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We process information from the world through multiple senses, and the brain must decide what information belongs together and what information should be segregated. One challenge in studying such multisensory integration is how to quantify the multisensory interactions, a challenge that is amplified by the host of methods that are now used to measure neural, behavioral, and perceptual responses. Many of the measures that have been developed to quantify multisensory integration (and which have been derived from single unit analyses), have been applied to these different measures without much consideration for the nature of the process being studied. Here, we provide a review focused on the means with which experimenters quantify multisensory processes and integration across a range of commonly used experimental methodologies. We emphasize the most commonly employed measures, including single- and multiunit responses, local field potentials, functional magnetic resonance imaging, and electroencephalography, along with behavioral measures of detection, accuracy, and response times. In each section, we will discuss the different metrics commonly used to quantify multisensory interactions, including the rationale for their use, their advantages, and the drawbacks and caveats associated with them. Also discussed are possible alternatives to the most commonly used metrics.
New magnetic resonance imaging (MRI) sequences are enabling clinical study of the in vivo spinal cord's internal structure. Yet, low contrast-to-noise ratio, artifacts, and imaging distortions have limited the applicability of tissue segmentation techniques pioneered elsewhere in the central nervous system. Recently, methods have been presented for cord/non-cord segmentation on MRI and the feasibility of gray matter/white matter tissue segmentation has been evaluated. To date, no automated algorithms have been presented. Herein, we present a non-local multi-atlas framework that robustly identifies the spinal cord and segments its internal structure with submillimetric accuracy. The proposed algorithm couples non-local fusion with a large number of slice-based atlases (as opposed to typical volumetric ones). To improve performance, the fusion process is interwoven with registration so that segmentation information guides registration and vice versa. We demonstrate statistically significant improvement over state-of-the-art benchmarks in a study of 67 patients. The primary contributions of this work are (1) innovation in non-volumetric atlas information, (2) advancement of label fusion theory to include iterative registration/segmentation, and (3) the first fully automated segmentation algorithm for spinal cord internal structure on MRI.
BACKGROUND - Next generation sequencing (NGS) is being widely used to identify genetic variants associated with human disease. Although the approach is cost effective, the underlying data is susceptible to many types of error. Importantly, since NGS technologies and protocols are rapidly evolving, with constantly changing steps ranging from sample preparation to data processing software updates, it is important to enable researchers to routinely assess the quality of sequencing and alignment data prior to downstream analyses.
RESULTS - Here we describe QPLOT, an automated tool that can facilitate the quality assessment of sequencing run performance. Taking standard sequence alignments as input, QPLOT generates a series of diagnostic metrics summarizing run quality and produces convenient graphical summaries for these metrics. QPLOT is computationally efficient, generates webpages for interactive exploration of detailed results, and can handle the joint output of many sequencing runs.
CONCLUSION - QPLOT is an automated tool that facilitates assessment of sequence run quality. We routinely apply QPLOT to ensure quick detection of diagnostic of sequencing run problems. We hope that QPLOT will be useful to the community as well.
Neurons in cortical ventral-stream area V4 are thought to contribute to important aspects of visual processing by integrating information from primary visual cortex (V1). However, how V4 neurons respond to visual stimulation after V1 injury remains unclear: While electrophysiological investigation of V4 neurons during reversible V1 inactivation suggests that virtually all responses are eliminated (Girard et al., 1991), fMRI in humans and monkeys with permanent lesions shows reliable V1-independent activity (Baseler et al., 1999; Goebel et al., 2001; Schmid et al., 2010). To resolve this apparent discrepancy, we longitudinally assessed neuronal functions of macaque area V4 using chronically implanted electrode arrays before and after creating a permanent aspiration lesion in V1. During the month after lesioning, we observed weak yet significant spiking activity in response to stimuli presented to the lesion-affected part of the visual field. These V1-independent responses showed sensitivity for motion and likely reflect the effect of V1-bypassing geniculate input into extrastriate areas.
