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.

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 92 individuals (68 depressed patients, 24 never-depressed controls) completed a sustained positive mood induction during fMRI. Directed functional connectivity paths 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. 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 controls;χ2=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 controls 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 controls in spite of biological overlap.

Psychologically stressful experiences evoke changes in cardiovascular physiology that may influence risk for cardiovascular disease (CVD). But what are the neural circuits and intermediate physiological pathways that link stressful experiences to cardiovascular changes that might in turn confer disease risk? This question is important because it has broader implications for our understanding of the neurophysiological pathways that link stressful and other psychological experiences to physical health. This review highlights selected findings from brain imaging studies of stressor-evoked cardiovascular reactivity and CVD risk. Converging evidence across these studies complements animal models and patient lesion studies to suggest that a network of cortical, limbic, and brainstem areas for central autonomic and physiological control are important for generating and regulating stressor-evoked cardiovascular reactivity via visceromotor and viscerosensory mechanisms. Emerging evidence further suggests that these brain areas may play a role in stress-related CVD risk, specifically by their involvement in mediating metabolically-dysregulated or extreme stressor-evoked cardiovascular reactions. Contextually, the research reviewed here offers an example of how brain imaging and health neuroscience methods can be integrated to address open and mechanistic questions about the neurophysiological pathways linking psychological stress and physical health.

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=3.89, p=0.049), comorbid anxiety diagnoses (42.9% of Subgroup B vs 17.5% of Subgroup A; χ=5.34, p=.02), and highly recurrent depression (63.2% of Subgroup B vs 31.8% of Subgroup A; χ=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.