Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups

STAT3-dependent reactive astrogliosis in the spinal dorsal horn underlies chronic itch

Chronic itch is an intractable symptom of inflammatory skin diseases, such as atopic and contact dermatitis1,2,3. Recent studies have revealed neuronal pathways selective for itch4,5,6,7,8, but the mechanisms by which itch turns into a pathological chronic state are poorly understood. Using mouse models of atopic and contact dermatitis, we demonstrate a long-term reactive state of astrocytes in the dorsal horn of the spinal segments that corresponds to lesioned, itchy skin. We found that reactive astrogliosis depended on the activation of signal transducer and activator of transcription 3 (STAT3). Conditional disruption of astrocytic STAT3 suppressed chronic itch, and pharmacological inhibition of spinal STAT3 ameliorated the fully developed chronic itch. Mice with atopic dermatitis exhibited an increase in scratching elicited by intrathecal administration of the itch-inducer gastrin-releasing peptide (GRP)4, and this enhancement was normalized by suppressing STAT3-mediated reactive astrogliosis. Moreover, we identified lipocalin-2 (LCN2) as an astrocytic STAT3-dependent upregulated factor that was crucial for chronic itch, and we demonstrated that intrathecal administration of LCN2 to normal mice increased spinal GRP-evoked scratching. Our findings indicate that STAT3-dependent reactive astrocytes act as critical amplifiers of itching through a mechanism involving the enhancement of spinal itch signals by LCN2, thereby providing a previously unrecognized target for treating chronic itch