fMRI Research Research Articles

Anhedonia is Associated With Attenuated Reward-Related Neural Activity During Cue-Evaluation and Motor-Preparation

Functional resonance magnetic imaging (fMRI) research indicates anhedonia is associated with blunted neural activity during reward-anticipation. However, due to low temporal resolution of fMRI, the time course of these associations remains largely unknown. We leveraged the strong temporal resolution of event-related potentials (ERPs) to examine associations between anhedonia and electrocortical activity across three temporally distinct stages of reward-anticipation: cue-evaluation, motor-preparation, and feedback-anticipation.

Hybrid auditory fMRI: In pursuit of increasing data acquisition while decreasing the impact of scanner noise

BackgroundTwo challenges in auditory fMRI include the loud scanner noise during sound presentation and slow data acquisition. Here, we introduce a new auditory imaging protocol, termed “hybrid”, that alleviates these obstacles. New methodWe designed a within-subject experiment (N = 14) wherein language-driven activity was measured by hybrid, interleaved silent (ISSS), and continuous multiband acquisition. To determine the advantage of noise attenuation during sound presentation, hybrid was compared to multiband. To identify the benefits of increased temporal resolution, hybrid was compared to ISSS. Data were evaluated by whole-brain univariate general linear modeling (GLM) and multivariate pattern analysis (MVPA). ResultsComparison with existing methods:•Hybrid vs. Multiband: in both GLM and MVPA, hybrid showed widespread activation throughout the language network including the left inferior frontal gyrus, bilateral superior temporal regions, thalamus, and inferior colliculus. By contrast, multiband showed activity mostly within the left frontotemporal cortices.•Hybrid vs. ISSS: in MVPA, hybrid yielded more activation than ISSS throughout the language network. However, in GLM, hybrid detected less activation than ISSS. Despite the reduction of activation, hybrid more specifically detected activity in the canonical language network compared to ISSS. ConclusionsOur data revealed that hybrid imaging restored neural activity in the canonical language network that was absent due to the loud noise or slow sampling in the conventional imaging protocols. With its noise-attenuated sound presentation windows and increased acquisition speed, the hybrid protocol is well-suited for auditory fMRI research tracking neural activity pertaining to fast, time-varying acoustic events.

Comparison of Four fMRI Paradigms Probing Emotion Processing.

Previous fMRI research has applied a variety of tasks to examine brain activity underlying emotion processing. While task characteristics are known to have a substantial influence on the elicited activations, direct comparisons of tasks that could guide study planning are scarce. We aimed to provide a comparison of four common emotion processing tasks based on the same analysis pipeline to suggest tasks best suited for the study of certain target brain regions. We studied an n-back task using emotional words (EMOBACK) as well as passive viewing tasks of emotional faces (FACES) and emotional scenes (OASIS and IAPS). We compared the activation patterns elicited by these tasks in four regions of interest (the amygdala, anterior insula, dorsolateral prefrontal cortex (dlPFC) and pregenual anterior cingulate cortex (pgACC)) in three samples of healthy adults (N = 45). The EMOBACK task elicited activation in the right dlPFC and bilateral anterior insula and deactivation in the pgACC while the FACES task recruited the bilateral amygdala. The IAPS and OASIS tasks showed similar activation patterns recruiting the bilateral amygdala and anterior insula. We conclude that these tasks can be used to study different regions involved in emotion processing and that the information provided is valuable for future research and the development of fMRI biomarkers.

