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In a perfect world, scientists would develop analyses that are guaranteed to reveal the ground truth of a research question. In reality, there are countless viable workflows that produce distinct, often conflicting, results. Although reproducibility places a necessary bound on the validity of results, it is not sufficient for claiming underlying validity, eventual utility, or generalizability. In this work we focus on how embracing variability in data analysis can improve the generalizability of results. We contextualize how design decisions in brain imaging can be made to capture variation, highlight examples, and discuss how variability capture may improve the quality of results., (© 2024. The Author(s).)

We describe the following shared data from N = 103 healthy adults who completed a broad set of cognitive tasks, surveys, and neuroimaging measurements to examine the construct of self-regulation. The neuroimaging acquisition involved task-based fMRI, resting state fMRI, and structural MRI. Each subject completed the following ten tasks in the scanner across two 90-minute scanning sessions: attention network test (ANT), cued task switching, Columbia card task, dot pattern expectancy (DPX), delay discounting, simple and motor selective stop signal, Stroop, a towers task, and a set of survey questions. The dataset is shared openly through the OpenNeuro project, and the dataset is formatted according to the Brain Imaging Data Structure (BIDS) standard., (© 2024. The Author(s).)

Mindfulness meditation training has been shown to increase resting-state functional connectivity between nodes of the frontoparietal executive control network (dorsolateral prefrontal cortex [DLPFC]) and the default mode network (posterior cingulate cortex [PCC]). We investigated whether these effects generalized to a Mindfulness-Based Stress Reduction (MBSR) course and tested for structural and behaviorally relevant consequences of change in connectivity. Healthy, meditation-naïve adults were randomized to either MBSR (N = 48), an active (N = 47) or waitlist (N = 45) control group. Participants completed behavioral testing, resting-state fMRI scans and diffusion tensor scans at pre-randomization (T1), post-intervention (T2) and ~5.5 months later (T3). We found increased T2-T1 PCC-DLPFC resting connectivity for MBSR relative to control groups. Although these effects did not persist through long-term follow-up (T3-T1), MBSR participants showed a significantly stronger relationship between days of practice (T1 to T3) and increased PCC-DLPFC resting connectivity than participants in the active control group. Increased PCC-DLPFC resting connectivity in MBSR participants was associated with increased microstructural connectivity of a white matter tract connecting these regions and increased self-reported attention. These data show that MBSR increases PCC-DLPFC resting connectivity, which is related to increased practice time, attention and structural connectivity., (© The Author(s) 2019. Published by Oxford University Press.)

We cannot see the minds of others, yet people often spontaneously interpret how they are viewed by other people (i.e., meta-perceptions) and often in a self-flattering manner. Very little is known about the neural associations of meta-perceptions, but a likely candidate is the ventromedial pFC (VMPFC). VMPFC has been associated with both self- and other-perception as well as motivated self-perception. Does this function extend to meta-perceptions? The current study examined neural activity while participants made meta-perceptive interpretations in various social scenarios. A drift-diffusion model was used to test whether the VMPFC is associated with two processes involved in interpreting meta-perceptions in a self-flattering manner: the extent to which the interpretation process involves the preferential accumulation of evidence in favor of a self-flattering interpretation versus the extent to which the interpretation process begins with an expectation that favors a self-flattering outcome. Increased VMPFC activity was associated with the extent to which people preferentially accumulate information when interpreting meta-perceptions under ambiguous conditions and marginally associated with self-flattering meta-perceptions. Together, the present findings illuminate the neural underpinnings of a social cognitive process that has received little attention to date: how we make meaning of others' minds when we think those minds are pointed at us.

