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The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Tobias Kober and Tom Hilbert are employees of Siemens Healthineers International AG, Switzerland. Yulin Chang is an employee of Siemens Medical Solutions USA Inc. Dan Rettmann and Ralph Noeske are employed by GE HealthCare. Guillaume Gilbert, Yansong Zhao, Sandeep Ganji, and Maarten Versluis are employed by Philips Healthcare. Carina Lucena, Lucky Heisler-Roman, and Dhruman Goradia are employed by PrimeNeuro Inc. Under a license agreement between Philips and the Johns Hopkins University, Dr. van Zijl and the University are entitled to fees related to an imaging device used in the study discussed for publication. Dr. van Zijl also is a paid lecturer for Philips and receives research support from Philips. This arrangement has been reviewed and approved by the Johns Hopkins University in accordance with its conflict of interest policies. Damien Fair is a patent holder on the Framwise Integrated Real-Time Motion Monitoring (FIRMM) software. He is also a co-founder of Turing Medical Technologies, Inc. The nature of this financial interest and the design of the study have been reviewed by two committees at the University of Minnesota. They have put in place a plan to help ensure that this research is not affected by the financial interest. All other authors report no biomedical financial interests or potential conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional magnetic resonance imaging. We show that, even when handling identical data, interpipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low interpipeline agreement can go unrecognized until the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially we show that, when interpipeline agreement is compromised, so too is the consistency of insights from brain-wide association studies. We highlight the importance of comparing analytic configurations, because both widely discussed and commonly overlooked decisions can lead to marked variation., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

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).)

Response times (RTs) are often the main signal of interest in cognitive psychology but are often ignored in functional MRI (fMRI) analyses. In fMRI analysis the intensity of the signal serves as a proxy for the intensity of local neuronal activity, but changes in either the intensity or the duration of neuronal activity can yield identical fMRI signals. Therefore, if RTs are ignored and pair with neuronal durations, fMRI results claiming intensity differences may be confounded by RTs. We show how ignoring RTs goes beyond this confound, where longer RTs are paired with larger activation estimates, to lesser-known issues where RTs become confounds in group-level analyses and, surprisingly, how the RT confound can induce other artificial group-level associations with variables that are not related to the condition contrast or RTs. We propose a new time-series model to address these issues and encourage increasing focus on what the widespread RT-based signal represents., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Functional magnetic resonance imaging (fMRI) has revolutionized cognitive neuroscience, but methodological barriers limit the generalizability of findings from the lab to the real world. Here, we present Neuroscout, an end-to-end platform for analysis of naturalistic fMRI data designed to facilitate the adoption of robust and generalizable research practices. Neuroscout leverages state-of-the-art machine learning models to automatically annotate stimuli from dozens of fMRI studies using naturalistic stimuli-such as movies and narratives-allowing researchers to easily test neuroscientific hypotheses across multiple ecologically-valid datasets. In addition, Neuroscout builds on a robust ecosystem of open tools and standards to provide an easy-to-use analysis builder and a fully automated execution engine that reduce the burden of reproducible research. Through a series of meta-analytic case studies, we validate the automatic feature extraction approach and demonstrate its potential to support more robust fMRI research. Owing to its ease of use and a high degree of automation, Neuroscout makes it possible to overcome modeling challenges commonly arising in naturalistic analysis and to easily scale analyses within and across datasets, democratizing generalizable fMRI research., Competing Interests: Ad, RR, RB, CM, JM, JK, PH, SG, RP, TY No competing interests declared, (© 2022, de la Vega, Rocca et al.)

Replicability and reproducibility of scientific findings is paramount for sustainable progress in neuroscience. Preregistration of the hypotheses and methods of an empirical study before analysis, the sharing of primary research data, and compliance with data standards such as the Brain Imaging Data Structure (BIDS), are considered effective practices to secure progress and to substantiate quality of research. We investigated the current level of adoption of open science practices in neuroimaging and the difficulties that prevent researchers from using them. Email invitations to participate in the survey were sent to addresses received through a PubMed search of human functional magnetic resonance imaging studies that were published between 2010 and 2020. 283 persons completed the questionnaire. Although half of the participants were experienced with preregistration, the willingness to preregister studies in the future was modest. The majority of participants had experience with the sharing of primary neuroimaging data. Most of the participants were interested in implementing a standardized data structure such as BIDS in their labs. Based on demographic variables, we compared participants on seven subscales, which had been generated through factor analysis. Exploratory analyses found that experienced researchers at lower career level had higher fear of being transparent and researchers with residence in the EU had a higher need for data governance. Additionally, researchers at medical faculties as compared to other university faculties reported a more unsupportive supervisor with regards to open science practices and a higher need for data governance. The results suggest growing adoption of open science practices but also highlight a number of important impediments., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2022. Published by Elsevier Inc.)

