Deep Learning of Resting-state Electroencephalogram Signals for 3-class Classification of Alzheimer’s Disease, Mild Cognitive Impairment and Healthy Ageing

Objective. This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairment (MCI) subjects and healthy ageing (HA) subjects using resting-state scalp electroencephalogram (EEG) signals. Approach. The raw EEG data were pre-processed to remove unwanted artefacts and sources of noise. The data were then processed with the continuous wavelet transform, using the Morse mother wavelet, to create time-frequency graphs with a wavelet coefficient scale range of 0-600. The graphs were combined into tiled topographical maps governed by the 10-20 system orientation for scalp electrodes. The application of this processing pipeline was used on a data set of resting-state EEG samples from age-matched groups of 52 AD subjects (82.3 ± 4.7 years of age), 37 MCI subjects (78.4 ± 5.1 years of age) and 52 HA subjects (79.6 ± 6.0 years of age). This resulted in the formation of a data set of 16197 topographical images. This image data set was then split into training, validation and test images and used as input to an AlexNet DL model. This model was comprised of five hidden convolutional layers and optimised for various parameters such as learning rate, learning rate schedule, optimiser, and batch size. Main results. The performance was assessed by a tenfold cross-validation strategy, which produced an average accuracy result of 98.9 ± 0.4% for the three-class classification of AD vs MCI vs HA. The results showed minimal overfitting and bias between classes, further indicating the strength of the model produced. Significance. These results provide significant improvement for this classification task compared to previous studies in this field and suggest that DL could contribute to the diagnosis of AD from EEG recordings

Relação entre doenças sistêmicas e manifestações periodontais: um enfoque em grupos de risco da COVID-19 / Relationship between systemic diseases and periodontal manifestations: a focus on COVID-19 risk groups

Objetivo: avaliar na literatura a relação entre algumas condições sistêmicas e a doença periodontal, enfocando em alguns grupos de risco à infecção pelo novo coronavírus. Métodos: utilizou-se artigos publicados entre os anos de 2010 e 2020 nos bancos de dados online PubMed (National Libary of Medicine) e Science Direct, usando descritores e termos Mesh organizados em lógica booleana: “periodontal diseases” OR “Periodontitis” OR “ Gingivitis” em associação com "Asthma”, "Diabetes mellitus", “Renal insufficiency chronic” e "Heart diseases”. Resultados: Mediante a análise dos estudos elegíveis, observou-se que doenças cardiovasculares, diabetes mellitus, doença renal crônica e asma estão relacionadas à doença periodontal, podendo esta, agravar as condições supracitadas. Uma vez que os pacientes acometidos por essas condições sistêmicas estão incluídos no grupo de risco à contaminação pelo SARS-CoV-2, é substancial que o cirurgião-dentista juntamente com uma equipe multiprofissional tenha conhecimento acerca disso para que medidas que evitem possíveis agravamentos no quadro respiratório sejam adotadas

Effect of high frequency Transcranial Magnetic Stimulation on sensory and motor function of individuals with incomplete Spinal Cord Injury

A Lesão Medular incompleta (LMi) é uma condição gerada por processos lesionais que afetam parcialmente a integridade da medula espinhal, ocasionando comprometimento na função sensório-motora devido ao declínio do funcionamento das vias medulares. Tal comprometimento impacta diretamente em aspectos físicos, psicológicos e sociais, com consequente redução da qualidade de vida e da independência funcional. Dessa forma, uma reabilitação efetiva requer a redução dos danos ocasionados ela LMi e, portanto, depende de técnicas capazes de favorecer a neuroplasticidade dos circuitos medulares remanescentes. A Estimulação Magnética Transcraniana repetitiva (EMTr) de alta frequência é uma técnica capaz de induzir aumento na excitabilidade do córtex motor primário, trato córtico-espinhal e medula espinhal, facilitando o desenvolvimento da conectividade responsável pela melhora sensório-motora e funcional. Objetivou-se avaliar os efeitos da EMTr de alta frequência aplicada sobre a área dos membros inferiores em M1 na função sensório-motora e nos níveis de espasticidade em indivíduos com LMi crônica. Esse estudo duplo-cego, placebo controlado avaliou quinze indivíduos com LMi crônica (35.3 ± 7.9 anos, média ± desvio padrão) incluídos sequencialmente em cinco sessões de EMTr placebo e cinco sessões de EMTr ativa à 5Hz, separadas por um período de repouso de uma semana. Avaliações clínicas foram feitas antes e depois de da EMTr placebo e ativa. Foram observadas mudanças estatisticamente significativas nos escores motores do International Standards for Neurological Classification of Spinal Cord Injury Patients/Padrões Internacionais para Classificação Neurológica de Pacientes com Lesão Medular (ISNCSCI) (T(1, 14) = 5.359, P < 0.001), as quais foram acompanhadas de tamanhos de efeito clinicamente significativos. A sensibilidade superficial avaliada pelo ISNCSCI também apresentou mudanças estatisticamente significativas nos escores após EMTr ativa (T(1, 14) = 2.223, P < 0.043). Não foram observadas mudanças nos níveis de espasticidade. Nenhum participante relatou efeitos adversos graves, com exceção de dor de cabeça transitória após algumas sessões. O presente estudo encontrou mudanças estatísticas e clinicas consistentes na função sensório-motora em indivíduos com LMi crônica após EMTr ativa. Dessa forma, essa técnica pode ser uma forma efetiva de reabilitação em indivíduos com LMiIncomplete Spinal Cord Injury (iSCI) is a condition generated by lesional processes that partially affect the integrity of the spinal cord, causing impairment in the sensorimotor function due to the decline in the functioning of the spinal cord. Such impairment directly impacts on physical, psychological and social aspects, with consequent reduction of quality of life and functional independence. Thus, effective rehabilitation requires the reduction of the damage caused by iSCI and, therefore, depends on techniques capable of favoring the neuroplasticity of the remaining medullary circuits. High frequency repetitive transcranial magnetic stimulation (rTMS) is a technique capable of inducing increased excitability of the primary motor cortex, corticospinal tract and spinal cord, facilitating the development of connectivity responsible for sensorimotor and functional improvement . The objective of this study was to evaluate the effects of high frequency applied rTMS on the lower limbs area in M1 on sensorimotor function and on spasticity levels in individuals with chronic iSCI. This double-blind, placebo-controlled study evaluated fifteen subjects with chronic iSCI (35.3 ± 7.9 years, mean ± standard deviation) included sequentially in five placebo rTMS sessions and five sessions of active rTMS at 5Hz separated by a washout period of one week. Clinical evaluations were done before and after the placebo and active rTMS. Statistically significant changes in the International Standards for Neurological Classification of Spinal Cord Injury Patients (ISNCSCI) motor scores (T (1, 14) = 5,359, P <0.001) were observed, which were accompanied by clinically significant effect sizes. The superficial sensitivity assessed by the ISNCSCI also showed statistically significant changes in the scores after active rTMS (T (1,14) = 2,223, P <0.043). No changes in spasticity were observed. No participant reported severe adverse events, except for transient headache after a few sessions. The present study found consistent statistical and clinical changes in sensorimotor function in individuals with chronic iSCI after active rTMS. Thus, this technique can be an effective form of rehabilitation in individuals with iSC

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer's Disease and Mild Cognitive Impairment

Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs

A novel deep learning approach using AlexNet for the classification of electroencephalograms in Alzheimer’s Disease and Mild Cognitive Impairmen

—Alzheimer's Disease (AD) is the most common form of dementia. Mild Cognitive Impairment (MCI) is the term given to the stage describing prodromal AD and represents a 'risk factor' in early-stage AD diagnosis from normal cognitive decline due to ageing. The electroencephalogram (EEG) has been studied extensively for AD characterization, but reliable early-stage diagnosis continues to present a challenge. The aim of this study was to introduce a novel way of classifying between AD patients, MCI subjects, and age-matched healthy control (HC) subjects using EEG-derived feature images and deep learning techniques. The EEG recordings of 141 age-matched subjects (52 AD, 37 MCI, 52 HC) were converted into 2D greyscale images representing the Pearson correlation coefficients and the distance Lempel-Ziv Complexity (dLZC) between the 21 EEG channels. Each feature type was computed from EEG epochs of 1s, 2s, 5s and 10s segmented from the original recording. The CNN architecture AlexNet was modified and employed for this three-way classification task and a 70/30 split was used for training and validation with each of the different epoch lengths and EEG-derived images. Whilst a maximum classification accuracy of 73.49% was obtained using dLZC-derived images from 10s epochs as input to the model, the classification accuracy reached 98.13% using the images obtained from Pearson correlation coefficients and 5s epochs. Clinical Relevance— The preliminary findings from this study show that deep learning applied to the analysis of the EEG can classify subjects with accuracies close to 100%

Effects of high-frequency transcranial magnetic stimulation on functional performance in individuals with incomplete spinal cord injury: study protocol for a randomized controlled trial

Background: Repetitive transcranial magnetic stimulation (rTMS) has been investigated as a new tool in neurological rehabilitation of individuals with spinal cord injury (SCI). However, due to the inconsistent results regarding the effects of rTMS in people with SCI, a randomized controlled double-blind crossover trial is needed to clarify the clinical utility and to assess the effect size of rTMS intervention in this population. Therefore, this paper describes a study protocol designed to investigate whether the use of rTMS can improve the motor and sensory function, as well as reduce spasticity in patients with incomplete SCI. Methods: A double-blind randomized sham-controlled crossover trial will be performed by enrolling 20 individuals with incomplete SCI. Patients who are at least six months post incomplete SCI (aged 18–60 years) will be recruited through referral by medical practitioners or therapists. Individuals will be randomly assigned to either group 1 or group 2 in a 1:1 ratio, with ten individuals in each group. The rTMS protocol will include ten sessions of high-frequency rTMS (5 Hz) over the bilateral lower-limb motor area positioned at the vertex (Cz). Clinical evaluations will be performed at baseline and after rTMS active and sham. Discussion: rTMS has produced positive results in treating individuals with physical impairments; thus, it might be promising in the SCI population. The results of this study may provide new insights to motor rehabilitation thereby contributing towards the better usage of rTMS in the SCI population. Trial registration: ClinicalTrials.gov, NCT02899637 . Registered on 25 August 2016.Medicine, Faculty ofNon UBCReviewedFacult