@article {819, title = {Analytic programming with FMRI data: a quick-start guide for statisticians using R.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e89470}, abstract = {

Functional magnetic resonance imaging (fMRI) is a thriving field that plays an important role in medical imaging analysis, biological and neuroscience research and practice. This manuscript gives a didactic introduction to the statistical analysis of fMRI data using the R project, along with the relevant R code. The goal is to give statisticians who would like to pursue research in this area a quick tutorial for programming with fMRI data. References of relevant packages and papers are provided for those interested in more advanced analysis.

}, keywords = {Brain, brain mapping, Humans, Magnetic Resonance Imaging, Programming Languages}, issn = {1932-6203}, doi = {10.1371/journal.pone.0089470}, author = {Eloyan, Ani and Li, Shanshan and Muschelli, John and Pekar, Jim J and Mostofsky, Stewart H and Caffo, Brian S} } @article {831, title = {Disruption of functional organization within the primary motor cortex in children with autism.}, journal = {Hum Brain Mapp}, volume = {35}, year = {2014}, month = {2014 Feb}, pages = {567-80}, abstract = {

Accumulating evidence suggests that motor impairments are prevalent in autism spectrum disorder (ASD), relate to the social and communicative deficits at the core of the diagnosis and may reflect abnormal connectivity within brain networks underlying motor control and learning. Parcellation of resting-state functional connectivity data using spectral clustering approaches has been shown to be an effective means of visualizing functional organization within the brain but has most commonly been applied to explorations of normal brain function. This article presents a parcellation of a key area of the motor network, the primary motor cortex (M1), a key area of the motor control network, in adults, typically developing (TD) children and children with ASD and introduces methods for selecting the number of parcels, matching parcels across groups and testing group differences. The parcellation is based solely on patterns of connectivity between individual M1 voxels and all voxels outside of M1, and within all groups, a gross dorsomedial to ventrolateral organization emerged within M1 which was left-right symmetric. Although this gross organizational scheme was present in both groups of children, statistically significant group differences in the size and segregation of M1 parcels within regions of the motor homunculus corresponding to the upper and lower limbs were observed. Qualitative comparison of the M1 parcellation for children with ASD with that of younger and older TD children suggests that these organizational differences, with a lack of differentiation between lower limb/trunk regions and upper limb/hand regions, may be due, at least in part, to a delay in functional specialization within the motor cortex.

}, keywords = {Adult, Autistic Disorder, brain mapping, Child, Developmental Disabilities, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Motor Cortex, Oxygen, Young Adult}, issn = {1097-0193}, doi = {10.1002/hbm.22188}, author = {Nebel, Mary Beth and Joel, Suresh E and Muschelli, John and Barber, Anita D and Caffo, Brian S and Pekar, James J and Mostofsky, Stewart H} } @article {822, title = {An evaluation of independent component analyses with an application to resting-state fMRI.}, journal = {Biometrics}, volume = {70}, year = {2014}, month = {2014 Mar}, pages = {224-36}, abstract = {

We examine differences between independent component analyses (ICAs) arising from different assumptions, measures of dependence, and starting points of the algorithms. ICA is a popular method with diverse applications including artifact removal in electrophysiology data, feature extraction in microarray data, and identifying brain networks in functional magnetic resonance imaging (fMRI). ICA can be viewed as a generalization of principal component analysis (PCA) that takes into account higher-order cross-correlations. Whereas the PCA solution is unique, there are many ICA methods-whose solutions may differ. Infomax, FastICA, and JADE are commonly applied to fMRI studies, with FastICA being arguably the most popular. Hastie and Tibshirani (2003) demonstrated that ProDenICA outperformed FastICA in simulations with two components. We introduce the application of ProDenICA to simulations with more components and to fMRI data. ProDenICA was more accurate in simulations, and we identified differences between biologically meaningful ICs from ProDenICA versus other methods in the fMRI analysis. ICA methods require nonconvex optimization, yet current practices do not recognize the importance of, nor adequately address sensitivity to, initial values. We found that local optima led to dramatically different estimates in both simulations and group ICA of fMRI, and we provide evidence that the global optimum from ProDenICA is the best estimate. We applied a modification of the Hungarian (Kuhn-Munkres) algorithm to match ICs from multiple estimates, thereby gaining novel insights into how brain networks vary in their sensitivity to initial values and ICA method.

