What is the best similarity measure for motion correction in fMRI time series?

Freire, Luís; Roche, Alexis; Mangin, Jean‐François

doi:10.1109/tmi.2002.1009383

Cited by 337 publications

(274 citation statements)

References 33 publications

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“…BrainVoyager and FSL also attempted a more sophisticated interpolation scheme. Freire et al (2002) point out that some types of cost functions may be more robust than others. However, changing the available parameters for the cost functions had relatively little effect.…”

Section: Discussionmentioning

confidence: 99%

Comparison of fMRI motion correction software tools

et al. 2005

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Motion correction of fMRI data is a widely used step prior to data analysis. In this study, a comparison of the motion correction tools provided by several leading fMRI analysis software packages was performed, including AFNI, AIR, BrainVoyager, FSL, and SPM2. Comparisons were performed using data from typical human studies as well as phantom data. The identical reconstruction, preprocessing, and analysis steps were used on every data set, except that motion correction was performed using various configurations from each software package. Each package was studied using default parameters, as well as parameters optimized for speed and accuracy. Forty subjects performed a Go/No-go task (an event-related design that investigates inhibitory motor response) and an N-back task (a block-design paradigm investigating working memory). The human data were analyzed by extracting a set of general linear model (GLM)-derived activation results and comparing the effect of motion correction on thresholded activation cluster size and maximum t value. In addition, a series of simulated phantom data sets were created with known activation locations, magnitudes, and realistic motion.Results from the phantom data indicate that AFNI and SPM2 yield the most accurate motion estimation parameters, while AFNI's interpolation algorithm introduces the least smoothing. AFNI is also the fastest of the packages tested. However, these advantages did not produce noticeably better activation results in motion-corrected data from typical human fMRI experiments. Although differences in performance between packages were apparent in the human data, no single software package produced dramatically better results than the others. The ''accurate'' parameters showed virtually no improvement in cluster t values compared to the standard parameters. While the ''fast'' parameters did not result in a substantial increase in speed, they did not degrade the cluster results very much either.The phantom and human data indicate that motion correction can be a valuable step in the data processing chain, yielding improvements of up to 20% in the magnitude and up to 100% in the cluster size of detected activations, but the choice of software package does not substantially affect this improvement. D

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Section: Discussionmentioning

confidence: 99%

Comparison of fMRI motion correction software tools

et al. 2005

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“…Images were realigned using INRIalign -a motion correction algorithm unbiased by local signal changes (Freire and Mangin, 2001;Freire et al, 2002). Data was spatially normalized (Ashburner and Friston, 1999) into the standard Montreal Neurological Institute space and spatially smoothed with a 10×10×10 mm 3 full width at half-maximum Gaussian kernel.…”

Section: Data Analysis: Pre-processingmentioning

confidence: 99%

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

et al. 2008

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We utilized a discrete dynamic Bayesian network (dDBN) approach (Burge et al., 2007) to determine differences in brain regions between patients with schizophrenia and healthy controls on a measure of effective connectivity, termed the approximate conditional likelihood score (ACL) (Burge and Lane, 2005). The ACL score represents a class-discriminative measure of effective connectivity by measuring the relative likelihood of the correlation between brain regions in one group versus another. The algorithm is capable of finding non-linear relationships between brain regions because it uses discrete rather than continuous values and attempts to model temporal relationships with a first-order Markov and stationary assumption constraint (Papoulis, 1991). Since Bayesian networks are overly sensitive to noisy data, we introduced an independent component analysis (ICA) filtering approach that attempted to reduce the noise found in fMRI data by unmixing the raw datasets into a set of independent spatial component maps. Components that represented noise were removed and the remaining components reconstructed into the dimensions of the original fMRI datasets. We applied the dDBN algorithm to a group of 35 patients with schizophrenia and 35 matched healthy controls using an ICA filtered and unfiltered approach. We determined that filtering the data significantly improved the magnitude of the ACL score. Patients showed the greatest ACL scores in several regions, most markedly the cerebellar vermis and hemispheres. Our findings suggest that schizophrenia patients exhibit weaker connectivity than healthy controls in multiple regions, including bilateral temporal and frontal cortices, plus cerebellum during an auditory paradigm.

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“…Images were realigned using INRIalign, a motion correction algorithm unbiased by local signal changes [Freire and Mangin, 2001;Freire et al, 2002]. Data were spatially normalized into the standard Montreal Neurological Institute (MNI) space [Friston et al, 1995], spatially smoothed with a 12 mm 3 full width at half-maximum (FWHM) Gaussian kernel.…”

Section: Preprocessingmentioning

confidence: 99%

A method for multitask fMRI data fusion applied to schizophrenia

Calhoun

Adalı

Kiehl

et al. 2005

Human Brain Mapping

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Abstract:It is becoming common to collect data from multiple functional magnetic resonance imaging (fMRI) paradigms on a single individual. The data from these experiments are typically analyzed separately and sometimes directly subtracted from one another on a voxel-by-voxel basis. These comparative approaches, although useful, do not directly attempt to examine potential commonalities between tasks and between voxels. To remedy this we propose a method to extract maximally spatially independent maps for each task that are "coupled" together by a shared loading parameter. We first compute an activation map for each task and each individual as "features," which are then used to perform joint independent component analysis (jICA) on the group data. We demonstrate our approach on a data set derived from healthy controls and schizophrenia patients, each of which carried out an auditory oddball task and a Sternberg working memory task. Our analysis approach revealed two interesting findings in the data that were missed with traditional analyses. First, consistent with our hypotheses, schizophrenia patients demonstrate "decreased" connectivity in a joint network including portions of regions implicated in two prevalent models of schizophrenia. A second finding is that for the voxels identified by the jICA analysis, the correlation between the two tasks was significantly higher in patients than in controls. This finding suggests that schizophrenia patients activate "more similarly" for both tasks than do controls. A possible synthesis of both findings is that patients are activating less, but also activating with a less-unique set of regions for these very different tasks. Both of the findings described support the claim that examination of joint activation across multiple tasks can enable new questions to be posed about fMRI data. Our approach can also be applied to data using more than two tasks. It thus provides a way to integrate and probe brain networks using a variety of tasks and may increase our understanding of coordinated brain networks and the impact of pathology upon them. Hum Brain Mapp 27:598-610, 2006.

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What is the best similarity measure for motion correction in fMRI time series?

Cited by 337 publications

References 33 publications

Comparison of fMRI motion correction software tools

Comparison of fMRI motion correction software tools

Hybrid ICA–Bayesian network approach reveals distinct effective connectivity differences in schizophrenia

A method for multitask fMRI data fusion applied to schizophrenia

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