Cerebrospinal fluid leak during stapes surgery: Gushing leaks and oozing leaks, two different phenomena.

Cerebrospinal fluid (CSF) leak is an uncommon event that can occur during stapes surgery. Such leaks can be classified as gushing leaks (stapes gushers) and oozing leaks. A stapes gusher is a massive flow of CSF through the perforated footplate that fills the middle ear suddenly, while an oozing leak is a slower and less profuse flow. We conducted a retrospective, observational, multicenter study of 38 patients—23 men and 15 women, aged 23 to 71 years (mean: 47)—who had experienced a CSF leak during stapes surgery. Patients were divided into various groups according to the type of surgical procedure performed and the type of postoperative complications they experienced. Audiometric and clinical evaluations were carried out pre- and postoperatively. Correlations among surgical variations (total or partial stapedectomy, placement of a prosthesis), hearing outcomes, and the incidence of postoperative complications (postoperative CSF leak and vertigo) were studied. Our statistical analysis revealed that gushing leaks and oozing leaks result in different degrees of hearing impairment and different rates of complications. We recommend that an individual approach be used to manage these complications

Chronic pain and spinal cord stimulation.

Chronic pain can have a devastating impact and lead to patient isolation. Many people with chronic pain are predisposed to anxiety-depressant symptoms, due to a lower quality life. The aim of the study is to demonstrate how neuromodulation methods, can encourage the reduction of chronic pain and an improvement in the quality of life, therefore advancing the restoration of psychological well-being.We involved 50 patients with a diagnosis of pain that not respond to traditional pharmacological therapies. Interventions: All subject had depression and anxiety symptoms and a low-quality life. We used the spinal cord stimulation treatment and a psychological evaluation for assessment of depression-anxiety symptomatology and the level of quality life.We observed a significant difference in physical functioning, role limitations due to physical health, general health perceptions, vitality, social functioning, role limitations due to emotional problems and mental health.Our study affirms that the perception of chronic pain has a great impact on the perception of psychological well-being, quality of life, and the performance of normal daily social and professional activities

Impaired Cerebral Haemodynamics in Vascular Depression: Insights From Transcranial Doppler Ultrasonography

Introduction: Late-life depression is a well-known risk factor for future dementia. Increasing evidences also show a link between cerebral hypoperfusion and neurodegeneration, although data on Transcranial Doppler ultrasonography (TCD)-derived measures in patients with “Vascular Depression” (VD) are lacking. The aim of this study was to assess and correlate TCD parameters with cognitive function and severity of subcortical ischemic vascular disease in a sample of VD patients.Methods: Seventy six patients (mean age 72.5 ± 5.3 years; 53.9% females) met the DSM-5 diagnostic criteria for unipolar major depression. Mean blood flow velocity (MBFv) and pulsatility index (PI) were recorded from the middle cerebral artery. Quantification of depressive symptoms (17-item Hamilton Depression Rating Scale –HDRS), neuropsychological test evaluating frontal lobe abilities (Stroop Color-Word test interference—Stroop T), and white matter lesions (WMLs) load according to the Fazekas visual score were also assessed.Results: WMLs severity was mild in 20 patients (group I), moderate in 32 (group II), and severe in 24 (group III). The groups were comparable in terms of clinical features, vascular risk factors profile, and HDRS score, whereas Stroop T score was worse in group III. An increased PI and a reduced MBFv were found in VD patients with severe WMLs. According to the regression analysis, a reduced MBFv was independently and significantly associated with depressive symptoms and executive dysfunction, even after adjusting for demographic features and vascular risk factors. Similarly, an independent and significant association was observed between the increase of PI and both Stroop T and WMLs severity.Conclusions: A TCD profile of low perfusion and high vascular resistance in VD patients suggests a diffuse cerebrovascular pathology likely arising from the small vessels and then extending to larger arteries. Hemodynamic dysfunction might play a pathogenic role in the development of cognitive impairment in patients with late-life depression and subcortical ischemic vascular disease. TCD represents a valuable tool in the early detection, assessment, and management of VD patients at risk for dementia

Functional evaluation of awareness in vegetative and minimally conscious state

Objective: The aim of this study was to assess differences in brain activation in a large sample of Vegetative State (VS) and Minimally Conscious State (MCS) patients, using functional magnetic resonance imaging (fMRI). Methods: We studied 50 patients four to seven months after brain injury. By using international clinical criteria and validated behavioural scales such as the Glasgow Coma Scale and the Clinical Unawareness Assessment Scale, the patients were grouped into VS (n=23) and MCS (n=27). All patients underwent to fMRI examination. After 6 months, the patients were reassessed using Glasgow Outcome Scale and Revised Coma Recovery Scale. Results: fMRI showed significant (p<0.01, cluster-corrected) brain activation in the primary auditory cortex bilaterally during the acoustic stimuli in patients with both VS and MCS. However, ten patients clinically classified as VS, showed a pattern of brain activation very similar to that of MCS patients. Six months later, these ten VS patients had significant clinical improvement, evolving into MCS, whereas the other VS patients and patients with MCS remained clinically stable. Conclusion: Brain activity could help in discerning whether the status of wakefulness in VS is also accompanied by partial awareness, as occurs in MCS. This may have very important prognostic implications