Empathy, the ability to understand others' emotions, can occur through perspective taking and experience sharing. Neural systems active when adults empathize include regions underlying perspective taking (e.g. medial prefrontal cortex; MPFC) and experience sharing (e.g. inferior parietal lobule; IPL). It is unknown whether adolescents utilize networks implicated in both experience sharing and perspective taking when accurately empathizing. This question is critical given the importance of accurately understanding others' emotions for developing and maintaining adaptive peer relationships during adolescence. We extend the literature on empathy in adolescence by determining the neural basis of empathic accuracy, a behavioral assay of empathy that does not bias participants toward the exclusive use of perspective taking or experience sharing. Participants (N = 155, aged 11.1-15.5 years) watched videos of 'targets' describing emotional events and continuously rated the targets' emotions during functional magnetic resonance imaging scanning. Empathic accuracy related to activation in regions underlying perspective taking (MPFC, temporoparietal junction and superior temporal sulcus), while activation in regions underlying experience sharing (IPL, anterior cingulate cortex and anterior insula) related to lower empathic accuracy. These results provide novel insight into the neural basis of empathic accuracy in adolescence and suggest that perspective taking processes may be effective for increasing empathy., (© The Author (2017). Published by Oxford University Press.)

Older and younger adults performed a state-based decision-making task while undergoing functional MRI (fMRI). We proposed that younger adults would be more prone to base their decisions on expected value comparisons, but that older adults would be more reactive decision-makers who would act in response to recent changes in rewards or states, rather than on a comparison of expected values. To test this we regressed BOLD activation on two measures from a sophisticated reinforcement learning (RL) model. A value-based regressor was computed by subtracting the immediate value of the selected alternative from its long-term value. The other regressor was a state-change uncertainty signal that served as a proxy for whether the participant's state improved or declined, relative to the previous trial. Younger adults' activation was modulated by the value-based regressor in ventral striatal and medial PFC regions implicated in reinforcement learning. Older adults' activation was modulated by state-change uncertainty signals in right dorsolateral PFC, and activation in this region was associated with improved performance in the task. This suggests that older adults may depart from standard expected-value based strategies and recruit lateral PFC regions to engage in reactive decision-making strategies., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Psychiatric disorders are characterized by major fluctuations in psychological function over the course of weeks and months, but the dynamic characteristics of brain function over this timescale in healthy individuals are unknown. Here, as a proof of concept to address this question, we present the MyConnectome project. An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics. A reproducible analysis workflow is provided, along with open access to the data and an online browser for results. We demonstrate dynamic changes in brain connectivity over the timescales of days to months, and relations between brain connectivity, gene expression and metabolites. This resource can serve as a testbed to study the joint dynamics of human brain and metabolic function over time, an approach that is critical for the development of precision medicine strategies for brain disorders.

Resting state functional MRI (fMRI) has enabled description of group-level functional brain organization at multiple spatial scales. However, cross-subject averaging may obscure patterns of brain organization specific to each individual. Here, we characterized the brain organization of a single individual repeatedly measured over more than a year. We report a reproducible and internally valid subject-specific areal-level parcellation that corresponds with subject-specific task activations. Highly convergent correlation network estimates can be derived from this parcellation if sufficient data are collected-considerably more than typically acquired. Notably, within-subject correlation variability across sessions exhibited a heterogeneous distribution across the cortex concentrated in visual and somato-motor regions, distinct from the pattern of intersubject variability. Further, although the individual's systems-level organization is broadly similar to the group, it demonstrates distinct topological features. These results provide a foundation for studies of individual differences in cortical organization and function, especially for special or rare individuals. VIDEO ABSTRACT., (Copyright © 2015 Elsevier Inc. All rights reserved.)

