Tor D. Wager

YNIMG Journal 2023 Journal Article

A multivariate brain signature for reward

• Sebastian P.H. Speer
• Christian Keysers
• Judit Campdepadrós Barrios
• Cas J.S. Teurlings
• Ale Smidts
• Maarten A.S. Boksem
• Tor D. Wager
• Valeria Gazzola

The processing of reinforcers and punishers is crucial to adapt to an ever changing environment and its dysregulation is 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 larger effect sizes and excellent reliability. To create such a predictive model for the processes of rewards and losses, termed the Brain Reward Signature (BRS), we trained a model to predict the signed magnitude of monetary rewards on the Monetary Incentive Delay task (MID; N = 39) and achieved a highly 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; N = 12) and on a gambling task from a large sample (73% decoding accuracy, N = 1084). We further provided preliminary data to characterize the specificity of the signature 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). Finally, we show that passively viewing positive and negatively valenced facial expressions loads positively on our signature, in line with previous studies on morbid curiosity. We thus created a BRS that can accurately predict brain responses to rewards and losses in active decision making tasks, and that possibly relates to information seeking in passive observational tasks.

YNIMG Journal 2022 Journal Article

Context-specific activations are a hallmark of the neural basis of individual differences in general executive function

• Andrew E. Reineberg
• Marie T. Banich
• Tor D. Wager
• Naomi P. Friedman

Common executive functioning (cEF) is a domain-general factor that captures shared variance in performance across diverse executive function tasks. To investigate the neural mechanisms of individual differences in cEF (e.g., goal maintenance, biasing), we conducted the largest fMRI study of multiple executive tasks to date (N = 546). Group average activation during response inhibition (antisaccade task), working memory updating (keep track task), and mental set shifting (number-letter switch task) overlapped in classic cognitive control regions. However, there were no areas across tasks that were consistently correlated with individual differences in cEF ability. Although similar brain areas are recruited when completing different executive function tasks, activation levels of those areas are not consistently associated with better performance. This pattern is inconsistent with a simple model in which higher cEF is associated with greater or less activation of a set of control regions across different task contexts; however, it is potentially consistent with a model in which individual differences in cEF primarily depend on activation of domain-specific targets of executive function. Brain features that explain commonalities in executive function performance across tasks remain to be discovered.

YNIMG Journal 2022 Journal Article

Cross-paradigm integration shows a common neural basis for aversive and appetitive conditioning

• Sanja Klein
• Onno Kruse
• Isabell Tapia León
• Lukas Van Oudenhove
• Sophie R. van ’t Hof
• Tim Klucken
• Tor D. Wager
• Rudolf Stark

Sharing imaging data and comparing them across different psychological tasks is becoming increasingly possible as the open science movement advances. Such cross-paradigm integration has the potential to identify commonalities in findings that neighboring areas of study thought to be paradigm-specific. However, even the integration of research from closely related paradigms, such as aversive and appetitive classical conditioning is rare - even though qualitative comparisons already hint at how similar the 'fear network' and 'reward network' may be. We aimed to validate these theories by taking a multivariate approach to assess commonalities across paradigms empirically. Specifically, we quantified the similarity of an aversive conditioning pattern derived from meta-analysis to appetitive conditioning fMRI data. We tested pattern expression in three independent appetitive conditioning studies with 29, 76 and 38 participants each. During fMRI scanning, participants in each cohorts performed an appetitive conditioning task in which a CS+ was repeatedly rewarded with money and a CS- was never rewarded. The aversive pattern was highly similar to appetitive CS+ > CS- contrast maps across samples and variations of the appetitive conditioning paradigms. Moreover, the pattern distinguished the CS+ from the CS- with above-chance accuracy in every sample. These findings provide robust empirical evidence for an underlying neural system common to appetitive and aversive learning. We believe that this approach provides a way to empirically integrate the steadily growing body of fMRI findings across paradigms.

YNIMG Journal 2022 Journal Article

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

• Xiaochun Han
• Yoni K. Ashar
• Philip Kragel
• Bogdan Petre
• Victoria Schelkun
• Lauren Y. Atlas
• Luke J. Chang
• Marieke Jepma

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.

YNIMG Journal 2021 Journal Article

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

• Erica L. Busch
• Lukas Slipski
• Ma Feilong
• J. Swaroop Guntupalli
• Matteo Visconti di Oleggio Castello
• Jeremy F. Huckins
• Samuel A. Nastase
• M. Ida Gobbini

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 patterns of activity or connectivity. The dimensions of this common model capture functional profiles that are shared across individuals such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Hyperalignment can use either response-based or connectivity-based input data to derive transformations that project individuals' neural data from anatomical space into the common model space. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we developed a new hyperalignment algorithm, hybrid hyperalignment, that derives transformations based on both response-based and connectivity-based information. We used three different movie-viewing fMRI datasets to test the performance of our new algorithm. Hybrid hyperalignment derives a single common model space that aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment. These results suggest that a single common information space can encode both shared cortical response and functional connectivity profiles across individuals.

