Small world properties of schizophrenia and OCD patients derived from fNIRS based functional brain network connectivity metrics


AKIN A., Yorgancıgil E., Öztürk O. C., SÜTÇÜBAŞI B., KIRIMLI C. E., Elgün Kırımlı E., ...Daha Fazla

Scientific Reports, cilt.14, sa.1, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 1
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1038/s41598-024-72199-0
  • Dergi Adı: Scientific Reports
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, Chemical Abstracts Core, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Anahtar Kelimeler: fNIRS, Functional connectivity, Global efficiency, Obsessive-compulsive disorder, Schizophrenia, Small worldness
  • Acıbadem Mehmet Ali Aydınlar Üniversitesi Adresli: Evet

Özet

Individuals suffering from obsessive compulsive disorder (OCD) and schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of functional integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain at rest is known to exhibit small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. The aim of this study was to compare the small-world properties of prefrontal cortical functional networks of healthy subjects with OCD and SCZ patient groups by use of hemodynamic data obtained with functional near infrared spectroscopy (fNIRS). 13 healthy subjects and 47 patients who were clinically diagnosed with either OCD (N = 21) or SCZ (N = 26) completed a Stroop test while their prefrontal cortex (PFC) hemodynamics were monitored with fNIRS. The Stroop test had a block design consisting of neutral, congruent and incongruent stimuli. For each subject and stimuli type, FCMs were derived separately which were then used to compute small world features that included (i) global efficiency (GE), (ii) clustering coefficient (CC), (iii) modularity (Q), and (iv) small-world parameter (σ). Small-world features of patients exhibited random networks which were indicated by higher GE and lower CC values when compared to healthy controls, implying a higher neuronal operational cost.