The Predictive Potential of Heart Rate Variability for Depression.

Heart rate variability (HRV),a measure of the fluctuations in the intervals between consecutive heartbeats, is an indicator of changes in the autonomic nervous system. A chronic reduction in HRV has been repeatedly linked to clinical depression. However, the chronological and mechanistic aspects of this relationship, between the neural, physiological, and psychopathological levels, remain unclear. In this review we present evidence by which changes in HRV might precede the onset of depression. We describe several pathways that can facilitate this relationship. First, we examine a theoretical model of the impact of autonomic imbalance on HRV and its role in contributing to mood dysregulation and depression. We then highlight brain regions that are regulating both HRV and emotion, suggesting these neural regions, and the Insula in particular, as potential mediators of this relationship. We also present additional possible mediating mechanisms involving the immune system and inflammation processes. Lastly, we support this model by showing evidence that modification of HRV with biofeedback leads to an improvement in some symptoms of depression. The possibility that changes in HRV precede the onset of depression is critical to put to the test, not only because it could provide insights into the mechanisms of the illness but also because it may offer a predictive anddiagnosticphysiological marker for depression. Importantly, it could also help to develop new effective clinical interventions for treating depression.

Neural responses to reward valence and magnitude from pre- to early adolescence.

BACKGROUND: Neural activation during reward processing is thought to underlie critical behavioral changes that take place during the transition to adolescence (e.g., learning, risk-taking). Though literature on the neural basis of reward processing in adolescence is booming, important gaps remain. First, more information is needed regarding changes in functional neuroanatomy in early adolescence. Another gap is understanding whether sensitivity to different aspects of the incentive (e.g., magnitude and valence) changes during the transition into adolescence. We used fMRI from a large sample of preadolescent children to characterize neural responses to incentive valence vs. magnitude during anticipation and feedback, and their change over a period of two years. METHODS: Data were taken from the Adolescent Cognitive and Brain DevelopmentSM (ABCD®) study release 3.0. Children completed the Monetary Incentive Delay task at baseline (ages 9-10) and year 2 follow-up (ages 11-12). Based on data from two sites (N = 491), we identified activation-based Regions of Interest (ROIs; e.g., striatum, prefrontal regions, etc.) that were sensitive to trial type (win $5, win $0.20, neutral, lose $0.20, lose $5) during anticipation and feedback phases. Then, in an independent subsample (N = 1470), we examined whether these ROIs were sensitive to valence and magnitude and whether that sensitivity changed over two years. RESULTS: Our results show that most ROIs involved in reward processing (including the striatum, prefrontal cortex, and insula) are specialized, i.e., mainly sensitive to either incentive valence or magnitude, and this sensitivity was consistent over a 2-year period. The effect sizes of time and its interactions were significantly smaller (0.002≤η2≤0.02) than the effect size of trial type (0.06≤η2≤0.30). Interestingly, specialization was moderated by reward processing phase but was stable across development. Biological sex and pubertal status differences were few and inconsistent. Developmental changes were mostly evident during success feedback, where neural reactivity increased over time. CONCLUSIONS: Our results suggest sub-specialization to valence vs. magnitude within many ROIs of the reward circuitry. Additionally, in line with theoretical models of adolescent development, our results suggest that the ability to benefit from success increases from pre- to early adolescence. These findings can inform educators and clinicians and facilitate empirical research of typical and atypical motivational behaviors during a critical time of development.

A highly replicable decline in mood during rest and simple tasks.

Does our mood change as time passes? This question is central to behavioural and affective science, yet it remains largely unexamined. To investigate, we intermixed subjective momentary mood ratings into repetitive psychology paradigms. Here we demonstrate that task and rest periods lowered participants' mood, an effect we call 'Mood Drift Over Time'. This finding was replicated in 19 cohorts totalling 28,482 adult and adolescent participants. The drift was relatively large (-13.8% after 7.3 min of rest, Cohen's d = 0.574) and was consistent across cohorts. Behaviour was also impacted: participants were less likely to gamble in a task that followed a rest period. Importantly, the drift slope was inversely related to reward sensitivity. We show that accounting for time using a linear term significantly improves the fit of a computational model of mood. Our work provides conceptual and methodological reasons for researchers to account for time's effects when studying mood and behaviour.