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Functional connectivity in disorders of consciousness: methodological aspects and clinical relevance

This is a Quick Guide about the role of the functional connectivity in the Disorders of Consciousness (DOC). Recent studies on resting state (RS) in DOC, by using functional magnetic resonance imaging (fMRI), showed that functional connectivity is severely impaired above all in the default mode network (DMN). In the vegetative and minimally conscious state, DMN integrity seems to correlate with the level of remaining consciousness, offering the possibility of using this information for diagnostic and prognostic purposes. Although the two principal approaches used in the RS analysis showed several methodological difficulties, especially in DOC patients, functional brain imaging is currently being validated as a valuable addition to the standardized clinical assessments that are already in use

Costs and Services Optimization in Neurological Telemedicine: GIS and Spatial Analysis

Telemedicine represents a means for providing healthcare and communicating healthcare information over often vast distances through the use of telecommunication technology. It is an integrated, biomedical and psychosocial, secondary prevention intervention, where a specialized team guides the patient and his family in the course of the disease by reducing the stress of caregivers and allowing patients to stay longer at home, reducing, consequently, costs related to go to the specialist clinic. Teleneurology is a part of Telemedicine which uses modern communication technologies to allow neurology to be practiced when the doctor and patient are not present in the same place, and possibly not at the same time. This study focuses on identifying regional spots as potential territorial stations for the telemedicine service through the GIS (Geographical Information System), a computerized systems that allows placement of data of different types and sources into one system to manage and form spatial relationships that allow the display and analysis of relationships of geographic, environmental, and population factors (Scholten & de Lepper, 1991). We analyzed the optimal location of territorial telemedicine spots in correspondence of general practitioner clinics. It could be intended as a way to involve general practitioners in the management of those patients. The aim of this study is to improve the quality of life of patients and their caregivers providing a continuity of care through this system that reduces the inconvenience of travel and related costs. We estimated, then, the reduction of costs for patients and care-givers joining the telemedicine service in terms of hours out of work

Wavelet algorithm for Magnetic Resonance Imaging

Compressing an image, namely reducing the amount of information needed to display the image itself, is a major issue in the development of applications, such as multimedia. Therefore the study of image compression approaches is of fundamental importance. In the present study, we developed a novel algorithm which is able to compress and to reconstruct an image by applying a wavelet transformation without compromising the diagnostic quality of the image itself. We showed that, according to the employed wavelet (Daubachies, Coiflet e Biorthogonal), the wavelet providing the best compression belongs to the third order of the db2 family, as indicated by the Compression Ratio (CR) is the 95%

Computer-Aided Diagnosis System for the Identification Carotid Atherosclerosis

Carotid atherosclerosis represents one of the most important cause of brain stroke. The degree of stenosis is up to now considered one of the most important features for determining the risk of brain stroke. Ultrasound (US) is a non-invasive technique, relatively inexpensive, portable, which has an excellent temporal resolution. Computer-Aided Diagnosis (CAD) has became one of the major research field in medical imaging and diagnostic radiology. This work presents a CAD system, an automatic prototype software used for carotid plaque detection. We studied US images of 44 subjects, 22 subjects with carotid artery stenosis and 22 subjects without carotid artery stenosis. The CAD system automatically performed the following processing steps: registration of images, detection of lesions by a segmentation method using Watershed algorithm, extraction of dynamic and morphologic features, and classification of plaques through Cluster Analysis. Investigations has been carried out on 287 suspect regions including 61 plaques and 226 non-plaques cases. The area under the Receiver Operationg Characteristic (ROC) curve was 0.89. The experimental results also show that the proposed methods is feasible, and have a good agreement with the expert neurologist. Without the need of any user-interaction, the proposed method generates a detection out-put that may be useful in second opinion

Watershed Segmentation and Cluster Analysis in neurological image

Image segmentation plays a crucial role in medical imaging by facilitating the delineation of regions of interest. The ultimate aim of an automatic image segmentation system is to mimic the human visual system in order to provide a meaningful image subdivision. The watershed transform is a well established tool for the segmentation of images. We have considered Magnetic Resonance Images (MRI) of four Multiple Sclerosis (MS) patients provided by IRCCS Centro Neurolesi "Bonino-Pulejo" of Messina in their original format and tested on them the watershed algorithm implemented using MATLAB 7.6. In this paper we propose an algorithm that use Watershed variant encapsulating Cluster Analysis, then region merging and edge detection procedures were used. This method uses an analysis of variance approach to evaluate the distances between clusters to identify the lesion and to support clinicians in the diagnosis of MS. The algorithm is able to segment or extract desired parts of only gray-scale images and is applied the Cluster Analysis for solved the problem of undesirable oversegmentation results produced by the watershed technique. From our results, we have seen that several analyzed regions have similar characteristics to be grouped together in same class. In particular, we saw that at a distance equal to the level of 0.084, you can find the MS regions. Then the set of parameters considered provides a good description of the regions selected by watershed and then through the cluster analysis allows the distinction between normal and suspect regions