@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 {833, title = {Cross-sectional and longitudinal association of body mass index and brain volume.}, journal = {Hum Brain Mapp}, volume = {35}, year = {2014}, month = {2014 Jan}, pages = {75-88}, abstract = {

Although a link between body mass index (BMI) and brain volume has been established in several cross-sectional studies, evidence of the association between change in BMI over time and changes in brain structure is limited. Using data from a cohort of 347 former lead workers and community controls with two magnetic resonance imaging scans over a period of ~5 years, we estimated cross-sectional and longitudinal associations of BMI and brain volume using both region of interest (ROI) and voxel-based morphometric (VBM) methods. We found that associations of BMI and brain volume were not significantly different in former lead workers when compared with community controls. In the cross-sectional analysis, higher BMIs were associated with smaller brain volumes in gray matter (GM) using both ROI and VBM approaches. No associations with white matter (WM) were observed. In the longitudinal analysis, higher baseline BMI was associated with greater decline in temporal and occipital GM ROI volumes. Change in BMI over the 5-year period was only associated with change in hippocampal volume and was not associated with change in any of the GM ROIs. Overall, higher BMI was associated with lower GM volume in several ROIs and with declines in volume in temporal and occipital GM over time. These results suggest that sustained high body mass may contribute to progressive temporal and occipital atrophy.

}, keywords = {Aged, Atrophy, Body Mass Index, Brain, Cross-Sectional Studies, Humans, Image Interpretation, Computer-Assisted, Longitudinal Studies, Magnetic Resonance Imaging, Male, Middle Aged, Obesity}, issn = {1097-0193}, doi = {10.1002/hbm.22159}, author = {Bobb, Jennifer F and Schwartz, Brian S and Davatzikos, Christos and Caffo, Brian} } @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 {843, title = {Cloak and DAG: a response to the comments on our comment.}, journal = {Neuroimage}, volume = {76}, year = {2013}, month = {2013 Aug 1}, pages = {446-9}, abstract = {

Our original comment (Lindquist and Sobel, 2011) made explicit the types of assumptions neuroimaging researchers are making when directed graphical models (DGMs), which include certain types of structural equation models (SEMs), are used to estimate causal effects. When these assumptions, which many researchers are not aware of, are not met, parameters of these models should not be interpreted as effects. Thus it is imperative that neuroimaging researchers interested in issues involving causation, for example, effective connectivity, consider the plausibility of these assumptions for their particular problem before using SEMs. In cases where these additional assumptions are not met, researchers may be able to use other methods and/or design experimental studies where the use of unrealistic assumptions can be avoided. Pearl does not disagree with anything we stated. However, he takes exception to our use of potential outcomes{\textquoteright} notation, which is the standard notation used in the statistical literature on causal inference, and his comment is devoted to promoting his alternative conventions. Glymour{\textquoteright}s comment is based on three claims that he inappropriately attributes to us. Glymour is also more optimistic than us about the potential of using directed graphical models (DGMs) to discover causal relations in neuroimaging research; we briefly address this issue toward the end of our rejoinder.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2011.11.027}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {684, title = {Corrected confidence bands for functional data using principal components.}, journal = {Biometrics}, volume = {69}, year = {2013}, month = {2013 Mar}, pages = {41-51}, abstract = {

Functional principal components (FPC) analysis is widely used to decompose and express functional observations. Curve estimates implicitly condition on basis functions and other quantities derived from FPC decompositions; however these objects are unknown in practice. In this article, we propose a method for obtaining correct curve estimates by accounting for uncertainty in FPC decompositions. Additionally, pointwise and simultaneous confidence intervals that account for both model- and decomposition-based variability are constructed. Standard mixed model representations of functional expansions are used to construct curve estimates and variances conditional on a specific decomposition. Iterated expectation and variance formulas combine model-based conditional estimates across the distribution of decompositions. A bootstrap procedure is implemented to understand the uncertainty in principal component decomposition quantities. Our method compares favorably to competing approaches in simulation studies that include both densely and sparsely observed functions. We apply our method to sparse observations of CD4 cell counts and to dense white-matter tract profiles. Code for the analyses and simulations is publicly available, and our method is implemented in the R package refund on CRAN.