A prerequisite for a pattern analysis using functional magnetic resonance imaging (fMRI) data is estimating the patterns from time series data, which then are input into the pattern analysis. Here we focus on how the combination of study design (order and spacing of trials) with pattern estimator impacts the Type I error rate of the subsequent pattern analysis. When Type I errors are inflated, the results are no longer valid, so this work serves as a guide for designing and analyzing MVPA studies with controlled false positive rates. The MVPA strategies examined are pattern classification and similarity, utilizing single trial activation patterns from the same functional run. Primarily focusing on the Least Squares Single and Least Square All pattern estimators, we show that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses. It may seem intuitive that increasing the interstimulus interval (ISI) would alleviate this issue, but remaining weak correlations between activation patterns persist and have a strong influence in pattern similarity analyses. Pattern similarity analyses using only activation patterns estimated from the same functional run of data are susceptible to inflated false positives unless trials are randomly ordered, with a different randomization for each subject. In other cases, where there is any structure to trial order, valid pattern similarity analysis results can only be obtained if similarity computations are restricted to pairs of activation patterns from independent runs. Likewise, for pattern classification, false positives are minimized when the testing and training sets in cross validation do not contain patterns estimated from the same run., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Background: Cardiorespiratory fitness is associated with increased frontal and parietal activation during executive function tasks. While these findings suggest fitness-related enhancement of neuronal response, the utility of functional magnetic resonance imaging (fMRI) may be limited by potential fitness-related differences in global vascular reactivity. The aim of this study was to determine if highly fit adults display differential activation during working memory after calibration for vascular reactivity relative to their sedentary counterparts., Methods: Thirty-two endurance-trained and 24 sedentary adults, aged 40-65 years, completed a 2-Back verbal working memory task and a breath-hold challenge during fMRI. Group differences in blood oxygen level-dependent (BOLD) response during working memory were examined across the whole brain and in a priori regions of interest (ROI) before and after breath-hold calibration using non-parametric permutation testing. Multiple regression was used to explore the association between cardiorespiratory fitness (VO2 max), age, and calibrated 2-Back-related activation within the one a priori ROI with significant group effects., Results: In comparison to the endurance-trained group, the sedentary group exhibited greater BOLD signal changes in response to the breath-hold task. After, but not before calibration, the endurance-trained group displayed significantly higher 2-Back-related activation in the right middle frontal gyrus (P = 0.049). Older age predicted lower 2-Back-related activation (ß = -0.308, P = 0.031), whereas fitness predicted higher activation (ß = 0.372, P = 0.021) in this region., Conclusions: Breath-hold calibration increased detection of working memory-related BOLD response differences between sedentary and endurance-trained adults. Moreover, cardiorespiratory fitness appeared to mitigate age-related changes in BOLD during working memory in this region., (Copyright © 2013 Wiley Periodicals, Inc.)

Studies of adults with attention-deficit/hyperactivity disorder (ADHD) have suggested that they have deficient response inhibition, but findings concerning the neural correlates of inhibition in this patient population are inconsistent. We used the Stop-Signal task and functional magnetic resonance imaging (fMRI) to compare neural activation associated with response inhibition between adults with ADHD (N=35) and healthy comparison subjects (N=62), and in follow-up tests to examine the effect of current medication use and symptom severity. There were no differences in Stop-Signal task performance or neural activation between ADHD and control participants. Among the ADHD participants, however, significant differences were associated with current medication, with individuals taking psychostimulants (N=25) showing less stopping-related activation than those not currently receiving psychostimulant medication (N=10). Follow-up analyses suggested that this difference in activation was independent of symptom severity. These results provide evidence that deficits in inhibition-related neural activation persist in a subset of adult ADHD individuals, namely those individuals currently taking psychostimulants. These findings help to explain some of the disparities in the literature, and advance our understanding of why deficits in response inhibition are more variable in adult, as compared with child and adolescent, ADHD patients., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Bayesian network analysis is an attractive approach for studying the functional integration of brain networks, as it includes both the locations of connections between regions of the brain (functional connectivity) and more importantly the direction of the causal relationship between the regions (directed functional connectivity). Further, these approaches are more attractive than other functional connectivity analyses in that they can often operate on larger sets of nodes and run searches over a wide range of candidate networks. An important study by Smith et al. (2011) illustrated that many Bayesian network approaches did not perform well in identifying the directionality of connections in simulated single-subject data. Since then, new Bayesian network approaches have been developed that have overcome the failures in the Smith work. Additionally, an important discovery was made that shows a preprocessing step used in the Smith data puts some of the Bayesian network methods at a disadvantage. This work provides a review of Bayesian network analyses, focusing on the methods used in the Smith work as well as methods developed since 2011 that have improved estimation performance. Importantly, only approaches that have been specifically designed for fMRI data perform well, as they have been tailored to meet the challenges of fMRI data. Although this work does not suggest a single best model, it describes the class of models that perform best and highlights the features of these models that allow them to perform well on fMRI data. Specifically, methods that rely on non-Gaussianity to direct causal relationships in the network perform well., (© 2013 Elsevier Inc. All rights reserved.)

