Cognitive and Neural Correlates of Loneliness and Wisdom during Emotional Bias

Grennan, Gillian; Balasubramani, Pragathi Priyadharsini; Alim, Fahad; Zafar-Khan, Mariam; Lee, Ellen; Jeste, Dilip V.; Mishra, Jyoti

doi:10.1093/cercor/bhab012

Cited by 30 publications

(29 citation statements)

References 120 publications

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“…The left lateralization for DLPFC and insula are concordant with our prior work and that of others showing left laterization linked with positive affect and self-regulation 25,29,30,[39][40][41] . Overall, these results show that stable interoceptive attention can be associated with higher levels of DMN Precuneus activity, akin to what has been observed on exteroceptive continuous performance tasks [42][43][44] .…”

Section: Resultssupporting

confidence: 91%

“…Finally, within-network connectivity of the cinguloopercular/salience network was observed to be greater during high interoceptive performance, consistent with a role of these brain regions in self-awareness and self-regulation 5,29,[45][46][47] . Furthermore, our prior research shows that ACC -Insula connectivity is strengthened when interoceptive attention abilities are trained via breath focused meditation 24 .…”

Section: Resultssupporting

confidence: 67%

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Default mode and frontal executive network interactions enable interoceptive attention & mindfulness

Grennan¹,

Balasubramani²,

Maric³

et al. 2021

Preprint

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Interoceptive attention to internal sensory signals is fundamental to mindfulness. In an attention-to-breathing task in 161 adults, we found that consistency of interoceptive attention significantly correlated with performance efficiency across several exteroceptive cognitive domain tasks. EEG source mapping within subjects showed that on low-consistency or distracted trials there was greater recruitment of frontal executive control activity in the dorsolateral prefrontal cortex (DLPFC) and suppression of the posterior default mode network (Precuneus), with increased functional connectivity between these regions. In contrast, high-consistency or attentive trials were associated with greater connectivity between Anterior Cingulate Cortex (ACC) and Insula, key nodes of the cognitive control network for attention monitoring. Notably, individual trait mindfulness was correlated with greater functional connectivity between DLPFC-Precuneus on distracted trials and greater ACC-Insula connectivity on attentive trials. These results showcase dynamic network interactions underlying objective markers of interoceptive attention and subjective rating mindfulness.

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Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 67%

Default mode and frontal executive network interactions enable interoceptive attention & mindfulness

Grennan¹,

Balasubramani²,

Maric³

et al. 2021

Preprint

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“…Participants accessed the game-like task, "Face Off", adapted from studies of attentional bias in emotional contexts [50][51][52] . The task integrated a standardized set of culturally diverse faces from the NimStim database 53 .…”

Section: Assessment 4: Emotion Biasmentioning

confidence: 99%

Personalized machine learning of depressed mood using wearables

et al. 2021

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Depression is a multifaceted illness with large interindividual variability in clinical response to treatment. In the era of digital medicine and precision therapeutics, new personalized treatment approaches are warranted for depression. Here, we use a combination of longitudinal ecological momentary assessments of depression, neurocognitive sampling synchronized with electroencephalography, and lifestyle data from wearables to generate individualized predictions of depressed mood over a 1-month time period. This study, thus, develops a systematic pipeline for N-of-1 personalized modeling of depression using multiple modalities of data. In the models, we integrate seven types of supervised machine learning (ML) approaches for each individual, including ensemble learning and regression-based methods. All models were verified using fourfold nested cross-validation. The best-fit as benchmarked by the lowest mean absolute percentage error, was obtained by a different type of ML model for each individual, demonstrating that there is no one-size-fits-all strategy. The voting regressor, which is a composite strategy across ML models, was best performing on-average across subjects. However, the individually selected best-fit models still showed significantly less error than the voting regressor performance across subjects. For each individual’s best-fit personalized model, we further extracted top-feature predictors using Shapley statistics. Shapley values revealed distinct feature determinants of depression over time for each person ranging from co-morbid anxiety, to physical exercise, diet, momentary stress and breathing performance, sleep times, and neurocognition. In future, these personalized features can serve as targets for a personalized ML-guided, multimodal treatment strategy for depression.

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“…A consistent finding in four published studies has been a strong inverse correlation between loneliness and wisdom, a multi-component personality trait, and its main component—pro-social behaviors of empathy and compassion [ 35 , 38 – 40 ]. A recent study using EEG also provided neurobiological support to the negative association between these traits [ 41 ]. Another study found that loneliness and wisdom, especially its compassion component, were associated with lower versus higher, respectively, levels of alpha and beta diversity of gut microbial taxa composition [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Compassion toward others and self-compassion predict mental and physical well-being: a 5-year longitudinal study of 1090 community-dwelling adults across the lifespan

et al. 2021

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There is growing interest in the role of compassion in promoting health and well-being, with cross-sectional data showing an inverse correlation with loneliness. This is the first longitudinal study examining both compassion toward others (CTO) and compassion toward self (CTS) as predictors of mental and physical health outcomes including loneliness, across adult lifespan. We followed 552 women and 538 men in San Diego County for up to 7.5 (mean 4.8 and SD 2.2) years, using validated rating scales for CTO, CTS, and loneliness. Linear mixed-effects models were employed to examine age- and sex-related trajectories of CTO and CTS over time. Linear regression models were used to evaluate baseline and longitudinal relationships of CTO and CTS with mental well-being, physical well-being, and loneliness. CTS and CTO were weakly intercorrelated. Women had higher baseline CTO than men. While CTO was stable over time and across the lifespan, CTS scores had an inverse U-shaped relationship with age, peaking around age 77. There were significant baseline × slope interactions of both CTO and CTS predicting improvements in physical well-being in adults <60 years old. Increases in CTO and CTS predicted improvements in mental well-being. Higher baseline CTO and CTS as well as increases in CTO and CTS scores predicted lower loneliness scores at follow-up. Thus, CTO and CTS were associated with better mental well-being and loneliness across the adult lifespan, and physical well-being in younger adults, and are promising targets for interventions to improve health outcomes.

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Cognitive and Neural Correlates of Loneliness and Wisdom during Emotional Bias

Cited by 30 publications

References 120 publications

Default mode and frontal executive network interactions enable interoceptive attention & mindfulness

Default mode and frontal executive network interactions enable interoceptive attention & mindfulness

Personalized machine learning of depressed mood using wearables

Compassion toward others and self-compassion predict mental and physical well-being: a 5-year longitudinal study of 1090 community-dwelling adults across the lifespan

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Cognitive and Neural Correlates of Loneliness and Wisdom during Emotional Bias

Cited by 30 publications

References 120 publications

Default mode and frontal executive network interactions enable interoceptive attention &amp; mindfulness

Default mode and frontal executive network interactions enable interoceptive attention &amp; mindfulness

Personalized machine learning of depressed mood using wearables

Compassion toward others and self-compassion predict mental and physical well-being: a 5-year longitudinal study of 1090 community-dwelling adults across the lifespan

Contact Info

Product

Resources

About

Default mode and frontal executive network interactions enable interoceptive attention & mindfulness

Default mode and frontal executive network interactions enable interoceptive attention & mindfulness