Publish in OALib Journal

ISSN: 2333-9721

APC: Only $99


Any time

2019 ( 64 )

2018 ( 97 )

2017 ( 101 )

2016 ( 145 )

Custom range...

Search Results: 1 - 10 of 33274 matches for " John-Dylan Haynes "
All listed articles are free for downloading (OA Articles)
Page 1 /33274
Display every page Item
Searchlight-based multi-voxel pattern analysis of fMRI by cross-validated MANOVA
Carsten Allefeld,John-Dylan Haynes
Quantitative Biology , 2014, DOI: 10.1016/j.neuroimage.2013.11.043
Abstract: Multi-voxel pattern analysis (MVPA) is a fruitful and increasingly popular complement to traditional univariate methods of analyzing neuroimaging data. We propose to replace the standard 'decoding' approach to searchlight-based MVPA, measuring the performance of a classifier by its accuracy, with a method based on the multivariate form of the general linear model. Following the well-established methodology of multivariate analysis of variance (MANOVA), we define a measure that directly characterizes the structure of multi-voxel data, the pattern distinctness $D$. Our measure is related to standard multivariate statistics, but we apply cross-validation to obtain an unbiased estimate of its population value, independent of the amount of data or its partitioning into 'training' and 'test' sets. The estimate $\hat D$ can therefore serve not only as a test statistic, but as an interpretable measure of multivariate effect size. The pattern distinctness generalizes the Mahalanobis distance to an arbitrary number of classes, but also the case where there are no classes of trials because the design is described by parametric regressors. It is defined for arbitrary estimable contrasts, including main effects (pattern differences) and interactions (pattern changes). In this way, our approach makes the full analytical power of complex factorial designs known from univariate fMRI analyses available to MVPA studies. Moreover, we show how the results of a factorial analysis can be used to obtain a measure of pattern stability, the equivalent of 'cross-decoding'.
Decoding Vigilance with NIRS
Carsten Bogler, Jan Mehnert, Jens Steinbrink, John-Dylan Haynes
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0101729
Abstract: Sustained, long-term cognitive workload is associated with variations and decrements in performance. Such fluctuations in vigilance can be a risk factor especially during dangerous attention demanding activities. Functional MRI studies have shown that attentional performance is correlated with BOLD-signals, especially in parietal and prefrontal cortical regions. An interesting question is whether these BOLD-signals could be measured in real-world scenarios, say to warn in a dangerous workplace whenever a subjects' vigilance is low. Because fMRI lacks the mobility needed for such applications, we tested whether the monitoring of vigilance might be possible using Near-Infrared Spectroscopy (NIRS). NIRS is a highly mobile technique that measures hemodynamics in the surface of the brain. We demonstrate that non-invasive NIRS signals correlate with vigilance. These signals carry enough information to decode subjects' reaction times at a single trial level.
Valid population inference for information-based imaging: Information prevalence inference
Carsten Allefeld,Kai G?rgen,John-Dylan Haynes
Statistics , 2015,
Abstract: In multivariate pattern analysis of neuroimaging data, 'second-level' inference is often performed by entering classification accuracies into a t-test vs chance level across subjects. We argue that while the random effects analysis implemented by the t-test does provide population inference if applied to activation differences, it fails to do so in the case of classification accuracy or other 'information-like' measures, because the true value of such measures can never be below chance level. This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population. Consequently, rejecting it only allows to infer that there are some subjects in which there is an information effect, but not that it generalizes. This statement is supported by theoretical arguments as well as simulations. We review possible alternative approaches to population inference for information-based imaging, converging on the idea that it should not target the mean, but the prevalence of the effect in the population. One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.
The Brightness of Colour
David Corney, John-Dylan Haynes, Geraint Rees, R. Beau Lotto
PLOS ONE , 2009, DOI: 10.1371/journal.pone.0005091