Five best practices for fMRI research: Towards a biologically grounded understanding of mental phenomena

The replication crisis in science has not spared functional magnetic resonance imaging (fMRI) research. A range of issues including insufficient control of false positives, code bugs, concern regarding generalizability and replicability of findings, inadequate characterization of physiological confounds, over-mining of repository datasets, and the small sample sizes/low power of many early studies have led to hearty debate in both the field and the press about the usefulness and viability of fMRI. Others still see enormous potential for fMRI in diagnosing conditions that do not otherwise lend themselves to non-invasive biological measurement, from chronic pain to neurological and psychiatric illness. How do we reconcile the limitations of fMRI with the hype over its potential? Despite many papers hailed by the press as the nail in the coffin for fMRI, from the dead salmon incident of 2009 to cluster failure more recently, funders, researchers, and the general public do not seem to have reduced their appetite for pictures of brain maps, or gadgets with the word “neuro” in the name. Multiple blogs exist for the sole purpose of criticizing such enterprise. The replicability crisis should certainly give ‘neuroimagers’ pause, and reason to soul-search. It is more important than ever to clarify when fMRI is and when it is not useful. The method remains the best noninvasive imaging tool for many research questions, however imperfect and imprecise it may be. However, to address past limitations, I argue neuroimaging researchers planning future studies need to consider the following five factors: power/effect size, design optimization, replicability, physiological confounds, and data sharing.

Resting-State fMRI in Studies of Acupuncture

Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.

Dorsal anterior cingulate cortex intrinsic functional connectivity linked to electrocortical measures of error monitoring.

The error-related negativity (ERN) is a response-locked event-related potential, occurring approximately 50ms following an erroneous response at frontocentral electrode sites. Source localization and functional magnetic resonance imaging (fMRI) research indicate that the ERN is likely generated by activity in the dorsal anterior cingulate cortex (dACC). The dACC is thought to be a part of a broader network of brain regions that collectively comprise an error monitoring network. However, little is known about how intrinsic connectivity within the dACC-based error monitoring network contributes to variability in ERN amplitude. The purpose of this study was to assess the relationship between dACC functional connectivity and ERN amplitude. In a sample of highly trait anxious individuals, the ERN was elicited in a flanker task and functional connectivity was assessed in a 10-min resting-state fMRI scan. Results suggest that the strength of dACC seeded functional connectivity with the supplementary motor area is correlated with the ΔERN (i.e., incorrect-correct responses) amplitude such that greater ΔERN amplitude was accompanied by greater functional coupling between these regions. In sum, ERN amplitude appears to be related to the strength of functional connectivity between error monitoring and motor control regions of the brain.

How to Interpret Resting-State fMRI: Ask Your Participants.

Resting-state fMRI (rsfMRI) reveals brain dynamics in a task-unconstrained environment as subjects let their minds wander freely. Consequently, resting subjects navigate a rich space of cognitive and perceptual states (i.e., ongoing experience). How this ongoing experience shapes rsfMRI summary metrics (e.g., functional connectivity) is unknown, yet likely to contribute uniquely to within- and between-subject differences. Here we argue that understanding the role of ongoing experience in rsfMRI requires access to standardized, temporally resolved, scientifically validated first-person descriptions of those experiences. We suggest best practices for obtaining those descriptions via introspective methods appropriately adapted for use in fMRI research. We conclude with a set of guidelines for fusing these two data types to answer pressing questions about the etiology of rsfMRI.

Jumping over baselines with new methods to predict activation maps from resting-state fMRI

Cognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

Removing the Reliability Bottleneck in Functional Magnetic Resonance Imaging Research to Achieve Clinical Utility

Our website uses cookies to enhance your experience. By continuing to use our site, or clicking "Continue," you are agreeing to our Cookie Policy | Continue

The Effect of Exercise on Neural Activation and Cognition: A Review of Task-Based fMRI Studies.

