Reproducible abnormalities of functional gradient reliably predict clinical and cognitive symptoms in schizophrenia
Posted 24 Nov 2020
bioRxiv DOI: 10.1101/2020.11.24.395251
Posted 24 Nov 2020
Background: Schizophrenia (SZ) typically manifests heterogeneous phenotypes involving positive, negative and cognitive symptoms. However, the underlying neural mechanisms of these symptoms keep unclear. Functional gradient is a fascinating measure to characterize continuous, hierarchical organization of brain. Methods: We aimed to investigate whether reproducible disruptions of functional gradient existed in SZ compared to normal controls (NC), and these abnormalities were associated with severity of clinical and cognitive symptoms in SZ. All analyses were implemented in two independent large-sample multi-site datasets (discovery dataset, 400 SZ and 336 NC; replication dataset, 279 SZ and 262 NC). First, functional gradient across cerebral cortex was calculated in each subject. Second, vertex-wise comparisons of cortical gradient between SZ and NC groups were performed to identify abnormalities in SZ. Meanwhile, reproducible and robustness analyses were implemented to validate these abnormalities. Finally, regression analyses were performed using generalized additive models to link these abnormalities to severity of clinical and cognitive symptoms in SZ. Results: We found an abnormal gradient map in SZ in the discovery dataset, which was reproducible in the replication dataset. The abnormal gradient pattern was also robust when performing methodological alternatives and control analyses. Further, these reproducible abnormalities can reliably predict symptoms of clinical and cognitive domains across the two independent datasets. Conclusion: These findings demonstrated that alterations in functional gradient can provide a reliable signature of SZ, characterizing the heterogenous symptoms of clinical or cognitive domains, and may be further investigated to understand the neurobiological mechanisms of these symptoms.
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