YNIMG Journal 2021 Journal Article

Neural mediators of subjective and autonomic responding during threat learning and regulation

• Hannah S. Savage
• Christopher G. Davey
• Tor D. Wager
• Sarah N. Garfinkel
• Bradford A. Moffat
• Rebecca K. Glarin
• Ben J. Harrison

Threat learning elicits robust changes across multiple affective domains, including changes in autonomic indices and subjective reports of fear and anxiety. It has been argued that the underlying causes of such changes may be dissociable at a neural level, but there is currently limited evidence to support this notion. To address this, we examined the neural mediators of trial-by-trial skin conductance responses (SCR), and subjective reports of anxious arousal and valence in participants (n = 27; 17 females) performing a threat reversal task during ultra-high field functional magnetic resonance imaging. This allowed us to identify brain mediators during initial threat learning and subsequent threat reversal. Significant neural mediators of anxious arousal during threat learning included the dorsal anterior cingulate, anterior insula cortex (AIC), and ventromedial prefrontal cortex (vmPFC), subcortical regions including the amygdala, ventral striatum, caudate and putamen, and brain-stem regions including the pons and midbrain. By comparison, autonomic changes (SCR) were mediated by a subset of regions embedded within this broader circuitry that included the caudate, putamen and thalamus, and two distinct clusters within the vmPFC. The neural mediators of subjective negative valence showed prominent effects in posterior cortical regions and, with the exception of the AIC, did not overlap with threat learning task effects. During threat reversal, positive mediators of both subjective anxious arousal and valence mapped to the default mode network; this included the vmPFC, posterior cingulate, temporoparietal junction, and angular gyrus. Decreased SCR during threat reversal was positively mediated by regions including the mid cingulate, AIC, two sub-regions of vmPFC, the thalamus, and the hippocampus. Our findings add novel evidence to support distinct underlying neural processes facilitating autonomic and subjective responding during threat learning and threat reversal. The results suggest that the brain systems engaged in threat learning mostly capture the subjective (anxious arousal) nature of the learning process, and that appropriate responding during threat reversal is facilitated by participants engaging self- and valence-based processes. Autonomic changes (SCR) appear to involve distinct facilitatory and regulatory contributions of vmPFC sub-regions.

YNIMG Journal 2020 Journal Article

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

• Nicolas Silvestrini
• Jen-I Chen
• Mathieu Piché
• Mathieu Roy
• Etienne Vachon-Presseau
• Choong-Wan Woo
• Tor D. Wager
• Pierre Rainville

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.

YNICL Journal 2019 Journal Article

Evidence for decreased Neurologic Pain Signature activation following thoracic spinal manipulation in healthy volunteers and participants with neck pain

• Kenneth A. Weber II
• Tor D. Wager
• Sean Mackey
• James M. Elliott
• Wen-Ching Liu
• Cheryl L. Sparks

BACKGROUND CONTEXT: Spinal manipulation (SM) is a common treatment for neck and back pain, theorized to mechanically affect the spine leading to therapeutic mechanical changes. The link between specific mechanical effects and clinical improvement is not well supported. SM's therapeutic action may instead be partially mediated within the central nervous system. PURPOSE: To introduce brain-based models of pain for spinal pain and manual therapy research, characterize the distributed central mechanisms of SM, and advance the preliminary validation of brain-based models as potential clinical biomarkers of pain. STUDY DESIGN: Secondary analysis of two functional magnetic resonance imaging studies investigating the effect of thoracic SM on pain-related brain activity: A non-controlled, non-blinded study in healthy volunteers (Study 1, n = 10, 5 females, and mean age = 31.2 ± 10.0 years) and a randomized controlled study in participants with acute to subacute neck pain (Study 2, n = 24, 16 females, mean age = 38.0 ± 15.1 years). METHODS: Functional magnetic resonance imaging was performed during noxious mechanical stimulation of the right index finger cuticle pre- and post-intervention. The effect of SM on pain-related activity was studied within brain regions defined by the Neurologic Pain Signature (NPS) that are predictive of physical pain. RESULTS: = 0.477, p = 0.019). In Study 2, neck pain (p = 0.046) and NPS (p = 0.033) activation decreased following verum but not sham SM. Associations between evoked pain, neck pain, and NPS activation, were not significant and less clear, possibly due to inadequate power, methodological limitations, or other confounding factors. CONCLUSIONS: The findings provide preliminary evidence that SM may alter the processing of pain-related brain activity within specific pain-related brain regions and support the use of brain-based models as clinical biomarkers of pain.

YNIMG Journal 2019 Journal Article

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

• Yong-Wook Hong
• Yejong Yoo
• Jihoon Han
• Tor D. Wager
• Choong-Wan Woo

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.

