@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 {835, title = {Evaluating dynamic bivariate correlations in resting-state fMRI: a comparison study and a new approach.}, journal = {Neuroimage}, volume = {101}, year = {2014}, month = {2014 Nov 1}, pages = {531-46}, abstract = {

To date, most functional Magnetic Resonance Imaging (fMRI) studies have assumed that the functional connectivity (FC) between time series from distinct brain regions is constant across time. However, recently, there has been an increased interest in quantifying possible dynamic changes in FC during fMRI experiments, as it is thought that this may provide insight into the fundamental workings of brain networks. In this work we focus on the specific problem of estimating the dynamic behavior of pair-wise correlations between time courses extracted from two different regions of the brain. We critique the commonly used sliding-window technique, and discuss some alternative methods used to model volatility in the finance literature that could also prove to be useful in the neuroimaging setting. In particular, we focus on the Dynamic Conditional Correlation (DCC) model, which provides a model-based approach towards estimating dynamic correlations. We investigate the properties of several techniques in a series of simulation studies and find that DCC achieves the best overall balance between sensitivity and specificity in detecting dynamic changes in correlations. We also investigate its scalability beyond the bivariate case to demonstrate its utility for studying dynamic correlations between more than two brain regions. Finally, we illustrate its performance in an application to test-retest resting state fMRI data.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.06.052}, author = {Lindquist, Martin A and Xu, Yuting and Nebel, Mary Beth and Caffo, Brain S} } @article {790, title = {Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment.}, journal = {PLoS One}, volume = {9}, year = {2014}, month = {2014}, pages = {e107263}, abstract = {

Brain lesion localization in multiple sclerosis (MS) is thought to be associated with the type and severity of adverse health effects. However, several factors hinder statistical analyses of such associations using large MRI datasets: 1) spatial registration algorithms developed for healthy individuals may be less effective on diseased brains and lead to different spatial distributions of lesions; 2) interpretation of results requires the careful selection of confounders; and 3) most approaches have focused on voxel-wise regression approaches. In this paper, we evaluated the performance of five registration algorithms and observed that conclusions regarding lesion localization can vary substantially with the choice of registration algorithm. Methods for dealing with confounding factors due to differences in disease duration and local lesion volume are introduced. Voxel-wise regression is then extended by the introduction of a metric that measures the distance between a patient-specific lesion mask and the population prevalence map.

}, issn = {1932-6203}, doi = {10.1371/journal.pone.0107263}, author = {Eloyan, Ani and Shou, Haochang and Shinohara, Russell T and Sweeney, Elizabeth M and Nebel, Mary Beth and Cuzzocreo, Jennifer L and Calabresi, Peter A and Reich, Daniel S and Lindquist, Martin A and Crainiceanu, Ciprian M} } @article {818, title = {Reduction of motion-related artifacts in resting state fMRI using aCompCor.}, journal = {Neuroimage}, volume = {96}, year = {2014}, month = {2014 Aug 1}, pages = {22-35}, abstract = {

Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.03.028}, author = {Muschelli, John and Nebel, Mary Beth and Caffo, Brian S and Barber, Anita D and Pekar, James J and Mostofsky, Stewart H} } @article {792, title = {Shrinkage prediction of seed-voxel brain connectivity using resting state fMRI.}, journal = {Neuroimage}, volume = {102 Pt 2}, year = {2014}, month = {2014 Nov 15}, pages = {938-44}, abstract = {

Resting-state functional magnetic resonance imaging (rs-fMRI) is used to investigate synchronous activations in spatially distinct regions of the brain, which are thought to reflect functional systems supporting cognitive processes. Analyses are often performed using seed-based correlation analysis, allowing researchers to explore functional connectivity between data in a seed region and the rest of the brain. Using scan-rescan rs-fMRI data, we investigate how well the subject-specific seed-based correlation map from the second replication of the study can be predicted using data from the first replication. We show that one can dramatically improve prediction of subject-specific connectivity by borrowing strength from the group correlation map computed using all other subjects in the study. Even more surprisingly, we found that the group correlation map provided a better prediction of a subject{\textquoteright}s connectivity than the individual{\textquoteright}s own data. While further discussion and experimentation are required to understand how this can be used in practice, results indicate that shrinkage-based methods that borrow strength from the population mean should play a role in rs-fMRI data analysis.

}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2014.05.043}, author = {Shou, Haochang and Eloyan, Ani and Nebel, Mary Beth and Mejia, Amanda and Pekar, James J and Mostofsky, Stewart and Caffo, Brian and Lindquist, Martin A and Crainiceanu, Ciprian M} }