Martin A. Lindquist

Prefrontal-Subcortical Pathways Mediating Successful Emotion Regulation

Although prefrontal cortex has been implicated in the cognitive regulation of emotion, the cortical-subcortical interactions that mediate this ability remain poorly understood. To address this issue, we identified a right ventrolateral prefrontal region (vlPFC) whose activity correlated with reduced negative emotional experience during cognitive reappraisal of aversive images. We then applied a pathway-mapping analysis on subcortical regions to locate mediators of the association between vlPFC activity and reappraisal success (i.e., reductions in reported emotion). Results identified two separable pathways that together explained approximately 50% of the reported variance in self-reported emotion: (1) a path through nucleus accumbens that predicted greater reappraisal success, and (2) a path through ventral amygdala that predicted reduced reappraisal success (i.e., more negative emotion). These results provide direct evidence that vlPFC is involved in both the generation and regulation of emotion through different subcortical pathways, suggesting a general role for this region in appraisal processes.

An fMRI-Based Neurologic Signature of Physical Pain

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

The Statistical Analysis of fMRI Data.

In recent years there has been explosive growth in the number of neuroimaging studies performed using functional Magnetic Resonance Imaging (fMRI). The field that has grown around the acquisition and analysis of fMRI data is intrinsically interdisciplinary in nature and involves contributions from researchers in neuroscience, psychology, physics and statistics, among others. A standard fMRI study gives rise to massive amounts of noisy data with a complicated spatio-temporal correlation structure. Statistics plays a crucial role in understanding the nature of the data and obtaining relevant results that can be used and interpreted by neuroscientists. In this paper we discuss the analysis of fMRI data, from the initial acquisition of the raw data to its use in locating brain activity, making inference about brain connectivity and predictions about psychological or disease states. Along the way, we illustrate interesting and important issues where statistics already plays a crucial role. We also seek to illustrate areas where statistics has perhaps been underutilized and will have an increased role in the future.

Meta-analysis of functional neuroimaging data: current and future directions

Meta-analysis is an increasingly popular and valuable tool for summarizing results across many neuroimaging studies. It can be used to establish consensus on the locations of functional regions, test hypotheses developed from patient and animal studies and develop new hypotheses on structure-function correspondence. It is particularly valuable in neuroimaging because most studies do not adequately correct for multiple comparisons; based on statistical thresholds used, we estimate that roughly 10-20% of reported activations in published studies are false positives. In this article, we briefly summarize some of the most popular meta-analytic approaches and their limitations, and we outline a revised multilevel approach with increased validity for establishing consistency across studies. We also discuss multivariate methods by which meta-analysis can be used to develop and test hypotheses about co-activity of brain regions. Finally, we argue that meta-analyses can make a uniquely valuable contribution to predicting psychological states from patterns of brain activity, and we briefly discuss some methods for making such predictions.

Spin-Echo fMRI in Humans Using High Spatial Resolutions and High Magnetic Fields

The Hahn spin‐echo (HSE)‐based BOLD effect at high magnetic fields is expected to provide functional images that originate exclusively from the microvasculature. The blood contribution that dominates HSE BOLD contrast at low magnetic fields (e.g., 1.5 T), and degrades specificity, is highly attenuated at high fields because the apparent T2 of venous blood in an HSE experiment decreases quadratically with increasing magnetic field. In contrast, the HSE BOLD contrast is believed to arise from the microvasculature and increase supralinearly with the magnetic field strength. In this work we report the results of detailed and quantitative evaluations of HSE BOLD signal changes for functional imaging in the human visual cortex at 4 and 7 T. This study used high spatial resolution, afforded by the increased signal‐to‐noise ratio (SNR) of higher field strengths and surface coils, to avoid partial volume effects (PVEs), and demonstrated increased contrast‐to‐noise ratio (CNR) and spatial specificity at the higher field strengths. The HSE BOLD signal changes induced by visual stimulation were predominantly linearly dependent on the echo time (TE). They increased in magnitude almost quadratically in going from 4 to 7 T when the blood contribution was suppressed using Stejskal‐Tanner gradients that suppress signals from the blood due to its inhomogeneous flow and higher diffusion constant relative to tissue. The HSE signal changes at 7 T were modeled accurately using a vascular volume of 1.5%, in agreement with the capillary volume of gray matter. Furthermore, high‐resolution acquisitions indicate that CNR increased with voxel sizes < 1 mm3 due to diminishing white matter or cerebrospinal fluid‐space vs. gray matter PVEs. It was concluded that the high‐field HSE functional MRI (fMRI) signals originated largely from the capillaries, and that the magnitude of the signal changes associated with brain function reached sufficiently high levels at 7 T to make it a useful approach for mapping on the millimeter to submillimeter spatial scale. Magn Reson Med 49:655–664, 2003.

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

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.

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

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.

Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask

Over the past year, a heated discussion about ‘circular’ or ‘nonindependent’ analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in ≤100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.

Validity and Power in Hemodynamic Response Modeling: A Comparison Study and a New Approach

One of the advantages of event‐related functional MRI (fMRI) is that it permits estimation of the shape of the hemodynamic response function (HRF) elicited by cognitive events. Although studies to date have focused almost exclusively on the magnitude of evoked HRFs across different tasks, there is growing interest in testing other statistics, such as the time‐to‐peak and duration of activation as well. Although there are many ways to estimate such parameters, we suggest three criteria for optimal estimation: 1) the relationship between parameter estimates and neural activity must be as transparent as possible; 2) parameter estimates should be independent of one another, so that true differences among conditions in one parameter (e.g., hemodynamic response delay) are not confused for apparent differences in other parameters (e.g., magnitude); and 3) statistical power should be maximized. In this work, we introduce a new modeling technique, based on the superposition of three inverse logit functions (IL), designed to achieve these criteria. In simulations based on real fMRI data, we compare the IL model with several other popular methods, including smooth finite impulse response (FIR) models, the canonical HRF with derivatives, nonlinear fits using a canonical HRF, and a standard canonical model. The IL model achieves the best overall balance between parameter interpretability and power. The FIR model was the next‐best choice, with gains in power at some cost to parameter independence. We provide software implementing the IL model. Hum Brain Mapp 2006.

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

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.