}, keywords = {Adolescent, Algorithms, Attention Deficit Disorder with Hyperactivity, brain mapping, Child, Child, Preschool, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Statistical, Principal Component Analysis}, issn = {1541-0420}, doi = {10.1111/biom.12111}, author = {Risk, Benjamin B and Matteson, David S and Ruppert, David and Eloyan, Ani and Caffo, Brian S} } @article {806, title = {Acute lesions that impair affective empathy.}, journal = {Brain}, volume = {136}, year = {2013}, month = {2013 Aug}, pages = {2539-49}, abstract = {

Functional imaging studies of healthy participants and previous lesion studies have provided evidence that empathy involves dissociable cognitive functions that rely on at least partially distinct neural networks that can be individually impaired by brain damage. These studies converge in support of the proposal that affective empathy--making inferences about how another person feels--engages at least the following areas: prefrontal cortex, orbitofrontal gyrus, anterior insula, anterior cingulate cortex, temporal pole, amygdala and temporoparietal junction. We hypothesized that right-sided lesions to any one of these structures, except temporoparietal junction, would cause impaired affective empathy (whereas bilateral damage to temporoparietal junction would be required to disrupt empathy). We studied 27 patients with acute right hemisphere ischaemic stroke and 24 neurologically intact inpatients on a test of affective empathy. Acute impairment of affective empathy was associated with infarcts in the hypothesized network, particularly temporal pole and anterior insula. All patients with impaired affective empathy were also impaired in comprehension of affective prosody, but many patients with impairments in prosodic comprehension had spared affective empathy. Patients with impaired affective empathy were older, but showed no difference in performance on tests of hemispatial neglect, volume of infarct or sex distribution compared with patients with intact affective empathy.

}, keywords = {Adult, Aged, Brain, Brain Ischemia, brain mapping, Cognition, Emotions, Empathy, Female, Functional Laterality, Humans, Male, Middle Aged, Nerve Net, Social Perception, Stroke}, issn = {1460-2156}, doi = {10.1093/brain/awt177}, author = {Leigh, Richard and Oishi, Kenichi and Hsu, John and Lindquist, Martin and Gottesman, Rebecca F and Jarso, Samson and Crainiceanu, Ciprian and Mori, Susumu and Hillis, Argye E} } @article {829, title = {Developmental changes in within- and between-network connectivity between late childhood and adulthood.}, journal = {Neuropsychologia}, volume = {51}, year = {2013}, month = {2013 Jan}, pages = {156-67}, abstract = {

A number of behavioral changes occur between late childhood and adulthood, including maturation of social cognition, reward receptivity, impulsiveness, risk-taking and cognitive control. Although some of these abilities show linear improvements with age, some abilities may temporarily worsen, reflecting both the restructuring and/or strengthening of connections within some brain systems. The current study uses resting state functional connectivity to examine developmental differences between late childhood and adulthood in task positive (TP) regions, which play a role in cognitive control functions, and task negative (TN) regions, which play a role in social cognition, self-referential, and internally-directed thought. Within the TP network, developmental differences in connectivity were found with the left dorsolateral prefrontal cortex. Within the TN network, developmental differences in connectivity were found with a broad area of the medial prefrontal cortex and the right parahippocampal gyrus. Connections between the two networks also showed significant developmental differences. Stronger anticorrelations were found in the TN maps of the adult group for the right anterior insula/inferior frontal gyrus, bilateral anterior inferior parietal lobule, bilateral superior parietal lobule and an anterior portion of the right posterior cingulate cortex. There was a significant brain-behavior relationship between the strength of anticorrelation in these regions and inhibitory control performance on two Go/No-go tasks suggesting that the development of anticorrelations between late childhood and adulthood supports mature inhibitory control. Overall, maturation of these networks occurred in specific regions which are associated with cognitive control of goal-directed behavior, including those involved in working memory, social cognition, and inhibitory control.

}, keywords = {Adolescent, Adult, Age Factors, Analysis of Variance, Brain, brain mapping, Child, Child Development, Cognition, Female, Humans, Image Processing, Computer-Assisted, Inhibition (Psychology), Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Neuropsychological Tests, Oxygen, Reaction Time, Young Adult}, issn = {1873-3514}, doi = {10.1016/j.neuropsychologia.2012.11.011}, author = {Barber, Anita D and Caffo, Brian S and Pekar, James J and Mostofsky, Stewart H} } @article {826, title = {Effects of working memory demand on neural mechanisms of motor response selection and control.}, journal = {J Cogn Neurosci}, volume = {25}, year = {2013}, month = {2013 Aug}, pages = {1235-48}, abstract = {