YNICL Journal 2018 Journal Article

Age of gray matters: Neuroprediction of recidivism

• Kent A. Kiehl
• Nathaniel E. Anderson
• Eyal Aharoni
• J.Michael Maurer
• Keith A. Harenski
• Vikram Rao
• Eric D. Claus
• Carla Harenski

Age is one of the best predictors of antisocial behavior. Risk models of recidivism often combine chronological age with demographic, social and psychological features to aid in judicial decision-making. Here we use independent component analyses (ICA) and machine learning techniques to demonstrate the utility of using brain-based measures of cerebral aging to predict recidivism. First, we developed a brain-age model that predicts chronological age based on structural MRI data from incarcerated males (n = 1332). We then test the model's ability to predict recidivism in a new sample of offenders with longitudinal outcome data (n = 93). Consistent with hypotheses, inclusion of brain-age measures of the inferior frontal cortex and anterior-medial temporal lobes (i. e. , amygdala) improved prediction models when compared with models using chronological age; and models that combined psychological, behavioral, and neuroimaging measures provided the most robust prediction of recidivism. These results verify the utility of brain measures in predicting future behavior, and suggest that brain-based data may more precisely account for important variation when compared with traditional proxy measures such as chronological age. This work also identifies new brain systems that contribute to recidivism which has clinical implications for treatment development.

YNIMG Journal 2017 Journal Article

Group-regularized individual prediction: theory and application to pain

• Martin A. Lindquist
• Anjali Krishnan
• Marina López-Solà
• Marieke Jepma
• Choong-Wan Woo
• Leonie Koban
• Mathieu Roy
• Lauren Y. Atlas

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.

YNICL Journal 2016 Journal Article

Neural changes in extinction recall following prolonged exposure treatment for PTSD: A longitudinal fMRI study

• Liat Helpman
• Marie-France Marin
• Santiago Papini
• Xi Zhu
• Gregory M. Sullivan
• Franklin Schneier
• Mariana Neria
• Erel Shvil

BACKGROUND: Neurobiological models of posttraumatic stress disorder (PTSD) implicate fear processing impairments in the maintenance of the disorder. Specific deficits in extinction recall, the retention of learned extinction, have been demonstrated. While deficient extinction recall, and the associated activation pattern of prefrontal and hippocampal regions, distinguishes individuals with PTSD from controls, research has not yet examined changes following treatment. We examined the behavioral and neural correlates of extinction recall before and after cognitive behavioral treatment of PTSD. METHODS: Fifty-eight participants (30 with PTSD, 28 trauma-exposed matched controls) underwent a 2-day behavioral fear conditioning, extinction, and recall paradigm during functional magnetic resonance imaging (fMRI). The same procedures were repeated 10 weeks later, after PTSD patients had completed prolonged exposure treatment. We analyzed fMRI data from 32 subjects (16 PTSD; 16 controls) and skin conductance response (SCR) data from 33 subjects (16 PTSD; 17 controls). Neural activity during extinction recall, SCR, and PTSD symptoms were compared across groups and over time. RESULTS: PTSD patients exhibited pre- to post-treatment reduction in rostral anterior cingulate cortex (rACC) activation during extinction recall, and increase in functional coherence between the rACC and the ventromedial prefrontal cortex (vmPFC) and subgenual anterior cingulate cortex (sgACC). Reduced PTSD symptom severity from pre- to post-treatment was significantly associated with reduced subgenual ACC and parahippocampal activation during this task. SCR during the extinction recall phase did not significantly change with treatment in the PTSD group, but change in SCR was associated with reduction in PTSD symptom severity. CONCLUSIONS: Prolonged exposure treatment appears to alter neural activation in PTSD patients during recall of fear extinction, and change in extinction recall (measured by SCR) is associated with symptom reduction. We discuss results in the context of neural systems involved in response to affective stimuli.

YNIMG Journal 2016 Journal Article

Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain

• Yina Ma
• Chenbo Wang
• Siyang Luo
• Bingfeng Li
• Tor D. Wager
• Wenxia Zhang
• Yi Rao
• Shihui Han

Humans exhibit substantial inter-individual differences in pain perception, which contributes to variability in analgesic efficacy. Individual differences in pain sensitivity have been linked with variation in the serotonin transporter gene (5-HTTLPR), and selective serotonin reuptake inhibitors (SSRIs) such as citalopram have been increasingly used as treatments for multiple pain conditions. We combined genotyping, pharmacological challenge, and neuroimaging during painful electrical stimulation to reveal how serotonin genetics and pharmacology interact to influence pain perception and its underlying neurobiological mechanisms. In a double-blind, placebo-controlled procedure, we acutely administrated citalopram (30mgpo) to short/short (s/s) and long/long (l/l) healthy male 5-HTTLPR homozygotes during functional MRI with painful and non-painful electrical stimulation. 5-HTTLPR genotype modulated citalopram effects on pain-related brain responses in the thalamus, cerebellum, anterior insula, midcingulate cortex and inferior frontal cortex. Specifically, citalopram significantly reduced pain-related brain responses in l/l but not in s/s homozygotes. Moreover, the interaction between 5-HTTLPR genotype and pain-related brain activity was a good predictor of the citalopram-induced reductions in pain reports. The genetic modulations of citalopram effects on brain-wide pain processing were paralleled by significant effects on the Neurological Pain Signature, a multivariate brain pattern validated to be sensitive and specific to physical pain. This work provides neurobiological mechanism by which genetic variation shapes brain responses to pain perception and treatment efficacy. These findings have important implications for the types of individuals for whom serotonergic treatments provide effective pain relief, which is critical for advancing personalized pain treatment.

