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Search Results: 1 - 10 of 224475 matches for " Cyril R. Pernet "
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Misconceptions in the use of the General Linear Model applied to functional MRI: a tutorial for junior neuro-imagers
Cyril R. Pernet
Frontiers in Neuroscience , 2014, DOI: 10.3389/fnins.2014.00001
Abstract: This tutorial presents several misconceptions related to the use the General Linear Model (GLM) in functional Magnetic Resonance Imaging (fMRI). The goal is not to present mathematical proofs but to educate using examples and computer code (in Matlab). In particular, I address issues related to (i) model parameterization (modelling baseline or null events) and scaling of the design matrix; (ii) hemodynamic modelling using basis functions, and (iii) computing percentage signal change. Using a simple controlled block design and an alternating block design, I first show why 'baseline' should not be modelled (model over-parameterization), and how this affects effect sizes. I also show that, depending on what is tested; over-parameterization does not necessarily impact upon statistical results. Next, using a simple periodic vs. random event related design, I show how the haemodynamic model (haemodynamic function only or using derivatives) can affects parameter estimates, as well as detail the role of orthogonalization. I then relate the above results to the computation of percentage signal change. Finally, I discuss how these issues affect group analysis and give some recommendations.
Modelling single-trial ERP reveals modulation of bottom-up face visual processing by top-down task constraints (in some subjects)
Guillaume A. Rousselet,Cyril R. Pernet
Frontiers in Psychology , 2011, DOI: 10.3389/fpsyg.2011.00137
Abstract: We studied how task constraints modulate the relationship between single-trial event-related potentials (ERPs) and image noise. Thirteen subjects performed two interleaved tasks: on different blocks, they saw the same stimuli, but they discriminated either between two faces or between two colors. Stimuli were two pictures of red or green faces that contained from 10 to 80% of phase noise, with 10% increments. Behavioral accuracy followed a noise dependent sigmoid in the identity task but was high and independent of noise level in the color task. EEG data recorded concurrently were analyzed using a single-trial ANCOVA: we assessed how changes in task constraints modulated ERP noise sensitivity while regressing out the main ERP differences due to identity, color, and task. Single-trial ERP sensitivity to image phase noise started at about 95–110 ms post-stimulus onset. Group analyses showed a significant reduction in noise sensitivity in the color task compared to the identity task from about 140 ms to 300 ms post-stimulus onset. However, statistical analyses in every subject revealed different results: significant task modulation occurred in 8/13 subjects, one showing an increase and seven showing a decrease in noise sensitivity in the color task. Onsets and durations of effects also differed between group and single-trial analyses: at any time point only a maximum of four subjects (31%) showed results consistent with group analyses. We provide detailed results for all 13 subjects, including a shift function analysis that revealed asymmetric task modulations of single-trial ERP distributions. We conclude that, during face processing, bottom-up sensitivity to phase noise can be modulated by top-down task constraints, in a broad window around the P2, at least in some subjects.
Quantifying the Time Course of Visual Object Processing Using ERPs: It's Time to Up the Game
Guillaume A. Rousselet,Cyril R. Pernet
Frontiers in Psychology , 2011, DOI: 10.3389/fpsyg.2011.00107
Abstract: Hundreds of studies have investigated the early ERPs to faces and objects using scalp and intracranial recordings. The vast majority of these studies have used uncontrolled stimuli, inappropriate designs, peak measurements, poor figures, and poor inferential and descriptive group statistics. These problems, together with a tendency to discuss any effect p < 0.05 rather than to report effect sizes, have led to a research field very much qualitative in nature, despite its quantitative inspirations, and in which predictions do not go beyond condition A > condition B. Here we describe the main limitations of face and object ERP research and suggest alternative strategies to move forward. The problems plague intracranial and surface ERP studies, but also studies using more advanced techniques – e.g., source space analyses and measurements of network dynamics, as well as many behavioral, fMRI, TMS, and LFP studies. In essence, it is time to stop amassing binary results and start using single-trial analyses to build models of visual perception.
LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data
Cyril R. Pernet,Nicolas Chauveau,Carl Gaspar,Guillaume A. Rousselet
Computational Intelligence and Neuroscience , 2011, DOI: 10.1155/2011/831409
Abstract: Magnetic- and electric-evoked brain responses have traditionally been analyzed by comparing the peaks or mean amplitudes of signals from selected channels and averaged across trials. More recently, tools have been developed to investigate single trial response variability (e.g., EEGLAB) and to test differences between averaged evoked responses over the entire scalp and time dimensions (e.g., SPM, Fieldtrip). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data. In addition, LIMO EEG provides robust parametric tests, therefore providing a new and complementary tool in the analysis of neural evoked responses. 1. Introduction LIMO EEG (https://gforge.dcn.ed.ac.uk/gf/project/limo_eeg/) is a toolbox for the statistical analysis of physiological data. The main goal of the toolbox is the analysis and formal testing for experimental effects at all electrodes/sensors and all time points of magneto- and electro encephalography (MEEG) recordings. This contrasts with traditional approaches that select peaks or mean amplitudes of averaged evoked responses. The toolbox is implemented in Matlab (http://www.mathworks.com/) and requires the Matlab statistical toolbox (free alternative to these functions can be found on the LIMO EEG server and corresponds to adapted version of Octave functions (http://www.gnu.org/software/octave/). The data structure and visualization makes use of the EEGLAB Matlab toolbox [1] (http://sccn.ucsd.edu/eeglab/); therefore LIMO EEG is better used as a plug-in of EEGLAB, although the statistical analyses can be performed independently. Similarly, the toolbox is primarily designed for EEG data although both EEGLAB and LIMO EEG can process MEG data. The toolbox offers a comprehensive range of statistical tests (Table 1), including many popular designs (ANOVAs, linear regressions, ANCOVAs). Some of the statistical methods, that is, massive univariate general linear analyses [2, 3] and spatiotemporal clustering for multiple comparisons correction [4–6] have existed for several years whereas others like bootstrapping were introduced only recently [7–9]. Table 1: Summary of statistical tests available in LIMO EEG via the GUI with the bootstrap procedures used at the univariate (one time frame on one electrode) and cluster levels. Contrary to other toolboxes dedicated to the analysis of event related potentials (ERPs), LIMO EEG deals both with within-subject variance (i.e., single trial
Brain classification reveals the right cerebellum as the best biomarker of dyslexia
Cyril R Pernet, Jean Poline, Jean Demonet, Guillaume A Rousselet
BMC Neuroscience , 2009, DOI: 10.1186/1471-2202-10-67
Abstract: The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes.These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries.Developmental dyslexia consists of a specific and persistent failure to acquire efficient reading skills despite conventional instruction, adequate intelligence, and socio-cultural opportunity [1]. Many competing neuro-cognitive hypotheses aim to explain dyslexia. The phonological hypothesis, which is the most influential account for reading problems, postulates deficits related to the access or the manipulation of phonemic information, or both, preventing efficient learning of gra
Re-Defining Dyslexia: Accounting for variability
Pernet,Cyril R.; Dufor,Olivier; Démonet,Jean-Francois;
Escritos de Psicología (Internet) , 2011,
Abstract: the scientific effervescence that reigns around developmental dyslexia is explained by the difficult challenge that consists of ascribing this handicap to a single cause whilst multiple profiles of dyslexic patients can be observed. in this chapter, we start by presenting the main neuro-cognitive hypotheses that aim to explain dyslexia. we then review the multidimensional nature of dyslexia, and discuss the necessity of using a common diagnostic criteria to improve our understanding of its true nature. we then conclude by presenting promising work connecting cerebral endophenotypes and behavioral phenotypes highlighting the need for a multi-factorial rather than mono-theoretical account of developmental dyslexia.
Re-Defining Dyslexia: Accounting for variability/Redefiniendo la dislexia: explicando la variabilidad
Cyril R. Pernet,Olivier Dufor,Jean-Francois Démonet
Escritos de Psicología , 2011,
Abstract: The scientific effervescence that reigns around developmental dyslexia is explained by the difficult challenge that consists of ascribing this handicap to a single cause whilst multiple profiles of dyslexic patients can be observed. In this chapter, we start by presenting the main neuro-cognitive hypotheses that aim to explain dyslexia. We then review the multidimensional nature of dyslexia, and discuss the necessity of using a common diagnostic criteria to improve our understanding of its true nature. We then conclude by presenting promising work connecting cerebral endophenotypes and behavioral phenotypes highlighting the need for a multi-factorial rather than mono-theoretical account of developmental dyslexia.
