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Methamphetamine abuse and dependence, frequently accompanied by schizophrenia-like psychotic symptoms [methamphetamine-associated psychosis (MAP)], is a serious public health problem worldwide. Few studies, however, have characterized brain dysfunction associated with MAP, nor investigated similarities and differences in brain dysfunction between MAP and schizophrenia. We compared prefrontal cortical activity associated with stop-signal inhibitory task in 21 patients with MAP, 14 patients with schizophrenia and 21 age- and gender-matched healthy controls using a 52-channel near-infrared spectroscopy (NIRS) system. Both the MAP and the schizophrenia groups showed significantly reduced activation in the bilateral ventrolateral prefrontal cortex compared with controls; however, only the MAP group showed reduced activation in the frontopolar prefrontal cortex. The MAP group demonstrated significant positive correlations between task performance and hemodynamic responses in the bilateral ventrolateral, polar and left dorsolateral regions of the prefrontal cortex. The MAP and schizophrenia groups demonstrated a significant difference in the relationship of impulsivity to hemodynamic changes in the bilateral premotor cortex. These findings characterize similarities and differences in prefrontal cortical dysfunction between psychosis associated with methamphetamine and schizophrenia. The reduced hemodynamic changes in the bilateral ventrolateral prefrontal cortex suggest a common underlying pathophysiology of MAP and schizophrenia, whereas those in the frontopolar prefrontal cortex point to an impaired state that is either inherent or caused specifically by methamphetamine use.

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Abstract. It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level-dependent signals was analyzed. A three-way analysis of variance (signal depth × Hb kind × task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F ð1;1286Þ ¼ 5.34, p < 0.05]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.