YNIMG Journal 2015 Journal Article

Dynamic functional connectivity using state-based dynamic community structure: Method and application to opioid analgesia

• Lucy F. Robinson
• Lauren Y. Atlas
• Tor D. Wager

We present a new method, State-based Dynamic Community Structure, that detects time-dependent community structure in networks of brain regions. Most analyses of functional connectivity assume that network behavior is static in time, or differs between task conditions with known timing. Our goal is to determine whether brain network topology remains stationary over time, or if changes in network organization occur at unknown time points. Changes in network organization may be related to shifts in neurological state, such as those associated with learning, drug uptake or experimental conditions. Using a hidden Markov stochastic blockmodel, we define a time-dependent community structure. We apply this approach to data from a functional magnetic resonance imaging experiment examining how contextual factors influence drug-induced analgesia. Results reveal that networks involved in pain, working memory, and emotion show distinct profiles of time-varying connectivity.

YNIMG Journal 2014 Journal Article

Characterization and reduction of cardiac- and respiratory-induced noise as a function of the sampling rate (TR) in fMRI

• Dietmar Cordes
• Rajesh R. Nandy
• Scott Schafer
• Tor D. Wager

It has recently been shown that both high-frequency and low-frequency cardiac and respiratory noise sources exist throughout the entire brain and can cause significant signal changes in fMRI data. It is also known that the brainstem, basal forebrain and spinal cord areas are problematic for fMRI because of the magnitude of cardiac-induced pulsations at these locations. In this study, the physiological noise contributions in the lower brain areas (covering the brainstem and adjacent regions) are investigated and a novel method is presented for computing both low-frequency and high-frequency physiological regressors accurately for each subject. In particular, using a novel optimization algorithm that penalizes curvature (i. e. the second derivative) of the physiological hemodynamic response functions, the cardiac- and respiratory-related response functions are computed. The physiological noise variance is determined for each voxel and the frequency-aliasing property of the high-frequency cardiac waveform as a function of the repetition time (TR) is investigated. It is shown that for the brainstem and other brain areas associated with large pulsations of the cardiac rate, the temporal SNR associated with the low-frequency range of the BOLD response has maxima at subject-specific TRs. At these values, the high-frequency aliased cardiac rate can be eliminated by digital filtering without affecting the BOLD-related signal.

YNIMG Journal 2014 Journal Article

Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations

• Choong-Wan Woo
• Anjali Krishnan
• Tor D. Wager

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.

YNIMG Journal 2012 Journal Article

Dynamic connectivity regression: Determining state-related changes in brain connectivity

• Ivor Cribben
• Ragnheidur Haraldsdottir
• Lauren Y. Atlas
• Tor D. Wager
• Martin A. Lindquist

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, often it is hard to specify this information a priori. In this work we introduce a data-driven technique for partitioning the experimental time course into distinct temporal intervals with different multivariate functional connectivity patterns between a set of regions of interest (ROIs). The technique, called Dynamic Connectivity Regression (DCR), detects temporal change points in functional connectivity and estimates a graph, or set of relationships between ROIs, for data in the temporal partition that falls between pairs of change points. Hence, DCR allows for estimation of both the time of change in connectivity and the connectivity graph for each partition, without requiring prior knowledge of the nature of the experimental design. Permutation and bootstrapping methods are used to perform inference on the change points. The method is applied to various simulated data sets as well as to an fMRI data set from a study (N =26) of a state anxiety induction using a socially evaluative threat challenge. The results illustrate the method's ability to observe how the networks between different brain regions changed with subjects' emotional state.

YNIMG Journal 2012 Journal Article

Estimating and testing variance components in a multi-level GLM

• Martin A. Lindquist
• Julie Spicer
• Iris Asllani
• Tor D. Wager

Most analysis of multi-subject fMRI data is concerned with determining whether there exists a significant population-wide ‘activation’ in a comparison between two or more conditions. This is typically assessed by testing the average value of a contrast of parameter estimates (COPE) against zero in a general linear model (GLM) analysis. However, important information can also be obtained by testing whether there exist significant individual differences in effect magnitude between subjects, i. e. whether the variance of a COPE is significantly different from zero. Intuitively, such a test amounts to testing whether inter-individual differences are larger than would be expected given the within-subject error variance. We compare several methods for estimating variance components, including a) a naïve estimate using ordinary least squares (OLS); b) linear mixed effects in R (LMER); c) a novel Matlab implementation of iterative generalized least squares (IGLS) and its restricted maximum likelihood variant (RIGLS). All methods produced reasonable estimates of within- and between-subject variance components, with IGLS providing an attractive balance between sensitivity and appropriate control of false positives. Finally, we use the IGLS method to estimate inter-subject variance in a perfusion fMRI study (N=18) of social evaluative threat, and show evidence for significant inter-individual differences in ventromedial prefrontal cortex (VMPFC), amygdala, hippocampus and medial temporal lobes, insula, and brainstem, with predicted inverse coupling between VMPFC and the midbrain periaqueductal gray only when high inter-individual variance was used to define the seed for functional connectivity analyses. In sum, tests of variance provides a way of selecting regions that show significant inter-individual variability for subsequent analyses that attempt to explain those individual differences.

