@article {836, title = {Brain mediators of the effects of noxious heat on pain.}, journal = {Pain}, volume = {155}, year = {2014}, month = {2014 Aug}, pages = {1632-48}, abstract = {

Recent human neuroimaging studies have investigated the neural correlates of either noxious stimulus intensity or reported pain. Although useful, analyzing brain relationships with stimulus intensity and behavior separately does not address how sensation and pain are linked in the central nervous system. In this study, we used multi-level mediation analysis to identify brain mediators of pain--regions in which trial-by-trial responses to heat explained variability in the relationship between noxious stimulus intensity (across 4 levels) and pain. This approach has the potential to identify multiple circuits with complementary roles in pain genesis. Brain mediators of noxious heat effects on pain included targets of ascending nociceptive pathways (anterior cingulate, insula, SII, and medial thalamus) and also prefrontal and subcortical regions not associated with nociceptive pathways per se. Cluster analysis revealed that mediators were grouped into several distinct functional networks, including the following: somatosensory, paralimbic, and striatal-cerebellar networks that increased with stimulus intensity; and 2 networks co-localized with "default mode" regions in which stimulus intensity-related decreases mediated increased pain. We also identified "thermosensory" regions that responded to increasing noxious heat but did not predict pain reports. Finally, several regions did not respond to noxious input, but their activity predicted pain; these included ventromedial prefrontal cortex, dorsolateral prefrontal cortex, cerebellar regions, and supplementary motor cortices. These regions likely underlie both nociceptive and non-nociceptive processes that contribute to pain, such as attention and decision-making processes. Overall, these results elucidate how multiple distinct brain systems jointly contribute to the central generation of pain.

}, issn = {1872-6623}, doi = {10.1016/j.pain.2014.05.015}, author = {Atlas, Lauren Y and Lindquist, Martin A and Bolger, Niall and Wager, Tor D} } @article {816, title = {Evidence for Specificity of Motor Impairments in Catching and Balance in Children with Autism.}, journal = {J Autism Dev Disord}, year = {2014}, month = {2014 Sep 18}, abstract = {

To evaluate evidence for motor impairment specificity in autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). Children completed performance-based assessment of motor functioning (Movement Assessment Battery for Children: MABC-2). Logistic regression models were used to predict group membership. In the models comparing typically developing and developmental disability (DD), all three MABC subscale scores were significantly negatively associated with having a DD. In the models comparing ADHD and ASD, catching and static balance items were associated with ASD group membership, with a 1 point decrease in performance increasing odds of ASD by 36 and 39~\%, respectively. Impairments in motor skills requiring the coupling of visual and temporal feedback to guide and adjust movement appear specifically deficient in ASD.

}, issn = {1573-3432}, doi = {10.1007/s10803-014-2229-0}, author = {Ament, Katarina and Mejia, Amanda and Buhlman, Rebecca and Erklin, Shannon and Caffo, Brian and Mostofsky, Stewart and Wodka, Ericka} } @article {815, title = {Neuroinflammation and brain atrophy in former NFL players: An in vivo multimodal imaging pilot study.}, journal = {Neurobiol Dis}, volume = {74C}, year = {2014}, month = {2014 Nov 7}, pages = {58-65}, abstract = {

There are growing concerns about potential delayed, neuropsychiatric consequences (e.g, cognitive decline, mood or anxiety disorders) of sports-related traumatic brain injury (TBI). Autopsy studies of brains from a limited number of former athletes have described characteristic, pathologic changes of chronic traumatic encephalopathy (CTE) leading to questions about the relationship between these pathologic and the neuropsychiatric disturbances seen in former athletes. Research in this area will depend on in vivo methods that characterize molecular changes in the brain, linking CTE and other sports-related pathologies with delayed emergence of neuropsychiatric symptoms. In this pilot project we studied former National Football League (NFL) players using new neuroimaging techniques and clinical measures of cognitive functioning. We hypothesized that former NFL players would show molecular and structural changes in medial temporal and parietal lobe structures as well as specific cognitive deficits, namely those of verbal learning and memory. We observed a significant increase in binding of [(11)C]DPA-713 to the translocator protein (TSPO), a marker of brain injury and repair, in several brain regions, such as the supramarginal gyrus and right amygdala, in 9 former NFL players compared to 9 age-matched, healthy controls. We also observed significant atrophy of the right hippocampus. Finally, we report that these same former players had varied performance on a test of verbal learning and memory, suggesting that these molecular and pathologic changes may play a role in cognitive decline. These results suggest that localized brain injury and repair, indicated by increased [(11)C]DPA-713 binding to TSPO, may be linked to history of NFL play. [(11)C]DPA-713 PET is a promising new tool that can be used in future study design to examine further the relationship between TSPO expression in brain injury and repair, selective regional brain atrophy, and the potential link to deficits in verbal learning and memory after NFL play.

