Electroencephalography-based machine learning for cognitive profiling in Parkinson's disease:Preliminary results

Background Cognitive symptoms are common in patients with Parkinson's disease. Characterization of a patient's cognitive profile is an essential step toward the identification of predictors of cognitive worsening. Objective The aim of this study was to investigate the use of the combination of resting-state EEG and data-mining techniques to build characterization models. Methods Dense EEG data from 118 patients with Parkinson's disease, classified into 5 different groups according to the severity of their cognitive impairments, were considered. Spectral power analysis within 7 frequency bands was performed on the EEG signals. The obtained quantitative EEG features of 100 patients were mined using 2 machine-learning algorithms to build and train characterization models, namely, support vector machines and k-nearest neighbors models. The models were then blindly tested on data from 18 patients. Results The overall classification accuracies were 84% and 88% for the support vector machines and k-nearest algorithms, respectively. The worst classifications were observed for patients from groups with small sample sizes, corresponding to patients with the severe cognitive deficits. Whereas for the remaining groups for whom an accurate diagnosis was required to plan the future healthcare, the classification was very accurate. Conclusion These results suggest that EEG features computed from a daily clinical practice exploration modality in-that it is nonexpensive, available anywhere, and requires minimal cooperation from the patient-can be used as a screening method to identify the severity of cognitive impairment in patients with Parkinson's disease. (c) 2018 International Parkinson and Movement Disorder Society</p

Optimizing the use of expert panel reference diagnoses in diagnostic studies of multidimensional syndromes

__Abstract__ Background: In the absence of a gold standard, a panel of experts can be invited to assign a reference diagnosis for use in research. Available literature offers limited guidance on assembling and working with an expert panel for this purpose. We aimed to develop a protocol for an expert panel consensus diagnosis and evaluated its applicability in a pilot project. Methods: An adjusted Delphi method was used, which started with the assessment of clinical vignettes by 3 experts individually, followed by a consensus discussion meeting to solve diagnostic discrepancies. A panel facilitator ensured that all experts were able to express their views, and encouraged the use of argumentation to arrive at a specific diagnosis, until consensus was reached by all experts. Eleven vignettes of patients suspected of having a primary neurodegenerative disease were presented to the experts. Clinical information was provided stepwise and included medical history, neurological, physical and cognitive function, brain MRI scan, and follow-up assessments over 2 years. After the consensus discussion meeting, the procedure was evaluated by the experts. Results: The average degree of consensus for the reference diagnosis increased from 52% after individual assessment of the vignettes to 94% after the consensus discussion meeting. Average confidence in the diagnosis after individual assessment was 85%. This did not increase after the consensus discussion meeting. The process evaluation led to several recommendations for improvement of the protocol. Conclusion: A protocol for attaining a reference diagnosis based on expert panel consensus was shown feasible in research practice

Description, Host-specificity, and Strain Selectivity of the Dinoflagellate Parasite Parvilucifera sinerae sp.nov. (Perkinsozoa)

17 pages, 7 figures, 2 tablesA new species of parasite, Parvilucifera sinerae sp. nov., isolated froma bloomof the toxic Dinoflagellate Alexandrium minutum in the harbor of Arenys de Mar (Mediterranean Sea, Spain), is described. This species is morphologically, behaviourally, and genetically (18S rDNA sequence) different from Parvilucifera infectans, until now the only species of the genus Parvilucifera to be genetically analyzed. Sequence análisis of the 18S ribosomal DNA supported P. Sinerae as a new species placed within the Perkinsozoa and close to P. infectans. Data on the seasonal occurrence of P. sinerae, its infective rates in natural and laboratory cultures, and intra-species strain-specific Resistance are presented. Life-cycle studies in field simples showed that the dinoflagellate resting zygote (restingcyst) was resistant to infection, but the mobile zygote (planozygote) orpelli clestage (temporary cyst) became infected. The effects of Light and salinity level son the growth of P. sinerae were examined, and the results showed that low salinity levels promote both sporangial germination and higher rates of infection. Our findings on this newly described parasite point to a complex host—parasite interaction and provide valuable information that leads to a reconsideration of the biological strategy to control dinoflagellate blooms by jeans of intentional parasitic infectionsThis research was funded by the EU Project SEED (GOCE-CT-2005-003875). R.I. Figueroa work is supported by a I3P contract and E. Garcés’ work is supported by a Ramon y Cajal grant, both from the Spanish Ministry of Education and SciencePeer reviewe

Surgical and Hardware-Related Adverse Events of Deep Brain Stimulation:A Ten-Year Single-Center Experience

INTRODUCTION: Although deep brain stimulation (DBS) is effective for treating a number of neurological and psychiatric indications, surgical and hardware-related adverse events (AEs) can occur that affect quality of life. This study aimed to give an overview of the nature and frequency of those AEs in our center and to describe the way they were managed. Furthermore, an attempt was made at identifying possible risk factors for AEs to inform possible future preventive measures. MATERIALS AND METHODS: Patients undergoing DBS-related procedures between January 2011 and July 2020 were retrospectively analyzed to inventory AEs. The mean follow-up time was 43 ± 31 months. Univariate logistic regression analysis was used to assess the predictive value of selected demographic and clinical variables. RESULTS: From January 2011 to July 2020, 508 DBS-related procedures were performed including 201 implantations of brain electrodes in 200 patients and 307 implantable pulse generator (IPG) replacements in 142 patients. Surgical or hardware-related AEs following initial implantation affected 40 of 200 patients (20%) and resolved without permanent sequelae in all instances. The most frequent AEs were surgical site infections (SSIs) (9.95%, 20/201) and wire tethering (2.49%, 5/201), followed by hardware failure (1.99%, 4/201), skin erosion (1.0%, 2/201), pain (0.5%, 1/201), lead migration (0.52%, 2/386 electrode sites), and hematoma (0.52%, 2/386 electrode sites). The overall rate of AEs for IPG replacement was 5.6% (17/305). No surgical, ie, staged or nonstaged, electrode fixation, or patient-related risk factors were identified for SSI or wire tethering. CONCLUSIONS: Major AEs including intracranial surgery-related AEs or AEs requiring surgical removal or revision of hardware are rare. In particular, aggressive treatment is required in SSIs involving multiple sites or when Staphylococcus aureus is identified. For future benchmarking, the development of a uniform reporting system for surgical and hardware-related AEs in DBS surgery would be useful

Will the biopsychosocial model of medicine survive in the age of artificial intelligence and machine learning?

Background: the biomedical model of medicine was replaced by the biopsychosocial model in order to better accommodate psychological and social aspects of illness. The introduction of machine learning techniques provides the perspective of truly personalized medicine. This poses new challenges to our medical model. Aim: to explore the implications of personalized medicine for the biopsychosocial model. Methods: scholarly reflection. Results: The ability of machine learning technology to integrate a wide diversity of data makes it possible to develop predictive models for presentation, course and treatment response in individual patients. Such models are based on individual risk factors and protective factors that may have diverging influences in different individuals. In a medical model adjusted to accommodate the possibilities of personalized medicine, it should be possible to highlight the importance and impact of each single factor in each individual patient. At present, the biopsychosocial model is not well prepared for this. When adopting machine learning technology in clinical practice, new skills and expertise will be required from physicians. They should be able to weigh and explain algorithms supported decisions to their patients. Moreover, new research should be designed in such a way that data will be suited for machine learning and can be integrated with existing databases in order to increase their size and scope. Conclusion: Currently, the biopsychosocial model is not well prepared to accommodate the possibilities of personalized medicine. Adaptations are needed to deal with the highly individual aspects of the patient's disease