YNIMG Journal 2011 Journal Article

Conflict, error likelihood, and RT: Response to Brown & Yeung et al.

• Jack Grinband
• Judith Savitskaya
• Tor D. Wager
• Tobias Teichert
• Vincent P. Ferrera
• Joy Hirsch

YNIMG Journal 2011 Journal Article

The dorsal medial frontal cortex is sensitive to time on task, not response conflict or error likelihood

• Jack Grinband
• Judith Savitskaya
• Tor D. Wager
• Tobias Teichert
• Vincent P. Ferrera
• Joy Hirsch

The dorsal medial frontal cortex (dMFC) is highly active during choice behavior. Though many models have been proposed to explain dMFC function, the conflict monitoring model is the most influential. It posits that dMFC is primarily involved in detecting interference between competing responses thus signaling the need for control. It accurately predicts increased neural activity and response time (RT) for incompatible (high-interference) vs. compatible (low-interference) decisions. However, it has been shown that neural activity can increase with time on task, even when no decisions are made. Thus, the greater dMFC activity on incompatible trials may stem from longer RTs rather than response conflict. This study shows that (1) the conflict monitoring model fails to predict the relationship between error likelihood and RT, and (2) the dMFC activity is not sensitive to congruency, error likelihood, or response conflict, but is monotonically related to time on task.

YNIMG Journal 2010 Journal Article

Change point estimation in multi-subject fMRI studies

• Lucy F. Robinson
• Tor D. Wager
• Martin A. Lindquist

Most statistical analyses of fMRI data assume that the nature, timing and duration of the psychological processes being studied are known. However, in many areas of psychological inquiry, it is hard to specify this information a priori. Examples include studies of drug uptake, emotional states or experiments with a sustained stimulus. In this paper we assume that the timing of a subject's activation onset and duration are random variables drawn from unknown population distributions. We propose a technique for estimating these distributions assuming no functional form, and allowing for the possibility that some subjects may show no response. We illustrate how these distributions can be used to approximate the probability that a voxel/region is activated as a function of time. Further a procedure is discussed that uses a hidden Markov random field model to cluster voxels based on characteristics of their onset, duration, and anatomical location. These methods are applied to an fMRI study (n =24) of state anxiety, and are well suited for investigating individual differences in state-related changes in fMRI activity and other measures.

YNIMG Journal 2009 Journal Article

Brain mediators of cardiovascular responses to social threat

• Tor D. Wager
• Christian E. Waugh
• Martin Lindquist
• Doug C. Noll
• Barbara L. Fredrickson
• Stephan F. Taylor

Social threat is a key component of mental “stress” and a potent generator of negative emotions and physiological responses in the body. How the human brain processes social context and drives peripheral physiology, however, is relatively poorly understood. Human neuroimaging and animal studies implicate the dorsal medial prefrontal cortex (MPFC), though this heterogeneous region is likely to contain multiple sub-regions with diverse relationships with physiological reactivity and regulation. We used fMRI combined with a novel multi-level path analysis approach to identify brain mediators of the effects of a public speech preparation task (social evaluative threat, SET) on heart rate (HR). This model provides tests of functional pathways linking experimentally manipulated threat, regional fMRI activity, and physiological output, both across time (within person) and across individuals (between persons). It thus integrates time series connectivity and individual difference analyses in the same path model. The results provide evidence for two dissociable, inversely coupled sub-regions of MPFC that independently mediated HR responses. SET caused activity increases in a more dorsal pregenual cingulate region, whose activity was coupled with HR increases. Conversely, SET caused activity decreases in a right ventromedial/medial orbital region, which were coupled with HR increases. Individual differences in coupling strength in each pathway independently predicted individual differences in HR reactivity. These results underscore both the importance and heterogeneity of MPFC in generating physiological responses to threat.

YNIMG Journal 2009 Journal Article

Brain mediators of cardiovascular responses to social threat, Part II: Prefrontal-subcortical pathways and relationship with anxiety

• Tor D. Wager
• Vanessa A. van Ast
• Brent L. Hughes
• Matthew L. Davidson
• Martin A. Lindquist
• Kevin N. Ochsner