Inhibitory control commonly recruits a number of frontal regions: pre-supplementary motor area (pre-SMA), frontal eye fields (FEFs), and right-lateralized posterior inferior frontal gyrus (IFG), dorsal anterior insula (DAI), dorsolateral prefrontal cortex (DLPFC), and inferior frontal junction (IFJ). These regions may directly implement inhibitory motor control or may be more generally involved in executive control functions. Two go/no-go tasks were used to distinguish regions specifically recruited for inhibition from those that additionally show increased activity with working memory demand. The pre-SMA and IFG were recruited for inhibition in both tasks and did not have greater activation for working memory demand on no-go trials, consistent with a role in inhibitory control. Activation in pre-SMA also responded to response selection demand and was increased with working memory on go trials specifically. The bilateral FEF and right DAI were commonly active for no-go trials. The FEF was also recruited to a greater degree with working memory demand on go trials and may bias top-down information when stimulus-response mappings change. The DAI, additionally responded to increased working memory demand on both go and no-go trials and may be involved in accessing sustained task information, alerting, or autonomic changes when cognitive demands increase. DLPFC activation was consistent with a role in working memory retrieval on both go and no-go trials. The inferior frontal junction, on the other hand, had greater activation with working memory specifically for no-go trials and may detect salient stimuli when the task requires frequent updating of working memory representations.

}, keywords = {Adult, Brain, brain mapping, Choice Behavior, Female, Humans, Image Processing, Computer-Assisted, Inhibition (Psychology), Magnetic Resonance Imaging, Male, Memory, Short-Term, Motor Activity, Neuropsychological Tests, Oxygen, Reaction Time, Young Adult}, issn = {1530-8898}, doi = {10.1162/jocn_a_00394}, author = {Barber, Anita D and Caffo, Brian S and Pekar, James J and Mostofsky, Stewart H} } @article {839, title = {An fMRI-based neurologic signature of physical pain.}, journal = {N Engl J Med}, volume = {368}, year = {2013}, month = {2013 Apr 11}, pages = {1388-97}, abstract = {

BACKGROUND: Persistent pain is measured by means of self-report, the sole reliance on which hampers diagnosis and treatment. Functional magnetic resonance imaging (fMRI) holds promise for identifying objective measures of pain, but brain measures that are sensitive and specific to physical pain have not yet been identified.

METHODS: In four studies involving a total of 114 participants, we developed an fMRI-based measure that predicts pain intensity at the level of the individual person. In study 1, we used machine-learning analyses to identify a pattern of fMRI activity across brain regions--a neurologic signature--that was associated with heat-induced pain. The pattern included the thalamus, the posterior and anterior insulae, the secondary somatosensory cortex, the anterior cingulate cortex, the periaqueductal gray matter, and other regions. In study 2, we tested the sensitivity and specificity of the signature to pain versus warmth in a new sample. In study 3, we assessed specificity relative to social pain, which activates many of the same brain regions as physical pain. In study 4, we assessed the responsiveness of the measure to the analgesic agent remifentanil.

RESULTS: In study 1, the neurologic signature showed sensitivity and specificity of 94\% or more (95\% confidence interval [CI], 89 to 98) in discriminating painful heat from nonpainful warmth, pain anticipation, and pain recall. In study 2, the signature discriminated between painful heat and nonpainful warmth with 93\% sensitivity and specificity (95\% CI, 84 to 100). In study 3, it discriminated between physical pain and social pain with 85\% sensitivity (95\% CI, 76 to 94) and 73\% specificity (95\% CI, 61 to 84) and with 95\% sensitivity and specificity in a forced-choice test of which of two conditions was more painful. In study 4, the strength of the signature response was substantially reduced when remifentanil was administered.

CONCLUSIONS: It is possible to use fMRI to assess pain elicited by noxious heat in healthy persons. Future studies are needed to assess whether the signature predicts clinical pain. (Funded by the National Institute on Drug Abuse and others.).

}, keywords = {Adult, Analgesics, Opioid, Artificial Intelligence, Brain, brain mapping, Female, Hot Temperature, Humans, Magnetic Resonance Imaging, Male, Pain, Pain Measurement, Piperidines, ROC Curve, Sensitivity and Specificity, Young Adult}, issn = {1533-4406}, doi = {10.1056/NEJMoa1204471}, author = {Wager, Tor D and Atlas, Lauren Y and Lindquist, Martin A and Roy, Mathieu and Woo, Choong-Wan and Kross, Ethan} } @article {803, title = {Quantifying the reliability of image replication studies: the image intraclass correlation coefficient (I2C2).}, journal = {Cogn Affect Behav Neurosci}, volume = {13}, year = {2013}, month = {2013 Dec}, pages = {714-24}, abstract = {

