Article
Computer Science, Interdisciplinary Applications
Yu Li, Xin Zhang, Jingxin Nie, Guowei Zhang, Ruiyan Fang, Xiangmin Xu, Zhengwang Wu, Dan Hu, Li Wang, Han Zhang, Weili Lin, Gang Li
Summary: This study utilizes a Graph Convolutional Network (GCN) to predict infant brain age based on resting-state fMRI data. The proposed Brain Connectivity Graph Convolutional Networks (BC-GCN) model incorporates information from different paths and is applied to dense graphs. Additionally, upgraded network structures and a two-stage framework are proposed to enhance the accuracy of age prediction. The experiments demonstrate significant improvements in prediction accuracy compared to state-of-the-art methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2022)
Article
Neurosciences
Bruno Hebling Vieira, Julien Dubois, Vince D. Calhoun, Carlos Ernesto Garrido Salmon
Summary: Predicting general intelligence from resting-state functional magnetic resonance imaging signals using an ensemble of recurrent neural networks and temporal variance of saliencies provides more reliable results than traditional approaches. The model's reliance on network size is a key factor, and the method allows for testing the effects of local alterations on data and derived metrics.
HUMAN BRAIN MAPPING
(2021)
Article
Neurosciences
David A. Wood, Sina Kafiabadi, Ayisha Al Busaidi, Emily Guilhem, Antanas Montvila, Jeremy Lynch, Matthew Townend, Siddharth Agarwal, Asif Mazumder, Gareth J. Barker, Sebastien Ourselin, James H. Cole, Thomas C. Booth
Summary: Convolutional neural networks can accurately predict age in healthy individuals, with implications for clinical decision-making and optimizing patient pathways. A brain-age framework suitable for routine clinical head MRI examinations was developed, enabling real-time detection of older-appearing brains.
Article
Clinical Neurology
Chun-yu Zhang, Bao-feng Yan, Nurehemaiti Mutalifu, Ya-wei Fu, Jiang Shao, Jun-jie Wu, Qi Guan, Song-hai Biedelehan, Ling-xiao Tong, Xin-ping Luan
Summary: In this study, a two-dimensional convolutional neural networks brain age prediction model was used to explore the law of brain development in children with cerebral palsy (CP). The results showed that the brain age of children with CP was generally higher than that of healthy peers, and male patients and those with bilateral spastic CP had higher brain ages.
FRONTIERS IN NEUROLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Han Peng, Weikang Gong, Christian F. Beckmann, Andrea Vedaldi, Stephen M. Smith
Summary: This study proposed a deep convolutional neural network model, SFCN, for accurate prediction of brain age, which achieved state-of-the-art performance through the combination of various performance-boosting techniques.
MEDICAL IMAGE ANALYSIS
(2021)
Article
Neurosciences
Keun-Soo Heo, Dong-Hee Shin, Sheng-Che Hung, Weili Lin, Han Zhang, Dinggang Shen, Tae-Eui Kam
Summary: Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive modality used to investigate functional connectomes in the brain. Effective noise removal is crucial in preprocessing rs-fMRI data. This study proposes an automatic deep learning framework for noise-related component identification, achieving remarkable performance and increasing noise detection speed.
Review
Biochemical Research Methods
Yurui Chen, Louxin Zhang
Summary: This article introduces the application of deep learning in drug response prediction and summarizes the latest deep learning methods. Although deep learning methods have shown some limitations in certain cases, combining them with established bioinformatics analyses can help overcome some of these challenges.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Psychiatry
Pedro L. Ballester, Laura Tomaz da Silva, Matheus Marcon, Nathalia Bianchini Esper, Benicio N. Frey, Augusto Buchweitz, Felipe Meneguzzi
Summary: In this study, multiple regressor models were trained using convolutional neural networks to predict brain age based on single slices of MRI images. Results indicated that the specific slice used for prediction, MRI site, and slice plane all influenced the accuracy of the model's predictions.
