Biology subjects

Wager, T. D.

Publications and source records attributed to Wager, T. D..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Odor imagery but not perception drives risk for food cue reactivity and increased adiposity

Mental imagery has been proposed to play a critical role in the amplification of cravings. Here we tested whether olfactory imagery drives food cue reactivity strength to promote adiposity in 45 healthy individuals. We measured odor perception, odor imagery ability, and food cue reactivity using self-report, perceptual testing, and neuroimaging. Adiposity was assessed at baseline and one year later. Brain responses to real and imagined odors were analyzed with univariate and multivariate decoding methods to identify pattern-based olfactory codes. We found that the accuracy of decoding imagined, but not real, odor quality correlated with a perceptual measure of odor imagery ability and with greater adiposity changes. This latter relationship was mediated by cue-potentiated craving and intake. Collectively, these findings establish odor imagery ability as a risk factor for weight gain and more specifically as a mechanism by which exposure to food cues promotes craving and overeating.

neuroscience↗

bioRxiv2022

Distinct spatial patterns of neural and physiology-related activity in the human periaqueductal grey during anticipatory social stress.

Periaqueductal gray (PAG) columns mediate affective experience, physiological regulation, and survival-related behavior; yet, only 7T imaging can resolve these small structures in humans. In a social stress task, participants prepared a speech, and we observed (a) bilateral ventrolateral PAG activity, relative to baseline, and (b) distinct spatial patterns of correlation between PAG activity and physiological response (i.e., cardiac interbeat interval, reparation rate, and tonic electrodermal activity).

neuroscience↗

bioRxiv2022

7-Tesla evidence for columnar and rostral--caudal organization of the human periaqueductal grey response in the absence of threat: a working memory study

The periaqueductal gray (PAG) is a small midbrain structure that surrounds the cerebral aqueduct, regulates brain-body communication, and is often studied for its role in "fight-or-flight" and "freezing" responses to threat. We used ultra-high field 7-Tesla fMRI to resolve the PAG in humans and distinguish it from the cerebral aqueduct, examining its in vivo function in humans during a working memory task (N = 87). Relative to baseline fixation, both mild and moderate task-elicited cognitive demands elicited bilateral BOLD increases in ventrolateral PAG (vlPAG), a region previously observed to show increased activity during anticipated painful threat in both non-human and human animals. The present task posed only the most minimal (if any) "threat". The mild-demand condition involved a task easier than remembering a phone number, elicited a heart rate decrease relative to baseline, yet nonetheless elicited a bilateral vlPAG response. Across PAG voxels, BOLD signal intensity correlated with changes in physiological reactivity (relative to baseline) and showed some evidence of spatial organization along the rostral-caudal axis. These findings suggest that the PAG may have a broader role in coordinating brain--body communication during a minimally to moderately demanding task, even in the absence of threat.

neuroscience↗

bioRxiv2022

The role of human superior colliculus in affective experiences during visual and somatosensory stimulation

The superior colliculus is often studied for its role in visually guided behaviors, but research in non-human animals indicates it is a midbrain hub for processing sensory information from multiple domains, including interoception (which is associated with affect). We used ultra-high field 7-Tesla fMRI to extend this work to humans, modeling superior colliculus BOLD signal intensity during visual or somatosensory stimulation (N = 40 in each sensory modality), both under aversive and neutral affective intensity. As hypothesized, the superior colliculus showed increased BOLD signal intensity in the dorsal and ventral subregions during visual and somatosensory stimulation, respectively. The entire superior colliculus also showed increased BOLD signal intensity during aversive compared to neural conditions. The superior colliculus BOLD signal intensity also correlated with a preregistered set of brain regions involved in visual, somatosensory, and interoceptive processing.

