Objective To explore the difference of white matter changes between bipolar affective disorder and schizophrenia using diffusion tensor imaging (DTI). Methods Patients with bipolar affective disorder and schizophrenia were selected from the Mental Health Center of West China Hospital of Sichuan University between October 2014 and January 2017. Volunteers were recruited from October 2014 to January 2017. The included patients were divided into bipolar affective disorder group and schizophrenia group according to their diagnosis. Volunteers were divided into normal control group. The bipolar affective disorder group was divided into two subgroups: manic episode and depressive episode. DTI was performed on the included patients and volunteers. Tract based spatial statistics (TBSS) was used to study the differences in fractional anisotropy (FA) of white matter between patients and normal controls, and FA values of two subgroups of bipolar affective disorder and schizophrenia were compared. Results A total of 99 patients and 40 normal controls were included in this study. Among them, there were 40 cases in schizophrenia group and 59 cases in bipolar affective disorder group (31 cases of manic episode and 28 cases of depressive episode). Compared with the normal control group, FA values decreased in corpus callosum, fornix, occipital forceps and left inferior longitudinal fasciculus with bipolar affective disorder group and schizophrenia group (P<0.05). There was no significant difference in FA values between bipolar affective disorder group and schizophrenia group (P>0.05), but the FA value in left posterior thalamic radiation decreased in depressive episode of bipolar affective disorder group compared with schizophrenia group (P=0.001). Conclusions There are similarities between white matter changes in bipolar affective disorder and schizophrenia. However, the white matter change in posterior thalamic radiation may be the characteristic change in depressive episode of bipolar affective disorder.
Objective To detect the contingent negative variation (CNV) in first episode deficit and non-deficit schizophrenia and the relationship between CNV and clinical symptoms. Methods Nihon Kohden evoked brain potentials machine were used to measure CNV in 60 patients with non-deficit schizophrenia (NDS), including 50 patients with deficit schizophrenia (DS) and 60 unrelated healthy controls (HC). Click-flashing paradigm was used to record the CNV and the differences among three groups were compared. The clinical status of patients with schizophrenia was determined using the Positive and Negative Syndrome Scale (PANSS). The overall functioning status was assessed using the Global Assessment of Functioning Scale (GAF). Partial correlations were computed to explore associations among the CNV in DS and the clinical data, controlling the sex, age, and education level. Results Compared to HC, both DS and NDS groups showed significantly reduced amplitude of B (F=27.38, P=0.00), significantly delayed reaction time (F=50.30, P=0.00). Compared to HC, the course of PINV in the DS group significantly shortened, while it was significantly delayed in the NDS group (F=15.32, P=0.00). Only in DS, when compared with that in HC, the latency of point A in CNV was delayed (F=61.01, P=0.00). There was no significant difference among three groups in both area of A-S2’ (F=2.34, P=0.10) and area of PINV (F=1.07, P=0.35). Amplitude of B and the course of PINV in the DS group correlated negatively with PANSS subscale of negative symptoms (r= –0.94, –0.89, respectively, Plt;0.05), whereas in the NDS group amplitude of B correlated negatively with PANSS subscale of positive symptoms (r= –0.87, Plt;0.05), but the course of PINV correlated positively with PANSS subscale of positive symptoms (r=0.88, Plt;0.05). Latency of point A in CNV, which was delayed in the DS group, correlated negatively with GAF (r= –0.48, Plt;0.05). Conclusion Generalized abnormalities of CNV existed in DS and NDS, while DS may cause more impairments in CNV than in NDS. The latency of point A in CNV may predict the social function outcomes of DS.