Brain decoding of spontaneous thought: Predictive modeling of self-relevance and valence using personal narratives.

The contents and dynamics of spontaneous thought are important factors for personality traits and mental health. However, assessing spontaneous thoughts is challenging due to their unconstrained nature, and directing participants' attention to report their thoughts may fundamentally alter them. Here, we aimed to decode two key content dimensions of spontaneous thought-self-relevance and valence-directly from brain activity. To train functional MRI-based predictive models, we used individually generated personal stories as stimuli in a story-reading task to mimic narrative-like spontaneous thoughts (n = 49). We then tested these models on multiple test datasets (total n = 199). The default mode, ventral attention, and frontoparietal networks played key roles in the predictions, with the anterior insula and midcingulate cortex contributing to self-relevance prediction and the left temporoparietal junction and dorsomedial prefrontal cortex contributing to valence prediction. Overall, this study presents brain models of internal thoughts and emotions, highlighting the potential for the brain decoding of spontaneous thought.

Brain representations of affective valence and intensity in sustained pleasure and pain.

Pleasure and pain are two fundamental, intertwined aspects of human emotions. Pleasurable sensations can reduce subjective feelings of pain and vice versa, and we often perceive the termination of pain as pleasant and the absence of pleasure as unpleasant. This implies the existence of brain systems that integrate them into modality-general representations of affective experiences. Here, we examined representations of affective valence and intensity in an functional MRI (fMRI) study (n = 58) of sustained pleasure and pain. We found that the distinct subpopulations of voxels within the ventromedial and lateral prefrontal cortices, the orbitofrontal cortex, the anterior insula, and the amygdala were involved in decoding affective valence versus intensity. Affective valence and intensity predictive models showed significant decoding performance in an independent test dataset (n = 62). These models were differentially connected to distinct large-scale brain networks-the intensity model to the ventral attention network and the valence model to the limbic and default mode networks. Overall, this study identified the brain representations of affective valence and intensity across pleasure and pain, promoting a systems-level understanding of human affective experiences.

A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems.

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.

The neural signature of the decision value of future pain.

Pain is a primary driver of action. We often must voluntarily accept pain to gain rewards. Conversely, we may sometimes forego potential rewards to avoid associated pain. In this study, we investigated how the brain represents the decision value of future pain. Participants (n = 57) performed an economic decision task, choosing to accept or reject offers combining various amounts of pain and money presented visually. Functional MRI (fMRI) was used to measure brain activity throughout the decision-making process. Using multivariate pattern analyses, we identified a distributed neural representation predicting the intensity of the potential future pain in each decision and participants' decisions to accept or avoid pain. This neural representation of the decision value of future pain included negative weights located in areas related to the valuation of rewards and positive weights in regions associated with saliency, negative affect, executive control, and goal-directed action. We further compared this representation to future monetary rewards, physical pain, and aversive pictures and found that the representation of future pain overlaps with that of aversive pictures but is distinct from experienced pain. Altogether, the findings of this study provide insights on the valuation processes of future pain and have broad potential implications for our understanding of disorders characterized by difficulties in balancing potential threats and rewards.

Neural signatures of individual variability in context-dependent perception of ambiguous facial expression.

How do we incorporate contextual information to infer others' emotional state? Here we employed a naturalistic context-dependent facial expression estimation task where participants estimated pleasantness levels of others' ambiguous expression faces when sniffing different contextual cues (e.g., urine, fish, water, and rose). Based on their pleasantness rating data, we placed participants on a context-dependency continuum and mapped the individual variability in the context-dependency onto the neural representation using a representational similarity analysis. We found that the individual variability in the context-dependency of facial expression estimation correlated with the activity level of the pregenual anterior cingulate cortex (pgACC) and the amygdala and was also decoded by the neural representation of the ventral anterior insula (vAI). A dynamic causal modeling revealed that those with higher context-dependency exhibited a greater degree of the modulation from vAI to the pgACC. These findings provide novel insights into the neural circuitry associated with the individual variability in context-dependent facial expression estimation and the first empirical evidence for individual variability in the predictive accounts of affective states.

Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature.

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

Prediction of psychosis: model development and internal validation of a personalized risk calculator.