Emotion-related impulsivity and risky decision-making: A systematic review and meta-regression.

Emotion-related impulsivity, the trait-like tendency toward regrettable behavior during states of high emotion, is a robust predictor of internalizing and externalizing psychopathology. Despite substantial evidence that emotion-related impulsivity is important transdiagnostically, relatively little is known about its cognitive correlates. This systematic review and meta-regression investigates one such candidate, risky decision-making. We analyzed 195 effect sizes from 51 studies of 14,957 total participants, including 105 newly calculated effect sizes that were not reported in the original publications. The meta-regression demonstrated evidence for a small, positive relationship of emotion-related impulsivity with behavioral indices of risky decision-making (ß = 0.086). Effects generalized across sample age, gender, Positive versus Negative Urgency, and clinical versus nonclinical samples. The average effect size varied by task type, with stronger effects for the Iowa Gambling Task and Delay Discounting Task. Experimental arousal manipulation was nearly a significant moderator, with stress and pharmacological manipulations yielding significant effect sizes. Analyses indicated that publication bias did not skew the current findings. Notwithstanding limitations, the data suggest that risky decision-making is a cognitive domain that relates to emotion-related impulsivity. We conclude with recommendations regarding the specific types of tasks and arousal inductions that will best capture emotion-related impulsivity in future experimental research.

Magnetoencephalographic correlates of mood and reward dynamics in human adolescents.

Despite its omnipresence in everyday interactions and its importance for mental health, mood and its neuronal underpinnings are poorly understood. Computational models can help identify parameters affecting self-reported mood during mood induction tasks. Here, we test if computationally modeled dynamics of self-reported mood during monetary gambling can be used to identify trial-by-trial variations in neuronal activity. To this end, we shifted mood in healthy (N = 24) and depressed (N = 30) adolescents by delivering individually tailored reward prediction errors while recording magnetoencephalography (MEG) data. Following a pre-registered analysis, we hypothesize that the expectation component of mood would be predictive of beta-gamma oscillatory power (25-40 Hz). We also hypothesize that trial variations in the source localized responses to reward feedback would be predicted by mood and by its reward prediction error component. Through our multilevel statistical analysis, we found confirmatory evidence that beta-gamma power is positively related to reward expectation during mood shifts, with localized sources in the posterior cingulate cortex. We also confirmed reward prediction error to be predictive of trial-level variations in the response of the paracentral lobule. To our knowledge, this is the first study to harness computational models of mood to relate mood fluctuations to variations in neural oscillations with MEG.

The temporal representation of experience in subjective mood.

Humans refer to their mood state regularly in day-to-day as well as clinical interactions. Theoretical accounts suggest that when reporting on our mood we integrate over the history of our experiences; yet, the temporal structure of this integration remains unexamined. Here, we use a computational approach to quantitatively answer this question and show that early events exert a stronger influence on reported mood (a primacy weighting) compared to recent events. We show that a Primacy model accounts better for mood reports compared to a range of alternative temporal representations across random, consistent, or dynamic reward environments, different age groups, and in both healthy and depressed participants. Moreover, we find evidence for neural encoding of the Primacy, but not the Recency, model in frontal brain regions related to mood regulation. These findings hold implications for the timing of events in experimental or clinical settings and suggest new directions for individualized mood interventions.

Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression.

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

Unstructured network topology begets order-based representation by privileged neurons.

How spiking activity reverberates through neuronal networks, how evoked and spontaneous activity interacts and blends, and how the combined activities represent external stimulation are pivotal questions in neuroscience. We simulated minimal models of unstructured spiking networks in silico, asking whether and how gentle external stimulation might be subsequently reflected in spontaneous activity fluctuations. Consistent with earlier findings in silico and in vitro, we observe a privileged subpopulation of 'pioneer neurons' that, by their firing order, reliably encode previous external stimulation. We also confirm that pioneer neurons are 'sensitive' in that they are recruited by small fluctuations of population activity. We show that order-based representations rely on a 'chain' of pioneer neurons with different degrees of sensitivity and thus constitute an emergent property of collective dynamics. The forming of such representations is greatly favoured by a broadly heterogeneous connection topology-a broad 'middle class' in degree of connectedness. In conclusion, we offer a minimal model for the representational role of pioneer neurons, as observed experimentally in vitro. In addition, we show that broadly heterogeneous connectivity enhances the representational capacity of unstructured networks.