Numerous studies have stressed the importance of exercise in promoting physical and mental health and for aiding in cognition. Encouragingly, physical exercise has been shown to reduce the risk of developing Alzheimer's disease and to mitigate hemiparesis experienced by stroke patients. Additionally, today where over 1.9 billion are overweight, physical exercise is imperative to save lives and to mitigate the burden on the healthcare system. Although the benefits of physical exercise have been explored, the underlying mechanisms to enact these benefits have not been well-characterized. Here we review exercise-induced changes in regional brain activation and modulation. Paradigms differing in intensity, duration, and type of motor movement have been used to assess exercise effects on memory, cognition, and disease mitigation in youth and elderly populations. To evaluate exercise-induced changes in neural activity, the noninvasive imaging technique, functional magnetic resonance imaging (fMRI), is employed. fMRI is recorded either during or after exercise intervention. Post-exercise fMRI is often paired with in-bore tests of cognition to provide insight into the associated brain regions. Whereas, during intervention, fMRI is used to detail muscle-associated neural activation profiles. Characterization of the region and magnitude of brain activation has been used to perform comparative studies and identify specific characteristics from individuals with varying motor and cognitive abilities. Further fMRI and exercise research, with the use of these metrics, could facilitate the development of tools for disease diagnosis or to assess level of dysfunction or progression.

Gender differences in functional connectivity of left-handers during the passage of an emotional stroop task

ntroduction. Neural correlates underlying the processing of emotional information, influence of emotional interference on cognitive control, gender difference in such activities remain a topic of research and discussion.Purpose. To study the connectivity of the brain regions involved in the processing of emotional information in left-handers, based on the EEG data obtained during their passage the emotional Stroop test (EST).Methods. EEG was registered during subjectpassage the EST successively two times. In EST a series including 240 words were presented at the center of the computer screen in one of two colors: red or green, words were emotional (negative) or neutral, shown in pseudorandom order. Some of these stimuli (target words names of animals and plants) participants were instructed to ignore (do not press any keys). Subjects were asked to respond with right hand (pressing P on a keyboard) for words printed in red and with left hand (pressing Q on a keyboard) for words printed in green. It was investigated source level functional connectivity (FC) in two groups of left-handers (17 to 22 years old): women (n=10) and men (n=10). FC was analysed between 21 regions of interest (ROI), selected on the base of fMRI research literature. Connections between ROIs were assessed using lagged phase synchronization (LPS) with eLORETA complex.Results. Statistically significant differences in FC by LPS between men and women groups were found only in first passage of EST. Particularly, FCof women, compared to men was: -higher between anterior cingulate cortex and left middle temporal area in delta band;-lower between left hippocampal formation and right frontal eye fields in beta-1;-and higher in beta-2 between right frontal eye fields and right anterior insula.were shorter for both emotional and neutral words. The interference effect was observed in both tests in women and only in the first test in men. Interference effect also was higher for responses with left hand for both groups. Repeated passage of the emotional Stroop test leads to reducing the effect of interference in men but not in women.Originality.The main neural networks have been identified, which in left-handed women are more involved in the processing of negative emotional information than in left-handed men.Conclusion. Compared to men, women have stronger connections of two areas of the brain involved in processing negatively colored emotional information, with other areas of the brain. The anterior cingulate cortex, which is considered to playan important role in attention and executive functions, is connected to the left middle temporal area, involved in the analysis of visual movement and words processing. Activation of theright anterior insular cortex, which is closely related to emotionalprocessing, is connected to the right frontal eye fields, which are known to play a key role in the goal directed eyes movements.Contrary, area of right frontal eye fields of men is more tightly connected to the contralateral left hippocampus, and is involved in semantic rather than emotional processing of information.Key words: emotional Stroop test;gender differences; left-handers; EEG; functional connectivity

The role of transcranial magnetic stimulation in understanding attention-related networks in single subjects.

Attention is a cognitive mechanism that has been studied through several methodological viewpoints, including animal models, MRI in stroke patients, and fMRI in healthy subjects. Activation-based fMRI research has also pointed to specific networks that activate during attention tasks. Most recently, network neuroscience has been used to study the functional connectivity of large-scale networks for attention to reveal how strongly correlated networks are to each other when engaged in specific behaviors. While neuroimaging has revealed important information about the neural correlates of attention, it is crucial to better understand how these processes are organized and executed in the brain in single subjects to guide theories and treatments for attention. Noninvasive brain stimulation is an effective tool to causally manipulate neural activity to detect the causal roles of circuits in behavior. We describe how combining transcranial magnetic stimulation (TMS) with modern precision network analysis in single-subject neuroimaging could test the roles of regions, circuits, and networks in regulating attention as a pathway to improve treatment effect magnitudes and specificity.