Social evaluative threat (SET) is a potent stressor in humans that causes autonomic changes, endocrine responses, and multiple health problems. Neuroimaging has recently begun to elucidate the brain correlates of SET, but as yet little is known about the mediating cortical-brainstem pathways in humans. This paper replicates and extends findings in a companion paper (Wager et al. , 2009) using an independent cohort of participants and different image acquisition parameters. Here, we focused specifically on relationships between the medial prefrontal cortex (MPFC), midbrain periaqueductal gray (PAG), and heart rate (HR). We applied multi-level path analysis to localize brain mediators of SET effects on HR and self-reported anxiety. HR responses were mediated by opposing signals in two distinct sub-regions of the MPFC—increases in rostral dorsal anterior cingulate cortex (rdACC) and de-activation in ventromedial prefrontal cortex (vmPFC). In addition, HR responses were mediated by PAG. Additional path analyses provided support for two cortical–subcortical pathways: one linking vmPFC, PAG, and HR, and another linking rdACC, thalamus, and HR. PAG responses were linked with HR changes both before and during SET, whereas cortical regions showed stronger connectivity with HR during threat. Self-reported anxiety showed a partially overlapping, but weaker, pattern of mediators, including the vmPFC, dorsomedial prefrontal cortex, and lateral frontal cortex, as well as substantial individual differences that were largely unexplained. Taken together, these data suggest pathways for the translation of social threats into both physiological and experiential responses, and provide targets for future research on the generation and regulation of emotion.

YNIMG Journal 2009 Journal Article

Evaluating the consistency and specificity of neuroimaging data using meta-analysis

• Tor D. Wager
• Martin A. Lindquist
• Thomas E. Nichols
• Hedy Kober
• Jared X. Van Snellenberg

Making sense of a neuroimaging literature that is growing in scope and complexity will require increasingly sophisticated tools for synthesizing findings across studies. Meta-analysis of neuroimaging studies fills a unique niche in this process: It can be used to evaluate the consistency of findings across different laboratories and task variants, and it can be used to evaluate the specificity of findings in brain regions or networks to particular task types. This review discusses examples, implementation, and considerations when choosing meta-analytic techniques. It focuses on the multilevel kernel density analysis (MKDA) framework, which has been used in recent studies to evaluate consistency and specificity of regional activation, identify distributed functional networks from patterns of co-activation, and test hypotheses about functional cortical-subcortical pathways in healthy individuals and patients with mental disorders. Several tests of consistency and specificity are described.

YNIMG Journal 2009 Journal Article

Introduction to a Special Issue of Neuroimage on Brain–Body Medicine

• Richard D. Lane
• Tor D. Wager

YNIMG Journal 2009 Journal Article

Meta-analysis of neuroimaging data: A comparison of image-based and coordinate-based pooling of studies

• Gholamreza Salimi-Khorshidi
• Stephen M. Smith
• John R. Keltner
• Tor D. Wager
• Thomas E. Nichols

With the rapid growth of neuroimaging research and accumulation of neuroinformatic databases the synthesis of consensus findings using meta-analysis is becoming increasingly important. Meta-analyses pool data across many studies to identify reliable experimental effects and characterize the degree of agreement across studies. Coordinate-based meta-analysis (CBMA) methods are the standard approach, where each study entered into the meta-analysis has been summarized using only the (x, y, z) locations of peak activations (with or without activation magnitude) reported in published reports. Image-based meta-analysis (IBMA) methods use the full statistic images, and allow the use of hierarchical mixed effects models that account for differing intra-study variance and modeling of random inter-study variation. The purpose of this work is to compare image-based and coordinate-based meta-analysis methods applied to the same dataset, a group of 15 fMRI studies of pain, and to quantify the information lost by working only with the coordinates of peak activations instead of the full statistic images. We apply a 3-level IBMA mixed model for a "mega-analysis", and highlight important considerations in the specification of each model and contrast. We compare the IBMA result to three CBMA methods: ALE (activation likelihood estimation), KDA (kernel density analysis) and MKDA (multi-level kernel density analysis), for various CBMA smoothing parameters. For the datasets considered, we find that ALE at σ =15 mm, KDA at ρ =25–30 mm and MKDA at ρ =15 mm give the greatest similarity to the IBMA result, and that ALE was the most similar for this particular dataset, though only with a Dice similarity coefficient of 0. 45 (Dice measure ranges from 0 to 1). Based on this poor similarity, and the greater modeling flexibility afforded by hierarchical mixed models, we suggest that IBMA is preferred over CBMA. To make IBMA analyses practical, however, the neuroimaging field needs to develop an effective mechanism for sharing image data, including whole-brain images of both effect estimates and their standard errors.

YNIMG Journal 2009 Journal Article

Modeling the hemodynamic response function in fMRI: Efficiency, bias and mis-modeling

• Martin A. Lindquist
• Ji Meng Loh
• Lauren Y. Atlas
• Tor D. Wager

Most brain research to date have focused on studying the amplitude of evoked fMRI responses, though there has recently been an increased interest in measuring onset, peak latency and duration of the responses as well. A number of modeling procedures provide measures of the latency and duration of fMRI responses. In this work we compare several techniques that vary in their assumptions, model complexity, and interpretation. For each method, we introduce methods for estimating amplitude, peak latency, and duration and for performing inference in a multi-subject fMRI setting. We then assess the techniques' relative sensitivity and their propensity for mis-attributing task effects on one parameter (e. g. , duration) to another (e. g. , amplitude). Finally, we introduce methods for quantifying model misspecification and assessing bias and power-loss related to the choice of model. Overall, the results show that it is surprisingly difficult to accurately recover true task-evoked changes in BOLD signal and that there are substantial differences among models in terms of power, bias and parameter confusability. Because virtually all fMRI studies in cognitive and affective neuroscience employ these models, the results bear on the interpretation of hemodynamic response estimates across a wide variety of psychological and neuroscientific studies.