}, issn = {1095-953X}, doi = {10.1016/j.nbd.2014.10.019}, author = {Coughlin, Jennifer M and Wang, Yuchuan and Munro, Cynthia A and Ma, Shuangchao and Yue, Chen and Chen, Shaojie and Airan, Raag and Kim, Pearl K and Adams, Ashley V and Garcia, Cinthya and Higgs, Cecilia and Sair, Haris I and Sawa, Akira and Smith, Gwenn and Lyketsos, Constantine G and Caffo, Brian and Kassiou, Michael and Guilarte, Tomas R and Pomper, Martin G} } @article {834, title = {Separate neural representations for physical pain and social rejection.}, journal = {Nat Commun}, volume = {5}, year = {2014}, month = {2014}, pages = {5380}, abstract = {

Current theories suggest that physical pain and social rejection share common neural mechanisms, largely by virtue of overlapping functional magnetic resonance imaging (fMRI) activity. Here we challenge this notion by identifying distinct multivariate fMRI patterns unique to pain and rejection. Sixty participants experience painful heat and warmth and view photos of ex-partners and friends on separate trials. FMRI pattern classifiers discriminate pain and rejection from their respective control conditions in out-of-sample individuals with 92\% and 80\% accuracy. The rejection classifier performs at chance on pain, and vice versa. Pain- and rejection-related representations are uncorrelated within regions thought to encode pain affect (for example, dorsal anterior cingulate) and show distinct functional connectivity with other regions in a separate resting-state data set (N=91). These findings demonstrate that separate representations underlie pain and rejection despite common fMRI activity at the gross anatomical level. Rather than co-opting pain circuitry, rejection involves distinct affective representations in humans.

}, issn = {2041-1723}, doi = {10.1038/ncomms6380}, author = {Woo, Choong-Wan and Koban, Leonie and Kross, Ethan and Lindquist, Martin A and Banich, Marie T and Ruzic, Luka and Andrews-Hanna, Jessica R and Wager, Tor D} } @article {786, title = {Statistical normalization techniques for magnetic resonance imaging.}, journal = {Neuroimage Clin}, volume = {6}, year = {2014}, month = {2014}, pages = {9-19}, abstract = {

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer{\textquoteright}s disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2014.08.008}, author = {Shinohara, Russell T and Sweeney, Elizabeth M and Goldsmith, Jeff and Shiee, Navid and Mateen, Farrah J and Calabresi, Peter A and Jarso, Samson and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {828, title = {Axial 3D gradient-echo imaging for improved multiple sclerosis lesion detection in the cervical spinal cord at 3T.}, journal = {Neuroradiology}, volume = {55}, year = {2013}, month = {2013 Mar}, pages = {431-9}, abstract = {

INTRODUCTION: In multiple sclerosis (MS), spinal cord imaging can help in diagnosis and follow-up evaluation. However, spinal cord magnetic resonance imaging (MRI) is technically challenging, and image quality, particularly in the axial plane, is typically poor compared to brain MRI. Because gradient-recalled echo (GRE) images might offer improved contrast resolution within the spinal cord at high magnetic field strength, both without and with a magnetization transfer prepulse, we compared them to T2-weighted fast-spin-echo (T2-FSE) images for the detection of MS lesions in the cervical cord at 3T.

METHODS: On a clinical 3T MRI scanner, we studied 62 MS cases and 19 healthy volunteers. Axial 3D GRE sequences were performed without and with off-resonance radiofrequency irradiation. To mimic clinical practice, all images were evaluated in conjunction with linked images from a sagittal short tau inversion recovery scan, which is considered the gold standard for lesion detection in MS. Two experienced observers recorded image quality, location and size of focal lesions, atrophy, swelling, and diffuse signal abnormality independently at first and then in consensus.

RESULTS: The number and volume of lesions detected with high confidence was more than three times as high on both GRE sequences compared to T2-FSE (p < 0.0001). Approximately 5 \% of GRE scans were affected by artifacts that interfered with image interpretation, not significantly different from T2W-FSE.

CONCLUSIONS: Axial 3D GRE sequences are useful for MS lesion detection when compared to 2D T2-FSE sequences in the cervical spinal cord at 3T and should be considered when examining intramedullary spinal cord lesions.

