Keyword search (3,619 papers available)


Combating childhood overweight and obesity: The role of Olympic Movement and bodily movement

Author(s): Tam BT; Wan K; Santosa S; Cai Z;

With over 420 million children (aged 0-19 years) worldwide living with overweight or obesity, the "obesity epidemic" or "globesity" is a defining public health challenge of this generation. While significant efforts have been made to address...

Article GUID: 39991475
A database of magnetic resonance imaging-transcranial ultrasound co-registration

Author(s): Alizadeh M; Collins DL; Kersten-Oertel M; Xiao Y;

Purpose: As a portable and cost-effective imaging modality with better accessibility than Magnetic Resonance Imaging (MRI), transcranial sonography (TCS) has demonstrated its flexibility and potential utility in various clinical diagnostic applications, inc...

Article GUID: 39920905
Sex differences in the metabolism of glucose and fatty acids by adipose tissue and skeletal muscle in humans

Author(s): Costa DN; Santosa S; Jensen MD;

Adult males and females have markedly different body composition, energy expenditure, and have different degrees of risk for metabolic diseases. A major aspect of metabolic regulation involves the appropriate storage and disposal of glucose and fatty acids....

Article GUID: 39869194
Dialogue mechanisms between astrocytic and neuronal networks: A whole-brain modelling approach

Author(s): Ali OBK; Vidal A; Grova C; Benali H;

Astrocytes critically shape whole-brain structure and function by forming extensive gap junctional networks that intimately and actively interact with neurons. Despite their importance, existing computational models of whole-brain activity ignore the roles ...

Article GUID: 39804928
Patterns of Cerebellar-Cortical Structural Covariance Mirror Anatomical Connectivity of Sensorimotor and Cognitive Networks

Author(s): Alasmar Z; Chakravarty MM; Penhune VB; Steele CJ;

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and ...

Article GUID: 39791308
Cognitive-behavioural therapy for insomnia mechanism of action: Exploring the homeostatic K-complex involvement

Author(s): Sforza M; Morin CM; Dang-Vu TT; Pomares FB; Perrault AA; Gouin JP; Bušková J; Janku K; Vgontzas A; Fernandez-Mendoza J; Bastien CH; Riemann ...

Investigating the mechanisms of action of cognitive-behavioural therapy for insomnia (CBT-I), the first-line treatment for chronic insomnia disorder (ID), can contribute to the overall understandin...

Article GUID: 39739397
Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology

Author(s): Hrtonova V; Nejedly P; Travnicek V; Cimbalnik J; Matouskova B; Pail M; Peter-Derex L; Grova C; Gotman J; Halamek J; Jurak P; Brazdil M; Klim...

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack...

Article GUID: 39608298
Feeling safe: a critical look at the effect of neighborhood safety features and perceptions on childhood symptoms of depression

Author(s): Infantino E; Barnett TA; Côté-Lussier C; Van Hulst A; Henderson M; Mathieu ME; Sabiston C; Kakinami L;...

Background: Physical characteristics and perceptions of an environment can have enduring effects on one's mental health. The present study aimed to determine whether a set of measures of neighb...

Article GUID: 39604905
The Immediate Effect of a Single Treatment of Neuromuscular Electrical Stimulation with the StimaWELL 120MTRS System on Multifidus Stiffness in Patients with Chronic Low Back Pain

Author(s): Wolfe D; Dover G; Boily M; Fortin M;

Background/objectives: Individuals with chronic low back pain (CLBP) have altered lumbar multifidus stiffness properties compared to healthy controls. Although neuromuscular electrical stimulation (NMES) application to the multifidus might affect stiffness,...

Article GUID: 39594260
Title:Metrics for evaluation of automatic epileptogenic zone localization in intracranial electrophysiology
Authors:Hrtonova V,  Nejedly P,  Travnicek V,  Cimbalnik J,  Matouskova B,  Pail M,  Peter-Derex L,  Grova C,  Gotman J,  Halamek J,  Jurak P,  Brazdil M,  Klimes P,  Frauscher B, 
Link:https://pubmed.ncbi.nlm.nih.gov/39608298/
DOI:10.1016/j.clinph.2024.11.007
Category:
PMID:39608298
Dept Affiliation: SOH
1 First Department of Neurology, Faculty of Medicine, Masaryk University, Pekarska 53, 602 00 Brno, Czech Republic; Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic; Department of Neurology, Duke University School of Medicine, 2424 Erwin Rd, Durham, NC 27705, the United States of America.
2 First Department of Neurology, Faculty of Medicine, Masaryk University, Pekarska 53, 602 00 Brno, Czech Republic; Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic.
3 Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic; International Clinical Research Center, St. Anne's University Hospital, Pekarska 53, 602 00 Brno, Czech Republic.
4 International Clinical Research Center, St. Anne's University Hospital, Pekarska 53, 602 00 Brno, Czech Republic.
5 Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Pekarska 53, 602 00 Brno, Czech Republic.
6 Center for Sleep Medicine, Lyon University Hospital, Lyon 1 University, 103 Grande Rue de la Croix-Rousse, 69004 Lyon, France; Lyon Neuroscience Research Center, CH Le Vinatier - Batiment 462 - Neurocampus, 95 Bd Pinel, 69500 Lyon, France.
7 Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University and Biomedical Engineering Department, McGill University, Montreal Neurological Hospital, Concordia University, 7141 Sherbrooke Street West, Montreal, QC H4B 1R6.
8 Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada.
9 Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic.
10 Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital, member of ERN-EpiCARE, Faculty of Medicine, Masaryk University, Pekarska 53, 602 00 Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC Central European Institute of Technology, Masaryk University, Zerotinovo nám 617/9, 601 77 Brno, Czech Republic.
11 Institute of Scientific Instruments of the CAS, v. v. i., Kralovopolska 147, 612 00 Brno, Czech Republic. Electronic address: petr.klimes@isibrno.cz.
12 Montreal Neurological Hospital, McGill University, 3801 Rue University, Montreal, QC H3A 2B4, Quebec, Canada; Department of Neurology, Duke University Medical School and Department of Biomedical Engineering, Pratt School of Engineering, 2424 Erwin Road, Durham, NC 27705, the United States of America. Electronic address: birgit.frauscher@duke.edu.

Description:

Introduction: Precise localization of the epileptogenic zone is critical for successful epilepsy surgery. However, imbalanced datasets in terms of epileptic vs. normal electrode contacts and a lack of standardized evaluation guidelines hinder the consistent evaluation of automatic machine learning localization models.

Methods: This study addresses these challenges by analyzing class imbalance in clinical datasets and evaluating common assessment metrics. Data from 139 drug-resistant epilepsy patients across two Institutions were analyzed. Metric behaviors were examined using clinical and simulated data.

Results: Complementary use of Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall Curve (AUPRC) provides an optimal evaluation approach. This must be paired with an analysis of class imbalance and its impact due to significant variations found in clinical datasets.

Conclusions: The proposed framework offers a comprehensive and reliable method for evaluating machine learning models in epileptogenic zone localization, improving their precision and clinical relevance.

Significance: Adopting this framework will improve the comparability and multicenter testing of machine learning models in epileptogenic zone localization, enhancing their reliability and ultimately leading to better surgical outcomes for epilepsy patients.