Quantitative EEG (QEEG) Measures Differentiate Parkinson`s Disease Patients from Healthy Controls

Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection. Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups. Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained. Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region. Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity. Keywords: Parkinson's disease, QEEG, cognitive decline, Parkinson's disease dementia, neurodegenerative disorders, machine learnin

Fine motor function and neuropsychological deficits in individuals at risk for schizophrenia

Deficits in fine motor function and neuropsychological performance have been described as risk factors for schizophrenia. In the Basel FEPSY study (Früherkennung von Psychosen; English: Early Detection of Psychosis) individuals at risk for psychosis were identified in a screening procedure (Riecher-Rössler et al. 2005). As a part of the multilevel assessment, 40 individuals at risk for psychosis and 42 healthy controls matched for age, sex and handedness were investigated with a fine motor function test battery and a neuropsychological test battery. Individuals at risk showed lower performances in all subtests of the fine motor function tests, predominantly in dexterity and velocity (wrist/fingers and arm/hand). In the neuropsychological test battery, individuals at risk performed less well compared to healthy controls regarding sustained attention, working memory and perseveration. The combined evaluation of the two test batteries (neuropsychological and fine motor function) separates the two groups into individuals at risk and healthy controls better than each test battery alone. A multilevel approach might therefore be a valuable contribution to detecting beginning schizophreni

EEG alterations during treatment with olanzapine

The aim of this naturalistic observational study was to investigate EEG alterations in patients under olanzapine treatment with a special regard to olanzapine dose and plasma concentration. Twenty-two in-patients of a psychiatric university ward with the monodiagnosis of paranoid schizophrenia (ICD-10: F20.0), who received a monotherapy of olanzapine were included in this study. All patients had a normal alpha-EEG before drug therapy, and did not suffer from brain-organic dysfunctions, as verified by clinical examination and cMRI scans. EEG and olanzapine plasma levels were determined under steady-state conditions (between 18 and 22 days after begin of treatment). In 9 patients (40.9%), pathological EEG changes (one with spike-waves) consecutive to olanzapine treatment were observed. The dose of olanzapine was significantly higher in patients with changes of the EEG than in patients without changes (24.4 mg/day (SD: 8.1) vs. 12.7 mg/day (SD: 4.8); T = −4.3, df = 21, P < 0.001). In patients with EEG changes, the blood plasma concentration of olanzapine (45.6 μg/l (SD: 30.9) vs. 26.3 μg/l (SD: 21.6) tended to be also higher. The sensitivity of olanzapine dosage to predict EEG changes was 66.7%, the specificity 100% (Youden-index: 0.67). EEG abnormalities during olanzapine treatment are common. These are significantly dose dependent. Thus, EEG control recordings should be mandatory during olanzapine treatment with special emphasis on dosages exceeding 20 mg per day, although keeping in mind that EEGs have only a limited predictive power regarding future epileptic seizures

Vorhersage von Psychosen durch stufenweise Mehrebenenabklärung - Das Basel FePsy (Früherkennung von Psychosen)-Projekt

Hintergrund: In Basel haben wir verschiedene Studien zur Verbesserung der Methodik zur Früherkennung von Psychosen (FePsy) durchgeführt. Methodik: Vom 01.03.2000 bis 29.02.2004 wurden 234 Individuen mithilfe des Basler Screening Instruments für Psychosen (BSIP) gescreent. Bei 10 6 Patienten konnte ein Risikostatus für Psychosen diagnostiziert werden, 53 davon konnten bis zu 7 Jahre (Mittel 5. 4 Jahre) nachuntersucht werden. Die weiteren Untersuchungen erfolgten u.a. mit einem spezifisch entwickelten Anamnese - Instrument, verschiedenen Skalen zur Psychopathologie, Untersuchungen der Neuropsychologie u n d Feinmotorik , klinische m und quantitative m EEG, MRI des Gehirns, Labor. Ergebnisse: Allein auf der Basis des BSIP konnte eine relativ zuverlässige Vorher sage getroffen werden: 21 (39.6 % ) der als „ Risikopatienten “Identifizierten entwickelten innerhalb der Beobachtungszeit tatsächlich eine Psychose. Post hoc konnte durch spezifischere Gewichtung der Psychopathologie und Einbezug neuropsychologischer Untersuchungen die Vorhersagegenauigkeit auf 81 % gesteigert werden. Die anderen oben genannten Verfahren können offensichtlich zur weiteren Verbesserung der Prädiktion beitragen. Schlussfolgerungen: Die Risikoabklärung für Psychose sollte stufenweise und unter Einbezug verschiedener Untersuchungsebenen erfolgen. Background: We have conducted various studies in Basel with the aim of improving the methods for the early detection of psychosis (Fruherkennung von Psychosen, FePsy).Methods: From 1.3.2000 to 29.2.2004 234 individuals were screened using the Basel Screening Instrument for Psychosis (BSIP). 106 patients were identified as at risk for psychosis; out of these 53 remained in follow-up for up to 7 years (mean 5.4 years). The assessments were done with a specifically developed instrument for history taking, various scales for the psychopathology, assessments of neuropsychology and fine motor functioning, clinical and quantitative EEG, MRI of the brain, laboratory etc.Results: Based on the BSIP alone, a relatively reliable prediction was possible: 21 (39.6 %) of the individuals identified as at risk developed psychosis within the follow-up time. Post-hoc prediction could be improved to 81 % by weighting psychopathology and including neuropsychology. Including the other domains obviously allows further improvements of prediction.Conclusions: The risk for psychosis should be assessed in a stepwise procedure. In a first step, a clinically oriented screening should be conducted. If an at-risk status is found, further assessments in various domains should be done in a specialised centre

