Subjective Cognitive and Communicative Complaints and Health-Related Quality of Life in Parkinson's Disease with and without Mild Cognitive Impairment.

INTRODUCTION: Mild Cognitive Impairment (MCI) is common in Parkinson's Disease (PD). Few studies have compared the Health-Related Quality of Life (HRQoL) in patients with and without MCI due to PD (PD-MCI), and its correlation to patients' subjective cognitive and communicative difficulties has not been explored. OBJECTIVE: We aimed to compare HRQoL in PD-MCI and PD without MCI (PD-nMCI), and explore its possible relationship to subjective cognitive and communicative complaints. METHODS: We included 29 PD-nMCI and 11 PD-MCI patients. The HRQoL was assessed with the Parkinson's Disease Questionnaire-39 (PDQ-39): its Cognition dimension was used as a measure of subjective cognitive complaints, its Communication dimension for subjective communicative complaints, and the summary index (PDQ-39 SI) as an indicator of HRQoL. Non-parametric partial correlations between the Cognition and Communication dimensions, and the adjusted PDQ-39 SI were conducted. RESULTS: PD-MCI patients had greater subjective cognitive and communicative complaints and worse HRQoL than PD-nMCI patients. In the PD-MCI group, both subjective cognitive and communicative complaints exhibited significant direct correlations with the adjusted HRQoL scores. CONCLUSIONS: HRQoL seems to be affected in PD-MCI, and it might be influenced by greater subjective cognitive and communicative complaints. Including patient-reported outcome measures of HRQoL, and providing cognitive and speech rehabilitation, as well as psychotherapeutic strategies to face these deficits can enhance the patient-centred approach in PD.

Spectral features of resting-state EEG in Parkinson's Disease: A multicenter study using functional data analysis.

OBJECTIVE: This study aims 1) To analyse differences in resting-state electroencephalogram (rs-EEG) spectral features of Parkinson's Disease (PD) and healthy subjects (non-PD) using Functional Data Analysis (FDA) and 2) To explore, in four independent cohorts, the external validity and reproducibility of the findings using both epoch-to-epoch FDA and averaged-epochs approach. METHODS: We included 169 subjects (85 non-PD; 84 PD) from four centres. Rs-EEG signals were preprocessed with a combination of automated pipelines. Sensor-level relative power spectral density (PSD), dominant frequency (DF), and DF variability (DFV) features were extracted. Differences in each feature were compared between PD and non-PD on averaged epochs and using FDA to model the epoch-to-epoch change of each feature. RESULTS: For averaged epochs, significantly higher theta relative PSD in PD was found across all datasets. Also, higher pre-alpha relative PSD was observed in three of four datasets in PD patients. For FDA, similar findings were achieved in theta, but all datasets showed consistently significant posterior pre-alpha differences across multiple epochs. CONCLUSIONS: Increased generalised theta, with posterior pre-alpha relative PSD, was the most reproducible finding in PD. SIGNIFICANCE: Rs-EEG theta and pre-alpha findings are generalisable in PD. FDA constitutes a reliable and powerful tool to analyse epoch-to-epoch the rs-EEG.

Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer's Disease Using Machine Learning and Resting State Electroencephalography.

BACKGROUND: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer's disease (AD). OBJECTIVE: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer's disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). METHODS: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. RESULTS: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. CONCLUSION: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.

Subjective Cognitive and Communicative Complaints and Health-Related Quality of Life in Parkinson's Disease with and without Mild Cognitive Impairment.

INTRODUCTION: Mild Cognitive Impairment (MCI) is common in Parkinson's Disease (PD). Few studies have compared the Health-Related Quality of Life (HRQoL) in patients with and without MCI due to PD (PD-MCI), and its correlation to patients' subjective cognitive and communicative difficulties has not been explored. OBJECTIVE: We aimed to compare HRQoL in PD-MCI and PD without MCI (PD-nMCI), and explore its possible relationship to subjective cognitive and communicative complaints. METHODS: We included 29 PD-nMCI and 11 PD-MCI patients. The HRQoL was assessed with the Parkinson's Disease Questionnaire-39 (PDQ-39): its Cognition dimension was used as a measure of subjective cognitive complaints, its Communication dimension for subjective communicative complaints, and the summary index (PDQ-39 SI) as an indicator of HRQoL. Non-parametric partial correlations between the Cognition and Communication dimensions, and the adjusted PDQ-39 SI were conducted. RESULTS: PD-MCI patients had greater subjective cognitive and communicative complaints and worse HRQoL than PD-nMCI patients. In the PD-MCI group, both subjective cognitive and communicative complaints exhibited significant direct correlations with the adjusted HRQoL scores. CONCLUSIONS: HRQoL seems to be affected in PD-MCI, and it might be influenced by greater subjective cognitive and communicative complaints. Including patient-reported outcome measures of HRQoL, and providing cognitive and speech rehabilitation, as well as psychotherapeutic strategies to face these deficits can enhance the patient-centred approach in PD.