BACKGROUND: Over the past two decades, early detection and early intervention in psychosis have become essential goals of psychiatry. However, clinical impressions are insufficient for predicting psychosis outcomes in clinical high-risk (CHR) individuals; a more rigorous and objective model is needed. This study aims to develop and internally validate a model for predicting the transition to psychosis within 10 years. METHODS: Two hundred and eight help-seeking individuals who fulfilled the CHR criteria were enrolled from the prospective, naturalistic cohort program for CHR at the Seoul Youth Clinic (SYC). The least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was used to develop a predictive model for a psychotic transition. We performed k-means clustering and survival analysis to stratify the risk of psychosis. RESULTS: The predictive model, which includes clinical and cognitive variables, identified the following six baseline variables as important predictors: 1-year percentage decrease in the Global Assessment of Functioning score, IQ, California Verbal Learning Test score, Strange Stories test score, and scores in two domains of the Social Functioning Scale. The predictive model showed a cross-validated Harrell's C-index of 0.78 and identified three subclusters with significantly different risk levels. CONCLUSIONS: Overall, our predictive model showed a predictive ability and could facilitate a personalized therapeutic approach to different risks in high-risk individuals.

Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging.

Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.

Optogenetic fMRI for Brain-Wide Circuit Analysis of Sensory Processing.

Sensory processing is a complex neurological process that receives, integrates, and responds to information from one's own body and environment, which is closely related to survival as well as neurological disorders. Brain-wide networks of sensory processing are difficult to investigate due to their dynamic regulation by multiple brain circuits. Optogenetics, a neuromodulation technique that uses light-sensitive proteins, can be combined with functional magnetic resonance imaging (ofMRI) to measure whole-brain activity. Since ofMRI has increasingly been used for investigating brain circuits underlying sensory processing for over a decade, we systematically reviewed recent ofMRI studies of sensory circuits and discussed the challenges of optogenetic fMRI in rodents.

Pain-Evoked Reorganization in Functional Brain Networks.

Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) "ventral attention network" regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced "small-worldness," modularity (fewer networks), and global network efficiency and in the emergence of an integrated "pain supersystem" (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized ("hub disruption") so that more hubs were localized in PS, and there was a shift from "connector" hubs linking disparate networks to "provincial" hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

Distinct fMRI patterns colocalized in the cingulate cortex underlie the after-effects of cognitive control on pain.

Demanding tasks can influence following behaviors but the underlying mechanisms remain largely unclear. In the present functional magnetic resonance imaging (fMRI) study, we used multivariate pattern analyses (MVPA) to compare patterns of brain activity associated with pain in response to noxious stimuli administered after a task requiring cognitive control (Stroop) and evaluate their functional interaction based on a mediation analysis model. We found that performing a difficult cognitive task leads to subsequent increases in pain and pain-related multivariate responses across the brain and within the anterior mid-cingulate cortex (aMCC). Moreover, an aMCC pattern predictive of task performance was further reactivated during pain and predicted ensuing increases in pain-related brain responses. This suggests functional interactions between distinct but partly co-localized neural networks underlying executive control and pain. These findings offer a new perspective on the functional role of the cingulate cortex in pain and cognition and provide a promising framework to investigate dynamical interactions between partly overlapping brain networks.

False-positive neuroimaging: Undisclosed flexibility in testing spatial hypotheses allows presenting anything as a replicated finding.

Hypothesis testing in neuroimaging studies relies heavily on treating named anatomical regions (e.g., "the amygdala") as unitary entities. Though data collection and analyses are conducted at the voxel level, inferences are often based on anatomical regions. The discrepancy between the unit of analysis and the unit of inference leads to ambiguity and flexibility in analyses that can create a false sense of reproducibility. For example, hypothesizing effects on "amygdala activity" does not provide a falsifiable and reproducible definition of precisely which voxels or which patterns of activation should be observed. Rather, it comprises a large number of unspecified sub-hypotheses, leaving room for flexible interpretation of findings, which we refer to as "model degrees of freedom." From a survey of 135 functional Magnetic Resonance Imaging studies in which researchers claimed replications of previous findings, we found that 42.2% of the studies did not report any quantitative evidence for replication such as activation peaks. Only 14.1% of the papers used exact coordinate-based or a priori pattern-based models. Of the studies that reported peak information, 42.9% of the 'replicated' findings had peak coordinates more than 15 mm away from the 'original' findings, suggesting that different brain locations were activated, even when studies claimed to replicate prior results. To reduce the flexible and qualitative region-level tests in neuroimaging studies, we recommend adopting quantitative spatial models and tests to assess the spatial reproducibility of findings. Techniques reviewed here include permutation tests on peak distance, Bayesian MANOVA, and a priori multivariate pattern-based models. These practices will help researchers to establish precise and falsifiable spatial hypotheses, promoting a cumulative science of neuroimaging.

