Incremental activation detection in FMRI series using kalman filtering

Roche, Alexis; Lahaye, Pierre-Jean; Poline, J.-B.

doi:10.1109/isbi.2004.1398553

Cited by 11 publications

(12 citation statements)

References 10 publications

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“…A General Linear Model (GLM) analysis was applied for the volume-and surface-based data using the same analysis code, namely the nipy package http://nipy.sourceforge.net/. The model included the ten conditions of the experiments convolved with a standard hemodynamic lter and its time derivative, a high-pass lter (cuto:128s) and the procedure included an estimation of the noise auto-correlation using an AR(1) model [31]. Activation maps were derived for the following six functional contrasts (we give short names in italic): i) left-right: left versus right button presses, ii) right-left: right versus left button presses, iii) audio-video: sentence listening versus sentence reading, iv) video-audio: sentence reading versus sentence listening, v) computation-sentences: computation versus sentence reading, vi) reading-visual: reading versus passive checkerboard viewing.…”

Section: Pre-processingmentioning

confidence: 99%

An empirical comparison of surface-based and volume-based group studies in neuroimaging

et al. 2012

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Thirion. An empirical comparison of surface-based and volume-based group studies in neuroimaging. NeuroImage, Elsevier, 2012, 63 (3) Being able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random eects (RFX) and mixed-eects analyses (MFX). We consider dierent schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We nd that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this eect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps.

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Section: Pre-processingmentioning

confidence: 99%

An empirical comparison of surface-based and volume-based group studies in neuroimaging

et al. 2012

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“…using SPM2 package [8]. Surface-based representations of functional data are then created using a volume-to-surface projection technique proposed in [9] and tmaps are built by an incremental statistical method [10]. Cortical localization and intersubject matching are performed via a surface-based coordinate system built on each cortical surface [2].…”

Section: Data Preparationmentioning

confidence: 99%

Surface-Based Structural Group Analysis of fMRI Data

Operto¹,

Clouchoux²,

Bulot³

et al. 2008

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008

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Abstract. As structural and surface-based analyses gain interest for activation detection, morphometry and intersubject matching purposes, this paper proposes a method to perform structural group analyses directly on the cortical surface. Scale-space blobs are extracted from surface-based functional maps and matched across subjects. The process aims at identifying activations within a population despite the various effects due to variability. Results of the method are presented with simulated activations and with data from a somatotopy protocol.

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“…A powerful estimation approach consists of maximizing the likelihood function or, equivalently, minimizing its negated logarithm given by [4]:…”

Section: Offline Fittingmentioning

confidence: 99%

“…We recently advocated Kalman filtering techniques as good candidates for online fMRI analysis [4]. In its standard form, the Kalman filter is an incremental solver for ordinary least-square (OLS) regression problems, and is therefore wellsuited for GLM fitting when assuming uncorrelated errors.…”

Section: Introductionmentioning

confidence: 99%

“…In its standard form, the Kalman filter is an incremental solver for ordinary least-square (OLS) regression problems, and is therefore wellsuited for GLM fitting when assuming uncorrelated errors. In the more general case where the noise autocorrelation is unknown and is therefore to be estimated, the regression problem becomes nonlinear, a situation that may be handled using an extended Kalman filter (EKF) [4]. This technique's main drawback is that it requires parameter initialization to work; we observed from practical experience that good initialization is difficult to tune, and is very much machine-dependent.…”

Section: Introductionmentioning

confidence: 99%

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Solving Incrementally the Fitting and Detection Problems in fMRI Time Series

Roche

Pinel

Dehaene

et al. 2004

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004

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Abstract. We tackle the problem of real-time statistical analysis of functional magnetic resonance imaging (fMRI) data. In a recent paper, we proposed an incremental algorithm based on the extended Kalman filter (EKF) to fit fMRI time series in terms of a general linear model with autoregressive errors (GLM-AR model). We here improve the technique using a new Kalman filter variant specifically tailored to the GLM-AR fitting problem, the Refined Kalman Filter (RKF), that avoids both the estimation bias and initialization issues typical from the EKF, at the price of increased memory load. We then demonstrate the ability of the method to perform online analysis on a "functional calibration" eventrelated fMRI protocol.

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Incremental activation detection in FMRI series using kalman filtering

Cited by 11 publications

References 10 publications

An empirical comparison of surface-based and volume-based group studies in neuroimaging

An empirical comparison of surface-based and volume-based group studies in neuroimaging

Surface-Based Structural Group Analysis of fMRI Data

Solving Incrementally the Fitting and Detection Problems in fMRI Time Series

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