Exploring Predictors of Outcome in the Psychosis Prodrome: Implications for Early Identification and Intervention

Functional disability is a key component of many psychiatric illnesses, particularly schizophrenia. Impairments in social and role functioning are linked to cognitive deficits, a core feature of psychosis. Retrospective analyses demonstrate that substantial functional decline precedes the onset of psychosis. Recent investigations reveal that individuals at clinical-high-risk (CHR) for psychosis show impairments in social relationships, work/school functioning and daily living skills. CHR youth also demonstrate a pattern of impairment across a range of cognitive domains, including social cognition, which is qualitatively similar to that of individuals with schizophrenia. While many studies have sought to elucidate predictors of clinical deterioration, specifically the development of schizophrenia, in such CHR samples, few have investigated factors relevant to psychosocial outcome. This review integrates recent findings regarding cognitive and social-cognitive predictors of outcome in CHR individuals, and proposes potential directions for future research that will contribute to targeted interventions and improved outcome for at-risk youth

Microstate connectivity alterations in patients with early Alzheimer's disease

Electroencephalography (EEG) microstates and brain network are altered in patients with Alzheimer's disease (AD) and discussed as potential biomarkers for AD. Microstates correspond to defined states of brain activity, and their connectivity patterns may change accordingly. Little is known about alteration of connectivity in microstates, especially in patients with amnestic mild cognitive impairment with stable or improving cognition within 30 months (aMCI).; Thirty-five outpatients with aMCI or mild dementia (mean age 77 ± 7 years, 47 % male, Mini Mental State Examination score ≥24) had comprehensive neuropsychological and clinical examinations. Subjects with cognitive decline over 30 months were allocated to the AD group, subjects with stable or improving cognition to the MCI-stable group. Results of neuropsychological testing at baseline were summarized in six domain scores. Resting state EEG was recorded with 256 electrodes and analyzed using TAPEEG. Five microstates were defined and individual data fitted. After phase transformation, the phase lag index (PLI) was calculated for the five microstates in every subject. Networks were reduced to 22 nodes for statistical analysis.; The domain score for verbal learning and memory and the microstate segmented PLI between the left centro-lateral and parieto-occipital regions in the theta band at baseline differentiated significantly between the groups. In the present sample, they separated in a logistic regression model with a 100 % positive predictive value, 60 % negative predictive value, 100 % specificity and 77 % sensitivity between AD and MCI-stable.; Combining neuropsychological and quantitative EEG test results allows differentiation between subjects with aMCI remaining stable and subjects with aMCI deteriorating over 30 months

Das Basel Screening Instrument für Psychosen (BSIP) : Entwicklung, Aufbau, Reliabilität und Validität

BACKGROUND: Early detection of psychosis is of growing clinical importance. So far there is, however, no screening instrument for detecting individuals with beginning psychosis in the atypical early stages of the disease with sufficient validity. We have therefore developed the Basel Screening Instrument for Psychosis (BSIP) and tested its feasibility, interrater-reliability and validity. AIM: Aim of this paper is to describe the development and structure of the instrument, as well as to report the results of the studies on reliability and validity. METHOD: The instrument was developed based on a comprehensive search of literature on the most important risk factors and early signs of schizophrenic psychoses. The interraterreliability study was conducted on 24 psychiatric cases. Validity was tested based on 206 individuals referred to our early detection clinic from 3/1/2000 until 2/28/2003. RESULTS: We identified seven categories of relevance for early detection of psychosis and used them to construct a semistructured interview. Interrater-reliability for high risk individuals was high (Kappa .87). Predictive validity was comparable to other, more comprehensive instruments: 16 (32 %) of 50 individuals classified as being at risk for psychosis by the BSIP have in fact developed frank psychosis within an follow-up period of two to five years. CONCLUSIONS: The BSIP is the first screening instrument for the early detection of psychosis which has been validated based on transition to psychosis. The BSIP is easy to use by experienced psychiatrists and has a very good interrater-reliability and predictive validity

Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer's disease

To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data.; A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender.; Differences in the delta band at 34 temporo-posterior source locations (p>.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results.; Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy.; qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments