干旱区生态环境知识资源中心(Arid): A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint

Arid

DOI	10.1016/j.jneumeth.2022.109475
	A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint
	Akti, Seymanur; Kamar, Dogay; Ozlu, Ozgur Anil; Soydemir, Ihsan; Akcan, Muhammet; Kul, Abdullah; Rekik, Islem
通讯作者	Rekik, I
来源期刊	JOURNAL OF NEUROSCIENCE METHODS
ISSN	0165-0270
EISSN	1872-678X
出版年	2022
卷号	368
英文摘要	Background: Predicting the evolution of the brain network, also called connectome, by foreseeing changes in the connectivity weights linking pairs of anatomical regions makes it possible to spot connectivity-related neurological disorders in earlier stages and detect the development of potential connectomic anomalies. Remarkably, such a challenging prediction problem remains least explored in the predictive connectomics literature. It is a known fact that machine learning (ML) methods have proven their predictive abilities in a wide variety of computer vision problems. However, ML techniques specifically tailored for the prediction of brain connectivity evolution trajectory from a single timepoint are almost absent. New method: To fill this gap, we organized a Kaggle competition where 20 competing teams designed advanced machine learning pipelines for predicting the brain connectivity evolution from a single timepoint. The teams developed their ML pipelines with combination of data pre-processing, dimensionality reduction and learning methods. Each ML framework inputs a baseline brain connectivity matrix observed at baseline timepoint t0 and outputs the brain connectivity map at a follow-up timepoint t1. The longitudinal OASIS-2 dataset was used for model training and evaluation. Both random data split and 5-fold cross-validation strategies were used for ranking and evaluating the generalizability and scalability of each competing ML pipeline. Results: Utilizing an inclusive approach, we ranked the methods based on two complementary evaluation metrics (mean absolute error (MAE) and Pearson Correlation Coefficient (PCC)) and their performances using different training and testing data perturbation strategies (single random split and cross-validation). The final rank was calculated using the rank product for each competing team across all evaluation measures and validation strategies. Furthermore, we added statistical significance values to each proposed pipeline. Conclusion: In support of open science, the developed 20 ML pipelines along with the connectomic dataset are made available on GitHub (https://github.com/basiralab/Kaggle-BrainNetPrediction-Toolbox). The outcomes of this competition are anticipated to lead the further development of predictive models that can foresee the evolution of the brain connectivity over time, as well as other types of networks (e.g., genetic networks).
英文关键词	Machine learning Brain connectivity evolution prediction Python toolbox Kaggle competition
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000788155700005
WOS关键词	NETWORKS ; MRI ; DIAGNOSIS
WOS类目	Biochemical Research Methods ; Neurosciences
WOS研究方向	Biochemistry & Molecular Biology ; Neurosciences & Neurology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393561
推荐引用方式 GB/T 7714	Akti, Seymanur,Kamar, Dogay,Ozlu, Ozgur Anil,et al. A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint[J],2022,368.
APA	Akti, Seymanur.,Kamar, Dogay.,Ozlu, Ozgur Anil.,Soydemir, Ihsan.,Akcan, Muhammet.,...&Rekik, Islem.(2022).A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint.JOURNAL OF NEUROSCIENCE METHODS,368.
MLA	Akti, Seymanur,et al."A comparative study of machine learning methods for predicting the evolution of brain connectivity from a baseline timepoint".JOURNAL OF NEUROSCIENCE METHODS 368(2022).