Arterial spin labeled (ASL) magnetic resonance imaging (MRI) is the primary method for noninvasively measuring regional brain perfusion in humans. We introduce ASLPrep, a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

The Adolescent Brain Cognitive Development (ABCD) study is an unprecedented longitudinal neuroimaging sample that tracks the brain development of over 9-10 year olds through adolescence. At the core of this study are the three tasks that are completed repeatedly within the MRI scanner, one of which is the stop-signal task. In analyzing the available stopping experimental code and data, we identified a set of design issues that we believe significantly compromise its value. These issues include but are not limited to variable stimulus durations that violate basic assumptions of dominant stopping models, trials in which stimuli are incorrectly not presented, and faulty stop-signal delays. We present eight issues, show their effect on the existing ABCD data, suggest prospective solutions including task changes for future data collection and preliminary computational models, and suggest retrospective solutions for data users who wish to make the most of the existing data., Competing Interests: PB, MH, HJ, RP No competing interests declared, (© 2021, Bissett et al.)

Mapping the causal effects of one brain region on another is a challenging problem in neuroscience that we approached through invasive direct manipulation of brain function together with concurrent whole-brain measurement of the effects produced. Here we establish a unique resource and present data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple runs exist for patients with different stimulation sites. We describe the resource, data collection process, preprocessing using the fMRIPrep analysis pipeline and management of artifacts, and provide end-user analyses to visualize distal brain activation produced by site-specific electrical stimulation. The data are organized according to the brain imaging data structure (BIDS) specification, and are available for analysis or future dataset contributions on openneuro.org including both raw and preprocessed data.

Functional magnetic resonance imaging (fMRI) is a standard tool to investigate the neural correlates of cognition. fMRI noninvasively measures brain activity, allowing identification of patterns evoked by tasks performed during scanning. Despite the long history of this technique, the idiosyncrasies of each dataset have led to the use of ad-hoc preprocessing protocols customized for nearly every different study. This approach is time consuming, error prone and unsuitable for combining datasets from many sources. Here we showcase fMRIPrep (http://fmriprep.org), a robust tool to prepare human fMRI data for statistical analysis. This software instrument addresses the reproducibility concerns of the established protocols for fMRI preprocessing. By leveraging the Brain Imaging Data Structure to standardize both the input datasets (MRI data as stored by the scanner) and the outputs (data ready for modeling and analysis), fMRIPrep is capable of preprocessing a diversity of datasets without manual intervention. In support of the growing popularity of fMRIPrep, this protocol describes how to integrate the tool in a task-based fMRI investigation workflow.

In network neuroscience, temporal network models have gained popularity. In these models, network properties have been related to cognition and behavior. Here, we demonstrate that calculating nodal properties that are dependent on temporal community structure (such as the participation coefficient [PC]) in time-varying contexts can potentially lead to misleading results. Specifically, with regards to the participation coefficient, increases in integration can be inferred when the opposite is occurring. Further, we present a temporal extension to the PC measure (temporal PC) that circumnavigates this problem by jointly considering all community partitions assigned to a node through time. The proposed method allows us to track a node's integration through time while adjusting for the possible changes in the community structure of the overall network., (© 2020 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses 1 . The flexibility of analytical approaches is exemplified by the fact that no two teams chose identical workflows to analyse the data. This flexibility resulted in sizeable variation in the results of hypothesis tests, even for teams whose statistical maps were highly correlated at intermediate stages of the analysis pipeline. Variation in reported results was related to several aspects of analysis methodology. Notably, a meta-analytical approach that aggregated information across teams yielded a significant consensus in activated regions. Furthermore, prediction markets of researchers in the field revealed an overestimation of the likelihood of significant findings, even by researchers with direct knowledge of the dataset 2-5 . Our findings show that analytical flexibility can have substantial effects on scientific conclusions, and identify factors that may be related to variability in the analysis of functional magnetic resonance imaging. The results emphasize the importance of validating and sharing complex analysis workflows, and demonstrate the need for performing and reporting multiple analyses of the same data. Potential approaches that could be used to mitigate issues related to analytical variability are discussed.

