Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： Heterogeneity among mental health issues has always attracted considerable attention, thereby restricting research on mental health and cognitive neuroscience. Additionally, the person-centred approach to personality research, which emphasizes population heterogeneity, has received more attention. On the other hand, the heterogeneity among depressive patients has been a problem that cannot be ignored (most studies ignored the actual situation and directly assumed sample homogeneity). A large number of empirical studies have provided evidence that isolated personality traits are often associated with depression. Only a few studies have considered the probable effect from a taxonomy perspective. Moreover, the neural mechanisms of personality types in depression remain unclear. This study aimed to reveal different personality subtypes of depressive disorders and elucidate subtypes from the perspective of resting-state functional connectivity.Personality and resting-state functional imaging data of 135 depressive patients and 133 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected nodes of the subcortical network as regions of interest according to the power-264 template and calculated the functional connectivity map of the region of interest to the whole brain. Based on the functional connectivity map, the differences in resting-state functional connectivity between the main types were compared.Personality and resting-state functional imaging data of 159 depressive patients and 156 controls were collected. First, combined with “depression diagnosis”, the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected the amygdala, hippocampus, insula (AAL atlas) and limbic network, default network, and control network (Schaefer-Yeo template) as regions of interest and calculated the functional connectivity of the subcortical regions to the networks. ANOVA was used to compare resting-state functional connectivity between the personality types.The results showed the following. (1) Depression was more common among individuals with high neuroticism and low extraversion tendencies, but there were also individuals with low neuroticism and high extraversion tendencies. The controls were more likely to be individuals with low neuroticism and high extraversion. (2) The results of resting-state functional connectivity showed no significant difference between depression and controls. (3) The functional connectivity strength of the left amygdala/insula-limbic network was significantly different across personality subtypes.In summary, the personality subtypes of depression identified by person-centred perspectives are more in line with reality and individual cognitive patterns, and they have potential clinical adaptive value. The findings of this study enhance the understanding of heterogeneity among depressive disorders.
Subjects: Psychology >> Social Psychology submitted time 2023-03-27 Cooperative journals: 《心理学报》
Abstract： In our complex social environments, life situations are ever-changing. When dealing with these changes, there is no one-size-fits-all response or regulatory strategy suitable for all situations. Emotion regulation flexibility (ERF)—a framework for understanding individual differences in adaptive responding to ever-changing life contexts—emphasizes that individuals can flexibly deploy and adjust emotion regulation strategies according to specific characteristics of stressful situations in daily life. To achieve regulatory efficacy, it is important that one can utilize a balanced profile of ER strategies and select strategies that fit well with particular stressful situations. Specifically, using multiple ER strategies in daily life, rather than relying on only single-strategies, would indicate higher ERF. Additionally, based on leading models of strategy-situation fit, certain ER strategies are more appropriate for high versus low intensity stressful events. For instance, distraction involves with shielding oneself from negative stimuli and replacing them with irrelevant things, which may have a greater regulatory effect in high-intensity negative situations. Conversely, strategies such as reappraisal, which involves the processing of negative situations through deep cognitive change, may be more effective in lower-intensity negative situations and as a cornerstone of longer-term ER. We used the experience-sampling method (ESM) to quantify individual’s ERF; more specifically we assess participants for 1) having more or less balanced ER strategy profiles and 2) showing greater strategy-situation fit, in regard to the use of distraction versus reappraisal in the regulation of high-intensity versus low-intensity negative life events. To test the adaptive value of ERF on negative emotions and mental health, we investigated the influence of ERF on depressive and anxiety symptoms in two samples. We hypothesized that individuals with a more balanced profile of ER strategy use and a great level of strategy-situation fit would have higher levels of mental health, indicated by low levels of anxiety and depressive feelings. In sample 1, two hundred eight college students finished the ESM procedure (2859 beeps). Intensity of negative situations was measured by self-reported negative feelings for the time points where participants reported an adverse event. Simultaneously, we assessed participants’ use of two ER strategies (i.e., distraction and reappraisal). Considering the negative impact of COVID-19 on people’s daily life, we collected another sample (sample 2, 3462 beeps) with one hundred people who lived in Hubei Province, where Wuhan was in lockdown during the severe phase of COVID-19 (March 7-13, 2020). We measured intensity of negative situations (by averaging individuals’ negative feelings), as well as