A Neural Model of How Horizontal and Interlaminar Connections of Visual Cortex Develop into Adult Circuits that Carry Out Perceptual Grouping and Learning

A neural model suggests how horizontal and interlaminar connections in visual cortical areas Vl and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms that control cortical development in the infant lead to properties of adult cortical anatomy, neurophysiology, and visual perception. The model clarifies how excitatory and inhibitory connections can develop stably by maintaining a balance between excitation and inhibition. The growth of long-range excitatory horizontal connections between layer 2/3 pyramidal cells is balanced against that of short-range disynaptic interneuronal connections. The growth of excitatory on-center connections from layer 6-to-4 is balanced against that of inhibitory interneuronal off-surround connections. These balanced connections interact via intracortical and intercortical feedback to realize properties of perceptual grouping, attention, and perceptual learning in the adult, and help to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. The model replicates cortical point spread functions and psychophysical data on the strength of real and illusory contours. The on-center off-surround layer 6-to-4 circuit enables top-clown attentional signals from area V2 to modulate, or attentionally prime, layer 4 cells in area Vl without fully activating them. This modulatory circuit also enables adult perceptual learning within cortical area Vl and V2 to proceed in a stable way.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Linking Visual Cortical Development to Visual Perception

Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-95-1-0657

Performance assessment in brain-computer interface-based augmentative and alternative communication

Abstract A large number of incommensurable metrics are currently used to report the performance of brain-computer interfaces (BCI) used for augmentative and alterative communication (AAC). The lack of standard metrics precludes the comparison of different BCI-based AAC systems, hindering rapid growth and development of this technology. This paper presents a review of the metrics that have been used to report performance of BCIs used for AAC from January 2005 to January 2012. We distinguish between Level 1 metrics used to report performance at the output of the BCI Control Module, which translates brain signals into logical control output, and Level 2 metrics at the Selection Enhancement Module, which translates logical control to semantic control. We recommend that: (1) the commensurate metrics Mutual Information or Information Transfer Rate (ITR) be used to report Level 1 BCI performance, as these metrics represent information throughput, which is of interest in BCIs for AAC; 2) the BCI-Utility metric be used to report Level 2 BCI performance, as it is capable of handling all current methods of improving BCI performance; (3) these metrics should be supplemented by information specific to each unique BCI configuration; and (4) studies involving Selection Enhancement Modules should report performance at both Level 1 and Level 2 in the BCI system. Following these recommendations will enable efficient comparison between both BCI Control and Selection Enhancement Modules, accelerating research and development of BCI-based AAC systems.http://deepblue.lib.umich.edu/bitstream/2027.42/115465/1/12938_2012_Article_658.pd

