Exploring the Lifelong Changes of Interaction between Cingulo-Opercular Network and Other Cognitive Control Related Functional Networks Based on Multiple Connectivity Indices.

BACKGROUND: The cingulo-opercular network (CON) has been proposed to play a central role in cognitive control. The lifetime change mechanism of its integrity and interaction with other cognitive control-related functional networks (CCRNs) is closely associated with developing cognitive control behaviors but needs further elucidation. METHODS: The resting-state functional magnetic resonance imaging data were recorded from 207 subjects, who were divided into three age groups: age 4-20, 21-59, and 60-85 years old. For each group, multiple indices (cross-correlation, total independence, and Granger causality) within CON and between CON and other cognitive control-related functional networks (dorsal attention network, DAN; central executive network, CEN; default mode network, DMN) were calculated and correlated with age to yield maps that delineated the changing pattern of CON-related interaction. RESULTS: We found three main results. (1) The connectivity indices within the CON and between CON and the other three CCRNs showed significant enhancement from childhood to early adulthood (age 4-20 years), (2) mild attenuation within CON from early adulthood to middle age (age 21-59 years), and (3) significant attenuation within CON and between CON and DMN in the elder group (age 60-85 years). CONCLUSIONS: The results indicated the prominently increased integrity of within-CON and CON-CCRNs communication, mildly weakened within-CON communication, and significantly attenuated within-CON and CON-DMN communication, characterizing distinct changing patterns of CON-interaction at three different stages that covered a life-long span.

Unbalanced amygdala communication in major depressive disorder.

BACKGROUND: Previous studies suggested an association between functional alteration of the amygdala and typical major depressive disorder (MDD) symptoms. Examining whether and how the interaction between the amygdala and regions/functional networks is altered in patients with MDD is important for understanding its neural basis. METHODS: Resting-state functional magnetic resonance imaging data were recorded from 67 patients with MDD and 74 age- and sex-matched healthy controls (HCs). A framework for large-scale network analysis based on seed mappings of amygdala sub-regions, using a multi-connectivity-indicator strategy (cross-correlation, total interdependencies (TI), Granger causality (GC), and machine learning), was employed. Multiple indicators were compared between the two groups. The altered indicators were ranked in a supporting-vector machine-based procedure and associated with the Hamilton Rating Scale for Depression scores. RESULTS: The amygdala connectivity with the default mode network and ventral attention network regions was enhanced and that with the somatomotor network, dorsal frontoparietal network, and putamen regions in patients with MDD was reduced. The machine learning analysis highlighted altered indicators that were most conducive to the classification between the two groups. LIMITATIONS: Most patients with MDD received different pharmacological treatments. It is difficult to illustrate the medication state's effect on the alteration model because of its complex situation. CONCLUSION: The results indicate an unbalanced interaction model between the amygdala and functional networks and regions essential for various emotional and cognitive functions. The model can help explain potential aberrancy in the neural mechanisms that underlie the functional impairments observed across various domains in patients with MDD.