Machine learning techniques are increasingly being used in making relevant predictions and inferences on individual subjects neuroimaging scan data. Previous studies have mostly focused on categorical discrimination of patients and matched healthy controls and more recently, on prediction of individual continuous variables such as clinical scores or age. However, these studies are greatly hampered by the large number of predictor variables (voxels) and low observations (subjects) also known as the curse-of-dimensionality or small-n-large-p problem. As a result, feature reduction techniques such as feature subset selection and dimensionality reduction are used to remove redundant predictor variables and experimental noise, a process which mitigates the curse-of-dimensionality and small-n-large-p effects. Feature reduction is an essential step before training a machine learning model to avoid overfitting and therefore improving model prediction accuracy and generalization ability. In this review, we discuss feature reduction techniques used with machine learning in neuroimaging studies.
Pyroglutamate helix B surface peptide (pHBSP) is an 11 amino acid peptide, designed to interact with a novel cell surface receptor, composed of the classical erythropoietin (EPO) receptor disulfide linked to the beta common receptor. pHBSP has the cytoprotective effects of EPO without stimulating erythropoiesis. Effects on early cerebral hemodynamics and neurological outcome at 2 weeks post-injury were compared in a rat model of mild cortical impact injury (3m/sec, 2.5 mm deformation) followed by 50 min of hemorrhagic hypotension (MAP 40 mm Hg for 50 min). Rats were randomly assigned to receive 5000 U/kg of EPO, 30 μg/kg of pHBSP, or an inactive substance every 12 h for 3 days, starting at the end of resuscitation from the hemorrhagic hypotension, which was 110 min post-injury. Both treatments reduced contusion volume at 2 weeks post-injury, from 20.8±2.8 mm(3) in the control groups to 7.7±2.0 mm(3) in the EPO-treated group and 5.9±1.5 mm(3) in the pHBSP-treated group (p=0.001). Both agents improved recovery of cerebral blood flow in the injured brain following resuscitation, and resulted in more rapid recovery of performance on beam balancing and beam walking tests. These studies suggest that pHBSP has neuroprotective effects similar to EPO in this model of combined brain injury and hypotension. pHBSP may be more useful in the clinical situation because there is less risk of thrombotic adverse effects.
The MEG inverse problem refers to the reconstruction of the neural activity of the brain from magnetoencephalography (MEG) measurements. We propose a two-way regularization (TWR) method to solve the MEG inverse problem under the assumptions that only a small number of locations in space are responsible for the measured signals (focality), and each source time course is smooth in time (smoothness). The focality and smoothness of the reconstructed signals are ensured respectively by imposing a sparsity-inducing penalty and a roughness penalty in the data fitting criterion. A two-stage algorithm is developed for fast computation, where a raw estimate of the source time course is obtained in the first stage and then refined in the second stage by the two-way regularization. The proposed method is shown to be effective on both synthetic and real-world examples.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS531 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org
Boxplots are a useful and widely used graphical technique to explore data in order to better understand the information we are working with. Boxplots display the first, second and third quartile as well as the interquartile range and outliers of a data set. The information displayed by the boxplot, and most of its variations, is based on the data’s median. However, much of scientific applications analyse and report data using the mean. In this paper, we propose a variation of the classical boxplot that displays information around the mean. Some information about the median is displayed as well.
Mild traumatic brain injury (mTBI) results in an estimated 75-90% of the 1.7 million TBI-related emergency room visits each year. Post-concussion symptoms, which can include impaired memory problems, may persist for prolonged periods of time in a fraction of these cases. The purpose of this study was to determine if an erythropoietin-mimetic peptide, pyroglutamate helix B surface peptide (pHBSP), would improve neurological outcomes following mTBI. Sixty-four rats were randomly assigned to pHBSP or control (inactive peptide) 30 lg/kg IP every 12 h for 3 days, starting at either 1 hour (early treatment) or 24 h (delayed treatment), after mTBI (cortical impact injury 3 m/sec, 2.5 mm deformation). Treatment with pHBSP resulted in significantly improved performance on the Morris water maze task. Rats that received pHBSP required 22.3 -1.3 sec to find the platform, compared to 26.3 -1.3 sec in control rats ( p = 0.022). The rats that received pHBSP also traveled a significantly shorter distance to get to the platform, 5.0 -0.3 meters, compared to 6.1 -0.3 meters in control rats ( p = 0.019). Motor tasks were only transiently impaired in this mTBI model, and no treatment effect on motor performance was observed with pHBSP. Despite the minimal tissue injury with this mTBI model, there was significant activation of inflammatory cells identified by labeling with CD68, which was reduced in the pHBSP-treated animals. The results suggest that pHBSP may improve cognitive function following mTBI.
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