☆ 4.7 Article

Toward interpretability of machine learning methods for the classification of patients with major depressive disorder based on functional network measures

CHAOS (2023)

Related references

Note: Only part of the references are listed.

Article Physics, Multidisciplinary

Features of the resting-state functional brain network of children with autism spectrum disorder: EEG source-level analysis

Semen Kurkin et al.

Summary: This study examines the organization of functional brain networks in children with autism spectrum disorder (ASD) compared to neurotypical children. The findings suggest that in the theta band, neurotypical children show stronger connections in areas responsible for emotions and consciousness than ASD children. Moreover, ASD children exhibit lower network global clustering in the delta + theta band, indicating more segregated but highly connected subnets compared to neurotypical individuals. Additionally, the study demonstrates age-dependent hyper-activation in the ASD group in the extended alpha band for younger ages, and hypo-activation for older ages. The study also discusses the importance of developing effective approaches to autism therapy by normalizing aberrant functional connections.

EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS (2023)

Add to Collection

Review Clinical Neurology

Functional brain networks in the evaluation of patients with neurodegenerative disorders

Matej Perovnik et al.

Summary: Network analytical tools are used to study brain diseases by analyzing imaging maps. Specific networks can be identified and validated, allowing for quantitative assessment of pathway changes over time and during treatment. Network abnormalities can be identified before symptoms and used to track disease progression and treatment efficacy. The development of analogous methods for functional MRI has expanded the application of network tools to diverse patient populations.

NATURE REVIEWS NEUROLOGY (2023)

Add to Collection

Article

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Oleg E. Karpov et al.

International Journal of Environmental Research and Public Health (2023)

Add to Collection

Article Chemistry, Multidisciplinary

Evaluation of Unsupervised Anomaly Detection Techniques in Labelling Epileptic Seizures on Human EEG

Oleg E. E. Karpov et al.

Summary: Automated labelling of epileptic seizures on electroencephalograms is a challenging task. The presence of artefacts complicates the analysis and separation of epileptic seizures. We propose using unsupervised anomaly detection algorithms to detect these rare events, which require minimal pre-processing and can achieve high recall rates.

APPLIED SCIENCES-BASEL (2023)

Add to Collection

Article Mathematics, Interdisciplinary Applications

The topology of fMRI-based networks defines the performance of a graph neural network for the classification of patients with major depressive disorder

Elena N. Pitsik et al.

Summary: Deep learning is widely used in neuroscience and medicine to recognize hidden patterns and nonlinear relations in brain functional connectivity. A graph neural network (GNN) was trained to classify major depressive disorder (MDD) based on topological features, with the shortest path identified as the most important feature. The proposed GNN-based classifier achieved an accuracy of 93% and the maximal F1-score when using a sparse graph consisting of 2.5% of the original connections, reducing overfitting.

CHAOS SOLITONS & FRACTALS (2023)

Add to Collection

Article Computer Science, Information Systems

A survey on the interpretability of deep learning in medical diagnosis

Qiaoying Teng et al.

Summary: This paper comprehensively reviews the interpretability of deep learning in medical diagnosis, including the application of interpretability methods, evaluation metrics, disease datasets, as well as the challenges and future research directions.

MULTIMEDIA SYSTEMS (2022)

Add to Collection

Article Multidisciplinary Sciences

Extreme value theory inspires explainable machine learning approach for seizure detection

Oleg E. Karpov et al.

Summary: Epilepsy is a manifestation of extreme behavior in living systems, characterized by sudden and unpredictable seizures. Treatment strategies involve analyzing brain activity during seizures, and machine learning methods are used for seizure detection. The combination of extreme value theory and outlier detection algorithms provides a promising approach for accurate detection in epilepsy.

SCIENTIFIC REPORTS (2022)

Add to Collection

Article Environmental Sciences

Resting-State Functional Connectivity Impairment in Patients with Major Depressive Episode

Drozdstoy Stoyanov et al.

