Cargando…

Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease

Exploring functional interactions among various brain regions is helpful for understanding the pathological underpinnings of neurological disorders. Brain networks provide an important representation of those functional interactions, and thus are widely applied in the diagnosis and classification of...

Descripción completa

Detalles Bibliográficos
Autores principales:	Guo, Hao, Zhang, Fan, Chen, Junjie, Xu, Yong, Xiang, Jie
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2017
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702364/ https://www.ncbi.nlm.nih.gov/pubmed/29209156 http://dx.doi.org/10.3389/fnins.2017.00615

Ejemplares similares

Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data
por: Li, Yao, et al.
Publicado: (2022)

Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset
por: Guo, Hao, et al.
Publicado: (2017)

Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction
por: Ding, Xiaoyu, et al.
Publicado: (2017)

Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)
por: Al-Zubaidi, Arkan, et al.
Publicado: (2019)

A Hybrid Machine Learning Method for Fusing fMRI and Genetic Data: Combining both Improves Classification of Schizophrenia
por: Yang, Honghui, et al.
Publicado: (2010)

Classification of Alzheimer’s Progression Using fMRI Data
por: Noh, Ju-Hyeon, et al.
Publicado: (2023)

Dimensionality of ICA in resting-state fMRI investigated by feature optimized classification of independent components with SVM
por: Wang, Yanlu, et al.
Publicado: (2015)

On the generalizability of resting-state fMRI machine learning classifiers
por: Huf, Wolfgang, et al.
Publicado: (2014)

Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset
por: Li, Yao, et al.
Publicado: (2020)

DC Shifts-fMRI: A Supplement to Event-Related fMRI
por: Li, Qiang, et al.
Publicado: (2019)

Editorial: Temporal Features in Resting State fMRI Data
por: Zhang, Nanyin, et al.
Publicado: (2020)

Topological Properties of Resting-State fMRI Functional Networks Improve Machine Learning-Based Autism Classification
por: Kazeminejad, Amirali, et al.
Publicado: (2019)

High Classification Accuracy for Schizophrenia with Rest and Task fMRI Data
por: Du, Wei, et al.
Publicado: (2012)

Classification of human chronotype based on fMRI network-based statistics
por: Mason, Sophie L., et al.
Publicado: (2023)

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping
por: Wang, Bin, et al.
Publicado: (2017)

Corrigendum: Hypernetwork Construction and Feature Fusion Analysis Based on Sparse Group Lasso Method on fMRI Dataset
por: Li, Yao, et al.
Publicado: (2020)

Network Connectivity in Epilepsy: Resting State fMRI and EEG–fMRI Contributions
por: Centeno, Maria, et al.
Publicado: (2014)

Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study
por: Yu, Qingbao, et al.
Publicado: (2016)

No Evidence for Automatic Remapping of Stimulus Features or Location Found with fMRI
por: Lescroart, Mark D., et al.
Publicado: (2016)

Prediction of Task-Related BOLD fMRI with Amplitude Signatures of Resting-State fMRI
por: Kannurpatti, Sridhar S., et al.
Publicado: (2012)

Toward literature-based feature selection for diagnostic classification: a meta-analysis of resting-state fMRI in depression
por: Sundermann, Benedikt, et al.
Publicado: (2014)

Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI
por: Bi, Xia-an, et al.
Publicado: (2018)

Advice Taking from Humans and Machines: An fMRI and Effective Connectivity Study
por: Goodyear, Kimberly, et al.
Publicado: (2016)

Exploring fMRI Results Space: 31 Variants of an fMRI Analysis in AFNI, FSL, and SPM
por: Pauli, Ruth, et al.
Publicado: (2016)

A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction
por: Cury, Claire, et al.
Publicado: (2020)

Spatial and temporal features of superordinate semantic processing studied with fMRI and EEG
por: Costanzo, Michelle E., et al.
Publicado: (2013)

Preservation of EEG spectral power features during simultaneous EEG-fMRI
por: Gallego-Rudolf, Jonathan, et al.
Publicado: (2022)

Classification and Extraction of Resting State Networks Using Healthy and Epilepsy fMRI Data
por: Vergun, Svyatoslav, et al.
Publicado: (2016)

A Tensor-Based Framework for rs-fMRI Classification and Functional Connectivity Construction
por: Noroozi, Ali, et al.
Publicado: (2020)

Physiological Noise in Brainstem fMRI
por: Brooks, Jonathan C. W., et al.
Publicado: (2013)

On the distinguishability of HRF models in fMRI
por: Rosa, Paulo N., et al.
Publicado: (2015)

Olfactory fMRI Activation Pattern Across Different Concentrations Changes in Alzheimer’s Disease
por: Zhang, Hui, et al.
Publicado: (2019)

Low-Rank Plus Sparse Decomposition of fMRI Data With Application to Alzheimer's Disease
por: Tu, Wei, et al.
Publicado: (2022)

fMRI in anxiety
por: Schunck, Thérèse, et al.
Publicado: (2003)

Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features
por: Bohaterewicz, Bartosz, et al.
Publicado: (2021)

Altered Directed Functional Connectivity of the Hippocampus in Mild Cognitive Impairment and Alzheimer’s Disease: A Resting-State fMRI Study
por: Xue, Jiayue, et al.
Publicado: (2019)

Phase fMRI Reveals More Sparseness and Balance of Rest Brain Functional Connectivity Than Magnitude fMRI
por: Chen, Zikuan, et al.
Publicado: (2019)

Locally Linear Embedding and fMRI Feature Selection in Psychiatric Classification
Publicado: (2019)

Investigating the Group-Level Impact of Advanced Dual-Echo fMRI Combinations
por: Kettinger, Ádám, et al.
Publicado: (2016)

Combined rTMS/fMRI Studies: An Overlooked Resource in Animal Models
por: Seewoo, Bhedita J., et al.
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_d0slu7982f6b7bd97fhp0ekvd2