Background: There is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes, and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thereby neglecting the potential influence of huge swaths of the brain., Methods: A multivariate data-driven approach (partial least squares) was used to identify latent components linking a large set of clinical, cognitive, and personality measures to whole-brain resting-state functional connectivity patterns across 224 participants. The participants were either healthy (n = 110) or diagnosed with bipolar disorder (n = 40), attention-deficit/hyperactivity disorder (n = 37), schizophrenia (n = 29), or schizoaffective disorder (n = 8). In contrast to traditional case-control analyses, the diagnostic categories were not used in the partial least squares analysis but were helpful for interpreting the components., Results: Our analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction, and impulsivity. Each component was associated with a unique whole-brain resting-state functional connectivity signature and was shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network and its connectivity with subcortical structures and cortical executive networks., Conclusions: We identified three distinct dimensions with dissociable (but overlapping) whole-brain resting-state functional connectivity signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks., (Copyright © 2019 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.)

The neuroimaging community is steering towards increasingly large sample sizes, which are highly heterogeneous because they can only be acquired by multi-site consortia. The visual assessment of every imaging scan is a necessary quality control step, yet arduous and time-consuming. A sizeable body of evidence shows that images of low quality are a source of variability that may be comparable to the effect size under study. We present the MRIQC Web-API, an open crowdsourced database that collects image quality metrics extracted from MR images and corresponding manual assessments by experts. The database is rapidly growing, and currently contains over 100,000 records of image quality metrics of functional and anatomical MRIs of the human brain, and over 200 expert ratings. The resource is designed for researchers to share image quality metrics and annotations that can readily be reused in training human experts and machine learning algorithms. The ultimate goal of the database is to allow the development of fully automated quality control tools that outperform expert ratings in identifying subpar images.

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to clean and standardize the data before statistical analysis. Generally, researchers create ad hoc preprocessing workflows for each dataset, building upon a large inventory of available tools. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. fMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing without manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than observed with commonly used preprocessing tools. fMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of results.

Head movements are typically viewed as a nuisance to functional magnetic resonance imaging (fMRI) analysis, and are particularly problematic for resting state fMRI. However, there is growing evidence that head motion is a behavioral trait with neural and genetic underpinnings. Using data from a large randomly ascertained extended pedigree sample of Mexican Americans (n = 689), we modeled the genetic structure of head motion during resting state fMRI and its relation to 48 other demographic and behavioral phenotypes. A replication analysis was performed using data from the Human Connectome Project, which uses an extended twin design (n = 864). In both samples, head motion was significantly heritable (h2 = 0.313 and 0.427, respectively), and phenotypically correlated with numerous traits. The most strongly replicated relationship was between head motion and body mass index, which showed evidence of shared genetic influences in both data sets. These results highlight the need to view head motion in fMRI as a complex neurobehavioral trait correlated with a number of other demographic and behavioral phenotypes. Given this, when examining individual differences in functional connectivity, the confounding of head motion with other traits of interest needs to be taken into consideration alongside the critical important of addressing head motion artifacts., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

To determine the spatiotemporal relationships among intrinsic networks of the human brain, we recruited seven neurosurgical patients (four males and three females) who were implanted with intracranial depth electrodes. We first identified canonical resting-state networks at the individual subject level using an iterative matching procedure on each subject's resting-state fMRI data. We then introduced single electrical pulses to fMRI pre-identified nodes of the default network (DN), frontoparietal network (FPN), and salience network (SN) while recording evoked responses in other recording sites within the same networks. We found bidirectional signal flow across the three networks, albeit with distinct patterns of evoked responses within different time windows. We used a data-driven clustering approach to show that stimulation of the FPN and SN evoked a rapid (<70 ms) response that was predominantly higher within the SN sites, whereas stimulation of the DN led to sustained responses in later time windows (85-200 ms). Stimulations in the medial temporal lobe components of the DN evoked relatively late effects (>130 ms) in other nodes of the DN, as well as FPN and SN. Our results provide temporal information about the patterns of signal flow between intrinsic networks that provide insights into the spatiotemporal dynamics that are likely to constrain the architecture of the brain networks supporting human cognition and behavior. SIGNIFICANCE STATEMENT Despite great progress in the functional neuroimaging of the human brain, we still do not know the precise set of rules that define the patterns of temporal organization between large-scale networks of the brain. In this study, we stimulated and then recorded electrical evoked potentials within and between three large-scale networks of the brain, the default network (DN), frontoparietal network (FPN), and salience network (SN), in seven subjects undergoing invasive neurosurgery. Using a data-driven clustering approach, we observed distinct temporal and directional patterns between the three networks, with FPN and SN activity predominant in early windows and DN stimulation affecting the network in later windows. These results provide important temporal information about the interactions between brain networks supporting human cognition and behavior., (Copyright © 2017 the authors 0270-6474/17/379667-08$15.00/0.)