neuroscience↗

bioRxiv2022

Layer-dependent activity in the human superior colliculus during working memory

We examined the superior colliculus (SC) with ultra-high resolution 7-Tesla fMRI during an N-back working memory task. We observed both increased BOLD signal intensity and functional connectivity that followed a layer-dependent pattern predicted from anatomical connections between SC and other brain structures important for visual processing, motor control, and executive function. Our results highlight a role for the human SC in cognitive functions that usually associated with the cerebral cortex.

neuroscience↗

bioRxiv2022

Replicable multivariate BWAS with moderate sample sizes

Brain-Wide Association Studies (BWAS) have become a dominant method for linking mind and brain over the past 30 years. Univariate models test tens to hundreds of thousands of brain voxels individually, whereas multivariate models ( multivariate BWAS) integrate signals across brain regions into a predictive model. Numerous problems have been raised with univariate BWAS, including lack of power and reliability and an inability to account for pattern-level information embedded in distributed neural circuits1-3. Multivariate predictive models address many of these concerns, and offer substantial promise for delivering brain-based measures of behavioral and clinical states and traits2,3. In their recent paper4, Marek et al. evaluated the effects of sample size on univariate and multivariate BWAS in three large-scale neuroimaging dataset and came to the general conclusion that "BWAS reproducibility requires samples with thousands of individuals". We applaud their comprehensive analysis, and we agree that (a) large samples are needed when conducting univariate BWAS of individual differences in trait measures, and (b) multivariate BWAS reveal substantially larger effects and are therefore more highly powered. However, we disagree with Marek et al.s claims that multivariate BWAS provide "inflated in-sample associations" that often fail to replicate (i.e., are underpowered), and that multivariate BWAS consequently require thousands of participants when predicting trait-level individual differences. Here we substantiate that (i) with appropriate methodology, the reported in-sample effect size inflation in multivariate BWAS can be entirely eliminated, and (ii) in most cases, multivariate BWAS effects are replicable with substantially smaller sample sizes (Figure 1). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=198 SRC="FIGDIR/small/497072v1_fig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@180764borg.highwire.dtl.DTLVardef@d64a2forg.highwire.dtl.DTLVardef@a0865aorg.highwire.dtl.DTLVardef@d4b14c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Multivariate BWAS provide unbiased effect sizes and high replicability with low-moderate sample sizes. (a) In-sample effects in multivariate BWAS are only inflated if estimates are obtained without cross-validation. (b) Cross-validation fully eliminates in-sample effect size inflation and, as a consequence, provides higher replicability. Each point in (a) and (b) corresponds to one bootstrap subsample, as in Fig. 4b of Marek et al. Dotted lines denote the threshold for p=0.05 with n=495. (c) The inflation of in-sample effect size obtained without cross-validation (red) is reduced, but does not disappear, at higher sample sizes. Conversely, cross-validated estimates (blue) are slightly pessimistic with low sample sizes and become quickly unbiased as sample size is increased. (d) Without cross-validation, in-sample effect size estimates are non-zero (r{approx}0.5, red) even when predicting permuted outcome data. Cross-validation eliminates systematic bias across all sample sizes (blue). Dashed lines in (c) and (d) denote 95% parametric confidence intervals, and shaded areas denote bootstrap and permutation-based confidence intervals. (e-f) Cross-validated analysis reveals that sufficient in-sample power (e) and out-of-sample replication probability (P(rep)) (f) can be achieved for a variety of phenotypes at low or moderate sample sizes. 80% power and P(rep) are achievable in <500 participants for half the phenotypes tested (colored bars) using the prediction algorithm in Marek et al. (top panels in (e) and (f), sample size required for 80% power or P(rep) shown). Other phenotypes require sample sizes >500 (bars with arrows). Power and P(rep) can be substantially improved with a ridge regression-based model recommended in some comparison studies10,11 (bottom panels in (e) and (f)), with 80% power and P(rep) with sample sizes as low as n=100 and n=75, respectively, when predicting cognitive ability, and sample sizes between 75 and 375 for other investigated variables, except inhibition assessed with the flanker task. (g) We estimated interactions between sample size and publication bias by computing effect size inflation (rdiscovery - rreplication) only for those bootstrap cases where prediction performance was significant (p>0.05) in the replication sample. Our analysis shows that the effect size inflation due to publication bias is modest (<10%) with <500 participants for half the phenotypes using the Marek et al. model and all phenotypes but the flanker using the ridge model. C_FIG