the use of two ER strategies at corresponding time points. After completing the ESM procedure, the participants were asked to fill out a series of emotional health questionnaires, including Beck Depression Inventory-II, Beck Anxiety Inventory and Spielberger State Anxiety Scale. Multilevel models were used to fit the covariation between the use of distraction versus reappraisal ER strategies and the intensity of negative events. Additionally, we used multiple level regression models to test whether high level of strategy-situation fit would result in lower negative feelings. To test whether a single-strategy preference would lead to higher levels of anxiety and depressive feelings compared to a multiple-strategy preference, latent profile analyses (LPA) was used. Results from the LPA indicated that individuals with preferences for rumination and express suppression reported higher levels depression and anxiety than individuals with a multi-strategy preference. In the multilevel models, results of the two independent samples both suggested individuals who were more inclined to use a higher level of distraction in response to high-intensity negative situations (e.g., adverse events or during COVID-19) and use higher levels of reappraisal during low-intensity situations (i.e., high level of ERF) reported lower levels of anxiety and depressive feelings. On the converse, individuals who tended to use more distraction in low intensity situations and more reappraisal in high intensity situations, (i.e., those showing lower ERF) reported a higher level of negative feelings. Together, our findings revealed a negative relationship between ERF and mental health problems in two samples, suggesting that having balanced ER profiles and flexibly deploying strategies in specific life contexts may have adaptive value in facilitating positive mental health. This work deepens our understanding of the interaction between ER strategies and situational demands, paving the way for future intervention research to help alleviate negative emotions associated with affective disorders or the experience of major traumatic events (such as epidemics, earthquakes, etc.).
Subjects: Psychology >> Personality Psychology submitted time 2022-11-18
Abstract：
Heterogeneity among mental health issues has always attracted considerable attention, thereby restricting research on mental health and cognitive neuroscience. Additionally, the person-centred approach to personality research, which emphasizes population heterogeneity, has received more attention. On the other hand, the heterogeneity among depressive patients has been a problem that cannot be ignored (most studies ignored the actual situation and directly assumed sample homogeneity). A large number of empirical studies have provided evidence that isolated personality traits are often associated with depression. Only a few studies have considered the probable effect from a taxonomy perspective. Moreover, the neural mechanisms of personality types in depression remain unclear. This study aimed to reveal different personality subtypes of depressive disorders and elucidate subtypes from the perspective of resting-state functional connectivity. Personality and resting-state functional imaging data of 135 depressive patients and 133 controls were collected. First, combined with "depression diagnosis", the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected nodes of the subcortical network as regions of interest according to the power-264 template and calculated the functional connectivity map of the region of interest to the whole brain. Based on the functional connectivity map, the differences in resting-state functional connectivity between the main types were compared. Personality and resting-state functional imaging data of 159 depressive patients and 156 controls were collected. First, combined with "depression diagnosis", the personality types in depressive patients and controls were identified through functional random forest. Specifically, neuroticism and extraversion (input features) were fitted with the diagnosis of depression by a random forest model. The random seeds were set to 1234, and 500 decision trees were fitted. The performance of the model was evaluated by tenfold cross-validation. Subsequently, the random forest algorithm generated a proximity matrix that represented the similarity between paired participants. Then, based on the proximity matrix, community detection clustering analysis was conducted on depressive patients and controls, and personality types associated with depression diagnosis were obtained. Finally, we selected the amygdala, hippocampus, insula (AAL atlas) and limbic network, default network, and control network (Schaefer-Yeo template) as regions of interest and calculated the functional connectivity of the subcortical regions to the networks. ANOVA was used to compare resting-state functional connectivity between the personality types. The results showed the following. (1) Depression was more common among individuals with high neuroticism and low extraversion tendencies, but there were also individuals with low neuroticism and high extraversion tendencies. The controls were more likely to be individuals with low neuroticism and high extraversion. (2) The results of resting-state functional connectivity showed no significant difference between depression and controls. (3) The functional connectivity strength of the left amygdala/insula-limbic network was significantly different across personality subtypes. In summary, the personality subtypes of depression identified by person-centred perspectives are more in line with reality and individual cognitive patterns, and they have potential clinical adaptive value. The findings of this study enhance the understanding of heterogeneity among depressive disorders.