}, keywords = {Adult, Aged, Algorithms, Cervical Vertebrae, Echo-Planar Imaging, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Male, Middle Aged, Multiple Sclerosis, Reproducibility of Results, Sensitivity and Specificity, Spinal Cord}, issn = {1432-1920}, doi = {10.1007/s00234-012-1118-5}, author = {Ozturk, Arzu and Aygun, Nafi and Smith, Seth A and Caffo, Brian and Calabresi, Peter A and Reich, Daniel S} } @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 {685, title = {Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling.}, journal = {BMC Bioinformatics}, volume = {13}, year = {2012}, month = {2012 Sep 14}, pages = {234}, abstract = {

ABSTRACT: BACKGROUND: Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential. RESULTS: We present an R Bioconductor package, waveTiling, which implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling the user is able to discover (1) group-wise expressed regions, (2) differentially expressed regions between any two groups in single-factor studies and in (3) multifactorial designs. Moreover, for time-course experiments it is also possible to detect (4) linear time effects and (5) a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, effect regions can be detected regardless of existing annotation. Three case studies with different experimental set-ups illustrate the use and the flexibility of the model-based transcriptome analysis. CONCLUSIONS: The waveTiling package provides the user with a convenient tool for the analysis of tiling array trancriptome data for a multitude of experimental set-ups. Regardless of the study design, the probe-wise analysis allows for the detection of transcriptional effects in both exonic, intronic and intergenic regions, without prior consultation of existing annotation.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-13-234}, author = {De Beuf, K. and Pipelers, Peter and Andriankaja, Megan and Thas, O. and Inz{\'e}, Dirk and Crainiceanu, C and Clement, Lieven} } @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 {nicole, title = {Distribution of cell-free and cell-associated HIV surrogates in the female genital tract following simulated vaginal intercourse}, journal = {Appeared online in the Journal of Infectious Diseases}, year = {2012}, author = {Louissaint, N and Nimmagadda, S and Bakshi, R and Du, Y and Macura, K and King, K and Wahl, R and Goldsmith, J and Caffo, B and Cao, Y and Anderson, J and Fuchs, E and Hendrix, C} } @article {cribben2012dynamic, title = {Dynamic connectivity regression: Determining state-related changes in brain connectivity}, journal = {NeuroImage}, year = {2012}, publisher = {Elsevier}, author = {Cribben, Ivor and Haraldsdottir, Ragnheidur and Atlas, Lauren Y and Wager, Tor D and Lindquist, Martin A} } @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 {lindquist2012estimating, title = {Estimating and testing variance components in a multi-level GLM}, journal = {Neuroimage}, volume = {59}, number = {1}, year = {2012}, pages = {490{\textendash}501}, publisher = {Elsevier}, author = {Lindquist, Martin A and Spicer, Julie and Asllani, Iris and Wager, Tor D} } @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 {nareshCaffeine, title = {Sleep-Disordered breathing and caffeine consumption: results of a community-based study}, journal = {Accepted in Chest}, year = {2012}, author = {Aurora, RN and Crainiceanu, C and Caffo, B and Punjabi, N} } @article {pmid21995019, title = {Assessment of bias for MRI diffusion tensor imaging using SIMEX}, journal = {Med Image Comput Comput Assist Interv}, volume = {14}, number = {Pt 2}, year = {2011}, pages = {107{\textendash}115}, author = {Lauzon, C and Asman, A. J. and Crainiceanu, C and Caffo, B and Landman, B} } @article {aurora2011correlating, title = {Correlating subjective and objective sleepiness: revisiting the association using survival analysis.}, journal = {Sleep}, volume = {34}, number = {12}, year = {2011}, pages = {1707}, author = {Aurora, RN and Caffo, B and Crainiceanu, C and Punjabi, N} } @article {AJT:AJT3735, title = {MELD Exceptions and Rates of Waiting List Outcomes}, journal = {American Journal of Transplantation}, volume = {11}, number = {11}, year = {2011}, pages = {2362{\textendash}2371}, publisher = {Blackwell Publishing Inc}, keywords = {Liver transplantation, MELD score, organ allocation}, issn = {1600-6143}, doi = {10.1111/j.1600-6143.2011.03735.x}, url = {http://dx.doi.org/10.1111/j.1600-6143.2011.03735.x}, author = {Massie, A and Caffo, B and Gentry, S and Hall, E and Axelrod, D and Lentine, K and Schnitzler, M and Gheorghian, A and Salvalaggio, P and Segev, D} } @article {atlas, title = {Brain mediators of predictive cue effects on perceived pain}, journal = {J Neurosci}, volume = {30}, year = {2010}, pages = {12964-77}, author = {Atlas, L.Y. and Bolger, N. and Lindquist, Martin A and Wager, T.D.} } @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 {Paninski2010, title = {A new look at state-space models for neural data.}, journal = {Journal of computational neuroscience}, volume = {29}, number = {1-2}, year = {2010}, pages = {107{\textendash}26}, abstract = {