Resting-state EEG alpha/theta ratio related to neuropsychological test performance in Parkinson's Disease.

OBJECTIVE: To determine possible associations of hemispheric-regional alpha/theta ratio (α/θ) with neuropsychological test performance in Parkinson's Disease (PD) non-demented patients. METHODS: 36 PD were matched to 36 Healthy Controls (HC). The α/θ in eight hemispheric regions was computed from the relative power spectral density of the resting-state quantitative electroencephalogram (qEEG). Correlations between α/θ and performance in several neuropsychological tests were conducted, significant findings were included in a moderation analysis. RESULTS: The α/θ in all regions was lower in PD than in HC, with larger effect sizes in the posterior regions. Right parietal, and right and left occipital α/θ had significant positive correlations with performance in Judgement of Line Orientation Test (JLOT) in PD. Adjusted moderation analysis indicated that right, but not left, occipital α/θ influenced the JLOT performance related to PD. CONCLUSIONS: Reduction of the occipital α/θ, in particular on the right side, was associated with visuospatial performance impairment in PD. SIGNIFICANCE: Visuospatial impairment in PD, which is highly correlated with the subsequent development of dementia, is reflected in α/θ in the right posterior regions. The right occipital α/θ may represent a useful qEEG marker for evaluating the presence of early signs of cognitive decline in PD and the subsequent risk of dementia.

Conectividad funcional en adultos mayores a partir de resonanciamagnética funcional como un posible indicador para la enfermedad deAlzheimer / Functional connectivity in senior adults from fMRI as a possible indicator for Alzheimer’s Disease

La enfermedad de Alzheimer es una enfermedad neurodegenerativa y la causa de demenciamás frecuente en el mundo. Esta enfermedad se caracteriza por muerte neuronal que lleva a una pérdida deconectividad cerebral general. Técnicas como la conectividad funcional a partir de imágenes de resonanciamagnética pueden brindar información acerca de la interacción entre regiones cerebrales y, por tanto, puedeser un indicador del Alzheimer.Objetivo: evaluar la conectividad funcional a partir de imágenes de resonancia magnética funcional en estadode reposo en adultos mayores como posible biomarcador para la enfermedad de Alzheimer.Materiales y métodos: en una población de 35 sujetos de edad avanzada (10 pacientes con Alzheimer de75 ± 2,87 años, 10 pacientes con deterioro cognitivo leve de 74,9 ± 2,88 años, y 15 personas sanas de 75,35 ±2,91 años), se compararon las redes de conectividad funcional obtenidas a través de la correlación temporal dela señal BOLD y elementos de la teoría de grafos. Se calcularon las medidas de las redes (costo y grado medio),y se correlacionaron estas medidas con las escalas neuropsicológicas ADNI-mem y ADAS-Cog.Resultados: en los pacientes con Alzheimer hay una disminución de la conectividad en comparación con loscontroles sanos y los pacientes con deterioro cognitivo leve. Se encontró una correlación significativa entre elcosto de las redes en los sujetos sanos y las escalas neuropsicológicas.Conclusión: se confirma la desconexión existente en la enfermedad de Alzheimer y se muestra que la alteraciónde la actividad cerebral en el deterioro cognitivo y Alzheimer se puede medir mediante el algoritmo basado engrafos desarrollado en este trabajo...

Alzheimer’s disease is a neurodegenerative disease and the most common cause of dementiain the world. This disease is characterized by neuronal cell death leading to an overall loss of brain connectivity.Functional connectivity from magnetic resonance images can provide information about the interactionbetween brain regions and therefore may be an indicator of Alzheimer. Objective: assessing functional connectivity from functional magnetic resonance images at rest in elderly asa potential biomarker for Alzheimer’s disease.Materials and methods: in a population of 35 elderly subjects (10 patients with Alzheimer 75 ± 2,87 years,10 patients with mild cognitive impairment 74,9 ± 2,88 years and 15 healthy individuals 75,35 ± 2,91years),functional connectivity networks obtained through the temporal correlation of BOLD signal and elementsof the graph theory were compared. Network indexes (cost and average degree) were calculated, and furthercorrelated with the neuropsychological scales, ADNI-mem, and ADAS-Cog...