Borderline personality disorder classification based on brain network measures during emotion regulation

Borderline Personality Disorder (BPD) is characterized by an increased emotional sensitivity and dysfunctional capacity to regulate emotions. While amygdala and prefrontal cortex interactions are regarded as the critical neural mechanisms underlying these problems, the empirical evidence hereof is inconsistent. In the current study, we aimed to systematically test different properties of brain connectivity and evaluate the predictive power to detect borderline personality disorder. Patients with borderline personality disorder (n = 51), cluster C personality disorder (n = 26) and non-patient controls (n = 44), performed an fMRI emotion regulation task. Brain network analyses focused on two properties of task-related connectivity: phasic refers to task-event dependent changes in connectivity, while tonic was defined as task-stable background connectivity. Three different network measures were estimated (strength, local efficiency, and participation coefficient) and entered as separate models in a nested cross-validated linear support vector machine classification analysis. Borderline personality disorder vs. non-patient controls classification showed a balanced accuracy of 55%, which was not significant under a permutation null-model, p = 0.23. Exploratory analyses did indicate that the tonic strength model was the highest performing model (balanced accuracy 62%), and the amygdala was one of the most important features. Despite being one of the largest data-sets in the field of BPD fMRI research, the sample size may have been limited for this type of classification analysis. The results and analytic procedures do provide starting points for future research, focusing on network measures of tonic connectivity, and potentially focusing on subgroups of BPD.

Neuroimaging Somatosensory and Therapeutic Alliance Mechanisms Supporting Acupuncture.

Acupuncture consists of multiple components, and neuroimaging studies are beginning to tease apart potential brain-based mechanisms supporting this nonpharmacologic intervention. Brain imaging in patient populations is important for the acupuncture neuroimaging field, and recent studies have incorporated neuroimaging into a clinical trial framework for improved ecological validity in extending results to be more relevant for clinical practice. For instance, functional magnetic resonance imaging (fMRI) research with patients suffering from carpal tunnel syndrome has suggested cortical plasticity as a mechanism supporting acupuncture analgesia. In turn, the patient-acupuncturist relationship is another important aspect of acupuncture analgesia, and recent hyperscan fMRI research has begun to explore brain-based mechanisms underlying therapeutic alliance between patient and clinician, and how this might impact acupuncture analgesia. Ultimately, neuroimaging has shown great promise in the past several decades in opening a window into brain function to better appreciate the mechanisms supporting acupuncture. As acupuncture likely operates via multiple distinct mechanisms of action, teasing apart the constituent aspects of acupuncture therapy in humans will be an important goal for current and future neuroimaging research studies.

An Important Step toward Understanding the Role of Body-based Cues on Human Spatial Memory for Large-Scale Environments

Moving our body through space is fundamental to human navigation; however, technical and physical limitations have hindered our ability to study the role of these body-based cues experimentally. We recently designed an experiment using novel immersive virtual-reality technology, which allowed us to tightly control the availability of body-based cues to determine how these cues influence human spatial memory [Huffman, D. J., & Ekstrom, A. D. A modality-independent network underlies the retrieval of large-scale spatial environments in the human brain. Neuron, 104, 611-622, 2019]. Our analysis of behavior and fMRI data revealed a similar pattern of results across a range of body-based cues conditions, thus suggesting that participants likely relied primarily on vision to form and retrieve abstract, holistic representations of the large-scale environments in our experiment. We ended our paper by discussing a number of caveats and future directions for research on the role of body-based cues in human spatial memory. Here, we reiterate and expand on this discussion, and we use a commentary in this issue by A. Steel, C. E. Robertson, and J. S. Taube (Current promises and limitations of combined virtual reality and functional magnetic resonance imaging research in humans: A commentary on Huffman and Ekstrom (2019). Journal of Cognitive Neuroscience, 2020) as a helpful discussion point regarding some of the questions that we think will be the most interesting in the coming years. We highlight the exciting possibility of taking a more naturalistic approach to study the behavior, cognition, and neuroscience of navigation. Moreover, we share the hope that researchers who study navigation in humans and nonhuman animals will synergize to provide more rapid advancements in our understanding of cognition and the brain.

