Clozapine (CLZ) is uniquely effective as a treatment for medication resistant schizophrenia. Information regarding its mechanism of action may offer clues to the pathophysiology of the disease and to improved treatment. In this study we employ a machine learning (ML) analysis of P300 evoked potentials obtained from quantitative electroencephalography (QEEG) data to identify changes in the brain induced by CLZ treatment. We employ brain source localization (BSL) on the EEG signals to extract source waveforms from specified regions of the brain. A subset of 8 features is selected from a large set of candidate features (consisting of spectral coherences between all identified source waveforms at multiple frequencies) that discriminate (by means of a classifier) between the pre- and post-treatment data for the schizophrenics (SCZ) most responsive to CLZ. We show these same selected features also discriminate between pre-treatment most responsive SCZ and healthy volunteers (HV), but not after treatment. Of note, these same features discriminate the least responsive SCZ from HV both pre- and post-treatment. This analysis suggests that the net beneficial effects of CLZ in SCZ are reflected in a normalization of P300 brain-source generators.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/EMBC.2012.6347339 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Identification of IGFBP3 and LGALS1 as potential secreted biomarkers for clear cell renal cell carcinoma based on bioinformatics analysis and machine learning.
Adv Clin Exp Med
January 2025
Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, USA.
Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Due to the lack of symptoms until advanced stages, early diagnosis of ccRCC is challenging. Therefore, the identification of novel secreted biomarkers for the early detection of ccRCC is urgently needed.
View Article and Find Full Text PDFHIV OctaScanner: A Machine Learning Approach to Unveil Proteolytic Cleavage Dynamics in HIV-1 Protease Substrates.
J Chem Inf Model
January 2025
State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China.
The rise of resistance to antiretroviral drugs due to mutations in human immunodeficiency virus-1 (HIV-1) protease is a major obstacle to effective treatment. These mutations alter the drug-binding pocket of the protease and reduce the drug efficacy by disrupting interactions with inhibitors. Traditional methods, such as biochemical assays and structural biology, are crucial for studying enzyme function but are time-consuming and labor-intensive.
View Article and Find Full Text PDFSeverity of metabolic derangement predicts survival after out-of-hospital cardiac arrest and the likelihood of benefiting from extracorporeal life support.
Emergencias
December 2024
Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seúl, República de Corea. Department of Digital Health, SAIHST, Sungkyunkwan University, Seúl, República de Corea.
Objective: To develop a Metabolic Derangement Score (MDS) based on parameters available after initial testing and assess the score's ability to predict survival after out-of hospital cardiac arrest (OHCA) and the likely usefulness of extracorporeal life support (ECLS).
Methods: A total of 5100 cases in the Korean Cardiac Arrest Research Consortium registry were included. Patients' mean age was 67 years, and 69% were men.
Background: Diabetic kidney disease (DKD) is one of the typical complications of type 2 diabetes (T2D), with approximately 10 % of DKD patients experiencing a Rapid decline (RD) in kidney function. RD leads to an increased risk of poor outcomes such as the need for dialysis. Albuminuria is a known kidney damage biomarker for DKD, yet RD cases do not always show changes in albuminuria, and the exact mechanism of RD remains unclear.
View Article and Find Full Text PDFIdentify characteristics of Vietnamese oral squamous cell carcinoma patients by machine learning on transcriptome and clinical-histopathological analysis.
J Dent Sci
December 2024
Blood Transfusion Haematology Hospital No. 2, Ho Chi Minh City, Viet Nam.
Background/purpose: Oral squamous cell carcinoma (OSCC) is notorious for its low survival rates, due to the advanced stage at which it is commonly diagnosed. To enhance early detection and improve prognostic assessments, our study harnesses the power of machine learning (ML) to dissect and interpret complex patterns within mRNA-sequencing (RNA-seq) data and clinical-histopathological features.
Materials And Methods: 206 retrospective Vietnamese OSCC formalin-fixed paraffin-embedded (FFPE) tumor samples, of which 101 were subjected to RNA-seq for classification based on gene expression.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!