Diagnostic classification of schizophrenia and bipolar disorder by using dynamic functional connectivity: An fNIRS study

Affiliations

¹ TOBB University of Economics and Technology, Biomedical Engineering Department, Ankara, Turkey; Ankara University, Brain Research Center (AÜBAUM), Ankara, Turkey.
² Health Sciences University, Gülhane Research and Education Hospital, Department of Psychiatry, Ankara, Turkey.
³ Ankara University, Brain Research Center (AÜBAUM), Ankara, Turkey; Ankara University, Faculty of Medicine Department of Psychiatry, Ankara, Turkey; Neuroscience and Neurotechnology Center of Excellence (NÖROM), Ankara, Turkey. Electronic address: bbaskak@gmail.com.
⁴ Harvard Medical School, Developmental Medicine Center, Boston Children's Hospital, Boston, USA.

Aykut Eken et al. J Neurosci Methods. 2022.

. 2022 Jul 1:376:109596.

doi: 10.1016/j.jneumeth.2022.109596. Epub 2022 Apr 14.

¹ TOBB University of Economics and Technology, Biomedical Engineering Department, Ankara, Turkey; Ankara University, Brain Research Center (AÜBAUM), Ankara, Turkey.
² Health Sciences University, Gülhane Research and Education Hospital, Department of Psychiatry, Ankara, Turkey.
³ Ankara University, Brain Research Center (AÜBAUM), Ankara, Turkey; Ankara University, Faculty of Medicine Department of Psychiatry, Ankara, Turkey; Neuroscience and Neurotechnology Center of Excellence (NÖROM), Ankara, Turkey. Electronic address: bbaskak@gmail.com.
⁴ Harvard Medical School, Developmental Medicine Center, Boston Children's Hospital, Boston, USA.

No abstract available

Keywords: Bipolar disorder; Dynamic Functional Connectivity; Machine learning; Mental State Judgement; Schizophrenia; fNIRS.

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