BackgroundThe negative impacts of spousal bereavement on the emotional health of the elderly (e.g., depression and anxiety) have been revealed. However, whether widowhood impairs emotional cognition among the elderly is less known. The purpose of this study is to reveal the emotional cognitive deficits among the widowed elderly.MethodsIn this study, we recruited 44 widowed elderly (WE) and 44 elder couples (non-widowed elderly, NWE) and examined their emotional cognition including attention and visual working memory, which were measured by the visual search task and delayed-match-to-sample task, respectively. Three kinds of emotional faces (i.e., sad, angry, and happy) were adopted as the attentional or mnemonic targets.ResultsIt revealed that WE had a general deficit in search efficiency across emotional types, while they showed mnemonic deficits in negative faces but not positive faces. Furthermore, the modeling analysis revealed that the level of depression or state anxiety of the elderly moderated the effects of widowhood on the deficits of mnemonic processing, i.e., the deficits were only evident among WE with the high level of depression or state anxiety.ConclusionThese findings reveal the attentional deficits in sad, angry, and happy faces and the mnemonic deficits in sad and angry faces among elderly who suffer from widowhood and point out the important role of emotional problems such as depression and state anxiety in modulating these emotional cognitive deficits.

Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the di culty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splittingmerging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject brain functional features for further analysis. After the user arranges functional magnetic resonance imaging (fMRI) data les and clicks a few buttons to set parameters, IABC will automatically output brain functional networks, their related time courses, and functional network connectivity. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.

Brain functional networks and connectivity have played an important role in exploring brain function for understanding the brain and disclosing the mechanisms of brain disorders. Independent component analysis (ICA) is one of the most widely applied data-driven methods to extract brain functional networks/connectivity. However, it is hard to guarantee the reliability of networks/connectivity due to the randomness of component order and the difficulty in selecting an optimal component number in ICA. To facilitate the analysis of brain functional networks and connectivity using ICA, we developed a MATLAB toolbox called Intelligent Analysis of Brain Connectivity (IABC). IABC incorporates our previously proposed group information guided independent component analysis (GIG-ICA), NeuroMark, and splitting-merging assisted reliable ICA (SMART ICA) methods, which can estimate reliable individual-subject brain functional features for further analysis. After the user arranges functional magnetic resonance imaging (fMRI) data files and clicks a few buttons to set parameters, IABC will automatically output brain functional networks, their related time courses, and functional network connectivity. All these neuroimaging measures are promising for providing clues in understanding brain function and differentiating brain disorders.