Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity.

BACKGROUND: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample. METHODS: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. RESULTS: The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). LIMITATIONS: This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression). CONCLUSIONS: This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications.

Background: Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Methods: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, mean±SD age=18.5±0.72). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). Limitations: This cross-sectional study included participants who were relatively healthy and relied on the self-reported assessment of depression symptoms. Conclusions: This study showcases the utility of network models in parsing heterogeneity in depressive symptoms, linking individual symptoms to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Aberrant functional brain network organization is associated with relapse during 1-year follow-up in alcohol-dependent patients.

Alcohol dependence (AD) is a debilitating disease associated with high relapse rates even after long periods of abstinence. Thus, elucidating neurobiological substrates of relapse risk is fundamental for the development of novel targeted interventions that could promote long-lasting abstinence. In the present study, we analysed resting-state functional magnetic resonance imaging (rsfMRI) data from a sample of recently detoxified patients with AD (n = 93) who were followed up for 12 months after rsfMRI assessment. Specifically, we employed graph theoretic analyses to compare functional brain network topology and functional connectivity between future relapsers (REL, n = 59), future abstainers (ABS, n = 28) and age- and gender-matched controls (CON, n = 83). Our results suggest increased whole-brain network segregation, decreased global network integration and overall blunted connectivity strength in REL compared with CON. Conversely, we found evidence for a comparable network architecture in ABS relative to CON. At the nodal level, REL exhibited decreased integration and decoupling between multiple brain systems compared with CON, encompassing regions associated with higher-order executive functions, sensory and reward processing. Among patients with AD, increased coupling between nodes implicated in reward valuation and salience attribution constitutes a particular risk factor for future relapse. Importantly, aberrant network organization in REL was consistently associated with shorter abstinence duration during follow-up, portending to a putative neural signature of relapse risk in AD. Future research should further evaluate the potential diagnostic value of the identified changes in network topology and functional connectivity for relapse prediction at the individual subject level.

A fast and intuitive method for calculating dynamic network reconfiguration and node flexibility.

Dynamic interactions between brain regions, either during rest or performance of cognitive tasks, have been studied extensively using a wide variance of methods. Although some of these methods allow elegant mathematical interpretations of the data, they can easily become computationally expensive or difficult to interpret and compare between subjects or groups. Here, we propose an intuitive and computationally efficient method to measure dynamic reconfiguration of brain regions, also termed flexibility. Our flexibility measure is defined in relation to an a-priori set of biologically plausible brain modules (or networks) and does not rely on a stochastic data-driven module estimation, which, in turn, minimizes computational burden. The change of affiliation of brain regions over time with respect to these a-priori template modules is used as an indicator of brain network flexibility. We demonstrate that our proposed method yields highly similar patterns of whole-brain network reconfiguration (i.e., flexibility) during a working memory task as compared to a previous study that uses a data-driven, but computationally more expensive method. This result illustrates that the use of a fixed modular framework allows for valid, yet more efficient estimation of whole-brain flexibility, while the method additionally supports more fine-grained (e.g. node and group of nodes scale) flexibility analyses restricted to biologically plausible brain networks.

The role of anticipated emotions in self-control: linking self-control and the anticipatory ability to engage emotions associated with upcoming events.

Self-control is typically attributed to "cold" cognitive control mechanisms that top-down influence "hot" affective impulses or emotions. In this study we tested an alternative view, assuming that self-control also rests on the ability to anticipate emotions directed toward future consequences. Using a behavioral within-subject design including an emotion regulation task measuring the ability to voluntarily engage anticipated emotions towards an upcoming event and a self-control task in which subjects were confronted with a variety of everyday conflict situations, we examined the relationship between self-control and anticipated emotions. We found that those individuals (n = 33 healthy individuals from the general population) who were better able to engage anticipated emotions to an upcoming event showed stronger levels of self-control in situations where it was necessary to resist short-term temptations or to endure short-term aversions to achieve long-term goals. This finding suggests that anticipated emotions may play a functional role in self-control-relevant deliberations with respect to possible future consequences and are not only inhibited top-down as implied by "dual system" views on self-control.