Bidirectional Associations Between Stress and Reward Processing in Children and Adolescents: A Longitudinal Neuroimaging Study.

BACKGROUND: Aberrations in both neural reward processing and stress reactivity are associated with increased risk for mental illness; yet, how these two factors relate to each other remains unclear. Several studies suggest that stress exposure impacts reward function, thus increasing risk for psychopathology. However, the alternative hypothesis, in which reward dysfunction impacts stress reactivity, has been rarely examined. The current study aimed to test both hypotheses using a longitudinal design. METHODS: Participants were 38 children (23 girls; 61%) from a prospective cohort study. A standard stress-exposure measure was collected at 7 years of age. Children performed a well-validated imaging reward paradigm at age 10, and a standardized acute psychological stress laboratory protocol was administered both at age 10 and at age 13. Structural equation modeling was used to examine bidirectional associations between stress and neural response to reward anticipation. RESULTS: Higher exposure to stressful life events at age 7 predicted lower neural response to reward anticipation in regions of the basal ganglia at age 10, which included ventral caudate, nucleus accumbens, putamen, and globus pallidus. Lower response to reward anticipation in medial prefrontal and anterior cingulate cortex predicted higher stress reactivity at age 13. CONCLUSIONS: Our findings provide support for bidirectional associations between stress and reward processing, in that stress may impact reward anticipation, but also in that reduced reward anticipation may increase susceptibility to stress.

A Biohybrid Setup for Coupling Biological and Neuromorphic Neural Networks.

Developing technologies for coupling neural activity and artificial neural components, is key for advancing neural interfaces and neuroprosthetics. We present a biohybrid experimental setting, where the activity of a biological neural network is coupled to a biomimetic hardware network. The implementation of the hardware network (denoted NeuroSoC) exhibits complex dynamics with a multiplicity of time-scales, emulating 2880 neurons and 12.7 M synapses, designed on a VLSI chip. This network is coupled to a neural network in vitro, where the activities of both the biological and the hardware networks can be recorded, processed, and integrated bidirectionally in real-time. This experimental setup enables an adjustable and well-monitored coupling, while providing access to key functional features of neural networks. We demonstrate the feasibility to functionally couple the two networks and to implement control circuits to modify the biohybrid activity. Overall, we provide an experimental model for neuromorphic-neural interfaces, hopefully to advance the capability to interface with neural activity, and with its irregularities in pathology.

Modulation of anterior cingulate cortex reward and penalty signalling in medication-naive young-adult subjects with depressive symptoms following acute dose lurasidone.

BACKGROUND: Aberrations in reward and penalty processing are implicated in depression and putatively reflect altered dopamine signalling. This study exploits the advantages of a placebo-controlled design to examine how a novel D2 antagonist with adjunctive antidepressant properties modifies activity in the brain's reward network in depression. METHODS: We recruited 43 medication-naïve subjects across the range of depression severity (Beck's Depression Inventory-II score range: 0-43), including healthy volunteers, as well as people meeting full-criteria for major depressive disorder. In a double-blind placebo-controlled cross-over design, all subjects received either placebo or lurasidone (20 mg) across two visits separated by 1 week. Functional magnetic resonance imaging with the Monetary Incentive Delay (MID) task assessed reward functions via neural responses during anticipation and receipt of gains and losses. Arterial spin labelling measured cerebral blood flow (CBF) at rest. RESULTS: Lurasidone altered fronto-striatal activity during anticipation and outcome phases of the MID task. A significant three-way Medication-by-Depression severity-by-Outcome interaction emerged in the anterior cingulate cortex (ACC) after correction for multiple comparisons. Follow-up analyses revealed significantly higher ACC activation to losses in high- v. low depression participants in the placebo condition, with a normalisation by lurasidone. This effect could not be accounted for by shifts in resting CBF. CONCLUSIONS: Lurasidone acutely normalises reward processing signals in individuals with depressive symptoms. Lurasidone's antidepressant effects may arise from reducing responses to penalty outcomes in individuals with depressive symptoms.

Reward Processing in Depression: A Conceptual and Meta-Analytic Review Across fMRI and EEG Studies.

