@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 = {<p>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.</p>},
	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 {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 = {<p>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.</p>},
	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}
}