Summary: This study identified significant differences in functional connectivity between healthy controls and patients with major depressive episode, with specific brain regions showing distinct patterns. Linear discriminant analysis indicated that full-connectivity matrices were the most precise in differentiating between the two groups.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2022)

Add to Collection

Article Medicine, General & Internal

Applications of Explainable Artificial Intelligence in Diagnosis and Surgery

Yiming Zhang et al.

Summary: In recent years, artificial intelligence has shown promise in medicine, but lack of explainability limits its clinical applications. Explainable artificial intelligence (XAI) has been developed to overcome this limitation by providing both decision-making and explanations. This review surveys the recent trends in medical diagnosis and surgical applications using XAI and summarizes the methods, challenges, and future research directions.

DIAGNOSTICS (2022)

Add to Collection

Review Physics, Multidisciplinary

Functional networks of the brain: from connectivity restoration to dynamic integration

A. E. Khramov et al.

Summary: This review discusses the physical and mathematical methods for reconstructing the functional networks of the brain based on recorded brain activity, analyzing the advantages, disadvantages, and limitations of various methods, as well as the application of complex network theory in this field and related issues. The study suggests that these methods may be used to explain brain function and pathological activity, and may help in developing new brain-computer interfaces.

PHYSICS-USPEKHI (2021)

Add to Collection

Article Mathematics, Applied

Machine learning evaluates changes in functional connectivity under a prolonged cognitive load

Nikita Frolov et al.

Summary: When applying machine learning models to analyze high-dimensional neuroimaging data, it is important to be aware of the black-box problem, which stems from a lack of understanding of internal algorithms and input features used for decision making. This study addresses the high-dimensional problem of classifying cognitive brain states based on functional connectivity by selecting and interpreting relevant input features. The research highlights advancements in building a robust classification model and identifying connectivity changes related to perception.

CHAOS (2021)

Add to Collection

Article Multidisciplinary Sciences

Biologically informed deep neural network for prostate cancer discovery

Haitham A. Elmarakeby et al.

Summary: The development of a biologically informed deep learning model P-NET enables stratification of prostate cancer patients by treatment-resistance state and evaluation of molecular drivers of treatment resistance for therapeutic targeting with complete model interpretability. This approach shows superior performance in cancer state prediction using molecular data compared to other modeling approaches.

NATURE (2021)

Add to Collection

Article Neurosciences

Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification

Alexander Kuc et al.

Summary: The study demonstrated the use of a pre-trained classifier to classify brain responses to ambiguous stimuli in brain-computer interfaces with high accuracy. Pre-trained classifiers can assist novice users in starting to use BCIs and improving accuracy during application.

FRONTIERS IN SYSTEMS NEUROSCIENCE (2021)

Add to Collection

Editorial Material Biochemistry & Molecular Biology

AI in medicine must be explainable

Shinjini Kundu

NATURE MEDICINE (2021)

Add to Collection

Article Multidisciplinary Sciences

Segregation, integration, and balance of large-scale resting brain networks configure different cognitive abilities

Rong Wang et al.

Summary: By analyzing functional brain networks in resting states, the study found that the brain networks are generally in a balanced state, allowing for both segregation and integration with flexible switching between the two. The research also showed that the balanced state of brain networks is associated with individual cognitive phenotypes, with integration linked to general cognitive ability, segregation to intelligence and processing speed, and balance tendency to memory.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2021)

Add to Collection

Article Neurosciences

Establishment of Effective Biomarkers for Depression Diagnosis With Fusion of Multiple Resting-State Connectivity Measures

Yanling Li et al.

Summary: This study investigated the functional abnormalities of the triple networks in Major Depressive Disorder (MDD) and found that fusion of multiple functional connectivities measures can effectively distinguish MDD from healthy controls. The results suggest that combining different functional connectivity measures may help establish clinical diagnosis biomarkers for depression.

FRONTIERS IN NEUROSCIENCE (2021)

Add to Collection

Article Computer Science, Artificial Intelligence

The importance of interpretability and visualization in machine learning for applications in medicine and health care

Alfredo Vellido

NEURAL COMPUTING & APPLICATIONS (2020)

Add to Collection

Article Neurosciences

Automated anatomical labelling atlas 3

Edmund T. Rolls et al.