}, keywords = {Brain, CD4 Lymphocyte Count, Computer Simulation, Confidence Intervals, HIV, HIV Infections, Humans, Magnetic Resonance Imaging, Models, Statistical, Multiple Sclerosis, Principal Component Analysis}, issn = {1541-0420}, doi = {10.1111/j.1541-0420.2012.01808.x}, author = {Goldsmith, J and Greven, S and Crainiceanu, C} } @article {824, title = {A decision-theory approach to interpretable set analysis for high-dimensional data.}, journal = {Biometrics}, volume = {69}, year = {2013}, month = {2013 Sep}, pages = {614-23}, abstract = {

A key problem in high-dimensional significance analysis is to find pre-defined sets that show enrichment for a statistical signal of interest; the classic example is the enrichment of gene sets for differentially expressed genes. Here, we propose a new decision-theory approach to the analysis of gene sets which focuses on estimating the fraction of non-null variables in a set. We introduce the idea of "atoms," non-overlapping sets based on the original pre-defined set annotations. Our approach focuses on finding the union of atoms that minimizes a weighted average of the number of false discoveries and missed discoveries. We introduce a new false discovery rate for sets, called the atomic false discovery rate (afdr), and prove that the optimal estimator in our decision-theory framework is to threshold the afdr. These results provide a coherent and interpretable framework for the analysis of sets that addresses the key issues of overlapping annotations and difficulty in interpreting p values in both competitive and self-contained tests. We illustrate our method and compare it to a popular existing method using simulated examples, as well as gene-set and brain ROI data analyses.

}, keywords = {Algorithms, Bayes Theorem, Biometry, Brain, Computer Simulation, Data Interpretation, Statistical, Decision Theory, Functional Neuroimaging, Gene Expression Profiling, Genomics, Humans, Magnetic Resonance Imaging, Models, Statistical, Oligonucleotide Array Sequence Analysis}, issn = {1541-0420}, doi = {10.1111/biom.12060}, author = {Boca, Simina M and Bravo, H{\'e}ctor C{\'e}orrada and Caffo, Brian and Leek, Jeffrey T and Parmigiani, Giovanni} } @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 {812, title = {Likelihood-based population independent component analysis.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {514-27}, abstract = {

Independent component analysis (ICA) is a widely used technique for blind source separation, used heavily in several scientific research areas including acoustics, electrophysiology, and functional neuroimaging. We propose a scalable two-stage iterative true group ICA methodology for analyzing population level functional magnetic resonance imaging (fMRI) data where the number of subjects is very large. The method is based on likelihood estimators of the underlying source densities and the mixing matrix. As opposed to many commonly used group ICA algorithms, the proposed method does not require significant data reduction by a 2-fold singular value decomposition. In addition, the method can be applied to a large group of subjects since the memory requirements are not restrictive. The performance of our approach is compared with a commonly used group ICA algorithm via simulation studies. Furthermore, the proposed method is applied to a large collection of resting state fMRI datasets. The results show that established brain networks are well recovered by the proposed algorithm.

}, keywords = {Adult, Algorithms, Attention Deficit Disorder with Hyperactivity, Biostatistics, Brain, Child, Computer Simulation, Databases, Factual, Humans, Imaging, Three-Dimensional, Likelihood Functions, Magnetic Resonance Imaging, Principal Component Analysis, Statistics, Nonparametric}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs055}, author = {Eloyan, Ani and Crainiceanu, Ciprian M and Caffo, Brian S} } @article {814, title = {Longitudinal scalar-on-functions regression with application to tractography data.}, journal = {Biostatistics}, volume = {14}, year = {2013}, month = {2013 Jul}, pages = {447-61}, abstract = {

We propose a class of estimation techniques for scalar-on-function regression where both outcomes and functional predictors may be observed at multiple visits. Our methods are motivated by a longitudinal brain diffusion tensor imaging tractography study. One of the study{\textquoteright}s primary goals is to evaluate the contemporaneous association between human function and brain imaging over time. The complexity of the study requires the development of methods that can simultaneously incorporate: (1) multiple functional (and scalar) regressors; (2) longitudinal outcome and predictor measurements per patient; (3) Gaussian or non-Gaussian outcomes; and (4) missing values within functional predictors. We propose two versions of a new method, longitudinal functional principal components regression (PCR). These methods extend the well-known functional PCR and allow for different effects of subject-specific trends in curves and of visit-specific deviations from that trend. The new methods are compared with existing approaches, and the most promising techniques are used for analyzing the tractography data.

}, keywords = {Anisotropy, Biostatistics, Brain, Diffusion Tensor Imaging, Humans, Linear Models, Models, Statistical, Multiple Sclerosis, Principal Component Analysis, Regression Analysis}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxs051}, author = {Gertheiss, Jan and Goldsmith, Jeff and Crainiceanu, Ciprian and Greven, Sonja} } @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 {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 {830, title = {Total white matter hyperintensity volume in bipolar disorder patients and their healthy relatives.}, journal = {Bipolar Disord}, volume = {14}, year = {2012}, month = {2012 Dec}, pages = {888-93}, abstract = {

OBJECTIVES: White matter hyperintensities (WMH) are more common in subjects with bipolar disorder (BP) than in healthy subjects (HS). Few studies have examined the effect of the diagnostic type of bipolar illness on WMH burden, and none have approached this question through a direct measurement of the volume of affected white matter in relationship to familiality. In this pilot study, we utilized a volumetric measurement of WMH to investigate the relationship between the total volume of WMH and the familiality and type of BP.