OBJECTIVE: A role for aberrant reward processing in the pathogenesis of depression has long been proposed. However, no review has yet examined its role in depression by integrating conceptual and quantitative findings across functional MRI (fMRI) and EEG methodologies. The authors quantified these effects, with an emphasis on development. METHOD: A total of 38 fMRI and 12 EEG studies were entered into fMRI and EEG meta-analyses. fMRI studies primarily examined reward anticipation and reward feedback. These were analyzed using the activation likelihood estimation method. EEG studies involved mainly the feedback-related negativity (FRN) event-related potential, and these studies were analyzed using random-effects meta-analysis of the association between FRN and depression. RESULTS: Analysis of fMRI studies revealed significantly reduced striatal activation in depressed compared with healthy individuals during reward feedback. When region-of-interest analyses were included, reduced activation was also observed in reward anticipation, an effect that was stronger in individuals under age 18. FRN was also significantly reduced in depression, with pronounced effects in individuals under age 18. In longitudinal studies, reduced striatal activation in fMRI and blunted FRN in EEG were found to precede the onset of depression in adolescents. CONCLUSIONS: Taken together, the findings show consistent neural aberrations during reward processing in depression, namely, reduced striatal signal during feedback and blunted FRN. These aberrations may underlie the pathogenesis of depression and have important implications for development of new treatments.

Is the encoding of Reward Prediction Error reliable during development?

Reward Prediction Errors (RPEs), defined as the difference between the expected and received outcomes, are integral to reinforcement learning models and play an important role in development and psychopathology. In humans, RPE encoding can be estimated using fMRI recordings, however, a basic measurement property of RPE signals, their test-retest reliability across different time scales, remains an open question. In this paper, we examine the 3-month and 3-year reliability of RPE encoding in youth (mean age at baseline = 10.6 ±â€¯0.3 years), a period of developmental transitions in reward processing. We show that RPE encoding is differentially distributed between the positive values being encoded predominantly in the striatum and negative RPEs primarily encoded in the insula. The encoding of negative RPE values is highly reliable in the right insula, across both the long and the short time intervals. Insula reliability for RPE encoding is the most robust finding, while other regions, such as the striatum, are less consistent. Striatal reliability appeared significant as well once covarying for factors, which were possibly confounding the signal to noise ratio. By contrast, task activation during feedback in the striatum is highly reliable across both time intervals. These results demonstrate the valence-dependent differential encoding of RPE signals between the insula and striatum, and the consistency of RPE signals or lack thereof, during childhood and into adolescence. Characterizing the regions where the RPE signal in BOLD fMRI is a reliable marker is key for estimating reward-processing alterations in longitudinal designs, such as developmental or treatment studies.

Long-range synchrony and emergence of neural reentry.

Neural synchronization across long distances is a functionally important phenomenon in health and disease. In order to access the basis of different modes of long-range synchrony, we monitor spiking activities over centimetre scale in cortical networks and show that the mode of synchrony depends upon a length scale, λ, which is the minimal path that activity should propagate through to find its point of origin ready for reactivation. When λ is larger than the physical dimension of the network, distant neuronal populations operate synchronously, giving rise to irregularly occurring network-wide events that last hundreds of milliseconds to several seconds. In contrast, when λ approaches the dimension of the network, a continuous self-sustained reentry propagation emerges, a regular seizure-like mode that is marked by precise spatiotemporal patterns ('synfire chains') and may last many minutes. Termination of a reentry phase is preceded by a decrease of propagation speed to a halt. Stimulation decreases both propagation speed and λ values, which modifies the synchrony mode respectively. The results contribute to the understanding of the origin and termination of different modes of neural synchrony as well as their long-range spatial patterns, while hopefully catering to manipulation of the phenomena in pathological conditions.

Controlling neural network responsiveness: tradeoffs and constraints.

In recent years much effort is invested in means to control neural population responses at the whole brain level, within the context of developing advanced medical applications. The tradeoffs and constraints involved, however, remain elusive due to obvious complications entailed by studying whole brain dynamics. Here, we present effective control of response features (probability and latency) of cortical networks in vitro over many hours, and offer this approach as an experimental toy for studying controllability of neural networks in the wider context. Exercising this approach we show that enforcement of stable high activity rates by means of closed loop control may enhance alteration of underlying global input-output relations and activity dependent dispersion of neuronal pair-wise correlations across the network.