neuroscience↗

bioRxiv2022

A multivariate brain signature for reward

The processing of rewards and losses are crucial for learning to adapt to an ever changing environment. Dysregulated reward processes are prevalent in mental health and substance use disorders. While many human brain measures related to reward have been based on activity in individual brain regions, recent studies indicate that many affective and motivational processes are encoded in distributed systems that span multiple regions. Consequently, decoding these processes using individual regions yields small effect sizes and limited reliability, whereas predictive models based on distributed patterns yield much larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, from now on termed the Brain Reward Signature (BRS), we trained a LASSO-PCR model to predict the signed magnitude of monetary rewards and losses on the Monetary Incentive Delay task (MID; N = 39) and achieved a high significant decoding performance (92% for decoding rewards versus losses). We subsequently demonstrate the generalizability of our signature on another version of the MID in a different sample (92% decoding accuracy for rewards versus losses; N = 12) and on a gambling task from a large sample (73% decoding accuracy for rewards versus losses, N = 1084) from the Human Connectome Project. Lastly, we also provided preliminary evidence for specificity to rewarding outcomes by illustrating that the signature map generates estimates that significantly differ between rewarding and negative feedback (92% decoding accuracy) but do not differ for conditions that differ in disgust rather than reward in a novel Disgust-Delay Task (N = 39). We thus created a BRS that can be used to make specific, generalizable and reproducible predictions about brain responses to rewards and losses.

neuroscience↗

bioRxiv2021

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 shortterm (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. Significance statementCurrent efforts towards translating brain biomarkers require identifying brain measures that can strongly and reliably predict outcomes of interest. We systematically examined the performance of a well-established brain activity pattern, the Neurological Pain Signature (NPS), in a large and diverse sample of participants. The NPS showed excellent reliability, and the reliability scaled with the number of trials within-person. The NPS responses showed strong correlations with pain reports at the within-person level but only modest correlations at the between-person level. The findings suggest that the NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of a pain-related physiological target. Author NoteThis project was supported by grants R01MH076136 (T.D.W.), R01DA046064, R01EB026549, and R01DA035484. Elizabeth A. Reynolds Losin was supported by a Mentored Research Scientist Development award from National Institute On Drug Abuse of the National Institutes of Health (K01DA045735). Lauren Y. Atlas was supported in part by funding from the Intramural Research Program of the National Center for Complementary and Integrative Health. Yoni K. Ashar was supported by NCATS Grant # TL1-TR-002386. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Code for all analyses and figures is available at https://github.com/XiaochunHan/NPS_measurement_properties. Data for all analyses and figures is available at https://osf.io/v9px7/.

neuroscience↗

bioRxiv2020

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

neuroscience↗

bioRxiv2020

Beyond fear centers: a distributed fMRI-based neuromarker for the subjective experience of fear

The specific neural systems underlying the subjective feeling of fear remain vigorously debated in affective neuroscience. Here, we combined functional MRI with machine learning to identify and evaluate a sensitive and generalizable neural signature predictive of the momentary self-reported subjective fear experience across discovery (n=67), validation (n=20) and generalization (n=31) cohorts. We systematically demonstrate that accurate fear prediction crucially requires distributed brain systems, with important contributions from cortical (e.g., prefrontal, midcingulate and insular cortices) and subcortical (e.g., thalamus, periaqueductal gray, basal forebrain and amygdala) regions. We further demonstrate that the neural representation of subjective fear is distinguishable from the representation of conditioned threat and general negative affect. Overall, our findings suggest that subjective fear, which exhibits distinct neural representation with some other aversive states, is encoded in distributed systems rather than isolated fear centers. This signature provides a neuromarker for monitoring fear-related neuropathology and evaluating novel treatments targeting pathological fear.

