Mcic | List of Frontiers open access articles

Original Research

Published on 05 Nov 2020

Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease

in Dementia and Neurodegenerative Diseases

Loris Nanni
Matteo Interlenghi
Sheryl Brahnam
Christian Salvatore
Sergio Papa
Raffaello Nemni
Isabella Castiglioni
The Alzheimer's Disease Neuroimaging Initiative

Frontiers in Neurology

doi 10.3389/fneur.2020.576194

13,087 views
78 citations

Original Research

Published on 13 May 2020

Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning

in Brain Imaging Methods

Dan Pan
An Zeng
Longfei Jia
Yin Huang
Tory Frizzell
Xiaowei Song

Frontiers in Neuroscience

doi 10.3389/fnins.2020.00259

44,626 views
308 citations

Original Research

Published on 01 Sep 2015

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach

in Brain Imaging Methods

Christian Salvatore
Antonio Cerasa
Petronilla Battista
Maria Carla Gilardi
Aldo Quattrone
Isabella Castiglioni

Frontiers in Neuroscience

doi 10.3389/fnins.2015.00307

24,168 views
259 citations

Original Research

Published on 30 Jul 2021

Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach

in Alzheimer's Disease and Related Dementias

Tingting Zhang
Qian Liao
Danmei Zhang
Chao Zhang
Jing Yan
Ronald Ngetich
Junjun Zhang
Zhenlan Jin
Ling Li

Frontiers in Aging Neuroscience

doi 10.3389/fnagi.2021.688926

13,061 views
79 citations

Original Research

Published on 23 Oct 2020

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

in Microbiotechnology

Mehdi Foroozandeh Shahraki
Shohreh Ariaeenejad
Fereshteh Fallah Atanaki
Behrouz Zolfaghari
Takeshi Koshiba
Kaveh Kavousi
Ghasem Hosseini Salekdeh

Frontiers in Microbiology

doi 10.3389/fmicb.2020.567863

6,021 views
34 citations

Original Research

Published on 24 Feb 2017

Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI

Zhuo Sun
Martijn van de Giessen
Boudewijn P. F. Lelieveldt
Marius Staring

Frontiers in Neuroinformatics

doi 10.3389/fninf.2017.00016

8,361 views
37 citations

Original Research

Published on 02 Jul 2020

Assessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortex

in Alzheimer's Disease and Related Dementias

Stephanos Leandrou
Demetris Lamnisos
Ioannis Mamais
Panicos A. Kyriacou
Constantinos S. Pattichis
for the Alzheimer’s Disease and Neuroimaging Initiative

Frontiers in Aging Neuroscience

doi 10.3389/fnagi.2020.00176

7,975 views
38 citations

Original Research

Published on 21 Jul 2021

Plasma Proteomic Biomarkers Relating to Alzheimer’s Disease: A Meta-Analysis Based on Our Own Studies

in Alzheimer's Disease and Related Dementias

Liu Shi
Noel J. Buckley
Isabelle Bos
Sebastiaan Engelborghs
Kristel Sleegers
Giovanni B. Frisoni
Anders Wallin
Alberto Lléo
Julius Popp
Pablo Martinez-Lage

Frontiers in Aging Neuroscience

doi 10.3389/fnagi.2021.712545

9,438 views
23 citations

Original Research

Published on 30 Jul 2020

Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

in Alzheimer's Disease and Related Dementias

Yubraj Gupta
Ji-In Kim
Byeong Chae Kim
Goo-Rak Kwon

Frontiers in Aging Neuroscience

doi 10.3389/fnagi.2020.00238

10,208 views
39 citations

Original Research

Published on 30 May 2022

Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

in Alzheimer's Disease and Related Dementias

Uttam Khatri
Goo-Rak Kwon

Frontiers in Aging Neuroscience

doi 10.3389/fnagi.2022.818871

17,048 views
70 citations

Original Research

Published on 16 Oct 2019

Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers

Yubraj Gupta
Ramesh Kumar Lama
Goo-Rak Kwon
Alzheimer's Disease Neuroimaging Initiative
Michael W. Weiner
Paul Aisen
Michael Weiner
Paul Aisen
Ronald Petersen
Clifford R. Jack

Frontiers in Computational Neuroscience

doi 10.3389/fncom.2019.00072

30,986 views
142 citations

Articles

Comparison of Transfer Learning and Conventional Machine Learning Applied to Structural Brain MRI for the Early Diagnosis and Prognosis of Alzheimer's Disease

Early Detection of Alzheimer’s Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning

Magnetic resonance imaging biomarkers for the early diagnosis of Alzheimer's disease: a machine learning approach

Predicting MCI to AD Conversation Using Integrated sMRI and rs-fMRI: Machine Learning and Graph Theory Approach

MCIC: Automated Identification of Cellulases From Metagenomic Data and Characterization Based on Temperature and pH Dependence

Detection of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Longitudinal Brain MRI

Assessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortex

Plasma Proteomic Biomarkers Relating to Alzheimer’s Disease: A Meta-Analysis Based on Our Own Studies

Classification and Graphical Analysis of Alzheimer’s Disease and Its Prodromal Stage Using Multimodal Features From Structural, Diffusion, and Functional Neuroimaging Data and the APOE Genotype

Alzheimer’s Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

Prediction and Classification of Alzheimer’s Disease Based on Combined Features From Apolipoprotein-E Genotype, Cerebrospinal Fluid, MR, and FDG-PET Imaging Biomarkers