Principles of sensorimotor learning.

The exploits of Martina Navratilova and Roger Federer represent the pinnacle of motor learning. However, when considering the range and complexity of the processes that are involved in motor learning, even the mere mortals among us exhibit abilities that are impressive. We exercise these abilities when taking up new activities - whether it is snowboarding or ballroom dancing - but also engage in substantial motor learning on a daily basis as we adapt to changes in our environment, manipulate new objects and refine existing skills. Here we review recent research in human motor learning with an emphasis on the computational mechanisms that are involved

The cognitive neuroscience of prehension: recent developments

Prehension, the capacity to reach and grasp, is the key behavior that allows humans to change their environment. It continues to serve as a remarkable experimental test case for probing the cognitive architecture of goal-oriented action. This review focuses on recent experimental evidence that enhances or modifies how we might conceptualize the neural substrates of prehension. Emphasis is placed on studies that consider how precision grasps are selected and transformed into motor commands. Then, the mechanisms that extract action relevant information from vision and touch are considered. These include consideration of how parallel perceptual networks within parietal cortex, along with the ventral stream, are connected and share information to achieve common motor goals. On-line control of grasping action is discussed within a state estimation framework. The review ends with a consideration about how prehension fits within larger action repertoires that solve more complex goals and the possible cortical architectures needed to organize these actions

Neural network and logistic regression diagnostic prediction models for giant cell arteritis: development and validation

Edsel B Ing,1 Neil R Miller,2 Angeline Nguyen,2 Wanhua Su,3 Lulu LCD Bursztyn,4 Meredith Poole,5 Vinay Kansal,6 Andrew Toren,7 Dana Albreki,8 Jack G Mouhanna,9 Alla Muladzanov,10 Mikaël Bernier,11 Mark Gans,10 Dongho Lee,12 Colten Wendel,13 Claire Sheldon,13 Marc Shields,14 Lorne Bellan,15 Matthew Lee-Wing,15 Yasaman Mohadjer,16 Navdeep Nijhawan,1 Felix Tyndel,17 Arun NE Sundaram,17 Martin W ten Hove,18 John J Chen,19 Amadeo R Rodriguez,20 Angela Hu,21 Nader Khalidi,21 Royce Ing,22 Samuel WK Wong,23 Nurhan Torun24 1Ophthalmology, University of Toronto, Toronto, ON, Canada; 2Ophthalmology, Johns Hopkins University, Baltimore, MD, USA; 3Statistics, MacEwan University, Edmonton, AB, Canada; 4Ophthalmology, Western University, London, ON, Canada; 5Queens University, Kingston, ON, Canada; 6Ophthalmology, University of Saskatchewan, Saskatoon, SK, Canada; 7Laval University, Quebec, QC, Canada; 8Ophthalmology, University of Ottawa, Ottawa, ON, Canada; 9University of Ottawa, Ottawa, ON, Canada; 10Ophthalmology, McGill University, Montreal, QC, Canada; 11University of Sherbrooke, QC, Canada; 12University of British Columbia, Vancouver, BC, Canada; 13Ophthalmology, University of British Columbia, Vancouver, BC, Canada; 14Ophthalmology, University of Virginia, Fisherville, VA, USA; 15Ophthalmology, University of Manitoba, Winnipeg, MB, Canada; 16Ophthalmology, Eye Institute of West Florida, Tampa, FL, USA; 17Neurology, University of Toronto, Toronto, ON, Canada; 18Ophthalmology, Queens University, Toronto, ON, Canada; 19Ophthalmology & Neurology, Mayo Clinic, Rochester, MN, USA; 20Ophthalmology, McMaster University, Hamilton, ON, Canada; 21Rheumatology, McMaster University, Hamilton, ON, Canada; 22Undergraduate Science, Ryerson University, Toronto, ON, Canada; 23Statistics, University of Waterloo, Waterloo, ON, Canada; 24Ophthalmology, Harvard University, Boston, MA, USA Purpose: To develop and validate neural network (NN) vs logistic regression (LR) diagnostic prediction models in patients with suspected giant cell arteritis (GCA). Design: Multicenter retrospective chart review.Methods: An audit of consecutive patients undergoing temporal artery biopsy (TABx) for suspected GCA was conducted at 14 international medical centers. The outcome variable was biopsy-proven GCA. The predictor variables were age, gender, headache, clinical temporal artery abnormality, jaw claudication, vision loss, diplopia, erythrocyte sedimentation rate, C-reactive protein, and platelet level. The data were divided into three groups to train, validate, and test the models. The NN model with the lowest false-negative rate was chosen. Internal and external validations were performed.Results: Of 1,833 patients who underwent TABx, there was complete information on 1,201 patients, 300 (25%) of whom had a positive TABx. On multivariable LR age, platelets, jaw claudication, vision loss, log C-reactive protein, log erythrocyte sedimentation rate, headache, and clinical temporal artery abnormality were statistically significant predictors of a positive TABx (P≤0.05). The area under the receiver operating characteristic curve/Hosmer–Lemeshow P for LR was 0.867 (95% CI, 0.794, 0.917)/0.119 vs NN 0.860 (95% CI, 0.786, 0.911)/0.805, with no statistically significant difference of the area under the curves (P=0.316). The misclassification rate/false-negative rate of LR was 20.6%/47.5% vs 18.1%/30.5% for NN. Missing data analysis did not change the results.Conclusion: Statistical models can aid in the triage of patients with suspected GCA. Misclassification remains a concern, but cutoff values for 95% and 99% sensitivities are provided (https://goo.gl/THCnuU). Keywords: giant cell arteritis, temporal artery biopsy, neural network, logistic regression, prediction models, ophthalmology, rheumatology &nbsp