One central goal in cognitive neuroscience of learning and memory is to characterize the neural processes that lead to long-lasting episodic memory. In addition to the stronger frontoparietal activity, greater category- or item-specific cortical representation during encoding, as measured by pattern similarity (PS), is also associated with better subsequent episodic memory. Nevertheless, it is unknown whether frontoparietal activity and cortical PS reflect distinct mechanisms. To address this issue, we reanalyzed previous data (Xue G, Dong Q, Chen C, Lu ZL, Mumford JA, Poldrack RA. 2010. Greater neural pattern similarity across repetitions is associated with better memory. Science. 330:97, Experiment 3) using a novel approach based on combined activation-based and information-based analyses. The results showed that across items, stronger frontoparietal activity was associated with greater PS in distributed brain regions, including those where the PS was predictive of better subsequent memory. Nevertheless, the item-specific PS was still associated with later episodic memory after controlling the effect of frontoparietal activity. Our results suggest that one possible mechanism of frontoparietal activity on episodic memory encoding is via enhancing PS, resulting in more unique and consistent input to the medial temporal lobe. In addition, they suggest that PS might index additional processes, such as pattern reinstatement as a result of study-phase retrieval, that contribute to episodic memory encoding.

A critical component of decision making is the ability to adjust criteria for classifying stimuli. fMRI and drift diffusion models were used to explore the neural representations of perceptual criteria in decision making. The specific focus was on the relative engagement of perceptual- and decision-related neural systems in response to adjustments in perceptual criteria. Human participants classified visual stimuli as big or small based on criteria of different sizes, which effectively biased their choices toward one response over the other. A drift diffusion model was fit to the behavioral data to extract estimates of stimulus size, criterion size, and difficulty for each participant and condition. These parameter values were used as modulated regressors to create a highly constrained model for the fMRI analysis that accounted for several components of the decision process. The results show that perceptual criteria values were reflected by activity in left inferior temporal cortex, a region known to represent objects and their physical properties, whereas stimulus size was reflected by activation in occipital cortex. A frontoparietal network of regions, including dorsolateral prefrontal cortex and superior parietal lobule, corresponded to the decision variables resulting from the downstream stimulus-criterion comparison, independent of stimulus type. The results provide novel evidence that perceptual criteria are represented in stimulus space and serve as inputs to be compared with the presented stimulus, recruiting a common network of decision regions shown to be active in other simple decisions. This work advances our understanding of the neural correlates of decision flexibility and adjustments of behavioral bias.