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

Functional neuroimaging techniques have transformed our ability to probe the neurobiological basis of behaviour and are increasingly being applied by the wider neuroscience community. However, concerns have recently been raised that the conclusions that are drawn from some human neuroimaging studies are either spurious or not generalizable. Problems such as low statistical power, flexibility in data analysis, software errors and a lack of direct replication apply to many fields, but perhaps particularly to functional MRI. Here, we discuss these problems, outline current and suggested best practices, and describe how we think the field should evolve to produce the most meaningful and reliable answers to neuroscientific questions.

Multivoxel pattern analysis (MVPA) has gained enormous popularity in the neuroimaging community over the past few years. At the group level, most MVPA studies adopt an "information based" approach in which the sign of the effect of individual subjects is discarded and a non-directional summary statistic is carried over to the second level. This is in contrast to a directional "activation based" approach typical in univariate group level analysis, in which both signal magnitude and sign are taken into account. The transition from examining effects in one voxel at a time vs. several voxels (univariate vs. multivariate) has thus tacitly entailed a transition from directional to non-directional signal definition at the group level. While a directional group-level MVPA approach implies that individuals have similar multivariate spatial patterns of activity, in a non-directional approach each individual may have a distinct spatial pattern. Using an experimental dataset, we show that directional and non-directional group-level MVPA approaches uncover distinct brain regions with only partial overlap. We propose a method to quantify the degree of spatial similarity in activation patterns over subjects. Applied to an auditory task, we find higher values in auditory regions compared to control regions., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

OpenfMRI is a repository for the open sharing of task-based fMRI data. Here we outline its goals, architecture, and current status of the repository, as well as outlining future plans for the project., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Little is currently known about the coordination of neural activity over longitudinal timescales and how these changes relate to behavior. To investigate this issue, we used resting-state fMRI data from a single individual to identify the presence of two distinct temporal states that fluctuated over the course of 18 mo. These temporal states were associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and also related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention. These patterns were replicated in a separate longitudinal dataset, providing additional supportive evidence for the presence of fluctuations in functional network topology over time. Together, our results underscore the importance of longitudinal phenotyping in cognitive neuroscience., Competing Interests: The authors declare no conflict of interest.

The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations., Competing Interests: The authors declare no competing financial interests.

Functional connectivity provides an informative and powerful framework for exploring brain organization. Despite this, few statistical methods are available for the accurate estimation of dynamic changes in functional network architecture. To date, the majority of existing statistical techniques have assumed that connectivity structure is stationary, which is in direct contrast to emerging data that suggests that the strength of connectivity between regions is variable over time. Therefore, the development of statistical methods that enable exploration of dynamic changes in functional connectivity is currently of great importance to the neuroscience community. In this paper, we introduce the 'Multiplication of Temporal Derivatives' (MTD) and then demonstrate the utility of this metric to: (i) detect dynamic changes in connectivity using data from a novel state-switching simulation; (ii) accurately estimate graph structure in a previously-described 'ground-truth' simulated dataset; and (iii) identify task-driven alterations in functional connectivity. We show that the MTD is more sensitive than existing sliding-window methods in detecting dynamic alterations in connectivity structure across a range of correlation strengths and window lengths in simulated data. In addition to the temporal precision offered by MTD, we demonstrate that the metric is also able to accurately estimate stationary network structure in both simulated and real task-based data, suggesting that the method may be used to identify dynamic changes in network structure as they evolve through time., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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.)