METHODS: Forty-five individuals with bipolar I disorder (BP-I) with psychotic features, BP-I without psychotic features, or bipolar II disorder (BP-II), seven of their unaffected relatives, and 32 HS were recruited for participation. T-2 weighted magnetic resonance imaging scans were obtained on all subjects, and the total volume of all WMH for each subject was measured in cubic centimeters. The significance of difference between groups was tested using ANOVA with post-hoc adjustment for multiple comparisons. Further, we used logistic regression to test for trends between symptom load and total WMH volume.

RESULTS: The mean total volume of WMH in BP-I patients with psychotic features was significantly higher (p < 0.05) than that of HS. Further, we observed a positive linear trend by familiality and type of affectedness when comparing mean total WMH volume of HS, unaffected family members, subjects with BP-II, and BP-I with and without a history of psychosis (p < 0.05).

CONCLUSIONS: Based on a quantitative technique, WMH burden appears to be associated with familiality and type of BP. The significance of these findings remains to be fully elucidated.

}, keywords = {Adult, Analysis of Variance, Bipolar Disorder, Brain, Female, Humans, Image Processing, Computer-Assisted, Leukoencephalopathies, Magnetic Resonance Imaging, Male, Nerve Fibers, Myelinated}, issn = {1399-5618}, doi = {10.1111/bdi.12019}, author = {Tighe, Sarah K and Reading, Sarah A and Rivkin, Paul and Caffo, Brian and Schweizer, Barbara and Pearlson, Godfrey and Potash, James B and Depaulo, J Raymond and Bassett, Susan S} } @article {845, title = {Graphical models, potential outcomes and causal inference: comment on Ramsey, Spirtes and Glymour.}, journal = {Neuroimage}, volume = {57}, year = {2011}, month = {2011 Jul 15}, pages = {334-6}, abstract = {

Ramsey, Spirtes and Glymour (RSG) critique a method proposed by Neumann et al. (2010) for the discovery of functional networks from fMRI meta-analysis data. We concur with this critique, but are unconvinced that directed graphical models (DGMs) are generally useful for estimating causal effects. We express our reservations using the "potential outcomes" framework for causal inference widely used in statistics.

}, keywords = {Artifacts, Brain, Computer Simulation, Humans, Image Interpretation, Computer-Assisted, Meta-Analysis as Topic}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.10.020}, author = {Lindquist, Martin A and Sobel, Michael E} } @article {846, title = {Brain mediators of predictive cue effects on perceived pain.}, journal = {J Neurosci}, volume = {30}, year = {2010}, month = {2010 Sep 29}, pages = {12964-77}, abstract = {

Information about upcoming pain strongly influences pain experience in experimental and clinical settings, but little is known about the brain mechanisms that link expectation and experience. To identify the pathways by which informational cues influence perception, analyses must jointly consider both the effects of cues on brain responses and the relationship between brain responses and changes in reported experience. Our task and analysis strategy were designed to test these relationships. Auditory cues elicited expectations for barely painful or highly painful thermal stimulation, and we assessed how cues influenced human subjects{\textquoteright} pain reports and brain responses to matched levels of noxious heat using functional magnetic resonance imaging. We used multilevel mediation analysis to identify brain regions that (1) are modulated by predictive cues, (2) predict trial-to-trial variations in pain reports, and (3) formally mediate the relationship between cues and reported pain. Cues influenced heat-evoked responses in most canonical pain-processing regions, including both medial and lateral pain pathways. Effects on several regions correlated with pretask expectations, suggesting that expectancy plays a prominent role. A subset of pain-processing regions, including anterior cingulate cortex, anterior insula, and thalamus, formally mediated cue effects on pain. Effects on these regions were in turn mediated by cue-evoked anticipatory activity in the medial orbitofrontal cortex (OFC) and ventral striatum, areas not previously directly implicated in nociception. These results suggest that activity in pain-processing regions reflects a combination of nociceptive input and top-down information related to expectations, and that anticipatory processes in OFC and striatum may play a key role in modulating pain processing.

}, keywords = {Adult, Brain, Cognition, Cues, Female, Hot Temperature, Humans, Male, Pain, Pain Threshold, Perception, Physical Stimulation}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.0057-10.2010}, author = {Atlas, Lauren Y and Bolger, Niall and Lindquist, Martin A and Wager, Tor D} } @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} }