Abstract: Background The perception of brightness depends on spatial context: the same stimulus can appear light or dark depending on what surrounds it. A less well-known but equally important contextual phenomenon is that the colour of a stimulus can also alter its brightness. Specifically, stimuli that are more saturated (i.e. purer in colour) appear brighter than stimuli that are less saturated at the same luminance. Similarly, stimuli that are red or blue appear brighter than equiluminant yellow and green stimuli. This non-linear relationship between stimulus intensity and brightness, called the Helmholtz-Kohlrausch (HK) effect, was first described in the nineteenth century but has never been explained. Here, we take advantage of the relative simplicity of this ‘illusion’ to explain it and contextual effects more generally, by using a simple Bayesian ideal observer model of the human visual ecology. We also use fMRI brain scans to identify the neural correlates of brightness without changing the spatial context of the stimulus, which has complicated the interpretation of related fMRI studies. Results Rather than modelling human vision directly, we use a Bayesian ideal observer to model human visual ecology. We show that the HK effect is a result of encoding the non-linear statistical relationship between retinal images and natural scenes that would have been experienced by the human visual system in the past. We further show that the complexity of this relationship is due to the response functions of the cone photoreceptors, which themselves are thought to represent an efficient solution to encoding the statistics of images. Finally, we show that the locus of the response to the relationship between images and scenes lies in the primary visual cortex (V1), if not earlier in the visual system, since the brightness of colours (as opposed to their luminance) accords with activity in V1 as measured with fMRI. Conclusions The data suggest that perceptions of brightness represent a robust visual response to the likely sources of stimuli, as determined, in this instance, by the known statistical relationship between scenes and their retinal responses. While the responses of the early visual system (receptors in this case) may represent specifically the statistics of images, post receptor responses are more likely represent the statistical relationship between images and scenes. A corollary of this suggestion is that the visual cortex is adapted to relate the retinal image to behaviour given the statistics of its past interactions with the sources of retinal images:
Tracking the Unconscious Generation of Free Decisions Using UItra-High Field fMRI
Stefan Bode, Anna Hanxi He, Chun Siong Soon, Robert Trampel, Robert Turner, John-Dylan Haynes
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021612
Abstract: Recently, we demonstrated using functional magnetic resonance imaging (fMRI) that the outcome of free decisions can be decoded from brain activity several seconds before reaching conscious awareness. Activity patterns in anterior frontopolar cortex (BA 10) were temporally the first to carry intention-related information and thus a candidate region for the unconscious generation of free decisions. In the present study, the original paradigm was replicated and multivariate pattern classification was applied to functional images of frontopolar cortex, acquired using ultra-high field fMRI at 7 Tesla. Here, we show that predictive activity patterns recorded before a decision was made became increasingly stable with increasing temporal proximity to the time point of the conscious decision. Furthermore, detailed questionnaires exploring subjects' thoughts before and during the decision confirmed that decisions were made spontaneously and subjects were unaware of the evolution of their decision outcomes. These results give further evidence that FPC stands at the top of the prefrontal executive hierarchy in the unconscious generation of free decisions.
Sequential dependencies between trials in free choice tasks
Carsten Allefeld,Chun Siong Soon,Carsten Bogler,Jakob Heinzle,John-Dylan Haynes
Quantitative Biology , 2013,
Abstract: In two previous experiments we investigated the neural precursors of subjects' "free" choices for one of two options (pressing one of two buttons, and choosing between adding and subtracting numbers). In these experiments the distribution of sequence lengths was taken as an approximate indicator of the randomness (or lack of sequential dependency) of the choice sequences. However, this method is limited in its ability to reveal sequential dependencies. Here we present a more detailed individual-subject analysis and conclude that despite of the presence of significant sequential dependencies the subjects' behavior still approximates randomness, as measured by an entropy rate (on pooled data) of 0.940 bit / trial and 0.965 bit / trial in the two experiments. We also provide the raw single-subject behavioral data.
MRI Pattern Recognition in Multiple Sclerosis Normal-Appearing Brain Areas
Martin Weygandt, Kerstin Hackmack, Caspar Pfüller, Judith Bellmann–Strobl, Friedemann Paul, Frauke Zipp, John-–Dylan Haynes
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0021138
Abstract: Objective Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques. Methods A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information. Results Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10?13). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10?7). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10?10). Interpretation We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.