Adaptive thresholding for reliable topological inference in single subject fMRI analysis
Krzysztof J. Gorgolewski,Mark E. Bastin,Cyril R. Pernet
Frontiers in Human Neuroscience , 2012, DOI: 10.3389/fnhum.2012.00245
Abstract: Single subject fMRI has proved to be a useful tool for mapping functional areas in clinical procedures such as tumor resection. Using fMRI data, clinicians assess the risk, plan and execute such procedures based on thresholded statistical maps. However, because current thresholding methods were developed mainly in the context of cognitive neuroscience group studies, most single subject fMRI maps are thresholded manually to satisfy specific criteria related to single subject analyzes. Here, we propose a new adaptive thresholding method which combines Gamma-Gaussian mixture modeling with topological thresholding to improve cluster delineation. In a series of simulations we show that by adapting to the signal and noise properties, the new method performs well in terms of total number of errors but also in terms of the trade-off between false negative and positive cluster error rates. Similarly, simulations show that adaptive thresholding performs better than fixed thresholding in terms of over and underestimation of the true activation border (i.e., higher spatial accuracy). Finally, through simulations and a motor test–retest study on 10 volunteer subjects, we show that adaptive thresholding improves reliability, mainly by accounting for the global signal variance. This in turn increases the likelihood that the true activation pattern can be determined offering an automatic yet flexible way to threshold single subject fMRI maps.
Parametric study of EEG sensitivity to phase noise during face processing
Guillaume A Rousselet, Cyril R Pernet, Patrick J Bennett, Allison B Sekuler
BMC Neuroscience , 2008, DOI: 10.1186/1471-2202-9-98
Abstract: Our results show that sensitivity to phase noise in faces emerges progressively in a short time window between the P1 and the N170 ERP visual components. The sensitivity to phase noise starts at about 120–130 ms after stimulus onset and continues for another 25–40 ms. This result was robust both within and across subjects. A control experiment using pink noise textures, which had the same second-order statistics as the faces used in Experiment 1, demonstrated that the sensitivity to phase noise observed for faces cannot be explained by the presence of global image structure alone. A second control experiment used wavelet textures that were matched to the face stimuli in terms of second- and higher-order image statistics. Results from this experiment suggest that higher-order statistics of faces are necessary but not sufficient to obtain the sensitivity to phase noise function observed in response to faces.Our results constitute the first quantitative assessment of the time course of phase information processing by the human visual brain. We interpret our results in a framework that focuses on image statistics and single-trial analyses.In primates, visual object processing unfolds from the retina to higher-order cortical areas through a hierarchy of processing steps. Although, at the neuronal level, lateral and feedback connections are integrated to the feedforward sweep of information [1,2], at the functional level, neuronal mechanisms can still be conceptualized as performing rapid transformations of the input retinal activation to achieve increasingly refined representations [3]. A fundamental question in vision science is thus how to uncover the mechanisms by which the pattern of retinal activation is progressively transformed into a code that is useful for making behavioural decisions. In recent years there has been an on-going debate as to what stimuli are best for probing visual neuronal mechanisms. This debate stems mostly from the study of neurons in V1, the
Age-related delay in information accrual for faces: Evidence from a parametric, single-trial EEG approach
Guillaume A Rousselet, Jesse S Husk, Cyril R Pernet, Carl M Gaspar, Patrick J Bennett, Allison B Sekuler
BMC Neuroscience , 2009, DOI: 10.1186/1471-2202-10-114
Abstract: Behavioural 75% correct thresholds were on average lower, and maximum accuracy was higher, in younger than older observers. ERPs from each subject were entered into a single-trial general linear regression model to identify variations in neural activity statistically associated with changes in image structure. The earliest age-related ERP differences occurred in the time window of the N170. Older observers had a significantly stronger N170 in response to noise, but this age difference decreased with increasing phase information. Overall, manipulating image phase information had a greater effect on ERPs from younger observers, which was quantified using a hierarchical modelling approach. Importantly, visual activity was modulated by the same stimulus parameters in younger and older subjects. The fit of the model, indexed by R2, was computed at multiple post-stimulus time points. The time-course of the R2 function showed a significantly slower processing in older observers starting around 120 ms after stimulus onset. This age-related delay increased over time to reach a maximum around 190 ms, at which latency younger observers had around 50 ms time lead over older observers.Using a component-free ERP analysis that provides a precise timing of the visual system sensitivity to image structure, the current study demonstrates that older observers accumulate face information more slowly than younger subjects. Additionally, the N170 appears to be less face-sensitive in older observers.Ageing has widespread effects on visual functions, both in terms of scale, from cellular to behavioural changes, and in terms of areas affected, from the structural integrity of the eye to the frontal cortex [1-3]. However, despite changes in optical factors in the retina, and in the lateral geniculate nuclei of the thalamus (LGN), declines in visual functions with age are mediated, to a large extent, by cortical changes [1,4-6]. At the moment, we have a very poor understanding of age-related
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