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Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent functional magnetic resonance imaging (fMRI) studies (n = 182 and n = 176): (1) an anterior prefrontal system selectively involved in cognitive reappraisal; (2) a fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) a largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions downregulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge 'limbic'-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

This study tested whether brain activity patterns evoked by affective stimuli relate to individual differences in an indicator of pre-clinical atherosclerosis: carotid artery intima-media thickness (CA-IMT). Adults (aged 30-54 years) completed functional magnetic resonance imaging (fMRI) tasks that involved viewing three sets of affective stimuli. Two sets included facial expressions of emotion, and one set included neutral and unpleasant images from the International Affective Picture System (IAPS). Cross-validated, multivariate and machine learning models showed that individual differences in CA-IMT were partially predicted by brain activity patterns evoked by unpleasant IAPS images, even after accounting for age, sex and known cardiovascular disease risk factors. CA-IMT was also predicted by brain activity patterns evoked by angry and fearful faces from one of the two stimulus sets of facial expressions, but this predictive association did not persist after accounting for known cardiovascular risk factors. The reliability (internal consistency) of brain activity patterns evoked by affective stimuli may have constrained their prediction of CA-IMT. Distributed brain activity patterns could comprise affective neural correlates of pre-clinical atherosclerosis; however, the interpretation of such correlates may depend on their psychometric properties, as well as the influence of other cardiovascular risk factors and specific affective cues., (© The Author(s) 2020. Published by Oxford University Press.)

Communication between the brain and peripheral mediators of systemic inflammation is implicated in numerous psychological, behavioral, and physiological processes. Functional neuroimaging studies have identified brain regions that associate with peripheral inflammation in humans, yet there are open questions about the consistency, specificity, and network characteristics of these findings. The present systematic review provides a meta-analysis to address these questions. Multilevel kernel density analysis of 24 studies (37 statistical maps; 264 coordinates; 457 participants) revealed consistent effects in the amygdala, hippocampus, hypothalamus, striatum, insula, midbrain, and brainstem, as well as prefrontal and temporal cortices. Effects in some regions were specific to particular study designs and tasks. Spatial pattern analysis revealed significant overlap of reported effects with limbic, default mode, ventral attention, and corticostriatal networks, and co-activation analyses revealed functional ensembles encompassing the prefrontal cortex, insula, and midbrain/brainstem. Together, these results characterize brain regions and networks associated with peripheral inflammation in humans, and they provide a functional neuroanatomical reference point for future neuroimaging studies on brain-body interactions., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Depressed patients show abnormalities in brain connectivity at rest, including hyperconnectivity within the default mode network (DMN). However, there is well-known heterogeneity in the clinical presentation of depression that is overlooked when averaging connectivity data. We used data-driven parsing of neural connectivity to reveal subgroups among 80 depressed patients completing resting state fMRI. Directed functional connectivity paths (eg, region A influences region B) within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation, a method shown to accurately recover the direction and presence of connectivity paths in individual participants. Individuals were clustered using community detection on neural connectivity estimates. Subgroups were compared on network features and on clinical and biological/demographic characteristics that influence depression prognosis. Two subgroups emerged. Subgroup A, containing 71% of the patients, showed a typical pattern of connectivity across DMN nodes, as previously reported in depressed patients on average. Subgroup B exhibited an atypical connectivity profile lacking DMN connectivity, with increased dorsal anterior cingulate-driven connectivity paths. Subgroup B members had an over-representation of females (87% of Subgroup B vs 65% of Subgroup A; χ 2 =3.89, p=0.049), comorbid anxiety diagnoses (42.9% of Subgroup B vs 17.5% of Subgroup A; χ 2 =5.34, p=.02), and highly recurrent depression (63.2% of Subgroup B vs 31.8% of Subgroup A; χ 2 =5.38, p=.02). Neural connectivity-based categorization revealed an atypical pattern of connectivity in a depressed patient subset that would be overlooked in group comparisons of depressed and healthy participants, and tracks with clinically relevant phenotypes including anxious depression and episodic recurrence. Data-driven parsing suggests heterogeneous substrates of depression; ideally, future work building on these findings will inform personalized treatment.

The default mode network (DMN) encompasses brain systems that exhibit coherent neural activity at rest. DMN brain systems have been implicated in diverse social, cognitive, and affective processes, as well as risk for forms of dementia and psychiatric disorders that associate with systemic inflammation. Areas of the anterior cingulate cortex (ACC) and surrounding medial prefrontal cortex (mPFC) within the DMN have been implicated specifically in regulating autonomic and neuroendocrine processes that relate to systemic inflammation via bidirectional signaling mechanisms. However, it is still unclear whether indicators of inflammation relate directly to coherent resting state activity of the ACC, mPFC, or other areas within the DMN. Accordingly, we tested whether plasma interleukin (IL)-6, an indicator of systemic inflammation, covaried with resting-state functional connectivity of the DMN among 98 adults aged 30-54 (39% male; 81% Caucasian). Independent component analyses were applied to resting state fMRI data to generate DMN connectivity maps. Voxel-wise regression analyses were then used to test for associations between IL-6 and DMN connectivity across individuals, controlling for age, sex, body mass index, and fMRI signal motion. Within the DMN, IL-6 covaried positively with connectivity of the sub-genual ACC and negatively with a region of the dorsal medial PFC at corrected statistical thresholds. These novel findings offer evidence for a unique association between a marker of systemic inflammation (IL-6) and ACC and mPFC functional connectivity within the DMN, a network that may be important for linking aspects of immune function to psychological and behavioral states in health and disease., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: There is well-known heterogeneity in affective mechanisms in depression that may extend to positive affect. We used data-driven parsing of neural connectivity to reveal subgroups present across depressed and healthy individuals during positive processing, informing targets for mechanistic intervention., Methods: Ninety-two individuals (68 depressed patients, 24 never-depressed control subjects) completed a sustained positive mood induction during functional magnetic resonance imaging. Directed functional connectivity paths within a depression-relevant network were characterized using Group Iterative Multiple Model Estimation (GIMME), a method shown to accurately recover the direction and presence of connectivity paths in individual participants. During model selection, individuals were clustered using community detection on neural connectivity estimates. Subgroups were externally tested across multiple levels of analysis., Results: Two connectivity-based subgroups emerged: subgroup A, characterized by weaker connectivity overall, and subgroup B, exhibiting hyperconnectivity (relative to subgroup A), particularly among ventral affective regions. Subgroup predicted diagnostic status (subgroup B contained 81% of patients; 50% of control subjects; χ 2 = 8.6, p = .003) and default mode network connectivity during a separate resting-state task. Among patients, subgroup B members had higher self-reported symptoms, lower sustained positive mood during the induction, and higher negative bias on a reaction-time task. Symptom-based depression subgroups did not predict these external variables., Conclusions: Neural connectivity-based categorization travels with diagnostic category and is clinically predictive, but not clinically deterministic. Both patients and control subjects showed heterogeneous, and overlapping, profiles. The larger and more severely affected patient subgroup was characterized by ventrally driven hyperconnectivity during positive processing. Data-driven parsing suggests heterogeneous substrates of depression and possible resilience in control subjects in spite of biological overlap., (Copyright © 2016 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)