神经影像学
多样性(控制论)
荟萃分析
透明度(行为)
神经功能成像
计算机科学
虚假关系
心理学
数据科学
人工智能
神经科学
机器学习
医学
计算机安全
内科学
作者
Veronika Müller,Edna C. Cieslik,Angela R. Laird,Peter T. Fox,Joaquim Raduà,David Mataix‐Cols,Christopher R. Tench,Tal Yarkoni,Thomas E. Nichols,Peter E. Turkeltaub,Tor D. Wager,Simon B. Eickhoff
标识
DOI:10.1016/j.neubiorev.2017.11.012
摘要
Neuroimaging has evolved into a widely used method to investigate the functional neuroanatomy, brain-behaviour relationships, and pathophysiology of brain disorders, yielding a literature of more than 30,000 papers. With such an explosion of data, it is increasingly difficult to sift through the literature and distinguish spurious from replicable findings. Furthermore, due to the large number of studies, it is challenging to keep track of the wealth of findings. A variety of meta-analytical methods (coordinate-based and image-based) have been developed to help summarise and integrate the vast amount of data arising from neuroimaging studies. However, the field lacks specific guidelines for the conduct of such meta-analyses. Based on our combined experience, we propose best-practice recommendations that researchers from multiple disciplines may find helpful. In addition, we provide specific guidelines and a checklist that will hopefully improve the transparency, traceability, replicability and reporting of meta-analytical results of neuroimaging data.
科研通智能强力驱动
Strongly Powered by AbleSci AI