities, e.g. lesions, are associated with cognitive impairment as measured by neuropsysiological tests [9]. Thus, evidences suggest that the cerebellum has an important role in monitoring sensory information and providing adaptation of both motor and non-motor functions to perform contextually relevant behaviours [10,11]. The cognitive role of the cerebellum is mainly due to its strong connections with several higher-level cortical regions, such as with the controlateral cerebral hemispheres in both feed forward and feedback directions. The feed forward loop (Figure 1 in blue) connects cortical areas via middle cerebellar peduncle (MCP) with deep cerebellar nuclei, including the dentate nucleus (DN) (afferent pathway). The feedback loop (Figure 1 in orange) connects the deep cerebellar nuclei with motor cortex, via the superior cerebellar peduncle (SCP), the red nucleus and the thalamus (Th) (efferent pathway) [12,13]. Disconnections of the fibers to the thalamus, negatively involve the limbic circuitry avoiding the mediation and regulation of many cognitive functions [14,15]. Although many studies have shown a strong association between thalamic atrophy and cognitive function [16-18], no one focused on its microstructural changes, as well as on abnormalities of MCP, SCP and DN, and on their relationships to cognitive functions in MS. Diffusion tensor imaging (DTI) is one of the most sensitive methods for detecting subtle alterations of white matter [19] (WM) by evaluating the directional movement of water molecules in the brain. Analysis of DTI images could indeed distinguish between regions where fibers present a strong alignment from those with a lower coherence. Diffusion properties are summarized by several indices, in particular by fractional anisotropy (FA) that refers to the degree to which diffusion is direction-dependent. FA is used to quantify the changes in WM microstructure, which might be related to development or progression of a specific disease [20]. The aim of this study was to investigate a group of patients affected by relapsing-remitting multiple sclerosis with (RR-MSc) and without (RR-MSnc) cerebellar signs, in order to determine the alteration of FA in the Th, MCP, SCP and DN and their correlation to cognitive functions, as assessed by neuropsysiological tests. In particular, we examined two hypotheses: Abstract Background: Using Diffusion Tensor Imaging (DTI), we tested the hypothesis that ...

Within the same homogeneous cohort of patients, we could identify three neuroimaging RRMS clusters characterized by different involvement of normal-appearing CC. Interestingly, these corresponded to three distinct levels of clinical and cognitive disability.