This article proposes the image intraclass correlation (I2C2) coefficient as a global measure of reliability for imaging studies. The I2C2 generalizes the classic intraclass correlation (ICC) coefficient to the case when the data of interest are images, thereby providing a measure that is both intuitive and convenient. Drawing a connection with classical measurement error models for replication experiments, the I2C2 can be computed quickly, even in high-dimensional imaging studies. A nonparametric bootstrap procedure is introduced to quantify the variability of the I2C2 estimator. Furthermore, a Monte Carlo permutation is utilized to test reproducibility versus a zero I2C2, representing complete lack of reproducibility. Methodologies are applied to three replication studies arising from different brain imaging modalities and settings: regional analysis of volumes in normalized space imaging for characterizing brain morphology, seed-voxel brain activation maps based on resting-state functional magnetic resonance imaging (fMRI), and fractional anisotropy in an area surrounding the corpus callosum via diffusion tensor imaging. Notably, resting-state fMRI brain activation maps are found to have low reliability, ranging from .2 to .4. Software and data are available to provide easy access to the proposed methods.

}, keywords = {Adult, Brain, brain mapping, Computer Simulation, Female, Humans, Male, Models, Biological, Neuroimaging, Reproducibility of Results, Statistics as Topic}, issn = {1531-135X}, doi = {10.3758/s13415-013-0196-0}, author = {Shou, H and Eloyan, A and Lee, S and Zipunnikov, V and Crainiceanu, A N and Nebel, N B and Caffo, B and Lindquist, M A and Crainiceanu, C M} } @article {827, title = {Analysis of group ICA-based connectivity measures from fMRI: application to Alzheimer{\textquoteright}s disease.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e49340}, abstract = {

Functional magnetic resonance imaging (fMRI) is a powerful tool for the in vivo study of the pathophysiology of brain disorders and disease. In this manuscript, we propose an analysis stream for fMRI functional connectivity data and apply it to a novel study of Alzheimer{\textquoteright}s disease. In the first stage, spatial independent component analysis is applied to group fMRI data to obtain common brain networks (spatial maps) and subject-specific mixing matrices (time courses). In the second stage, functional principal component analysis is utilized to decompose the mixing matrices into population-level eigenvectors and subject-specific loadings. Inference is performed using permutation-based exact logistic regression for matched pairs data. The method is applied to a novel fMRI study of Alzheimer{\textquoteright}s disease risk under a verbal paired associates task. We found empirical evidence of alternative ICA-based metrics of connectivity when comparing subjects evidencing mild cognitive impairment relative to carefully matched controls.

}, keywords = {Aged, Algorithms, Alzheimer Disease, Brain, brain mapping, Case-Control Studies, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Mild Cognitive Impairment, Nerve Net, Principal Component Analysis, Task Performance and Analysis, Time Factors}, issn = {1932-6203}, doi = {10.1371/journal.pone.0049340}, author = {Li, Shanshan and Eloyan, Ani and Joel, Suresh and Mostofsky, Stewart and Pekar, James and Bassett, Susan Spear and Caffo, Brian} } @article {841, title = {Dissociable influences of opiates and expectations on pain.}, journal = {J Neurosci}, volume = {32}, year = {2012}, month = {2012 Jun 6}, pages = {8053-64}, abstract = {

Placebo treatments and opiate drugs are thought to have common effects on the opioid system and pain-related brain processes. This has created excitement about the potential for expectations to modulate drug effects themselves. If drug effects differ as a function of belief, this would challenge the assumptions underlying the standard clinical trial. We conducted two studies to directly examine the relationship between expectations and opioid analgesia. We administered the opioid agonist remifentanil to human subjects during experimental thermal pain and manipulated participants{\textquoteright} knowledge of drug delivery using an open-hidden design. This allowed us to test drug effects, expectancy (knowledge) effects, and their interactions on pain reports and pain-related responses in the brain. Remifentanil and expectancy both reduced pain, but drug effects on pain reports and fMRI activity did not interact with expectancy. Regions associated with pain processing showed drug-induced modulation during both Open and Hidden conditions, with no differences in drug effects as a function of expectation. Instead, expectancy modulated activity in frontal cortex, with a separable time course from drug effects. These findings reveal that opiates and placebo treatments both influence clinically relevant outcomes and operate without mutual interference.