NEUROIMAGE (2020)

Add to Collection

Review Chemistry, Medicinal

The Value of Neuroimaging Techniques in the Translation and Trans-diagnostic Validation of Psychiatric Diagnoses - Selective Review

Anna Todeva-Radneva et al.

CURRENT TOPICS IN MEDICINAL CHEMISTRY (2020)

Add to Collection

Article Clinical Neurology

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification

Shangran Qiu et al.

BRAIN (2020)

Add to Collection

Article Biotechnology & Applied Microbiology

Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study

Anupama Jha et al.

GENOME BIOLOGY (2020)

Add to Collection

Review Computer Science, Artificial Intelligence

Causability and explainability of artificial intelligence in medicine

Andreas Holzinger et al.

WILEY INTERDISCIPLINARY REVIEWS-DATA MINING AND KNOWLEDGE DISCOVERY (2019)

Add to Collection

Article Computer Science, Artificial Intelligence

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

NATURE MACHINE INTELLIGENCE (2019)

Add to Collection

Article Biochemistry & Molecular Biology

Resting-state connectivity biomarkers define neurophysiological subtypes of depression

Andrew T. Drysdale et al.

NATURE MEDICINE (2017)

Add to Collection

Review Clinical Neurology

Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers

Takashi Yamada et al.

INTERNATIONAL JOURNAL OF NEUROPSYCHOPHARMACOLOGY (2017)

Add to Collection

Article Neurosciences

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls

Mohammad R. Arbabshirani et al.

NEUROIMAGE (2017)

Add to Collection

Review Neurosciences

A Tutorial Review of Functional Connectivity Analysis Methods and Their Interpretational Pitfalls

Andre M. Bastos et al.

FRONTIERS IN SYSTEMS NEUROSCIENCE (2016)

Add to Collection

Article Neurosciences

Unsupervised Classification of Major Depression Using Functional Connectivity MRI

Ling-Li Zeng et al.

HUMAN BRAIN MAPPING (2014)

Add to Collection

Article Multidisciplinary Sciences

Generalization of Clustering Coefficients to Signed Correlation Networks

Giulio Costantini et al.

PLOS ONE (2014)

Add to Collection

Review Multidisciplinary Sciences

Structural and Functional Brain Networks: From Connections to Cognition

Hae-Jeong Park et al.

SCIENCE (2013)

Add to Collection

Review Neurosciences

The economy of brain network organization

Edward T. Bullmore et al.

NATURE REVIEWS NEUROSCIENCE (2012)

Add to Collection

Article Multidisciplinary Sciences

Changes in Community Structure of Resting State Functional Connectivity in Unipolar Depression

Anton Lord et al.

PLOS ONE (2012)

Add to Collection

Article Multidisciplinary Sciences

Optimizing Functional Network Representation of Multivariate Time Series

Massimiliano Zanin et al.

SCIENTIFIC REPORTS (2012)

Add to Collection

Article Neurosciences

Weight-conserving characterization of complex functional brain networks

Mikail Rubinov et al.

NEUROIMAGE (2011)

Add to Collection

Review Neurosciences

Graph-based network analysis of resting-state functional MRI

Jinhui Wang et al.

FRONTIERS IN SYSTEMS NEUROSCIENCE (2010)

Add to Collection

Article Neurosciences

Efficiency of Functional Brain Networks and Intellectual Performance

Martijn P. van den Heuvel et al.

JOURNAL OF NEUROSCIENCE (2009)

Add to Collection

Article Computer Science, Artificial Intelligence

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

S. Garcia et al.

SOFT COMPUTING (2009)

Add to Collection

Article Multidisciplinary Sciences

Network 'Small-World-Ness': A Quantitative Method for Determining Canonical Network Equivalence

Mark D. Humphries et al.

PLOS ONE (2008)

Add to Collection

Review Biochemical Research Methods

A review of feature selection techniques in bioinformatics

Yvan Saeys et al.

BIOINFORMATICS (2007)

Add to Collection

© Peeref 2019-2024. All rights reserved.