Frontal-dependent task performance is typically modulated by dopamine (DA) according to an inverted-U pattern, whereby intermediate levels of DA signaling optimizes performance. Numerous studies implicate trait differences in DA signaling based on differences in the catechol-O-methyltransferase (COMT) gene in executive function task performance. However, little work has investigated genetic variations in DA signaling downstream from COMT. One candidate is the DA- and cAMP-regulated phosphoprotein of molecular weight 32 kDa (DARPP-32), which mediates signaling through the D1-type DA receptor, the dominant DA receptor in the frontal cortex. Using an n-back task, we used signal detection theory to measure performance in a healthy adult population (n = 97) genotyped for single nucleotide polymorphisms in the COMT (rs4680) and DARPP-32 (rs907094) genes. Correct target detection (hits) and false alarms were used to calculate d' measures for each working memory load (0-, 2-, and 3-back). At the highest load (3-back) only, we observed a significant COMT × DARPP-32 interaction, such that the DARPP-32 T/T genotype enhanced target detection in COMT(ValVal) individuals, but impaired target detection in COMT(Met) carriers. These findings suggest that enhanced dopaminergic signaling via the DARPP-32 T allele aids target detection in individuals with presumed low frontal DA (COMT(ValVal)) but impairs target detection in those with putatively higher frontal DA levels (COMT(Met) carriers). Moreover, these data support an inverted-U model with intermediate levels of DA signaling optimizing performance on tasks requiring maintenance of mental representations in working memory.
Over the last decade there has been considerable progress in the discovery and development of biomarkers of kidney disease, and several have now been evaluated in different clinical settings. Although there is a growing literature on the performance of various biomarkers in clinical studies, there is limited information on how these biomarkers would be utilized by clinicians to manage patients with acute kidney injury (AKI). Recognizing this gap in knowledge, we convened the 10th Acute Dialysis Quality Initiative meeting to review the literature on biomarkers in AKI and their application in clinical practice. We asked an international group of experts to assess four broad areas for biomarker utilization for AKI: risk assessment, diagnosis, and staging; differential diagnosis; prognosis and management; and novel physiological techniques including imaging. This article provides a summary of the key findings and recommendations of the group, to equip clinicians to effectively use biomarkers in AKI.
BACKGROUND - Diagnosing coarctation of the aorta (CoA) in the presence of a patent ductus arteriosus (PDA) may require observation until PDA closure. The aim of this study was to create a model incorporating previously published indices to estimate the probability of neonatal CoA in the presence of a PDA.
METHODS - A retrospective "investigation" cohort of 80 neonates was divided into two groups: (1) neonates with PDA and suspicion for CoA requiring observation to confirm the presence or absence of CoA and (2) neonates with PDA and confirmed diagnosis of either CoA or unobstructed aortic arch. Multivariate logistic regression was used to create the coarctation probability model (CPM), which was used to calculate a neonate's probability of CoA. The CPM was validated internally using bootstrapping and subsequently validated prospectively using a "validation" cohort of 74 neonates with PDA.
RESULTS - The CPM had an area under the receiver operating characteristic curve of 0.96 and demonstrated good clinical significance in the risk stratification of neonates with PDA and CoA. No neonate with a CPM probability of <15% had CoA after PDA closure. Neonates with CPM probability < 15% were classified at low risk, between 15% and 60% at moderate risk, and >60% at high risk for CoA.
CONCLUSIONS - On the basis of these results, the authors recommend measurement of the CPM in all neonates with PDA. Those with CPM probability < 15% no longer require observation, which could decrease observation in as many as half of neonates with unobstructed aortic arches; those with CPM probabilities between 15% and 60% require follow-up imaging, while those with CPM probabilities > 60% should be observed as inpatients until PDA closure.
Copyright © 2013 American Society of Echocardiography. Published by Mosby, Inc. All rights reserved.
