@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 {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 {838, title = {Detecting functional connectivity change points for single-subject fMRI data.}, journal = {Front Comput Neurosci}, volume = {7}, year = {2013}, month = {2013}, pages = {143}, abstract = {

Recently in functional magnetic resonance imaging (fMRI) studies there has been an increased interest in understanding the dynamic manner in which brain regions communicate with one another, as subjects perform a set of experimental tasks or as their psychological state changes. Dynamic Connectivity Regression (DCR) is a data-driven technique used for detecting temporal change points in functional connectivity between brain regions where the number and location of the change points are unknown a priori. After finding the change points, DCR estimates a graph or set of relationships between the brain regions for data that falls between pairs of change points. In previous work, the method was predominantly validated using multi-subject data. In this paper, we concentrate on single-subject data and introduce a new DCR algorithm. The new algorithm increases accuracy for individual subject data with a small number of observations and reduces the number of false positives in the estimated undirected graphs. We also introduce a new Likelihood Ratio test for comparing sparse graphs across (or within) subjects; thus allowing us to determine whether data should be combined across subjects. We perform an extensive simulation analysis on vector autoregression (VAR) data as well as to an fMRI data set from a study (n = 23) of a state anxiety induction using a socially evaluative threat challenge. The focus on single-subject data allows us to study the variation between individuals and may provide us with a deeper knowledge of the workings of the brain.

}, issn = {1662-5188}, doi = {10.3389/fncom.2013.00143}, author = {Cribben, Ivor and Wager, Tor D and Lindquist, Martin A} } @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 {cstechn2012, title = {Discussion of {\textquoteleft}Clustering random curves under spatial interdependence with application to service accessibility{\textquoteright}}, journal = {Technometrics}, year = {2012}, pages = {to appear}, author = {Crainiceanu, C and Staicu, A. M.} } @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 {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 {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 {lewis2011differential, title = {Differential involvement of striato-and cerebello-thalamo-cortical pathways in tremor-and akinetic/rigid-predominant Parkinson{\textquoteright}s disease}, journal = {Neuroscience}, year = {2011}, publisher = {Elsevier}, author = {Lewis, M.M. and Du, G. and Sen, S. and Kawaguchi, A. and Truong, Y. and Lee, S. and Mailman, R.B. and Huang, X.} } @article {nicole2, title = {Distribution of Cell-free and Cell-associated HIV surrogates in the Colon Following Simulated Receptive Anal Intercourse in Healthy Men}, journal = {Journal of Acquired Immune Defficiency Syndromes, Basic and Translational Science}, volume = {59}, number = {1}, year = {2011}, pages = {10{\textendash}17}, author = {Louissaint, N and Nimmagadda, S and Fuchs, E and Bashki, R and Cao, Y and Lee, L and Goldsmith, J and Caffo, B and Du, Y and King, K and Menendez, F and Torbenson, M and Hendrix, C} } @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 {reich2009damage, title = {Damage to the Optic Radiation in Multiple Sclerosis Is Associated With Retinal Injury and Visual Disability}, journal = {Archives of Neurology, PMCID: PMC2784485}, volume = {66}, number = {8}, year = {2009}, pages = {998{\textendash}1006}, publisher = {Am Med Assoc}, author = {Reich, D. and Smith, S and Gordon-Lipkin, E and Ozturk, A and Caffo, B and Balcer, L and Calabresi, P} } @article {mostofsky2009decreased, title = {Decreased connectivity and cerebellar activity in autism during motor task performance}, journal = {Brain, PMCID: PMC2732264}, volume = {132}, number = {9}, year = {2009}, pages = {2413{\textendash}2425}, publisher = {Oxford Univ Press}, author = {Mostofsky, S and Powell, S and Simmonds, D and Goldberg, M and Caffo, B and Pekar, J} } @article {grinband2008detection, title = {Detection of time-varying signals in event-related fMRI designs}, journal = {Neuroimage}, volume = {43}, number = {3}, year = {2008}, pages = {509{\textendash}520}, publisher = {Elsevier}, author = {Grinband, Jack and Wager, Tor D and Lindquist, Martin A and Ferrera, Vincent P and Hirsch, Joy} } @article {redgrave2008differential, title = {Differential brain activation in anorexia nervosa to Fat and Thin words during a Stroop task}, journal = {NeuroReport, PMCID: PMC2891098}, volume = {19}, number = {12}, year = {2008}, pages = {1181{\textendash}1185}, author = {Redgrave, G and Bakker, A and Bello, N and Caffo, B and Coughlin, J and Guarda, A and McEntee, J and Pekar, J and Reinblatt, S and Verduzco, G. and Moran, T} } @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.} } @article {guilarte2008dysregulation, title = {Dysregulation of Glutamate Carboxypeptidase II in Psychiatric Disease}, journal = {Schizophrenia Research, PMCID: PMC2287371}, volume = {99}, number = {1-3}, year = {2008}, pages = {324{\textendash}332}, publisher = {NIH Public Access}, author = {Guilarte, T and Hammoud, D and McGlothan, J and Caffo, B and Foss, C and Kozikowski, A and Pomper, M} } @article {Vogelstein2007, title = {Dynamically reconfigurable silicon array of spiking neurons with conductance-based synapses.}, journal = {IEEE Transactions on Neural Networks}, volume = {18}, number = {1}, year = {2007}, pages = {253{\textendash}65}, publisher = {IEEE INSTITUTE OF ELECTRICAL AND ELECTRONICS}, abstract = {

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking neural networks is presented. The chip contains 2400 silicon neurons with fully programmable and reconfigurable synaptic connectivity. Each neuron implements a discrete-time model of a single-compartment cell. The model allows for analog membrane dynamics and an arbitrary number of synaptic connections, each with tunable conductance and reversal potential. The array of silicon neurons functions as an address-event (AE) transceiver, with incoming and outgoing spikes communicated over an asynchronous event-driven digital bus. Address encoding and conflict resolution of spiking events are implemented via a randomized arbitration scheme that ensures balanced servicing of event requests across the array. Routing of events is implemented externally using dynamically programmable random-access memory that stores a postsynaptic address, the conductance, and the reversal potential of each synaptic connection. Here, we describe the silicon neuron circuits, present experimental data characterizing the 3 mm x 3 mm chip fabricated in 0.5-microm complementary metal-oxide-semiconductor (CMOS) technology, and demonstrate its utility by configuring the hardware to emulate a model of attractor dynamics and waves of neural activity during sleep in rat hippocampus.

}, issn = {10459227}, doi = {10.1109/TNN.2006.883007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278476}, author = {Vogelstein, Joshua T and Mallik, Udayan and Vogelstein, Joshua T and Cauwenberghs, Gert} }