preliminary clinical evaluation of the ASTRA4D algorithm

Objectives. To propose and evaluate a four-dimensional (4D) algorithm for joint motion elimination and spatiotemporal noise reduction in low-dose dynamic myocardial computed tomography perfusion (CTP). Methods. Thirty patients with suspected or confirmed coronary artery disease were prospectively included und underwent dynamic contrast-enhanced 320-row CTP. The presented deformable image registration method ASTRA4D identifies a low-dimensional linear model of contrast propagation (by principal component analysis, PCA) of the ex-ante temporally smoothed time-intensity curves (by local polynomial regression). Quantitative (standard deviation, signal-to-noise ratio (SNR), temporal variation, volumetric deformation) and qualitative (motion, contrast, contour sharpness; 1, poor; 5, excellent) measures of CTP quality were assessed for the original and motion-compensated volumes (without and with temporal filtering, PCA/ASTRA4D). Following visual myocardial perfusion deficit detection by two readers, diagnostic accuracy was evaluated using 1.5T magnetic resonance (MR) myocardial perfusion imaging as the reference standard in 15 patients. Results. Registration using ASTRA4D was successful in all 30 patients and resulted in comparison with the benchmark PCA in significantly (p<0.001) reduced noise over time (-83%, 178.5 vs 29.9) and spatially (-34%, 21.4 vs 14.1) as well as improved SNR (+47%, 3.6 vs 5.3) and subjective image quality (motion, contrast, contour sharpness: +1.0, +1.0, +0.5). ASTRA4D resulted in significantly improved per-segment sensitivity of 91% (58/64) and similar specificity of 96% (429/446) compared with PCA (52%, 33/64; 98%, 435/446; p=0.011) and the original sequence (45%, 29/64; 98%, 438/446; p=0.003) in the visual detection of perfusion deficits. Conclusions. The proposed functional approach to temporal denoising and morphologic alignment was shown to improve quality metrics and sensitivity of 4D CTP in the detection of myocardial ischemia.Zielsetzung. Die Entwicklung und Bewertung einer Methode zur simultanen Rauschreduktion und Bewegungskorrektur für niedrig dosierte dynamische CT Myokardperfusion. Methoden. Dreißig prospektiv eingeschlossene Patienten mit vermuteter oder bestätigter koronarer Herzkrankheit wurden einer dynamischen CT Myokardperfusionsuntersuchung unterzogen. Die präsentierte Registrierungsmethode ASTRA4D ermittelt ein niedrigdimensionales Modell des Kontrastmittelflusses (mittels einer Hauptkomponentenanalyse, PCA) der vorab zeitlich geglätteten Intensitätskurven (mittels lokaler polynomialer Regression). Quantitative (Standardabweichung, Signal-Rausch-Verhältnis (SNR), zeitliche Schwankung, räumliche Verformung) und qualitative (Bewegung, Kontrast, Kantenschärfe; 1, schlecht; 5, ausgezeichnet) Kennzahlen der unbearbeiteten und bewegungskorrigierten Perfusionsdatensätze (ohne und mit zeitlicher Glättung PCA/ASTRA4D) wurden ermittelt. Nach visueller Beurteilung von myokardialen Perfusionsdefiziten durch zwei Radiologen wurde die diagnostische Genauigkeit im Verhältnis zu 1.5T Magnetresonanztomographie in 15 Patienten ermittelt. Resultate. Bewegungskorrektur mit ASTRA4D war in allen 30 Patienten erfolgreich und resultierte im Vergleich mit der PCA Methode in signifikant (p<0.001) verringerter zeitlicher Schwankung (-83%, 178.5 gegenüber 29.9) und räumlichem Rauschen (-34%, 21.4 gegenüber 14.1) sowie verbesserter SNR (+47%, 3.6 gegenüber 5.3) und subjektiven Qualitätskriterien (Bewegung, Kontrast, Kantenschärfe: +1.0, +1.0, +0.5). ASTRA4D resultierte in signifikant verbesserter segmentweiser Sensitivität 91% (58/64) und ähnlicher Spezifizität 96% (429/446) verglichen mit der PCA Methode (52%, 33/64; 98%, 435/446; p=0.011) und dem unbearbeiteten Perfusionsdatensatz (45%, 29/64; 98%, 438/446; p=0.003) in der visuellen Beurteilung von myokardialen Perfusionsdefiziten. Schlussfolgerungen. Der vorgeschlagene funktionale Ansatz zur simultanen Rauschreduktion und Bewegungskorrektur verbesserte Qualitätskriterien und Sensitivität von dynamischer CT Perfusion in der visuellen Erkennung von Myokardischämie

Adaptive Physics-Based Non-Rigid Registration for Immersive Image-Guided Neuronavigation Systems

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby handling deformation in the presence of resection is presented. To improve the user experience, we also present a new way of using mixed reality with ultrasound, MRI, and CT. Materials and methods: This study focuses on 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. An Adaptive Physics-Based Non-Rigid Registration method (A-PBNRR) registers preoperative and intraoperative MRI for each patient. The results are compared with three other readily available registration methods: a rigid registration implemented in 3D Slicer v4.4.0; a B-Spline non-rigid registration implemented in 3D Slicer v4.4.0; and PBNRR implemented in ITKv4.7.0, upon which A-PBNRR was based. Three measures were employed to facilitate a comprehensive evaluation of the registration accuracy: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Results: The A-PBNRR using multi-tissue mesh adaptation improved the accuracy of deformable registration by more than five times compared to rigid and traditional physics based non-rigid registration, and four times compared to B-Spline interpolation methods which are part of ITK and 3D Slicer. Performance analysis showed that A-PBNRR could be applied, on average, in \u3c2 min, achieving desirable speed for use in a clinical setting. Conclusions: The A-PBNRR method performed significantly better than other readily available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room. A-PBNRR, coupled with the mixed reality system, presents a powerful and affordable solution compared to current neuronavigation systems