Subjects: Psychology >> Cognitive Psychology submitted time 2022-08-01
Abstract： In our complex social environments, life situations are ever-changing. When dealing with these changes, there is no one-size-fits-all response or regulatory strategy suitable for all situations. Emotion regulation flexibility (ERF)—a framework for understanding individual differences in adaptive responding to ever-changing life contexts—emphasizes that individuals can flexibly deploy and adjust emotion regulation strategies according to specific characteristics of stressful situations in daily life. To achieve regulatory efficacy, it is important that one can utilize a balanced profile of ER strategies and select strategies that fit well with particular stressful situations. Specifically, using multiple ER strategies in daily life, rather than relying on only single-strategies, would indicate higher ERF. Additionally, based on leading models of strategy-situation fit, certain ER strategies are more appropriate for high versus low intensity stressful events. For instance, distraction involves with shielding oneself from negative stimuli and replacing them with irrelevant things, which may have a greater regulatory effect in high-intensity negative situations. Conversely, strategies such as reappraisal, which involves the processing of negative situations through deep cognitive change, may be more effective in lower-intensity negative situations and as a cornerstone of longer-term ER. We used the experience-sampling method (ESM) to quantify individual’s ERF; more specifically we assess participants for 1) having more or less balanced ER strategy profiles and 2) showing greater strategy-situation fit, in regard to the use of distraction versus reappraisal in the regulation of high-intensity versus low-intensity negative life events. To test the adaptive value of ERF on negative emotions and mental health, we investigated the influence of ERF on depressive and anxiety symptoms in two samples. We hypothesized that individuals with a more balanced profile of ER strategy use and a great level of strategy-situation fit would have higher levels of mental health, indicated by low levels of anxiety and depressive feelings. In sample 1, two hundred eight college students finished the ESM procedure (2859 beeps). Intensity of negative situations was measured by self-reported negative feelings for the time points where participants reported an adverse event. Simultaneously, we assessed participants’ use of two ER strategies (i.e., distraction and reappraisal). Considering the negative impact of COVID-19 on people’s daily life, we collected another sample (sample 2, 3462 beeps) with one hundred people who lived in Hubei Province, where Wuhan was in lockdown during the severe phase of COVID-19 (March 7-13, 2020). We measured intensity of negative situations (by averaging individuals’ negative feelings), as well as the use of two ER strategies at corresponding time points. After completing the ESM procedure, the participants were asked to fill out a series of emotional health questionnaires, including Beck Depression Inventory-II, Beck Anxiety Inventory and Spielberg State Anxiety Scale. Multilevel models were used to fit the covariation between the use of distraction versus reappraisal ER strategies and the intensity of negative events. Additionally, we used multiple level regression models to test whether high level of strategy-situation fit would result in lower negative feelings. To test whether a single-strategy preference would lead to higher levels of anxiety and depressive feelings compared to a multiple-strategy preference, latent profile analyses (LPA) was used. Results from the LPA indicated that individuals with preferences for rumination and express suppression reported higher levels depression and anxiety than individuals with a multi-strategy preference. In the multilevel models, results of the two independent samples both suggested individuals who were more inclined to use a higher level of distraction in response to high-intensity negative situations (e.g., adverse events or during COVID-19) and use higher levels of reappraisal during low-intensity situations (i.e., high level of ERF) reported lower levels of anxiety and depressive feelings. On the converse, individuals who tended to use more distraction in low intensity situations and more reappraisal in high intensity situations, (i.e., those showing lower ERF) reported a higher level of negative feelings. Together, our findings revealed a negative relationship between ERF and mental health problems in two samples, suggesting that having balanced ER profiles and flexibly deploying strategies in specific life contexts may have adaptive value in facilitating positive mental health. This work deepens our understanding of the interaction between ER strategies and situational demands, paving the way for future intervention research to help alleviate negative emotions associated with affective disorders or the experience of major traumatic events (such as epidemics, earthquakes, etc.).
Peer Review Status:
Awaiting Review
Subjects: Psychology >> Physiological Psychology Subjects: Computer Science >> Other Disciplines of Computer Science submitted time 2020-08-07
Abstract： Neuroscience have great inspiration for artificial intelligence. By drawing on the research results of these disciplines, we designed a new artificial neural network to simulate the amygdala in human brain. The neural network consists of two parts, a long-term memory network and a activation network. The memory network records the neurons sending and receiving signals and the weight between them, while the activation network records the neurons sending and receiving signals and the time point when the signals were sent. The activation network retains only a short time memory of the event and modifies the weights in the long-term memory network according to the set rules. Using this approach, we have successfully endows the agent the ability of fear emotion learning and classical conditioning learning, which is very similar to the ability of amygdala".
Peer Review Status:Awaiting Review
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