neuroscience↗

bioRxiv2020

The Brain Activation-based Sexual Image Classifier (BASIC): A sensitive and specific fMRI activity pattern for sexual image perception

Sexual stimuli processing is a key element in the repertoire of human affective and motivational states. Previous neuroimaging studies of sexual stimulus processing have revealed a complicated mosaic of activated regions, leaving unresolved questions about their sensitivity and specificity to sexual stimuli per se, generalizability across individuals, and potential utility as neuromarkers for sexual stimulus processing. In this study, data on sexual, negative, non-sexual positive, and neutral images from Wehrum et al. (2013) (N = 100) were re-analyzed with multivariate Support Vector Machine models to create the Brain Activation-based Sexual Image Classifier (BASIC) model. This model was tested for sensitivity, specificity, and generalizability in cross-validation (N = 100) and an independent test cohort (N = 18; Kragel et al. 2019). The BASIC model showed highly accurate performance (94-100%) in classifying sexual versus neutral or nonsexual affective images in both datasets. Virtual lesions and test of individual large-scale networks (e.g., visual or attention networks) show that these individual networks are neither necessary nor sufficient to capture sexual stimulus processing. These findings suggest that brain responses to sexual stimuli constitute a category of mental event that is distinct from general affect and involves multiple brain networks. It is, however, largely conserved across individuals, permitting the development of neuromarkers for sexual processing in individual persons. Future studies could assess performance of BASIC to a broader array of affective/motivational stimuli and link brain responses with physiological and subjective measures of sexual arousal.

neuroscience↗

bioRxiv2020

An Approach to Automatically Label & Order Brain Activity/Component Maps

Functional magnetic resonance imaging (fMRI) is a brain imaging technique which provides detailed in-sights into brain function and its disruption in various brain disorders. fMRI data can be analyzed using data-driven or region-of-interest based methods. The data-driven analysis of brain activity maps involves several steps, the first of which is identifying whether the maps capture what might be interpreted as intrinsic connectivity networks (ICNs) or artifacts. This is followed by linking the ICNs to known anatomical and/or functional parcellations. Optionally, as in the study of functional network connectivity (FNC), rearranging the connectivity graph is also necessary for systematic interpretation. Here we present a toolbox that automates all these processes under minimal or no supervision with high accuracy. We provide a pretrained cross-validated elastic-net regularized general linear model for the noisecloud toolbox to separate the ICNs from artifacts. We include several well-known anatomical and functional parcellations from which researchers can choose to label the activity maps. Finally, we integrate a method for maximizing the within-domain modularity to generate a more systematically structured FNC matrix. We improve upon and integrate existing techniques and new methods to design this toolbox which can take care of all the above needs. Specifically, we show that our pretrained model achieves 89% accuracy and 100% precision at classifying ICNs from artifacts in a validation dataset. Researchers are generating brain imaging data and analyzing brain activity at an ever-increasing rate. The Autolabeller toolbox can help automate such analyses for faster and reproducible research.

neuroscience↗

bioRxiv2020

Evoked pain intensity representation is distributed across brain systems: A multistudy mega-analysis

