Detection of advanced brain aging in schizophrenia and its structural underpinning by using normative brain age metrics.

Conceptualizing mental disorders as deviations from normative functioning provides a statistical perspective for understanding the individual heterogeneity underlying psychiatric disorders. To broaden the understanding of the idiosyncrasy of brain aging in schizophrenia, we introduced an imaging-derived brain age paradigm combined with normative modeling as novel brain age metrics. We constructed brain age models based on GM, WM, and their combination (multimodality) features of 482 normal participants. The normalized predicted age difference (nPAD) was estimated in 147 individuals with schizophrenia and their 130 demographically matched controls through normative models of brain age metrics and compared between the groups. Regression analyses were also performed to investigate the associations of nPAD with illness duration, onset age, symptom severity, and intelligence quotient. Finally, regional contributions to advanced brain aging in schizophrenia were investigated. The results showed that the individuals exhibited significantly higher nPAD (P < 0.001), indicating advanced normative brain age than the normal controls in GM, WM, and multimodality models. The nPAD measure based on WM was positively associated with the negative symptom score (P = 0.009), and negatively associated with the intelligence quotient (P = 0.039) and onset age (P = 0.006). The imaging features that contributed to nPAD mostly involved the prefrontal, temporal, and parietal lobes, especially the precuneus and uncinate fasciculus. This study demonstrates that normative brain age metrics could detect advanced brain aging and associated clinical and neuroanatomical features in schizophrenia. The proposed nPAD measures may be useful to investigate aberrant brain aging in mental disorders and their brain-phenotype relationships.

Genetic associations and expression of extra-short isoforms of disrupted-in-schizophrenia 1 in a neurocognitive subgroup of schizophrenia.

Disrupted-in-schizophrenia 1 (DISC1) was reported to be associated with schizophrenia. In a previous study, we found significant association with schizophrenia patients with deficient sustained attention assessed by continuous performance test (CPT). This study aimed to identify risk polymorphisms in this specific neurocognitive subgroup and investigate the expression of different isoforms of DISC1. A total of 83 genetic variants were identified through direct sequencing in 50 controls and 100 schizophrenia patients. Fourteen variants were genotyped in 600 controls and 912 patients. Patients were subgrouped by familial loading (multiplex or simplex) and performance on CPT. The frequency of AA genotype of rs11122324 at the 3'-UTR of Es and Esv1 isoforms and of rs2793091 at intron 4 were significantly higher in multiplex schizophrenia patients than those in controls (corrected p < 0.05). In further subgrouping, the frequency of AA genotype of the two SNPs were significantly higher in multiplex schizophrenia patients with deficient sustained attention than those in controls (corrected p < 0.005). The mRNA expression levels of two extra-short isoforms (Es and Esv1) in the EBV-transformed lymphocytes of schizophrenia were significantly higher than those of controls. Luciferase reporter assays demonstrated that the A-allele of rs11122324 significantly upregulated DISC1 extra-short isoforms transcription compared with the G-allele. We found two SNPs (rs11122324 and rs2793091) of DISC1 may be specifically associated with multiplex schizophrenia patients with deficient sustained attention. The SNP rs11122324 may be a risk polymorphism, which may have functional influence on the transcription of Es and Esv1 through increasing their expression.

Shared and distinct alterations of white matter tracts in remitted and nonremitted patients with schizophrenia.

Patients with schizophrenia do not usually achieve remission state even after adequate antipsychotics treatment. Previous studies found significant difference in white matter integrity between patients with good outcomes and those with poor outcomes, but difference is still unclear at individual tract level. This study aimed to use a systematic approach to identify the tracts that were associated with remission state in patients with schizophrenia. We evaluated 91 patients with schizophrenia (remitted, 50; nonremitted, 41) and 50 healthy controls through diffusion spectrum imaging. White matter tract integrity was assessed through an automatic tract-specific analysis method to determine the mean generalized fractional anisotropy (GFA) values of the 76 white matter tract bundles in each participant. Analysis of covariance among the 3 groups revealed 12 tracts that were significantly different in GFA values. Post-hoc analysis showed that compared with the healthy controls, the nonremission group had reduced integrity in all 12 tracts, whereas the remission group had reduced integrity in only 4 tracts. Comparison between the remission and nonremission groups revealed 4 tracts with significant difference (i.e., the right fornix, bilateral uncinate fasciculi, and callosal fibers connecting the temporal poles) even after adjusting age, sex, education year, illness duration, and medication dose. Furthermore, all the 4 tracts were correlated with negative symptoms scores of the positive and negative syndrome scale. In conclusion, our study identified the tracts that were associated with remission state of schizophrenia. These tracts might be a potential prognostic marker for the symptomatic remission in patients with schizophrenia.

Individualized prediction of schizophrenia based on the whole-brain pattern of altered white matter tract integrity.

BACKGROUND: A schizophrenia diagnosis relies on characteristic symptoms identified by trained physicians, and is thus prone to subjectivity. This study developed a procedure for the individualized prediction of schizophrenia based on whole-brain patterns of altered white matter tract integrity. METHODS: The study comprised training (108 patients and 144 controls) and testing (60 patients and 60 controls) groups. Male and female participants were comparable in each group and were analyzed separately. All participants underwent diffusion spectrum imaging of the head, and the data were analyzed using the tract-based automatic analysis method to generate a standardized two-dimensional array of white matter tract integrity, called the connectogram. Unique patterns in the connectogram that most accurately identified schizophrenia were systematically reviewed in the training group. Then, the diagnostic performance of the patterns was individually verified in the testing group by using receiver-operating characteristic curve analysis. RESULTS: The performance was high in men (accuracy = 0.85) and satisfactory in women (accuracy = 0.75). In men, the pattern was located in discrete fiber tracts, as has been consistently reported in the literature; by contrast, the pattern was widespread over all tracts in women. These distinct patterns suggest that there is a higher variability in the microstructural alterations in female patients than in male patients. CONCLUSIONS: The individualized prediction of schizophrenia is feasible based on the different whole-brain patterns of tract integrity. The optimal masks and their corresponding regions in the fiber tracts could serve as potential imaging biomarkers for schizophrenia. Hum Brain Mapp 39:575-587, 2018. © 2017 Wiley Periodicals, Inc.