Despite growing interest in multi-voxel pattern analysis (MVPA) methods for fMRI, a major problem remains--that of generating estimates in rapid event-related (ER) designs, where the BOLD responses of temporally adjacent events will overlap. While this problem has been investigated for methods that reduce each event to a single parameter per voxel (Mumford et al., 2012), most of these methods make strong parametric assumptions about the shape of the hemodynamic response, and require exact knowledge of the temporal profile of the underlying neural activity. A second class of methods uses multiple parameters per event (per voxel) to capture temporal information more faithfully. In addition to enabling a more accurate estimate of ER responses, this allows for the extension of the standard classification paradigm into the temporal domain (e.g., Mourão-Miranda et al., 2007). However, existing methods in this class were developed for use with block and slow ER data, and there has not yet been an exploration of how to adapt such methods to data collected using rapid ER designs. Here, we demonstrate that the use of multiple parameters preserves or improves classification accuracy, while additionally providing information on the evolution of class discrimination. Additionally, we explore an alternative to the method of Mourão-Miranda et al. tailored to use in rapid ER designs that yields equivalent classification accuracies, but is better at unmixing responses to temporally adjacent events. The current work paves the way for wider adoption of spatiotemporal classification analyses, and greater use of MVPA with rapid ER designs., (Copyright © 2012 Elsevier Inc. All rights reserved.)

In the past, power analyses were not that common for fMRI studies, but recent advances in power calculation techniques and software development are making power analyses much more accessible. As a result, power analyses are more commonly expected in grant applications proposing fMRI studies. Even though the software is somewhat automated, there are important decisions to be made when setting up and carrying out a power analysis. This guide provides tips on carrying out power analyses, including obtaining pilot data, defining a region of interest and other choices to help create reliable power calculations.

Neuroimaging research has largely focused on the identification of associations between brain activation and specific mental functions. Here we show that data mining techniques applied to a large database of neuroimaging results can be used to identify the conceptual structure of mental functions and their mapping to brain systems. This analysis confirms many current ideas regarding the neural organization of cognition, but also provides some new insights into the roles of particular brain systems in mental function. We further show that the same methods can be used to identify the relations between mental disorders. Finally, we show that these two approaches can be combined to empirically identify novel relations between mental disorders and mental functions via their common involvement of particular brain networks. This approach has the potential to discover novel endophenotypes for neuropsychiatric disorders and to better characterize the structure of these disorders and the relations between them.

Repeated study improves memory, but the underlying neural mechanisms of this improvement are not well understood. Using functional magnetic resonance imaging and representational similarity analysis of brain activity, we found that, compared with forgotten items, subsequently remembered faces and words showed greater similarity in neural activation across multiple study in many brain regions, including (but not limited to) the regions whose mean activities were correlated with subsequent memory. This result addresses a longstanding debate in the study of memory by showing that successful episodic memory encoding occurs when the same neural representations are more precisely reactivated across study episodes, rather than when patterns of activation are more variable across time.

Many network analyses of fMRI data begin by defining a set of regions, extracting the mean signal from each region and then analyzing the correlations between regions. One essential question that has not been addressed in the literature is how to best define the network neighborhoods over which a signal is combined for network analyses. Here we present a novel unsupervised method for the identification of tightly interconnected voxels, or modules, from fMRI data. This approach, weighted voxel coactivation network analysis (WVCNA), is based on a method that was originally developed to find modules of genes in gene networks. This approach differs from many of the standard network approaches in fMRI in that connections between voxels are described by a continuous measure, whereas typically voxels are considered to be either connected or not connected depending on whether the correlation between the two voxels survives a hard threshold value. Additionally, instead of simply using pairwise correlations to describe the connection between two voxels, WVCNA relies on a measure of topological overlap, which not only compares how correlated two voxels are but also the degree to which the pair of voxels is highly correlated with the same other voxels. We demonstrate the use of WVCNA to parcellate the brain into a set of modules that are reliably detected across data within the same subject and across subjects. In addition we compare WVCNA to ICA and show that the WVCNA modules have some of the same structure as the ICA components, but tend to be more spatially focused. We also demonstrate the use of some of the WVCNA network metrics for assessing a voxel's membership to a module and also how that voxel relates to other modules. Last, we illustrate how WVCNA modules can be used in a network analysis to find connections between regions of the brain and show that it produces reasonable results., (Copyright 2010 Elsevier Inc. All rights reserved.)

We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.