Frontal-Brainstem Pathways Mediating Placebo Effects on Social Rejection.

Placebo treatments can strongly affect clinical outcomes, but research on how they shape other life experiences and emotional well-being is in its infancy. We used fMRI in humans to examine placebo effects on a particularly impactful life experience, social pain elicited by a recent romantic rejection. We compared these effects with placebo effects on physical (heat) pain, which are thought to depend on pathways connecting prefrontal cortex and periaqueductal gray (PAG). Placebo treatment, compared with control, reduced both social and physical pain, and increased activity in the dorsolateral prefrontal cortex (dlPFC) in both modalities. Placebo further altered the relationship between affect and both dlPFC and PAG activity during social pain, and effects on behavior were mediated by a pathway connecting dlPFC to the PAG, building on recent work implicating opioidergic PAG activity in the regulation of social pain. These findings suggest that placebo treatments reduce emotional distress by altering affective representations in frontal-brainstem systems.SIGNIFICANCE STATEMENT Placebo effects are improvements due to expectations and the socio-medical context in which treatment takes place. Whereas they have been extensively studied in the context of somatic conditions such as pain, much less is known of how treatment expectations shape the emotional experience of other important stressors and life events. Here, we use brain imaging to show that placebo treatment reduces the painful feelings associated with a recent romantic rejection by recruiting a prefrontal-brainstem network and by shifting the relationship between brain activity and affect. Our findings suggest that this brain network may be important for nonspecific treatment effects across a wide range of therapeutic approaches and mental health conditions.

Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain.

Cognitive self-regulation can strongly modulate pain and emotion. However, it is unclear whether self-regulation primarily influences primary nociceptive and affective processes or evaluative ones. In this study, participants engaged in self-regulation to increase or decrease pain while experiencing multiple levels of painful heat during functional magnetic resonance imaging (fMRI) imaging. Both heat intensity and self-regulation strongly influenced reported pain, but they did so via two distinct brain pathways. The effects of stimulus intensity were mediated by the neurologic pain signature (NPS), an a priori distributed brain network shown to predict physical pain with over 90% sensitivity and specificity across four studies. Self-regulation did not influence NPS responses; instead, its effects were mediated through functional connections between the nucleus accumbens and ventromedial prefrontal cortex. This pathway was unresponsive to noxious input, and has been broadly implicated in valuation, emotional appraisal, and functional outcomes in pain and other types of affective processes. These findings provide evidence that pain reports are associated with two dissociable functional systems: nociceptive/affective aspects mediated by the NPS, and evaluative/functional aspects mediated by a fronto-striatal system.

Group-regularized individual prediction: theory and application to pain.

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or 'decode' psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction-based on population-level predictive maps from prior groups-and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N=180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker-in this case, the Neurologic Pain Signature (NPS)-improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.

An fMRI-based neurologic signature of physical pain.

BACKGROUND: Persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment. Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified. METHODS: In four studies involving a total of 114 participants, we developed an fMRI-based measure that predicts pain intensity at the level of the individual person. In study 1, we used machine-learning analyses to identify a pattern of fMRI activity across brain regions--a neurologic signature--that was associated with heat-induced pain. The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions. In study 2, we tested the sensitivity and specificity of the signature to pain versus warmth in a new sample. In study 3, we assessed specificity relative to social pain, which activates many of the same brain regions as physical pain. In study 4, we assessed the responsiveness of the measure to the analgesic agent remifentanil. RESULTS: In study 1, the neurologic signature showed sensitivity and specificity of 94% or more (95% confidence interval [CI], 89 to 98) in discriminating painful heat from nonpainful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and nonpainful warmth with 93% sensitivity and specificity (95% CI, 84 to 100). In study 3, it discriminated between physical pain and social pain with 85% sensitivity (95% CI, 76 to 94) and 73% specificity (95% CI, 61 to 84) and with 95% sensitivity and specificity in a forced-choice test of which of two conditions was more painful. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered. CONCLUSIONS: It is possible to use fMRI to assess pain elicited by noxious heat in healthy persons. Future studies are needed to assess whether the signature predicts clinical pain. (Funded by the National Institute on Drug Abuse and others.).