Proteome Profiling—Pitfalls and Progress
Paul A. Haynes,John R. Yates III
Comparative and Functional Genomics , 2000, DOI: 10.1002/1097-0061(20000630)17:2<81::aid-yea22>3.0.co;2-z
Abstract: In this review we examine the current state of analytical methods in proteomics. The conventional methodology using two-dimensional electrophoresis gels and mass spectrometry is discussed, with particular reference to the advantages and shortcomings thereof. Two recently published methods which offer an alternative approach are presented and discussed, with emphasis on how they can provide information not available via two-dimensional gel electrophoresis. These two methods are the isotope-coded affinity tags approach of Gygi et al. and the two-dimensional liquid chromatography–tandem mass spectrometry approach as presented by Link et al. We conclude that both of these new techniques represent significant advances in analytical methodology for proteome analysis. Furthermore, we believe that in the future biological research will continue to be enhanced by the continuation of such developments in proteomic analytical technology.
Microwave Breast Imaging System Prototype with Integrated Numerical Characterization
Mark Haynes,John Stang,Mahta Moghaddam
International Journal of Biomedical Imaging , 2012, DOI: 10.1155/2012/706365
Abstract: The increasing number of experimental microwave breast imaging systems and the need to properly model them have motivated our development of an integrated numerical characterization technique. We use Ansoft HFSS and a formalism we developed previously to numerically characterize an S-parameter- based breast imaging system and link it to an inverse scattering algorithm. We show successful reconstructions of simple test objects using synthetic and experimental data. We demonstrate the sensitivity of image reconstructions to knowledge of the background dielectric properties and show the limits of the current model. 1. Introduction A number of experimental systems for microwave breast imaging have been developed in recent years. These systems test full-wave inverse scattering algorithms [1–4] as well as synthetic aperture beam focusing techniques [5]. While imaging algorithms abound in the literature, techniques to properly model, characterize, and calibrate these systems have lagged behind algorithm development. Investigators have started to identify characterization as a major task, which must be addressed in order to fully evaluate the efficacy of microwave imaging for breast cancer detection. Part of this evaluation involves separating modeling errors from intrinsic algorithm artifacts in the final images. Thus, there is a need for accurate models of experimental systems, as well as methods that efficiently incorporate these models into the imaging algorithms. The task of characterizing a microwave breast imaging system for inverse scattering, as compared to a free-space system, is complicated by several factors. Specifically, the antennas are not isolated in the background media but exist as part of the surrounding structure. Also, compact arrangements of many antennas create a cavity-like imaging geometry, and the transmitter incident fields include all background multiple scattering. Finally, the antennas and object are in each others near-fields, so object-cavity scattering should be modeled. In trying to characterize breast imaging systems, investigators have turned to full numerical simulation. The antenna cavity in [6] was modeled using Ansoft HFSS and only used for antenna design and sensitivity analysis. In [7], dipole sources of an inverse scattering experiment were modeled with HFSS and calibration constants used to scale the antenna incident fields. HFSS has also been used to obtain antenna incident fields in a near-field and open, antenna setup [8]; however, ad hoc methods have been used to calibrate the scattered field S-parameter data for
Invariant barriers to reactive front propagation in fluid flows
John Mahoney,Dylan Bargteil,Mark Kingsbury,Kevin Mitchell,Tom Solomon
Physics , 2011, DOI: 10.1209/0295-5075/98/44005
Abstract: We present theory and experiments on the dynamics of reaction fronts in two-dimensional, vortex-dominated flows, for both time-independent and periodically driven cases. We find that the front propagation process is controlled by one-sided barriers that are either fixed in the laboratory frame (time-independent flows) or oscillate periodically (periodically driven flows). We call these barriers \emph{burning invariant manifolds} (BIMs), since their role in front propagation is analogous to that of invariant manifolds in the transport and mixing of passive impurities under advection. Theoretically, the BIMs emerge from a dynamical systems approach when the advection-reaction-diffusion dynamics is recast as an ODE for front element dynamics. Experimentally, we measure the location of BIMs for several laboratory flows and confirm their role as barriers to front propagation.
Page 1 /33274
Display every page Item

Copyright © 2008-2017 Open Access Library. All rights reserved.