YNIMG Journal 2009 Journal Article

The new field of Brain–Body Medicine: What have we learned and where are we headed?

• Richard D. Lane
• Tor D. Wager

YNIMG Journal 2009 Journal Article

WITHDRAWN: Introduction to a Special Issue of Neuroimage on Brain-Body Medicine

• Richard D. Lane
• Tor D. Wager

This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http: //www. elsevier. com/locate/withdrawalpolicy.

YNIMG Journal 2008 Journal Article

Detection of time-varying signals in event-related fMRI designs

• Jack Grinband
• Tor D. Wager
• Martin Lindquist
• Vincent P. Ferrera
• Joy Hirsch

In neuroimaging research on attention, cognitive control, decision-making, and other areas where response time (RT) is a critical variable, the temporal variability associated with the decision is often assumed to be inconsequential to the hemodynamic response (HDR) in rapid event-related designs. On this basis, the majority of published studies model brain activity lasting less than 4 s with brief impulses representing the onset of neural or cognitive events, which are then convolved with the hemodynamic impulse response function (HRF). However, electrophysiological studies have shown that decision-related neuronal activity is not instantaneous, but in fact, often lasts until the motor response. It is therefore possible that small differences in neural processing durations, similar to human RTs, will produce noticeable changes in the HDR, and therefore in the results of regression analyses. In this study we compare the effectiveness of traditional models that assume no temporal variance with a model that explicitly accounts for the duration of very brief epochs of neural activity. Using both simulations and fMRI data, we show that brief differences in duration are detectable, making it possible to dissociate the effects of stimulus intensity from stimulus duration, and that optimizing the model for the type of activity being detected improves the statistical power, consistency, and interpretability of results.

YNIMG Journal 2008 Journal Article

Functional grouping and cortical–subcortical interactions in emotion: A meta-analysis of neuroimaging studies

• Hedy Kober
• Lisa Feldman Barrett
• Josh Joseph
• Eliza Bliss-Moreau
• Kristen Lindquist
• Tor D. Wager

We performed an updated quantitative meta-analysis of 162 neuroimaging studies of emotion using a novel multi-level kernel-based approach, focusing on locating brain regions consistently activated in emotional tasks and their functional organization into distributed functional groups, independent of semantically defined emotion category labels (e. g. , “anger, ” “fear”). Such brain-based analyses are critical if our ways of labeling emotions are to be evaluated and revised based on consistency with brain data. Consistent activations were limited to specific cortical sub-regions, including multiple functional areas within medial, orbital, and inferior lateral frontal cortices. Consistent with a wealth of animal literature, multiple subcortical activations were identified, including amygdala, ventral striatum, thalamus, hypothalamus, and periaqueductal gray. We used multivariate parcellation and clustering techniques to identify groups of co-activated brain regions across studies. These analyses identified six distributed functional groups, including medial and lateral frontal groups, two posterior cortical groups, and paralimbic and core limbic/brainstem groups. These functional groups provide information on potential organization of brain regions into large-scale networks. Specific follow-up analyses focused on amygdala, periaqueductal gray (PAG), and hypothalamic (Hy) activations, and identified frontal cortical areas co-activated with these core limbic structures. While multiple areas of frontal cortex co-activated with amygdala sub-regions, a specific region of dorsomedial prefrontal cortex (dmPFC, Brodmann's Area 9/32) was the only area co-activated with both PAG and Hy. Subsequent mediation analyses were consistent with a pathway from dmPFC through PAG to Hy. These results suggest that medial frontal areas are more closely associated with core limbic activation than their lateral counterparts, and that dmPFC may play a particularly important role in the cognitive generation of emotional states.

YNIMG Journal 2007 Journal Article

Modeling state-related fMRI activity using change-point theory

• Martin A. Lindquist
• Christian Waugh
• Tor D. Wager

The general linear model (GLM) approach has arguably become the dominant way to analyze functional magnetic resonance imaging (fMRI) data. It tests whether activity in a brain region is systematically related to some known input function. However, the GLM becomes impractical when the precise timing and duration of psychological events cannot be specified a priori. In this work, we introduce a new analysis approach that allows the predicted signal to depend non-linearly on the input. The approach uses ideas from statistical control theory and change-point theory to model slowly varying processes for which the onset times and durations of underlying psychological activity are uncertain. Our approach is exploratory in nature, while retaining the inferential capabilities of the more rigid modeling approach. It is a multi-subject extension of the exponentially weighted moving average (EWMA) method used in change-point analysis. We extend existing EWMA models for individual subjects (a single time series) so that they are applicable to fMRI data, and develop a group analysis using a hierarchical model, which we term HEWMA (Hierarchical EWMA). The HEWMA method can be used to analyze fMRI data voxel-wise throughout the brain, data from regions of interest, or temporal components extracted using ICA or similar methods. We validate the false-positive rate control of the method and provide power estimates using simulations based on real fMRI data. We further apply this method to an fMRI study (n =24) of state anxiety. A toolbox implementing all functions in Matlab is freely available from the authors.