Information is coded in the brain at different scales for different phenomena: locally, distributed across regions and networks, and globally. For pain, the scale of representation is controversial. Although generally believed to be an integrated cognitive and sensory phenomenon implicating diverse brain systems, quantitative characterizations of which regions and networks are sufficient to represent pain are lacking. In this meta-analysis (or mega-analysis) using data from 289 participants across 10 studies, we use model comparison combined with multivariate predictive models to investigate the spatial scale and location of acute pain representation. We compare models based on (a) a single most pain-predictive module, either previously identified elementary regions or a single best large-scale cortical resting-state network module; (b) selected cortical-subcortical systems related to evoked pain in prior literature ( multi-system models); and (c) a model spanning the full brain. We estimate the accuracy of pain intensity predictions using cross validation (7 studies) and subsequently validate in three independent holdout studies. All spatial scales convey information about pain intensity, but distributed, multi-system models better characterize pain representations than any individual region or network (e.g. multisystem models explain >20% more of individual subject pain ratings than the best elementary region). Full brain models showed no predictive advantage over multi-system models. These findings quantify the extent that representation of evoked pain experience is distributed across multiple cortical and subcortical systems, show that pain representation is not circumscribed by any elementary region or conical network, and provide a blueprint for identifying the spatial scale of information in other domains. Significance StatementWe define modular, multisystem and global views of brain function, use multivariate fMRI decoding to characterize pain representations at each level, and provide evidence for a multisystem representation of evoked pain. We further show that local views necessarily exclude important components of pain representation, while a global full brain representation is superfluous, even though both are viable frameworks for representing pain. These findings quantitatively juxtapose and reconcile divergent conclusions from evoked pain studies within a generalized neuroscientific framework, and provide a blueprint for investigating representational architecture for diverse brain processes.

neuroscience↗

bioRxiv2020

Pain and breathlessness: Salient, somatosensory and similar, but not the same

Quantifying pain currently relies upon subjective self-report. Alongside the inherent variability embedded within these metrics, added complications include the influence of ambiguous or prolonged noxious inputs, or in situations when communication may be compromised. As such, there is continued interest in the development of brain biomarkers of pain, such as in the form of neural signatures of brain activity. However, issues pertaining to pain-related specificity remain, and by understanding the current limits of these signatures we can both progress their development and investigate the potentially generalizable properties of pain to other salient and/or somatomotor tasks. Here, we utilized two independent datasets to test one of the established Neural Pain Signatures (the NPS (Wager et al. 2013)). In Study 1, brain activity was measured using functional magnetic resonance imaging (fMRI) in 40 healthy subjects during experimentally induced breathlessness, conditioned anticipation of breathlessness and a simple finger opposition task. In Study 2, brain activity was again measured during anticipation and breathlessness in 19 healthy subjects, as well as a modulation with the opioid remifentanil. We were able to identify significant NPS-related brain activity during anticipation and perception of breathlessness, as well as during finger opposition using the global NPS. Furthermore, localised NPS responses were found in early somatomotor regions, bilateral insula and dorsal anterior cingulate for breathlessness and finger opposition. In contrast, no conditions were able to activate the local signature in the dorsal posterior insula - thought to be critical for pain perception. These results provide properties of the present boundaries of the NPS, and offer insight into the overlap between breathlessness and somatomotor conditions with pain.

neuroscience↗

bioRxiv2020

Inferring the infant pain experience: a translational fMRI-based signature approach

