Guney, GokhanYigin, Busra OzgodeGuven, NecdetColak, BurcinAlici, Yasemin HosgorenErzin, GamzeSaygili, Gorkem2022-09-122022-09-1220211738-1088https://www.cpn.or.kr/journal/view.html?doi=10.9758/cpn.2021.19.2.206http://hdl.handle.net/11727/7679Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.enginfo:eu-repo/semantics/openAccessDeep learningsNeuropsychiatryArtificial neural networksConvolutional neural networksRecurrent neural networksGenerative adversarial networkarticle1922062190006441371000032-s2.0-85105957416