YNIMG Journal 2005 Journal Article

Accounting for nonlinear BOLD effects in fMRI: parameter estimates and a model for prediction in rapid event-related studies

• Tor D. Wager
• Alberto Vazquez
• Luis Hernandez
• Douglas C. Noll

Nonlinear effects in fMRI BOLD data may substantially influence estimates of task-related activations, particularly in rapid event-related designs. If the BOLD response to each stimulus is assumed to be independent of the stimulation history, nonlinear interactions create a prediction error that may reduce sensitivity. When stimulus density differs among conditions, nonlinear effects can cause artifactual differences in activation. This situation can occur in rapid event-related designs or when comparing blocks of unequal lengths. We present data showing substantial nonlinear history effects for stimuli 1 s apart and use estimates of nonlinearities in response magnitude, onset time, and time to peak to form a low-dimensional parameterization of these nonlinear effects. Our estimates of nonlinearity appear relatively consistent throughout the brain, and these estimates can be used to form adjusted linear predictors for future rapid event-related fMRI studies. Adjusting the linear model for these known nonlinear effects results in a substantially better model fit. The biggest advantages to using predictors adjusted for known nonlinear effects are (1) higher sensitivity at the individual subject level of analysis, (2) better control of confounds related to nonlinear effects, and (3) more accurate estimates of design efficiency in experimental fMRI design.

YNIMG Journal 2005 Journal Article

Common and unique components of response inhibition revealed by fMRI

• Tor D. Wager
• Ching-Yune C. Sylvester
• Steven C. Lacey
• Derek Evan Nee
• Michael Franklin
• John Jonides

The ability to inhibit inappropriate responses is central to cognitive control, but whether the same brain mechanisms mediate inhibition across different tasks is not known. We present evidence for a common set of frontal and parietal regions engaged in response inhibition across three tasks: a go/no-go task, a flanker task, and a stimulus–response compatibility task. Regions included bilateral anterior insula/frontal operculum and anterior prefrontal, right dorsolateral and premotor, and parietal cortices. Insula activity was positively correlated with interference costs in behavioral performance in each task. Principal components analysis showed a coherent pattern of individual differences in these regions that was also positively correlated with performance in all three tasks. However, correlations among tasks were low, for both brain activity and performance. We suggest that common interference detection and/or resolution mechanisms are engaged across tasks, and that inter-task correlations in behavioral performance are low because they conflate measurements of common mechanisms with measurements of individual biases unique to each task.

YNIMG Journal 2005 Journal Article

Increased sensitivity in neuroimaging analyses using robust regression

• Tor D. Wager
• Matthew C. Keller
• Steven C. Lacey
• John Jonides

Robust regression techniques are a class of estimators that are relatively insensitive to the presence of one or more outliers in the data. They are especially well suited to data that require large numbers of statistical tests and may contain outliers due to factors not of experimental interest. Both these issues apply particularly to neuroimaging data analysis. We use simulations to compare several robust techniques against ordinary least squares (OLS) regression, and we apply robust regression to second-level (group “random effects”) analyses in three fMRI datasets. Our results show that robust iteratively reweighted least squares (IRLS) at the 2nd level is a computationally efficient technique that both increases statistical power and decreases false positive rates in the presence of outliers. The benefits of IRLS are apparent with small samples (n = 10) and increase with larger sample sizes (n = 40) in the typical range of group neuroimaging experiments. When no true effects are present, IRLS controls false positive rates at an appropriate level. We show that IRLS can have substantial benefits in analysis of group data and in estimating hemodynamic response shapes from time series data. We provide software to implement IRLS in group neuroimaging analyses.

YNIMG Journal 2003 Journal Article

Optimization of experimental design in fMRI: a general framework using a genetic algorithm

• Tor D. Wager
• Thomas E. Nichols

This article describes a method for selecting design parameters and a particular sequence of events in fMRI so as to maximize statistical power and psychological validity. Our approach uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect to single or multiple measures of fitness. Two strengths of the GA framework are that (1) it operates with any sort of model, allowing for very specific parameterization of experimental conditions, including nonstandard trial types and experimentally observed scanner autocorrelation, and (2) it is flexible with respect to fitness criteria, allowing optimization over known or novel fitness measures. We describe how genetic algorithms may be applied to experimental design for fMRI, and we use the framework to explore the space of possible fMRI design parameters, with the goal of providing information about optimal design choices for several types of designs. In our simulations, we considered three fitness measures: contrast estimation efficiency, hemodynamic response estimation efficiency, and design counterbalancing. Although there are inherent trade-offs between these three fitness measures, GA optimization can produce designs that outperform random designs on all three criteria simultaneously.

YNIMG Journal 2001 Journal Article

Characterizing nonlinear BOLD responses in human visual cortex

• Tor D. Wager
• Thomas E. Nichols
• Richard Bryck
• John Jonides
• Edward E. Smith