BackgroundIn the absence of verbal communication it is challenging to infer an individuals sensory and emotional experience. In adults, fMRI has been used to develop multivariate brain activity signatures, which reliably capture elements of human pain experience. We translate whole-brain fMRI signatures that encode pain perception in adults to the newborn infant brain, to advance understanding of functional brain development and pain perception in early life. MethodsA cohort of adults (n=10; mean age=28.3 years) and 2 cohorts of healthy infants (Cohort A: n=15; Cohort B: n=22; mean postnatal age=3 days) were stimulated with low intensity nociceptive stimuli (64-512mN) during acquisition of functional MRI data. fMRI pain signatures were applied directly to the adult data and transformed such that they could be applied to the infant brain. In each cohort, we assessed the concordance of the signatures with the brain responses using cosine-similarity scores, and we assessed stimulus intensity encoding of the signature responses using Spearman rank correlation. Brain activity in pro-pain and anti-pain brain regions were also examined. FindingsThe Neurologic Pain Signature (NPS), which reflects aspects of nociceptive pain experience, was activated in both the adults and infants, and reliably encoded stimulus intensity. However, the Stimulus Intensity Independent Pain Signature (SIIPS1), which reflects higher-level cognitive modulation of nociceptive input, was only expressed in adults. Pro-pain brain regions showed similar activation patterns in adults and infants, whereas, anti-pain brain regions exhibited divergent responses. InterpretationBasic intensity encoding of nociceptive information is similar in adults and infants. However, translation of adult brain signatures into infants reveals significant differences in infant cerebral processing of nociceptive information, which may reflect their lack of expectation, motivation and contextualisation. This study expands the use of brain activity pain signatures to non-verbal patients and provides a potential approach to assess analgesic interventions in infancy. FundingThis work was funded by Wellcome (Senior Research Fellowship awarded to Prof. Rebeccah Slater) and SSNAP "Support for Sick and Newborn Infants and their Parents" Medical Research Fund (University of Oxford Excellence Fellowship awarded to Dr Eugene Duff). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for research articles published prior to March 2020 using terms including fMRI, infant or neonate, and pain or nociception in the title or abstract. Due to the relatively new emergence of this field, and the experimental and analytical challenges involved in studying cerebral processing of pain in the MRI environment in healthy newborn infants, only five fMRI studies have examined infant brain responses to nociceptive input. In a foundational pilot study, Williams et al., applied an experimental noxious stimulus to a single infant, evoking widespread brain activity that included several brain regions involved in pain processing in adults. Goksan et al., subsequently performed an observational cohort study and used regional analyses to compare active brain regions in infants (n=10) and adults (n=10), concluding that the evoked patterns of brain activity were broadly similar in infants and adults. Further follow-up analysis in the infant cohort revealed that the functional connectivity of brain regions involved in descending pain modulation influences the magnitude of pain-related brain activity. Two further studies focused on methodological advances, providing evidence-based recommendations for fMRI acquisition parameters and image processing in order to maximise the quality of infant data, and these methods have been implemented in this study. Added value of this studyThis study translates validated adult pain fMRI brain signatures to a nonverbal patient population in which the assessment and management of pain presents a significant clinical challenge. Application of fMRI brain signatures to newborn infants expands on previous fMRI studies that provided only qualitative evidence that noxious stimulation commonly activates brain regions in the adult and infant brain. Here we demonstrate that the basic encoding of the sensory discriminative aspects of pain, as represented by the Neurologic Pain Signature (NPS), occurs in both adults and infants, whereas higher-level cognitive modulation of pain, represented by the Stimulus Intensity Independent Pain Signature (SIIPS1) is only present in adults and not observed in infants. The differences in how the immature infant brain processes pain, relative to the mature adult brain, are likely to reflect differences in their expectation, motivation and contextualisation of external events rather than differences in their core nociceptive cerebral processing of pain. This work allows us to use quantitative fMRI observations to make stronger inferences related to pain experience in nonverbal infants. Implications of all the available evidenceBehavioural pain scores used in neonatal clinical care offer limited sensitivity and specificity to pain. Neonatal clinical trials that use these scores as outcome measures frequently report a lack of efficacy of common analgesic interventions, resulting in few evidence-based drugs for treating pain. The value of using brain-based neuroimaging markers of pain as a means of providing objective evidence of analgesic efficacy in early proof of concept studies is well recognised in adults, even in the absence of behavioural pain modulation. Similarly, in infants EEG-based measures of noxious-evoked brain activity have been used as outcome measures in clinical trials of analgesics to overcome some of the inherent limitations of using behavioural observations to quantify analgesic efficacy. Considering the successful translation of the Neurologic Pain Signature (NPS) and its sensitivity to analgesic modulation in adults, this novel methodology represents an objective brain-based fMRI approach that could be used to advance the discovery and assessment of analgesic interventions in infancy.