NBS-Predict: A prediction-based extension of the network-based statistic.

Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects' functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.

Self-control and interoception: Linking the neural substrates of craving regulation and the prediction of aversive interoceptive states induced by inspiratory breathing restriction.

Following the interoceptive inference framework, we set out to replicate our previously reported association of self-control and interoceptive prediction and strived to investigate the neural underpinnings subserving the relationship between self-control and aversive interoceptive predictive models. To this end, we used fMRI and a within-subject design including an inspiratory breathing-load task to examine the prediction of aversive interoceptive perturbation and a craving-regulation for palatable foods task to measure self-control. In this current study, we could successfully replicate previous effects with an independent sample (n â€‹= â€‹39) and observed that individuals who 'over-estimated' their upcoming interoceptive state with respect to experienced dyspnea (i.e., anticipated versus experienced) were more effective in the down-regulation of craving using negative future-thinking strategies. These individuals, again, obtained higher scores on a measure of trait self-control, i.e. self-regulation to achieve long-term goals. On a neural level, we found evidence that the anterior insula (AI) and the presupplementary motor area (preSMA), which were recruited in both tasks, partly accounted for these effects. Specifically, levels of AI activation during the anticipation of the aversive interoceptive state (breathing restriction) were associated with self-controlled behavior in the craving task, whereas levels of interoceptive prediction during the breathing task were conversely associated with activation in preSMA during the down-regulation of craving, whose anticipatory activity was correlated with self-control success. Moreover, during the self-control task, levels of interoceptive prediction were associated with connectivity in a spatially distributed network including among other areas the insula and regions of cognitive control, while during the interoceptive prediction task, levels of self-control were associated with connectivity in a spatially distributed network including among other regions the insula and preSMA. In sum, these findings consolidate the notion that self-control is directly linked to interoceptive inference and highlight the contribution of AI and preSMA as candidate regions underlying this relationship possibly creating processing advantages in self-control situations referring to the prediction of future internal states.

Self-control is linked to interoceptive inference: Craving regulation and the prediction of aversive interoceptive states induced with inspiratory breathing load.

The interoceptive inference framework suggests that our brain continuously anticipates future states of our body and aims to avoid events that might counteract homeostasis by minimizing prediction errors through active inference; e.g. appropriate actions. If predicted interoceptive models are inaccurate, behavior inconsistent with our long-term homeostatic goals may result; e.g. in failures in self-control. Using a within-subject design including an inspiratory breathing-load task to examine the prediction of aversive interoceptive perturbation and a craving-regulation for palatable foods task, we examined the relationship between self-control and aversive interoceptive predictive models. Those individuals (n = 51 healthy individuals from the general population) who were more accurate in predicting their interoceptive state with respect to anticipated versus experienced dyspnea were significantly more effective in the down-regulation of craving using negative future-thinking strategies. These individuals also scored higher on a measure of trait self-control, i.e. self-regulation to achieve long-term goals. Thus, individuals with more accurate predictive interoceptive models are better able to modulate cravings and thus exert better self-control.

Regulating Craving by Anticipating Positive and Negative Outcomes: A Multivariate Pattern Analysis and Network Connectivity Approach.

During self-control, we may resist short-term temptations in order to reach a favorable future (e.g., resisting cake to stay healthy). The neural basis of self-control is typically attributed to "cold," unemotional cognitive control mechanisms which inhibit affect-related regions via the prefrontal cortex (PFC). Here, we investigate the neural underpinnings of regulating cravings by mentally evoking the positive consequences of resisting a temptation (e.g., being healthy) as opposed to evoking the negative consequences of giving in to a temptation (e.g., becoming overweight). It is conceivable that when using these types of strategies, regions associated with emotional processing [e.g., striatum, ventromedial prefrontal cortex (vmPFC)] are involved in addition to control-related prefrontal and parietal regions. Thirty-one participants saw pictures of unhealthy snacks in the fMRI scanner and, depending on the trial, regulated their craving by thinking of the positive consequences of resisting, or the negative consequences of not resisting. In a control condition, they anticipated the pleasure of eating and thus, allowed the craving to occur (now-condition). In line with previous studies, we found activation of a cognitive control network during self-regulation. In the negative future thinking condition, the insula was more active than in the positive condition, while there were no activations that were stronger in the positive (> negative) future thinking condition. However, additionally, multivariate pattern analysis showed that during craving regulation, information about the valence of anticipated emotions was present in the vmPFC, the posterior cingulate cortex (PCC) and the insula. Moreover, a network including vmPFC and PCC showed higher connectivity during the positive (> negative) future thinking condition. Since these regions are often associated with affective processing, these findings suggest that "hot," affective processes may, at least in certain circumstances, play a role in self-control.

