Motion Correction Algorithms May Create Spurious Brain Activations in the Absence of Subject Motion

Freire, Luís; Mangin, Jean‐François

doi:10.1006/nimg.2001.0869

Cited by 287 publications

(216 citation statements)

References 32 publications

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“…Imaging data were collected on a Siemens Allegra 3-T system located at the Olin Neuropsychiatry Research Center (Hartford, CT). The echoplanar image gradient-echo pulse sequence (repetition/echo times, 1,500/28 ms; flip angle, 65°) effectively covered the entire brain (150 mm) in 1.5 s. Functional image runs were motion-corrected by using an algorithm unbiased by local signal changes (INRIAlign) (30) as implemented in the SPM2 software (Wellcome Trust Centre for Neuroimaging, University College London). Normalized images were smoothed at 9 mm full width at half maximum.…”

Section: Methodsmentioning

confidence: 99%

Neuroprediction of future rearrest

Aharoni

Vincent

Harenski

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

221

198

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Identification of factors that predict recurrent antisocial behavior is integral to the social sciences, criminal justice procedures, and the effective treatment of high-risk individuals. Here we show that error-related brain activity elicited during performance of an inhibitory task prospectively predicted subsequent rearrest among adult offenders within 4 y of release (N = 96). The odds that an offender with relatively low anterior cingulate activity would be rearrested were approximately double that of an offender with high activity in this region, holding constant other observed risk factors. These results suggest a potential neurocognitive biomarker for persistent antisocial behavior.R isk assessment is a major component of criminal justice and treatment decisions. One crucial application of such predictions is the ability to identify, manage, and remediate antisocial behavior. Decisions that rely on antisocial risk prediction pervade the justice system, beginning with recommendations for bail, jail, and probation to sentencing, civil commitment, parole decisions, diversion, and treatment program assignments, to name a few. Initial attempts to predict future antisocial behavior based purely on clinicians' opinions have been shown to be highly inaccurate (1). Subsequent research that used evidence-based static (e.g., age, sex, criminal history) and dynamic (e.g., impulsivity, drug use, social support) risk factors have led to significant improvements in predicting future antisocial behavior (2-4).One of the strongest and most widely studied risk factors for recidivism is impulsivity, or behavioral disinhibition, the persistent lack of restraint and consideration of consequences (3). Risk assessments, personality tests, and neuropsychological measures have been used to assess impulsivity and have demonstrated the ability to predict future antisocial behavior. However, these latter measures serve only as proxies for direct measurement of the brain's inhibitory and cognitive control systems. Indeed, neuroscientists have suggested that endophenotypes carry the potential to characterize underlying traits and abnormalities independently of behavioral phenotypes (5). This stance has been supported by recent functional MRI (fMRI) studies that have, for instance, accurately predicted choices in a motor-decision task (6), substance abuse relapse (7-10), and consumer purchases (i.e., neuromarketing) (11). These results raise the possibility that more direct measures of brain activity associated with impulse control may lend incremental utility to the prediction of future antisocial behavior.The brain regions associated with impulse control have been well characterized. Consistent among these regions is the anterior cingulate cortex (ACC), a limbic region associated with error processing, conflict monitoring, response selection, and avoidance learning (12-16). Neurobiological models suggest that the ACC is central to an error-monitoring circuit wherein it relays error information from the basal ganglia and inferior fron...

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Section: Methodsmentioning

confidence: 99%

Neuroprediction of future rearrest

Aharoni

Vincent

Harenski

et al. 2013

Proc. Natl. Acad. Sci. U.S.A.

221

198

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“…A simulation (phantom) study was also undertaken to inform the results from the human study as well as to obtain absolute measures of registration and interpolation accuracy. In particular, the phantom data were used to obtain a data set with a known interplay between motion and the activation signal (see e.g., Freire and Mangin, 2001). The phantom and human data agree on several points, allowing us to bridge results from previous authors who used either phantom or human data but not both.…”

Section: Data Sourcesmentioning

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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“…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

153

122

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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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Motion Correction Algorithms May Create Spurious Brain Activations in the Absence of Subject Motion

Cited by 287 publications

References 32 publications

Neuroprediction of future rearrest

Neuroprediction of future rearrest

Comparison of fMRI motion correction software tools

A method for multitask fMRI data fusion applied to schizophrenia

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