The occurrence of collinearity in fMRI-based GLMs (general linear models) may reduce power or produce unreliable parameter estimates. It is commonly believed that orthogonalizing collinear regressors in the model will solve this problem, and some software packages apply automatic orthogonalization. However, the effects of orthogonalization on the interpretation of the resulting parameter estimates is widely unappreciated or misunderstood. Here we discuss the nature and causes of collinearity in fMRI models, with a focus on the appropriate uses of orthogonalization. Special attention is given to how the two popular fMRI data analysis software packages, SPM and FSL, handle orthogonalization, and pitfalls that may be encountered in their usage. Strategies are discussed for reducing collinearity in fMRI designs and addressing their effects when they occur.

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.)

Multi-voxel pattern analysis (MVPA) has led to major changes in how fMRI data are analyzed and interpreted. Many studies now report both MVPA results and results from standard univariate voxel-wise analysis, often with the goal of drawing different conclusions from each. Because MVPA results can be sensitive to latent multidimensional representations and processes whereas univariate voxel-wise analysis cannot, one conclusion that is often drawn when MVPA and univariate results differ is that the activation patterns underlying MVPA results contain a multidimensional code. In the current study, we conducted simulations to formally test this assumption. Our findings reveal that MVPA tests are sensitive to the magnitude of voxel-level variability in the effect of a condition within subjects, even when the same linear relationship is coded in all voxels. We also find that MVPA is insensitive to subject-level variability in mean activation across an ROI, which is the primary variance component of interest in many standard univariate tests. Together, these results illustrate that differences between MVPA and univariate tests do not afford conclusions about the nature or dimensionality of the neural code. Instead, targeted tests of the informational content and/or dimensionality of activation patterns are critical for drawing strong conclusions about the representational codes that are indicated by significant MVPA results., (Copyright © 2014 Elsevier Inc. All rights reserved.)

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.)

Recently, there has been a dramatic increase in the number of functional magnetic resonance imaging studies seeking to answer questions about how the brain represents information. Representational questions are of particular importance in connecting neuroscientific and cognitive levels of analysis because it is at the representational level that many formal models of cognition make distinct predictions. This review discusses techniques for univariate, adaptation, and multivoxel analysis, and how they have been used to answer questions about content specificity in different regions of the brain, how this content is organized, and how representations are shaped by and contribute to cognitive processes. Each of the analysis techniques makes different assumptions about the underlying neural code and thus differ in how they can be applied to specific questions. We also discuss the many pitfalls of representational analysis, from the flexibility in data analysis pipelines to emergent nonrepresentational relationships that can arise between stimuli in a task., (© 2013 New York Academy of Sciences.)

Over the last 20 years, fMRI has revolutionized cognitive neuroscience. Here I outline a vision for what the next 20 years of fMRI in cognitive neuroscience might look like. Some developments that I hope for include increased methodological rigor, an increasing focus on connectivity and pattern analysis as opposed to "blobology", a greater focus on selective inference powered by open databases, and increased use of ontologies and computational models to describe underlying processes., (Copyright © 2011 Elsevier Inc. All rights reserved.)

We examined whether anterior and posterior hippocampal subregions in humans show distinct relationships to state and trait anxiety. In rodents, the ventral (but not dorsal) hippocampus is critically involved in contextual anxiety, whereas dorsal hippocampus is affected by chronic stress and genetically bred trait anxiety. These studies suggest that state forms of anxiety may be more associated with anterior (ventral in rodents) hippocampus, whereas trait forms of anxiety maybe more associated with posterior (dorsal in rodents) hippocampus. Participants were placed under alternating blocks of threat of shock and safety conditions while performing a secondary task, and state and trait anxiety measures were obtained. Using subject-specific anatomically defined masks, we found that state anxiety was related to activity in anterior but not posterior hippocampus, whereas trait anxiety showed the opposite pattern. Additionally, a psychophysiological connectivity analysis showed that activity in anterior hippocampus was more strongly related to activity in ventromedial prefrontal cortex under threat than under safety conditions, significantly more so than activity in posterior hippocampus was. Hence, anterior hippocampus shows a distinct moment-to-moment connectivity profile with other neural regions during threat relative to posterior hippocampus. The findings provide several lines of evidence for functional differentiation of anterior and posterior hippocampal involvement across state and trait components of anxiety in humans., (PsycINFO Database Record (c) 2012 APA, all rights reserved)

The rapid growth of the literature on neuroimaging in humans has led to major advances in our understanding of human brain function but has also made it increasingly difficult to aggregate and synthesize neuroimaging findings. Here we describe and validate an automated brain-mapping framework that uses text-mining, meta-analysis and machine-learning techniques to generate a large database of mappings between neural and cognitive states. We show that our approach can be used to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature and support accurate 'decoding' of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results have validated a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.