neuroscience↗

bioRxiv2020

Emotional contagion of pain across different social cues shares common and process-specific neural representations

Insular and anterior cingulate cortex activation across vicarious pain induction procedures suggests that they are core pain empathy nodes. However, pain empathic responses encompass emotional contagion as well as unspecific arousal and overlapping functional activations are not sufficient to determine shared and process-specific neural representations. We employed multivariate pattern analyses to fMRI data acquired during physical and affective vicarious pain induction and found spatially and functionally similar cross-modality (physical versus affective) whole-brain vicarious pain-predictive patterns. Further analyses consistently identified shared neural representations in the bilateral mid-insula. Mid-insula vicarious pain patterns were not sensitive to capture non-painful arousing negative stimuli but predicted self-experienced pain during thermal stimulation, suggesting process-specific representation of emotional contagion for pain. Finally, a domain-general vicarious pain pattern which predicted vicarious as well as self-experienced pain was developed. Our findings demonstrate a generalizable neural expression of vicarious pain and suggest that the mid-insula encodes emotional contagion for pain.

neuroscience↗

bioRxiv2019

Inferring the Genetic influences on Psychological Traits Using MRI Connectivity Predictive Models: Demonstration with Cognition

Genetic correlations between brain and behavioral phenotypes in analyses from major genetic consortia have been weak and mostly non-significant. fMRI models of systems-level brain patterns may help improve our ability to link genes, brains, and behavior by identifying reliable and reproducible endophenotypes. Work using connectivity-based predictive modeling (CBPM) has generated brain-based proxies of behavioral and neuropsychological variables. If such models capture activity in inherited brain systems, they may offer a more powerful link between genes and behavior. As a proof of concept, we develop models predicting intelligence (IQ) based on fMRI connectivity and test their effectiveness as endophenotypes. We link brain and IQ in a model development dataset of N=3,000 individuals; and test the genetic correlations between brain models and measured IQ in a genetic validation sample of N=13,092 individuals from the UKBiobank. We compare an additive connectivity-based model to multivariate LASSO and ridge models phenotypically and genetically. We also compare these approaches to single \"candidate\" brain areas. We find that predictive brain models were significantly phenotypically correlated with IQ and showed much stronger correlations than individual edges. Further, brain models were more heritable than single brain regions (h2=.155-.181) and capture about half of the genetic variance in IQ (rG=.422-.576), while rGs with single brain measures were smaller and non-significant. For the different approaches, LASSO and Ridge were similarly predictive, with slightly weaker performance of the additive model. LASSO model weights were highly theoretically interpretable and replicated known brain IQ associations. Finally, functional connectivity models trained in midlife showed genetic correlations with early life correlates of IQ, suggesting some stability in the prediction of fMRI models. We conclude that multi-system predictive models hold promise as imaging endophenotypes that offer complex and theoretically relevant conclusions for future imaging genetics research.

genetics↗

bioRxiv2019

Behavioral and neural signatures of working memory in childhood

Working memory function changes across development and varies across individuals. The patterns of behavior and brain function that track individual differences in working memory during development, however, are not well understood. Here we establish associations between working memory, cognitive abilities, and functional MRI activation in data from over 4,000 9-10-year-olds enrolled in the Adolescent Brain Cognitive Development study, an ongoing longitudinal study in the United States. Behavioral analyses reveal robust relationships between working memory, short-term memory, language skills, and fluid intelligence. Analyses relating out-of-scanner working memory performance to memory-related fMRI activation in an emotional n-back task demonstrate that frontoparietal activity in response to an explicit memory challenge indexes working memory ability. Furthermore, this relationship is domain-specific, such that fMRI activation related to emotion processing during the emotional n-back task, inhibitory control during a stop-signal task, and reward processing during a monetary incentive delay task does not track memory abilities. Together these results inform our understanding of the emergence of individual differences in working memory and lay the groundwork for characterizing the ways in which they change across adolescence.

neuroscience↗