Parkinson's disease is known to be associated with abnormal electrical spiking activities of basal ganglia neurons, including changes in firing rate, bursting activities and oscillatory firing patterns and changes in entropy. We explored the relative importance of these measures through optimal feature selection and discrimination analysis methods. We identified key characteristics of basal ganglia activity that predicted whether the neurons were recorded in the normal or parkinsonian state. Starting with 29 features extracted from the spike timing of neurons recorded in normal and parkinsonian monkeys in the internal or external segment of the globus pallidus or the subthalamic nucleus (STN), we used a method that incorporates a support vector machine algorithm to find feature combinations that optimally discriminate between the normal and parkinsonian states. Our results demonstrate that the discrimination power of combinations of specific features is higher than that of single features, or of all features combined, and that the most discriminative feature sets differ substantially between basal ganglia structures. Each nucleus or class of neurons in the basal ganglia may react differently to the parkinsonian condition, and the features used to describe this state should be adapted to the neuron type under study. The feature that was overall most predictive of the parkinsonian state in our data set was a high STN intraburst frequency. Interestingly, this feature was not correlated with parameters describing oscillatory firing properties in recordings made in the normal condition but was significantly correlated with spectral power in specific frequency bands in recordings from the parkinsonian state (specifically with power in the 8-13 Hz band).
Despite its anatomical prominence, the function of the primate pulvinar is poorly understood. A few electrophysiological studies in simian primates have investigated the functional organization of pulvinar by examining visuotopic maps. Multiple visuotopic maps have been found for all studied simians, with differences in organization reported between New and Old World simians. Given that prosimians are considered closer to the common ancestors of New and Old World primates, we investigated the visuotopic organization of pulvinar in the prosimian bush baby (Otolemur garnettii). Single-electrode extracellular recording was used to find the retinotopic maps in the lateral (PL) and inferior (PI) pulvinar. Based on recordings across cases, a 3D model of the map was constructed. From sections stained for Nissl bodies, myelin, acetylcholinesterase, calbindin, or cytochrome oxidase, we identified three PI chemoarchitectonic subdivisions, lateral central (PIcl), medial central (PIcm), and medial (PIm) inferior pulvinar. Two major retinotopic maps were identified that cover PL and PIcl, the dorsal one in dorsal PL and the ventral one in PIcl and ventral PL. Both maps represent central vision at the posterior end of the border between the maps, the upper visual field in the lateral half and the lower visual field in the medial half. They share many features with the maps reported for the pulvinar of simians, including the location in pulvinar and the representation of the upper-lower and central-peripheral visual field axes. The second-order representation in the lateral map and a laminar organization are likely features specific to Old World simians.
Copyright © 2013 Wiley Periodicals, Inc.
PURPOSE - Multi-atlas segmentation has been shown to be highly robust and accurate across an extraordinary range of potential applications. However, it is limited to the segmentation of structures that are anatomically consistent across a large population of potential target subjects (i.e., multi-atlas segmentation is limited to "in-atlas" applications). Herein, the authors propose a technique to determine the likelihood that a multi-atlas segmentation estimate is representative of the problem at hand, and, therefore, identify anomalous regions that are not well represented within the atlases.
METHODS - The authors derive a technique to estimate the out-of-atlas (OOA) likelihood for every voxel in the target image. These estimated likelihoods can be used to determine and localize the probability of an abnormality being present on the target image.
RESULTS - Using a collection of manually labeled whole-brain datasets, the authors demonstrate the efficacy of the proposed framework on two distinct applications. First, the authors demonstrate the ability to accurately and robustly detect malignant gliomas in the human brain-an aggressive class of central nervous system neoplasms. Second, the authors demonstrate how this OOA likelihood estimation process can be used within a quality control context for diffusion tensor imaging datasets to detect large-scale imaging artifacts (e.g., aliasing and image shading).
CONCLUSIONS - The proposed OOA likelihood estimation framework shows great promise for robust and rapid identification of brain abnormalities and imaging artifacts using only weak dependencies on anomaly morphometry and appearance. The authors envision that this approach would allow for application-specific algorithms to focus directly on regions of high OOA likelihood, which would (1) reduce the need for human intervention, and (2) reduce the propensity for false positives. Using the dual perspective, this technique would allow for algorithms to focus on regions of normal anatomy to ascertain image quality and adapt to image appearance characteristics.