Anticipating the good and the bad: A study on the neural correlates of bivalent emotion anticipation and their malleability via attentional deployment.

In everyday life, we often deliberate about affective outcomes of decisions which can be described as ambivalent; i.e. positive and negative at the same time. For example, when looking forward to meet a dear friend at her/his favorite concert although one dislikes the music that is being performed. Thus, anticipation of bivalent emotions and their volitional regulation is an important ingredient of everyday choices. However, previous studies investigating neural substrates involved in anticipating emotional events mostly focused on anticipating either negative emotions (punishment) or positive emotions (reward) in isolation, thus inducing either of them separately. Furthermore, these studies rather focused on the effortful down-regulation of affect (i.e. reducing negative or positive affect), whereas such conflict situations may also require us to deploy attention on and thereby upregulate anticipated emotions in order to resolve a decision conflict (e.g., by focusing on positive consequences while orienting away from negative consequences of that same situation). To address this gap, we performed a series of three fMRI-experiments using simple visual and auditory stimuli in order to (i) determine the neural correlates involved when anticipating a bivalent affective outcome that is both positive and negative at the same time - related to a conflict situation and (ii) investigate their malleability during anticipation via voluntary emotion regulation using attentional focusing. In these studies, we (i) demonstrate that brain areas involved in anticipating positive (ventral striatum) and negative (anterior insula) emotional events are co-activated when anticipating the occurrence of both punishment and reward at the same time and (ii) provide evidence that attention on either the positive or the negative correlates with a shift in activations of these co-activated neural networks and associated anticipated emotions towards either the positive (increased activity in ventral striatum, ventromedial prefrontal cortex, posterior cingulate cortex) or the negative (increased activity in insula) aspect of the upcoming bivalent outcome. In summary, we provide self-report and neural evidence for the assumption that affective brain systems associated with the processing of bivalent anticipated emotions can be voluntarily controlled by cognitive emotion regulation strategies.

Evaluating the replicability, specificity, and generalizability of connectome fingerprints.

Establishing reliable, robust, and unique brain signatures from neuroimaging data is a prerequisite for precision psychiatry, and therefore a highly sought-after goal in contemporary neuroscience. Recently, the procedure of connectome fingerprinting, using brain functional connectivity profiles as such signatures, was shown to be able to accurately identify individuals from a group of 126 subjects from the Human Connectome Project (HCP). However, the specificity and generalizability of this procedure were not tested. In this replication study, we show both for the original and an extended HCP data set (n = 900 subjects), as well as for an additional data set of more commonly acquired imaging quality (n = 84) that (i) although the high accuracy can be replicated for the larger HCP 900 data set, accuracy is (ii) lower for standard neuroimaging data, and, that (iii) connectome fingerprinting may not be specific enough to distinguish between individuals. In addition, both accuracy and specificity are projected to drop considerably as the size of a data set increases. Although the moderate-to-high accuracies do suggest there is a portion of unique variance, our results suggest that connectomes may actually be quite similar across individuals. This outcome may be relevant to how precision psychiatry could benefit from inferences based on functional connectomes.

Influence of Familial Risk for Depression on Cortico-Limbic Connectivity During Implicit Emotional Processing.