Stimulus-Specific Visual Working Memory Representations in Human Cerebellar Lobule VIIb/VIIIa.

fMRI research has revealed that cerebellar lobule VIIb/VIIIa exhibits load-dependent activity that increases with the number of items held in visual working memory (VWM). However, it remains unclear whether these cerebellar responses reflect processes specific to VWM or more general visual attentional mechanisms. To investigate this question, we examined whether cerebellar activity during the delay period of a VWM task is selective for stimuli held in working memory. A sample of male and female human subjects performed a VWM continuous report task in which they were retroactively cued to remember the direction of motion of moving dot stimuli. Cerebellar lobule VIIb/VIIIa delay-period activation accurately decoded the direction of the remembered stimulus, as did frontal and parietal regions of the dorsal attention network. Arguing against a motor explanation, no other cerebellar area exhibited stimulus specificity, including the oculomotor vermis, a key area associated with eye movement control. Finer-scale analysis revealed that the medial portion of lobule VIIb and to a lesser degree the lateral most portion of lobules VIIb and VIIIa, which exhibit robust resting state connectivity with frontal and parietal regions of the dorsal attention network, encoded the identity of the remembered stimulus, while intermediate portions of lobule VIIb/VIIIa did not. These findings of stimulus-specific coding of VWM within lobule VIIb/VIIIa indicate for the first time that the distributed network responsible for the encoding and maintenance of mnemonic representations extends to the cerebellum.SIGNIFICANCE STATEMENT There is considerable debate concerning where in the brain the contents of visual working memory (VWM) are stored. To date, this literature has primarily focused on the role of regions located within cerebral cortex. There is growing evidence for cerebellar involvement in higher-order cognitive functions including working memory. While the cerebellum has been previously shown to be recruited by VWM paradigms, it is unclear whether any portion of cerebellum actively encodes and maintains mnemonic representations. The present study demonstrates that cerebellar lobule VIIb/VIIIa activity patterns are selective for remembered stimuli and that this selectivity persists in the absence of perceptual input. These findings provide novel evidence for the participation of cerebellar structures in the persistent storage of visual information.

Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: A multicenter study

From molecular mechanisms to global brain networks, atypical fluctuations are the hallmark of neurodegeneration. Yet, traditional fMRI research on resting-state networks (RSNs) has favored static and average connectivity methods, which by overlooking the fluctuation dynamics triggered by neurodegeneration, have yielded inconsistent results. The present multicenter study introduces a data-driven machine learning pipeline based on dynamic connectivity fluctuation analysis (DCFA) on RS-fMRI data from 300 participants belonging to three groups: behavioral variant frontotemporal dementia (bvFTD) patients, Alzheimer's disease (AD) patients, and healthy controls. We considered non-linear oscillatory patterns across combined and individual resting-state networks (RSNs), namely: the salience network (SN), mostly affected in bvFTD; the default mode network (DMN), mostly affected in AD; the executive network (EN), partially compromised in both conditions; the motor network (MN); and the visual network (VN). These RSNs were entered as features for dementia classification using a recent robust machine learning approach (a Bayesian hyperparameter tuned Gradient Boosting Machines (GBM) algorithm), across four independent datasets with different MR scanners and recording parameters. The machine learning classification accuracy analysis revealed a systematic and unique tailored architecture of RSN disruption. The classification accuracy ranking showed that the most affected networks for bvFTD were the SN + EN network pair (mean accuracy = 86.43%, AUC = 0.91, sensitivity = 86.45%, specificity = 87.54%); for AD, the DMN + EN network pair (mean accuracy = 86.63%, AUC = 0.89, sensitivity = 88.37%, specificity = 84.62%); and for the bvFTD vs. AD classification, the DMN + SN network pair (mean accuracy = 82.67%, AUC = 0.86, sensitivity = 81.27%, specificity = 83.01%). Moreover, the DFCA classification systematically outperformed canonical connectivity approaches (including both static and linear dynamic connectivity). Our findings suggest that non-linear dynamical fluctuations surpass two traditional seed-based functional connectivity approaches and provide a pathophysiological characterization of global brain networks in neurodegenerative conditions (AD and bvFTD) across multicenter data.