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.)

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

Since the observation of the blood oxygenation level dependent (BOLD) effect on measured MR signal in the brain, functional magnetic resonance imaging (fMRI) has rapidly become the tool of choice for exploring brain function in cognitive neuroscience. Although fMRI is an exciting and powerful means to examining the brain in vivo, the field has sometimes permitted itself to believe that patterns of BOLD activity reveal more than it is possible to measure given the method's spatial and temporal sampling, while concurrently not fully exploring the amount of information it provides. In this article, we examine some of the constraints on the kinds of inferences that can be supported by fMRI. We critique the concept of reverse inference that is often employed to claim some cognitive function must be present given activity in a specific region. We review the consideration of functional and effective connectivity that remain infrequently applied in cognitive neuroimaging, highlighting recent thinking on the ways in which functional imaging can be used to characterize inter-regional communication. Recent advances in neuroimaging that make it possible to assess anatomical connectivity using diffusion tensor imaging (DTI) and we discuss how these may inform interpretation of fMRI results. Descriptions of fMRI studies in the media, in some instances, serve to misrepresent fMRI's capabilities. We comment on how researchers need to faithfully represent fMRI's promise and limitations in dealing with the media. Finally, as we stand at the crossroads of fMRI research, where one pathway leads toward a rigorous understanding of cognitive operations using fMRI and another leads us to a predictable collection of observations absent of clear insight, we offer our impressions of a fruitful path for future functional imaging research.

In this editorial, we outline a set of guidelines for the reporting of methods and results in functional magnetic resonance imaging studies and provide a checklist to assist authors in preparing manuscripts that meet these guidelines.

Functional magnetic resonance imaging (fMRI) has quickly become the most prominent tool in cognitive neuroscience. In this article, I outline some of the limits on the kinds of inferences that can be supported by fMRI, focusing particularly on reverse inference, in which the engagement of specific mental processes is inferred from patterns of brain activation. Although this form of inference is weak, newly developed methods from the field of machine learning offer the potential to formalize and strengthen reverse inferences. I conclude by discussing the increasing presence of fMRI results in the popular media and the ethical implications of the increasing predictive power of fMRI.

Blood oxygenation level dependent (BOLD) signals in functional magnetic resonance imaging (fMRI) are often small compared to the level of noise in the data. The sources of noise are numerous including different kinds of motion artifacts and physiological noise with complex patterns. This complicates the statistical analysis of the fMRI data. In this study, we propose an automatic method to reduce fMRI artifacts based on independent component analysis (ICA). We trained a supervised classifier to distinguish between independent components relating to a potentially task-related signal and independent components clearly relating to structured noise. After the components had been classified as either signal or noise, a denoised fMR time-series was reconstructed based only on the independent components classified as potentially task-related. The classifier was a novel global (fixed structure) decision tree trained in a Neyman-Pearson (NP) framework, which allowed the shape of the decision regions to be controlled effectively. Additionally, the conservativeness of the classifier could be tuned by modifying the NP threshold. The classifier was tested against the component classifications by an expert with the data from a category learning task. The test set as well as the expert were different from the data used for classifier training and the expert labeling the training set. The misclassification rate was between 0.2 and 0.3 for both the event-related and blocked designs and it was consistent among variety of different NP thresholds. The effects of denoising on the group-level statistical analyses were as expected: The denoising generally decreased Z-scores in the white matter, where extreme Z-values can be expected to reflect artifacts. A similar but weaker decrease in Z-scores was observed in the gray matter on average. These two observations suggest that denoising was likely to reduce artifacts from gray matter and could be useful to improve the detection of activations. We conclude that automatic ICA-based denoising offers a potentially useful approach to improve the quality of fMRI data and consequently increase the accuracy of the statistical analysis of these data.

The analysis of group fMRI data requires a statistical model known as the mixed effects model. This article motivates the need for a mixed effects model and outlines the different stages of the mixed model used to analyze group fMRI data. Different modeling options and their impact on analysis results are also described.