Imbalances in cortico-limbic activity and functional connectivity (FC) supposedly underlie biased emotional processing and present putative intermediate phenotypes (IPs) for major depressive disorder (MDD). To prove the validity of these IPs, we assessed them in familial risk. In 70 healthy first-degree relatives of MDD patients and 70 controls, brain activity and seed-based amygdala FC were assessed during an implicit emotional processing task for fMRI containing angry and fearful faces. Using the generalized psychophysiological interaction approach, amygdala FC was assessed (a) across conditions to provide comparable data to previous studies and (b) compared between conditions to elucidate its implications for emotional processing. Associations of amygdala FC with self-reported negative affect were explored post hoc. Groups did not differ in brain activation. In relatives, amygdala FC across conditions was decreased with superior and medial frontal gyrus (SFG, MFG) and increased with subgenual and perigenual anterior cingulate cortex (sgACC, pgACC). NA was inversely correlated with amygdala FC with MFG, pgACC and their interaction in relatives. Relatives showed aberrant condition-dependent modulations of amygdala FC with visual cortex, thalamus and orbitofrontal cortex. Our results do not support imbalanced cortico-limbic activity as IP for MDD. Diminished amygdala-dorsomedial prefrontal FC in relatives might indicate insufficient regulatory capacity, which appears to be compensated by ventromedial prefrontal regions. Differential task-dependent modulations of amygdala FC are discussed as a stronger involvement of automatic instead of voluntary emotional processing pathways. Reliability and etiological implications of these results should be investigated in future studies including longitudinal designs and patient-risk-control comparisons.

Segregation of face sensitive areas within the fusiform gyrus using global signal regression? A study on amygdala resting-state functional connectivity.

The application of global signal regression (GSR) to resting-state functional magnetic resonance imaging data and its usefulness is a widely discussed topic. In this article, we report an observation of segregated distribution of amygdala resting-state functional connectivity (rs-FC) within the fusiform gyrus (FFG) as an effect of GSR in a multi-center-sample of 276 healthy subjects. Specifically, we observed that amygdala rs-FC was distributed within the FFG as distinct anterior versus posterior clusters delineated by positive versus negative rs-FC polarity when GSR was performed. To characterize this effect in more detail, post hoc analyses revealed the following: first, direct overlays of task-functional magnetic resonance imaging derived face sensitive areas and clusters of positive versus negative amygdala rs-FC showed that the positive amygdala rs-FC cluster corresponded best with the fusiform face area, whereas the occipital face area corresponded to the negative amygdala rs-FC cluster. Second, as expected from a hierarchical face perception model, these amygdala rs-FC defined clusters showed differential rs-FC with other regions of the visual stream. Third, dynamic connectivity analyses revealed that these amygdala rs-FC defined clusters also differed in their rs-FC variance across time to the amygdala. Furthermore, subsample analyses of three independent research sites confirmed reliability of the effect of GSR, as revealed by similar patterns of distinct amygdala rs-FC polarity within the FFG. In this article, we discuss the potential of GSR to segregate face sensitive areas within the FFG and furthermore discuss how our results may relate to the functional organization of the face-perception circuit.

Nothing to lose: processing blindness to potential losses drives thrill and adventure seekers.

Sensation seeking has been linked to increased risk taking and is therefore crucial in influencing behavioral outcomes of risk-taking behavior. Using functional magnetic resonance imaging (fMRI), the neural underpinnings of risk appraisal were studied in a large subject sample (n=188), stratified according to thrill and adventure seeking (TAS) ratings. As defined by a median split of the sample, low and high TAS groups were compared on a simple decision-making task completed during fMRI. The task was designed such that risk (i.e., magnitude of outcome) and gains (i.e., direction of outcome) could be mapped independently. Behavioral analysis indicated that high TAS individuals are more sensitive to rewards but less discriminating between risk with and without punishment and that low TAS individuals are less sensitive to rewards but quite sensitive to receiving punishments in risky situations. Imaging results on the group differences for the interaction between level of risk and level of gain showed differences in the right superior frontal gyrus (BA6), left insula (BA21), right nucleus accumbens, left lentiform nucleus, and left precuneus (BA7). The presented data suggest a neural model of risk processing in sensation seeking individuals such that the positive response to reward outweighs the impact of equivalent loss. This imbalance in approach/avoidance is evident in differences in the underlying neural substrates in TAS individuals and leads to greater risk behavior in the face of potential loss.