FRONTIERS IN PSYCHIATRY
(2021)
Article
Computer Science, Information Systems
Weiping Ding, Xinjie Shen, Jiashuang Huang, Hengrong Ju, Yuepeng Chen, Tao Yin
Summary: This paper proposes a brain age prediction model using a similarity metric convolutional neural network. By introducing a Siamese convolutional neural network, the features of two groups of resting-state functional MRI (rs-fMRI) are learned simultaneously, and a similarity measurement network is designed. Experimental results show that this method has low mean absolute error and high correlation coefficient on the longitudinal imaging data set of Southwest University.
Article
Computer Science, Artificial Intelligence
Patrick Oliveira de Paula, Thiago Bulhoes da Silva Costa, Romis Ribeiro de Faissol Attux, Denis Gustavo Fantinato
Summary: Research on brain-computer interface (BCI) systems based on electroencephalography (EEG) signals is rapidly advancing, with a focus on achieving robust performance. Recently, deep learning methods, specifically convolutional neural networks (CNNs), have been applied to BCI systems to enhance performance. This study encodes EEG data as images and uses 2D-kernel-based CNNs for classification, yielding favorable results.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
M. Tanveer, M. A. Ganaie, Iman Beheshti, Tripti Goel, Nehal Ahmad, Kuan-Ting Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin
Summary: Over the years, Machine Learning models have been successfully used for predicting brain age accurately based on neuroimaging data. This review comprehensively analyzes the adoption of deep learning for brain age estimation and explores different deep learning architectures and frameworks used in this field. The paper aims to establish a common reference for newcomers and experienced researchers interested in utilizing deep learning models for brain age estimation.
INFORMATION FUSION
(2023)
Article
Agriculture, Multidisciplinary
Yura Perugachi-Diaz, Jakub M. Tomczak, Sandjai Bhulai
Summary: In this study, white cabbage seedling images were classified using convolutional neural networks. The research found that AlexNet is the best performing model, accurately classifying 94% of the seedlings.
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2021)
Article
Ecology
Ali Seydi Keceli, Aydin Kaya, Cagatay Catal, Bedir Tekinerdogan
Summary: The manual prediction of plant species and diseases is costly and time-consuming, and expertise may not always be available. Automated approaches, such as machine learning and deep learning, are being used to overcome these challenges. This study proposes a novel multi-task learning approach that combines plant species and disease prediction tasks using shared representations. The results show that this approach improves efficiency and learning speed.
ECOLOGICAL INFORMATICS
(2022)
Article
Multidisciplinary Sciences
Fabian Eitel, Jan Philipp Albrecht, Martin Weygandt, Friedemann Paul, Kerstin Ritter
Summary: A new CNN architecture is proposed to combine hierarchical abstraction idea with spatial homogeneity in neuroimaging data, introducing patch individual filters (PIF) for faster learning of abstract features specific to regions. Results show that CNNs with PIF layers converge faster and achieve better performance than standard CNNs and patch-based CNNs for sex classification, Alzheimer's disease detection, and multiple sclerosis detection tasks on different data sets.
SCIENTIFIC REPORTS
(2021)
Article
Neurosciences
Suyu Bi, Yun Guan, Lixia Tian
Summary: Both movie and resting-state functional MRI are effective and promising techniques for predicting brain age, but there are some differences in connectivity properties, particularly involving components of the default mode network.
Article
Mathematical & Computational Biology
Regina J. Meszlenyi, Krisztian Buza, Zoltan Vidnyanszky
FRONTIERS IN NEUROINFORMATICS
(2017)
Article
Neurosciences
Regina J. Meszlenyi, Petra Hermann, Krisztian Buza, Viktor Gal, Zoltan Vidnyanszky
FRONTIERS IN NEUROSCIENCE
(2017)
Proceedings Paper
Computer Science, Hardware & Architecture
Regina Meszlenyi, Ladislav Peska, Viktor Gal, Zoltan Vidnyanszky, Krisztian Buza
2016 THIRD EUROPEAN NETWORK INTELLIGENCE CONFERENCE (ENIC 2016)
(2016)
Proceedings Paper
Engineering, Electrical & Electronic
Regina Meszlenyi, Ladislav Peska, Viktor Gal, Zoltan Vidnyanszky, Krisztian Buza
2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO)
(2016)
Proceedings Paper
Computer Science, Artificial Intelligence
Rodica Ioana Lung, Mihai Suciu, Regina Meszlenyi, Krisztian Buza, Noemi Gasko
PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XIV
(2016)