State space methods have proven indispensable in neural data analysis. However, common methods for performing inference in state-space models with non-Gaussian observations rely on certain approximations which are not always accurate. Here we review direct optimization methods that avoid these approximations, but that nonetheless retain the computational efficiency of the approximate methods. We discuss a variety of examples, applying these direct optimization techniques to problems in spike train smoothing, stimulus decoding, parameter estimation, and inference of synaptic properties. Along the way, we point out connections to some related standard statistical methods, including spline smoothing and isotonic regression. Finally, we note that the computational methods reviewed here do not in fact depend on the state-space setting at all; instead, the key property we are exploiting involves the bandedness of certain matrices. We close by discussing some applications of this more general point of view, including Markov chain Monte Carlo methods for neural decoding and efficient estimation of spatially-varying firing rates.

}, keywords = {1 introduction, Action Potentials, Action Potentials: physiology, Animals, Computer Simulation, for inference in state-space, forward-backward methods, hidden markov model, models, neural coding, Neurological, Neurons, Neurons: physiology, Retinal Ganglion Cells, Retinal Ganglion Cells: physiology, state-space models, Statistical, Synapses, Synapses: physiology, tridiagonal matrix}, issn = {1573-6873}, doi = {10.1007/s10827-009-0179-x}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19649698}, author = {Paninski, Liam and Ahmadian, Yashar and Gil, Daniel and Shinsuke, Ferreira and Rahnama, Kamiar and Michael, Rad and Vogelstein, Joshua T and Wu, Wei and Ferreira, Daniel Gil and Koyama, Shinsuke and {Rahnama Rad}, Kamiar and Vidne, Michael} } @article {Wager09b, title = {Brain mediators of cardiovascular responses to social threat, Part II: Prefrontal subcortical pathways and relationship with anxiety}, journal = {NeuroImage}, volume = {47}, year = {2009}, pages = {836-851}, author = {Wager, T.D. and van Ast, V. and Davidson, M.L. and Lindquist, Martin A and Ochsner, K.N.} } @article {lindquist09, title = {Modeling the Hemodynamic Response Function in fMRI: Efficiency, Bias and Mis-modeling}, journal = {NeuroImage}, volume = {45}, year = {2009}, pages = {S187-S198}, author = {Lindquist, Martin A and Loh, J.M. and Atlas, L. and Wager, T.D.} } @article {caffo2007flexible, title = {Flexible random intercept models for binary outcomes using mixtures of normals}, journal = {Computational Statistics and Data Analysis, PMCID: PMC2031853}, volume = {51}, number = {11}, year = {2007}, pages = {5220{\textendash}5235}, publisher = {Elsevier}, author = {Caffo, B and An, M and Rohde, C} } @article {agresti2004examples, title = {Examples in which misspecification of a random effects distribution reduces efficiency, and possible remedies}, journal = {Computational Statistics and Data Analysis}, volume = {47}, number = {3}, year = {2004}, pages = {639{\textendash}653}, publisher = {Elsevier}, author = {Agresti, A and Caffo, B and Ohman-Strickland, P} } @article {yacoub2003spin, title = {Spin-echo fMRI in humans using high spatial resolutions and high magnetic fields}, journal = {Magnetic resonance in medicine}, volume = {49}, number = {4}, year = {2003}, pages = {655{\textendash}664}, publisher = {Wiley Online Library}, author = {Yacoub, Essa and Duong, Timothy Q and De Moortele, Van and Lindquist, Martin A and Adriany, Gregor and Kim, Seong-Gi and U{\u g}urbil, K{\^a}mil and Hu, Xiaoping and others} } @article {agresti2002measures, title = {Measures of relative model fit}, journal = {Computational Statistics and Data Analysis}, volume = {39}, number = {2}, year = {2002}, pages = {127{\textendash}136}, publisher = {Elsevier}, author = {Agresti, A and Caffo, B} } @article {hartzel2001multinomial, title = {Multinomial logit random effects models}, journal = {Statistical Modelling}, volume = {1}, number = {2}, year = {2001}, pages = {81}, author = {Hartzel, J and Agresti, A and Caffo, B} } @article {agresti2000random, title = {Random-effects modeling of categorical response data}, journal = {Sociological Methodology}, volume = {30}, year = {2000}, pages = {27{\textendash}80}, publisher = {Blackwell Publishers}, author = {Agresti, A and Booth, J and Hobert, J and Caffo, B} } @article {agresti2000simple, title = {Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures}, journal = {The American Statistician}, volume = {54}, number = {4}, year = {2000}, pages = {280{\textendash}288}, publisher = {American Statistical Association}, author = {Agresti, A and Caffo, B} }