Cluster-extent based thresholding in fMRI analyses: pitfalls and recommendations.

Cluster-extent based thresholding is currently the most popular method for multiple comparisons correction of statistical maps in neuroimaging studies, due to its high sensitivity to weak and diffuse signals. However, cluster-extent based thresholding provides low spatial specificity; researchers can only infer that there is signal somewhere within a significant cluster and cannot make inferences about the statistical significance of specific locations within the cluster. This poses a particular problem when one uses a liberal cluster-defining primary threshold (i.e., higher p-values), which often produces large clusters spanning multiple anatomical regions. In such cases, it is impossible to reliably infer which anatomical regions show true effects. From a survey of 814 functional magnetic resonance imaging (fMRI) studies published in 2010 and 2011, we show that the use of liberal primary thresholds (e.g., p<.01) is endemic, and that the largest determinant of the primary threshold level is the default option in the software used. We illustrate the problems with liberal primary thresholds using an fMRI dataset from our laboratory (N=33), and present simulations demonstrating the detrimental effects of liberal primary thresholds on false positives, localization, and interpretation of fMRI findings. To avoid these pitfalls, we recommend several analysis and reporting procedures, including 1) setting primary p<.001 as a default lower limit; 2) using more stringent primary thresholds or voxel-wise correction methods for highly powered studies; and 3) adopting reporting practices that make the level of spatial precision transparent to readers. We also suggest alternative and supplementary analysis methods.

Psychological well-being and salivary markers of inflammation: The moderating effect of age.

Increasing evidence suggests a significant impact of higher psychological well-being (PWB) on health outcomes; however, such associations have been studied exclusively in middle-aged to older adults. This study examined the aging effect on PWB measures as well as the moderating effect of age on the link between PWB and inflammation, using salivary markers by comparing the younger adults (n = 127; Mage = 22.98 years) versus older adults (n = 75; Mage = 75.60 years). Older adults showed significantly lower levels of PWB, particularly regarding purpose in life and personal growth. Moreover, higher purpose in life was associated with lower salivary IL-1ß and IL-6 (b = 0.83, p < .001; b = 0.81, p < .01) only in the older adult group but not in younger adults. These findings highlight the potential buffering effect of the sense of living well on physiological pathways in later life.

A dorsomedial prefrontal cortex-based dynamic functional connectivity model of rumination.

Rumination is a cognitive style characterized by repetitive thoughts about one's negative internal states and is a common symptom of depression. Previous studies have linked trait rumination to alterations in the default mode network, but predictive brain markers of rumination are lacking. Here, we adopt a predictive modeling approach to develop a neuroimaging marker of rumination based on the variance of dynamic resting-state functional connectivity and test it across 5 diverse subclinical and clinical samples (total n = 288). A whole-brain marker based on dynamic connectivity with the dorsomedial prefrontal cortex (dmPFC) emerges as generalizable across the subclinical datasets. A refined marker consisting of the most important features from a virtual lesion analysis further predicts depression scores of adults with major depressive disorder (n = 35). This study highlights the role of the dmPFC in trait rumination and provides a dynamic functional connectivity marker for rumination.

Neural mechanisms of pain relief through paying attention to painful stimuli.

ABSTRACT: A commonly held belief suggests that turning one's attention away from pain reduces it, whereas paying attention to pain increases it. However, some attention-based therapeutic strategies for pain, such as mindfulness-based interventions, suggest that paying attention to painful stimuli can reduce pain, resulting in seemingly contradictory conclusions regarding attention and pain. Here, we investigated the analgesic effects of attention modulation and provide behavioral and neural evidence that paying attention to pain can reduce pain when attention is directed toward the specific features of painful stimuli. The analgesic effects of paying attention to painful stimuli were mediated by the primary somatosensory cortex and goal-directed attention regions in the prefrontal and parietal cortex. These findings suggest that suppressing early somatosensory processing through top-down modulation is the key mechanism of the analgesic effects of paying attention to painful stimuli, providing evidence that pain itself can be used as a component of pain management.