IT IS NOT A SMALL WORLD FOR PSYCHIATRIC PATIENTS

Abstract

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 integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients. AUTHOR SUMMARYIndividuals 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 integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain with an effective connectivity pattern exhibits small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. We analyzed neuroimaging data from 60 subjects (13healthy controls, 21 OCD and 26 SCZ) using functional near-infrared spectroscopy (fNIRS) during a color word matching Stroop Task and computed FCMs. Small-world features were evaluated using the Global Efficiency (GE), Clustering Coefficient (CC), Modularity (Q), and small-world parameter ({sigma}). The proposed pipeline in this study for fNIRS data processing demonstrates that patients with OCD and SCZ exhibit small-world features resembling random networks, as indicated by higher GE and lower CC values compared to healthy controls, implying a higher operational cost for these patients.