Caffeine enhances BOLD responses to electrical whisker pad stimulation in rats during alpha‐chloralose anaesthesia

By reducing the cerebral blood flow and thereby increasing the resting deoxyhaemoglobin concentration, many human studies have shown that caffeine has a beneficial effect on enhancing the magnitude of blood-oxygenation-level-dependent (BOLD) responses. However, the effect of caffeine on BOLD responses in animals under anaesthesia has not been demonstrated. In this study, we aimed to determine the effect of systemic caffeine administration on BOLD responses in rats under alpha-chloralose. By applying electric whisker pad stimulation to male Sprague-Dawley rats, we performed fMRI measurements before and after the caffeine injection (40mg/kg, n=7) or an equivalent volume of saline (n=6) at 7T. To understand the potential perturbation of animal physiology during stimulation, arterial blood pressure was measured in a separate group of animals (n=3) outside the scanner. Caffeine significantly decreased baseline BOLD signals (p=.05) due to the increased deoxyhaemoglobin level. Both BOLD responses and t-values in the primary somatosensory cortex were significantly increased (both p<.05). The blood pressure changed insignificantly (p>.05). No significant differences in BOLD responses and t-values were observed in the control condition of saline injection (both p>.05). These findings suggested that, although the cerebral activity was lower under alpha-chloralose anaesthesia, the higher level of deoxygemoglobin at the baseline under the caffeinated condition can benefit the magnitude of BOLD responses in rats. These findings suggest that animal models might serve as potential platforms for further caffeine-related fMRI research studies.

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

AbstractFunctional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.

Influence of sample size and analytic approach on stability and interpretation of brain-behavior correlations in task-related fMRI data.

Limited statistical power due to small sample sizes is a problem in fMRI research. Most of the work to date has examined the impact of sample size on task‐related activation, with less attention paid to the influence of sample size on brain‐behavior correlations, especially in actual experimental fMRI data. We addressed this issue using two large data sets (a working memory task, N = 171, and a relational processing task, N = 865) and both univariate and multivariate approaches to voxel‐wise correlations. We created subsamples of different sizes and calculated correlations between task‐related activity at each voxel and task performance. Across both data sets the magnitude of the brain‐behavior correlations decreased and similarity across spatial maps increased with larger sample sizes. The multivariate technique identified more extensive correlated areas and more similarity across spatial maps, suggesting that a multivariate approach would provide a consistent advantage over univariate approaches in the stability of brain‐behavior correlations. In addition, the multivariate analyses showed that a sample size of roughly 80 or more participants would be needed for stable estimates of correlation magnitude in these data sets. Importantly, a number of additional factors would likely influence the choice of sample size for assessing such correlations in any given experiment, including the cognitive task of interest and the amount of data collected per participant. Our results provide novel experimental evidence in two independent data sets that the sample size commonly used in fMRI studies of 20–30 participants is very unlikely to be sufficient for obtaining reproducible brain‐behavior correlations, regardless of analytic approach.