@article {817, title = {The neural correlates of learned motor acuity.}, journal = {J Neurophysiol}, volume = {112}, year = {2014}, month = {2014 Aug 15}, pages = {971-80}, abstract = {

We recently defined a component of motor skill learning as "motor acuity," quantified as a shift in the speed-accuracy trade-off function for a task. These shifts are primarily driven by reductions in movement variability. To determine the neural correlates of improvement in motor acuity, we devised a motor task compatible with magnetic resonance brain imaging that required subjects to make finely controlled wrist movements under visual guidance. Subjects were imaged on day 1 and day 5 while they performed this task and were trained outside the scanner on intervening days 2, 3, and 4. The potential confound of performance changes between days 1 and 5 was avoided by constraining movement time to a fixed duration. After training, subjects showed a marked increase in success rate and a reduction in trial-by-trial variability for the trained task but not for an untrained control task, without changes in mean trajectory. The decrease in variability for the trained task was associated with increased activation in contralateral primary motor and premotor cortical areas and in ipsilateral cerebellum. A global nonlocalizing multivariate analysis confirmed that learning was associated with increased overall brain activation. We suggest that motor acuity is acquired through increases in the number of neurons recruited in contralateral motor cortical areas and in ipsilateral cerebellum, which could reflect increased signal-to-noise ratio in motor output and improved state estimation for feedback corrections, respectively.

}, issn = {1522-1598}, doi = {10.1152/jn.00897.2013}, author = {Shmuelof, Lior and Yang, Juemin and Caffo, Brian and Mazzoni, Pietro and Krakauer, John W} } @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 {804, title = {Normalization and extraction of interpretable metrics from raw accelerometry data.}, journal = {Biostatistics}, volume = {15}, year = {2014}, month = {2014 Jan}, pages = {102-16}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity}, issn = {1468-4357}, doi = {10.1093/biostatistics/kxt029}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M} } @article {Bai2014Metrics, title = {Normalization and extraction of interpretable metrics from raw accelerometry data}, journal = {Biostatistics}, volume = {15}, number = {1}, year = {2014}, pages = {102{\textendash}116}, abstract = {

We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.

}, keywords = {Activity intensity, Movelets, Movement, Signal processing, Time active, Tri-axial accelerometer}, issn = {14654644}, author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A. and Crainiceanu, Ciprian M.} } @article {goldsmithAAS, title = {Nonlinear tube-fitting for the analysis of anatomical and functional structures}, journal = {Annals of Applied Statistics, PMCID: PMC3119905}, volume = {5}, number = {1}, year = {2011}, pages = {337-363}, issn = {1932-6157}, doi = {10.1214/10-AOAS384}, author = {Goldsmith, J and Caffo, B and Crainiceanu, C and Reich, D. and Du, Y and Hendrix, C} } @article {caffoSleep, title = {A novel approach to prediction of mild sleep disorders in a population-based sample: the Sleep Heart Health Study}, journal = {Sleep, PMCID: PMC2982734}, volume = {33}, number = {12}, year = {2011}, pages = {1641{\textendash}1648}, author = {Caffo, B and Diener-West, M and Punjabi, N and Samet, J} } @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 {crainiceanu2009nonparametric, title = {Nonparametric signal extraction and measurement error in the analysis of electroencephalographic activity during sleep}, journal = {Journal of the American Statistical Association, PMCID: PMC2802498}, volume = {104}, number = {486}, year = {2009}, pages = {541{\textendash}555}, publisher = {ASA}, author = {Crainiceanu, C and Caffo, B and Di, C and Punjabi, N} } @article {Crai:Caff:Di:Punj:nonp:2009, title = {Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep}, journal = {Journal of the American Statistical Association}, volume = {104}, number = {486}, year = {2009}, pages = {541{\textendash}555}, keywords = {Hierarchical smoothing, Measurement error, penalized splines, Sleep}, author = {Crainiceanu, C and Caffo, B and Di, Chong-Zhi and Punjabi, Naresh M} } @article {cao2008noninvasive, title = {Noninvasive Quantitation of Drug Concentration in Prostate and Seminal Vesicles: Improvement and Validation With Desipramine and Aspirin}, journal = {The Journal of Clinical Pharmacology}, volume = {48}, number = {2}, year = {2008}, pages = {176-183}, publisher = {Am Coll Clin Pharm}, author = {Cao, Y and Caffo, B and Choi, L and Radebaugh, C and Fuchs, E and Hendrix, C} } @article {ndovi2007new, title = {A new method to estimate quantitatively seminal vesicle and prostate gland contributions to ejaculate}, journal = {British Journal of Clinical Pharmacology, PMCID: PMC2203235}, volume = {63}, number = {4}, year = {2007}, pages = {404-420}, publisher = {Blackwell Publishing}, author = {Ndovi, T and Parsons, T and Choi, L and Caffo, B and Rohde, C and Hendrix, C} } @article {Crai:nonp:2007, title = {Nonparametric Regression Methods for Longitudinal Data Analysis. Mixed-effects Modeling Approaches: Hulin Wu and Jin-Ting Zhang}, journal = {Journal of the American Statistical Association}, volume = {102}, number = {478}, year = {2007}, pages = {761{\textendash}761}, author = {Crainiceanu, C} } @article {Carr:Rupp:Crai:Tost:Kara:nonl:2004, title = {Nonlinear and Nonparametric Regression and Instrumental Variables}, journal = {Journal of the American Statistical Association}, volume = {99}, number = {467}, year = {2004}, pages = {736{\textendash}750}, keywords = {Bayesian methods, Identifiability, Measurement error, Simulation extrapolation, Splines, structural modeling}, author = {Carroll, Raymond J. and Ruppert, D. and Crainiceanu, C and Tosteson, Tor D. and Karagas, Margaret R.} }