The ability to stop motor responses depends critically on the right inferior frontal cortex (IFC) and also engages a midbrain region consistent with the subthalamic nucleus (STN). Here we used diffusion-weighted imaging (DWI) tractography to show that the IFC and the STN region are connected via a white matter tract, which could underlie a "hyperdirect" pathway for basal ganglia control. Using a novel method of "triangulation" analysis of tractography data, we also found that both the IFC and the STN region are connected with the presupplementary motor area (preSMA). We hypothesized that the preSMA could play a conflict detection/resolution role within a network between the preSMA, the IFC, and the STN region. A second experiment tested this idea with functional magnetic resonance imaging (fMRI) using a conditional stop-signal paradigm, enabling examination of behavioral and neural signatures of conflict-induced slowing. The preSMA, IFC, and STN region were significantly activated the greater the conflict-induced slowing. Activation corresponded strongly with spatial foci predicted by the DWI tract analysis, as well as with foci activated by complete response inhibition. The results illustrate how tractography can reveal connections that are verifiable with fMRI. The results also demonstrate a three-way functional-anatomical network in the right hemisphere that could either brake or completely stop responses.

A common approach to the analysis of fMRI data involves the extraction of signal from specified regions of interest (or ROI's). Three approaches to ROI analysis are described, and the strengths and assumptions of each method are outlined.

Background: First-degree relatives of persons with schizophrenia are at elevated risk for the illness, demonstrate deficits in verbal memory, and exhibit structural abnormalities in the medial temporal lobe (MTL). We used functional magnetic resonance imaging (fMRI) to assess brain activity in the MTL during novel and repeated word-pair encoding., Methods: Participants were 21 non-psychotic, first-degree relatives of persons with schizophrenia and 26 matched healthy controls (ages 13-28). fMRI signal change was measured using a Siemens 1.5T MR scanner, and data were analyzed using SPM-2. Verbal memory was assessed using the Miller Selfridge (MS) Context Memory test prior to scanning., Results: The groups were comparable on demographics, intelligence and post-scan word recognition. Relatives at genetic risk (GR) had significantly more psychopathology than controls and worse performance on the MS test (p < .05). GR participants exhibited greater repetition suppression of activation in the left and right anterior parahippocampus (PHA, in the region of the entorhinal cortex region), after controlling for possible confounders. Controls and GR participants with above-median MS performance showed significantly greater repetition suppression of activation in left inferior frontal gyrus than those scoring below the median., Conclusions: This is the first study to demonstrate an alteration of brain activity in the PHA in persons at GR for schizophrenia.

Objective: Adult first-degree relatives of persons with schizophrenia carry elevated genetic risk for the illness, demonstrate working memory (WM) impairments, and manifest alterations in dorsolateral prefrontal cortical (DLPFC) function during WM. Because substantially less is known about these phenotypes in adolescent subjects we sought to demonstrate that young relatives of persons with schizophrenia manifest impaired WM and altered prefrontal activation., Methods: Participants were 21 non-psychotic, unmedicated first-degree relatives of persons with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder, depressed type and 24 unmedicated controls, recruited from the community and hospitals in metropolitan Boston (ages 13-28). We compared groups on an auditory WM task with interference prior to scanning and used functional magnetic resonance imaging (fMRI) to compare groups while performing visual 2-back WM and control vigilance tasks. Blood oxygen level dependent signal change was measured using two whole-brain gradient echo EPI pulse acquisitions (21 contiguous, 5mm axial slices), acquired on a Siemens 1.5T MR scanner. Data were analyzed using Statistical Parametric Mapping-99., Results: The high risk subjects were significantly impaired on the auditory WM task, had significantly greater Phobic Anxiety, and marginally greater Psychoticism than controls on the Symptom Checklist-90-Revised, and showed significantly greater task-elicited activation in the right DLPFC (BA 46). Psychopathology, IQ, and in-scanner WM performance did not account for group differences in brain activation., Conclusions: Data support a physiological difference (an exaggerated fMRI response) in DLPFC in adolescents at genetic risk for schizophrenia, independent of psychosis. Future work can study the relationship of these measures to possible onset of schizophrenia.