}, keywords = {Analgesics, Opioid, Anticipation, Psychological, Behavior, brain mapping, Dose-Response Relationship, Drug, Female, Hemodynamics, Hot Temperature, Humans, Image Processing, Computer-Assisted, Injections, Intravenous, Linear Models, Magnetic Resonance Imaging, Male, Pain, Pain Measurement, Piperidines, Young Adult}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0383-12.2012}, author = {Atlas, Lauren Y and Whittington, Robert A and Lindquist, Martin A and Wielgosz, Joe and Sonty, Nomita and Wager, Tor D} } @article {842, title = {Dynamic connectivity regression: determining state-related changes in brain connectivity.}, journal = {Neuroimage}, volume = {61}, year = {2012}, month = {2012 Jul 16}, pages = {907-20}, abstract = {

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

}, keywords = {Algorithms, Brain, brain mapping, Computer Simulation, Emotions, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Neural Pathways}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2012.03.070}, author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A} } @article {844, title = {Estimating and testing variance components in a multi-level GLM.}, journal = {Neuroimage}, volume = {59}, year = {2012}, month = {2012 Jan 2}, pages = {490-501}, abstract = {

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

}, keywords = {Brain, brain mapping, Humans, Image Interpretation, Computer-Assisted, Linear Models, Magnetic Resonance Imaging, Models, Neurological, Sensitivity and Specificity}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.07.077}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris and Wager, Tor D} } @article {622, title = {Dynamics of large-scale cortical interactions at high gamma frequencies during word production: event related causality (ERC) analysis of human electrocorticography (ECoG).}, journal = {Neuroimage}, volume = {56}, year = {2011}, month = {2011 Jun 15}, pages = {2218-37}, abstract = {

Intracranial EEG studies in humans have shown that functional brain activation in a variety of functional-anatomic domains of human cortex is associated with an increase in power at a broad range of high gamma (\>60Hz) frequencies. Although these electrophysiological responses are highly specific for the location and timing of cortical processing and in animal recordings are highly correlated with increased population firing rates, there has been little direct empirical evidence for causal interactions between different recording sites at high gamma frequencies. Such causal interactions are hypothesized to occur during cognitive tasks that activate multiple brain regions. To determine whether such causal interactions occur at high gamma frequencies and to investigate their functional significance, we used event-related causality (ERC) analysis to estimate the dynamics, directionality, and magnitude of event-related causal interactions using subdural electrocorticography (ECoG) recorded during two word production tasks: picture naming and auditory word repetition. A clinical subject who had normal hearing but was skilled in American Signed Language (ASL) provided a unique opportunity to test our hypothesis with reference to a predictable pattern of causal interactions, i.e. that language cortex interacts with different areas of sensorimotor cortex during spoken vs. signed responses. Our ERC analyses confirmed this prediction. During word production with spoken responses, perisylvian language sites had prominent causal interactions with mouth/tongue areas of motor cortex, and when responses were gestured in sign language, the most prominent interactions involved hand and arm areas of motor cortex. Furthermore, we found that the sites from which the most numerous and prominent causal interactions originated, i.e. sites with a pattern of ERC "divergence", were also sites where high gamma power increases were most prominent and where electrocortical stimulation mapping interfered with word production. These findings suggest that the number, strength and directionality of event-related causal interactions may help identify network nodes that are not only activated by a task but are critical to its performance.

}, keywords = {brain mapping, Cerebral Cortex, Electroencephalography, Female, Humans, Middle Aged, Neural Pathways, Seizures, Signal Processing, Computer-Assisted, Speech}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.03.030}, author = {Korzeniewska, A. and Franaszczuk, Piotr J. and Crainiceanu, C and Ku{\'s}, Rafa{\l} and Crone, Nathan E.} } @article {847, title = {Everything you never wanted to know about circular analysis, but were afraid to ask.}, journal = {J Cereb Blood Flow Metab}, volume = {30}, year = {2010}, month = {2010 Sep}, pages = {1551-7}, abstract = {

Over the past year, a heated discussion about {\textquoteright}circular{\textquoteright} or {\textquoteright}nonindependent{\textquoteright} analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in }, keywords = {Brain, brain mapping, Data Interpretation, Statistical, Guidelines as Topic, Image Processing, Computer-Assisted, Neurosciences, Publications, Research Design, Selection Bias}, issn = {1559-7016}, doi = {10.1038/jcbfm.2010.86}, author = {Kriegeskorte, Nikolaus and Lindquist, Martin A and Nichols, Thomas E and Poldrack, Russell A and Vul, Edward} } @article {Korz:Crai:Ku:Fran:Cron:dyna:2008, title = {Dynamics of Event-related Causality in Brain Electrical Activity}, journal = {Human Brain Mapping}, volume = {29}, number = {10}, year = {2008}, pages = {1170{\textendash}1192}, keywords = {brain mapping, cortical synchronization, EEG, Language, Multivariate analysis, Neural network, neural transmission, Signal processing}, author = {Korzeniewska, A. and Crainiceanu, C and Ku{\'s}, Rafa{\l}{} and Franaszczuk, Piotr J. and Crone, Nathan E.} }