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Introduction: Chronic exposure to excessive endogenous cortisol leads to brain changes in Cushing's disease (CD). However, it remains unclear how CD affects large-scale functional networks (FNs) and whether these effects are reversible after treatment. This study aimed to investigate functional network changes of CD patients and their reversibility in a longitudinal cohort. Methods: Active CD patients (N = 37) were treated by transsphenoidal pituitary surgery and reexamined 3 months later. FNs were computed from resting-state fMRI data of the CD patients and matched normal controls (NCs, N = 37). A pattern classifier was built on the FNs to distinguish active CD patients from controls and applied to FNs of the CD patients at the 3-month follow-up. Two subgroups of endocrine-remitted CD patients were identified according to their classification scores, referred to as image-based phenotypically (IBP) recovered and unrecovered CD patients, respectively. The informative FNs identified by the classification model were compared between NCs, active CD patients, and endocrine-remitted patients as well as between IBP recovered and unrecovered CD patients to explore their functional network reversibility. Results: All 37 CD patients reached endocrine remission after treatment. The classification model identified three informative FNs, including cerebellar network (CerebN), fronto-parietal network (FPN), and default mode network. Among them, CerebN and FPN partially recovered toward normal at 3 months after treatment. Moreover, the informative FNs were correlated with 24-h urinary-free cortisol and emotion scales in CD patients. Conclusion: These findings suggest that CD patients have aberrant FNs that are partially reversible toward normal after treatment. [ABSTRACT FROM AUTHOR]

It remains challenging to identify depression accurately due to its biological heterogeneity. As people suffering from depression are associated with functional brain network alterations, we investigated subtypes of patients with first‐episode drug‐naive (FEDN) depression based on brain network characteristics. This study included data from 91 FEDN patients and 91 matched healthy individuals obtained from the International Big‐Data Center for Depression Research. Twenty large‐scale functional connectivity networks were computed using group information guided independent component analysis. A multivariate unsupervised normative modeling method was used to identify subtypes of FEDN and their associated networks, focusing on individual‐level variability among the patients for quantifying deviations of their brain networks from the normative range. Two patient subtypes were identified with distinctive abnormal functional network patterns, consisting of 10 informative connectivity networks, including the default mode network and frontoparietal network. 16% of patients belonged to subtype I with larger extreme deviations from the normal range and shorter illness duration, while 84% belonged to subtype II with weaker extreme deviations and longer illness duration. Moreover, the structural changes in subtype II patients were more complex than the subtype I patients. Compared with healthy controls, both increased and decreased gray matter (GM) abnormalities were identified in widely distributed brain regions in subtype II patients. In contrast, most abnormalities were decreased GM in subtype I. The informative functional network connectivity patterns gleaned from the imaging data can facilitate the accurate identification of FEDN‐MDD subtypes and their associated neurobiological heterogeneity. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD., (© 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)