There is much interest currently in using functional neuroimaging techniques to understand better the nature of cognition. One particular practice that has become common is 'reverse inference', by which the engagement of a particular cognitive process is inferred from the activation of a particular brain region. Such inferences are not deductively valid, but can still provide some information. Using a Bayesian analysis of the BrainMap neuroimaging database, I characterize the amount of additional evidence in favor of the engagement of a cognitive process that can be offered by a reverse inference. Its usefulness is particularly limited by the selectivity of activation in the region of interest. I argue that cognitive neuroscientists should be circumspect in the use of reverse inference, particularly when selectivity of the region in question cannot be established or is known to be weak.

Functional MRI is widely used for imaging the neural correlates of psychological processes and how these brain processes change with learning, development and neuropsychiatric disorder. In order to interpret changes in imaging signals over time, for example, in patient studies, the long-term reliability of fMRI must first be established. Here, eight healthy adult subjects were scanned on two sessions, 1 year apart, while performing a classification learning task known to activate frontostriatal circuitry. We show that behavioral performance and frontostriatal activation were highly concordant at a group level at both time-points. Furthermore, intra-class correlation coefficients (ICCs), which index the degree of correlation between subjects at different time-points, were high for behavior and for functional activation. ICC was significantly higher within the network recruited by learning than outside that network. We conclude that fMRI can have high long-term test-retest reliability, making it suitable as a biomarker for brain development and neurodegeneration.

Acquisition of phonological processing skills, such as the ability to segment words into corresponding speech sounds, is critical to the development of efficient reading. Prior neuroimaging studies of phonological processing have often relied on auditory stimuli or print-mediated tasks that may be problematic for various theoretical and empirical reasons. For the current study, we developed a task to evaluate phonological processing that used visual stimuli but did not require interpretation of orthographic forms. This task requires the subject to retrieve the names of objects and to compare their first sounds; then, the subject must indicate if the initial sounds of the names of the pictures are the same. The phonological analysis task was compared to both a baseline matching task and a more complex control condition in which the participants evaluated two different pictures and indicated whether they represented the same object. The complex picture-matching condition controls for the visual complexity of the stimuli but does not require phonological analysis of the names of the objects. While both frontal and ventral posterior areas were activated in response to phonological analysis of the names of pictures, only inferior and superior frontal gyrus exhibited differential sensitivity to the phonological comparison task as compared to the complex picture-matching control task. These findings suggest that phonological processing that is not mediated by print relies primarily on frontal language processing areas among skilled readers.

Introduction: Studies of prefrontal cortical (PFC) function in schizophrenia have been inconsistent, with studies showing both increased and decreased PFC activation compared to healthy controls. Discrepant findings may be due to task performance effects or demographic differences between samples. We report functional magnetic resonance imaging (fMRI) data comparing subjects with schizophrenia and healthy controls performing a 2-back working memory (WM) task, addressing the effects of task performance., Methods: Twenty-two controls and 14 patients with DSM-IV schizophrenia, scanned on a Siemens 1.5 T scanner, performed a visual letter 2-back task and control task (CPT-X) during fMRI. Data were analyzed using Statistical Parametric Mapping (SPM)-99., Results: After statistical adjustment for performance differences, persons with schizophrenia showed significantly greater activation than controls in the right medial frontal gyrus and left inferior parietal lobule/medial temporal gyrus region (BA 39/40), and a trend toward greater activation in the left ventrolateral PFC. This pattern was also observed in demographically matched subgroups of participants., Conclusions: Data are consistent with findings reported in recent studies showing increased PFC and parietal activation in schizophrenia when the effects of reduced WM task performance in patients with schizophrenia are addressed. Further studies are needed to clarify the pathophysiological basis of WM load sensitivity in schizophrenia and its relationship to genes.

Mesencephalic dopaminergic system (MDS) neurons may participate in learning by providing a prediction error signal to their targets, which include ventral striatal, orbital, and medial frontal regions, as well as by showing sensitivity to the degree of uncertainty associated with individual stimuli. We investigated the mechanisms of probabilistic classification learning in humans using functional magnetic resonance imaging to examine the effects of feedback and uncertainty. The design was optimized for separating neural responses to stimulus, delay, and negative and positive feedback components. Compared with fixation, stimulus and feedback activated brain regions consistent with the MDS, whereas the delay period did not. Midbrain activity was significantly different for negative versus positive feedback (consistent with coding of the "prediction error") and was reliably correlated with the degree of uncertainty as well as with activity in MDS target regions. Purely cognitive feedback apparently engages the same regions as rewarding stimuli, consistent with a broader characterization of this network.