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Assessment and quantification of head motion in neuropsychiatric functional imaging research as applied to schizophrenia

Published online by Cambridge University Press:  14 August 2007

ANDREW R. MAYER ,
ALEXANDRE R. FRANCO ,
JOSEF LING  and
JOSE M. CAÑIVE
ANDREW R. MAYER
Affiliation:
The MIND Institute, Albuquerque, New Mexico Neurology Department, University of New Mexico School of Medicine, Albuquerque, New Mexico
ALEXANDRE R. FRANCO
Affiliation:
The MIND Institute, Albuquerque, New Mexico Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
JOSEF LING
Affiliation:
The MIND Institute, Albuquerque, New Mexico
JOSE M. CAÑIVE
Affiliation:
Center for Functional Brain Imaging, New Mexico VA Health Care System, and Departments of Psychiatry and Neurosciences, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
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Abstract

Differing degrees of head motion have long been recognized as a potential confound in functional neuroimaging studies comparing neuropsychiatric populations to healthy normal volunteers, and studies often cite excessive head motion as a possible reason for the different patterns of functional activation frequently observed between groups. We empirically tested the degree of head motion in 16 patients with chronic schizophrenia and 16, age- and education-matched controls during the acquisition of functional magnetic resonance imaging data. We examined the degree of motion across three different indices (total motion, relative motion, task-correlated motion) during a complex attentional task and the effect of entering the motion parameters as additional regressors in a general linear model analysis. Results indicate that individuals with schizophrenia did not exhibit more task-correlated or total motion compared with controls. Moreover, the residual error term from the general linear model analysis was similar for both groups of subjects. In conclusion, current results suggest that stable patients with schizophrenia are capable of controlling head motion compared with matched normal controls. However, a direct comparison of the motion parameters is an essential step for any quality assurance protocol to determine whether additional corrective techniques need to be implemented. (JINS, 2007, 13, 839–845.)

Keywords

fMRIMotionArtifactNeuropsychiatricSchizophreniaQuality assurance
Type
Research Article
Information
Journal of the International Neuropsychological Society , Volume 13 , Issue 5 , September 2007 , pp. 839 - 845
Copyright
2007 The International Neuropsychological Society

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References

REFERENCES

Ardekani, B.A., Bachman, A.H., & Helpern, J.A. (2001). A quantitative comparison of motion detection algorithms in fMRI. Magnetic Resonance Imaging, 19, 959–963.Google Scholar
Bullmore, E.T., Brammer, M.J., Rabe-Hesketh, S., Curtis, V.A., Morris, R.G., Williams, S.C., Sharma, T., & McGuire, P.K. (1999). Methods for diagnosis and treatment of stimulus-correlated motion in generic brain activation studies using fMRI. Human Brain Mapping, 7, 38–48.Google Scholar
Bullmore, E., Brammer, M., Williams, S.C., Rabe-Hesketh, S., Janot, N., David, A., Mellers, J., Howard, R., & Sham, P. (1996). Statistical methods of estimation and inference for functional MR image analysis. Magnetic Resonance in Medicine, 35, 261–277.Google Scholar
Callicott, J.H., Bertolino, A., Mattay, V.S., Langheim, F.J., Duyn, J., Coppola, R., Goldberg, T.E., & Weinberger, D.R. (2000). Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cerebral Cortex, 10, 1078–1092.Google Scholar
Callicott, J.H., Ramsey, N.F., Tallent, K., Bertolino, A., Knable, M.B., Coppola, R., Goldberg, T., van Gelderen, P., Mattay, V.S., Frank, J.A., Moonen, C.T., & Weinberger, D.R. (1998). Functional magnetic resonance imaging brain mapping in psychiatry: Methodological issues illustrated in a study of working memory in schizophrenia. Neuropsychopharmacology, 18, 186–196.Google Scholar
Callicott, J.H. & Weinberger, D.R. (1999). Neuropsychiatric dynamics: The study of mental illness using functional magnetic resonance imaging. European Journal of Radiology, 30, 95–104.Google Scholar
Callicott, J.H. & Weinberger, D.R. (2003). Brain imaging as an approach to phenotype characterization for genetic studies of schizophrenia. Methods in Molecular Medicine, 77, 227–247.Google Scholar
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, an International Journal, 29, 162–173.Google Scholar
Cox, R.W. & Jesmanowicz, A. (1999). Real-time 3D image registration for functional MRI. Magnetic Resonance in Medicine, 42, 1014–1018.Google Scholar
Davidson, L.L. & Heinrichs, R.W. (2003). Quantification of frontal and temporal lobe brain-imaging findings in schizophrenia: A meta-analysis. Psychiatry Research, 122, 69–87.Google Scholar
Eberhard, J., Lindstrom, E., & Levander, S. (2006). Tardive dyskinesia and antipsychotics: A 5-year longitudinal study of frequency, correlates and course. International Clinical Psychopharmacology, 21, 35–42.Google Scholar
Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S., & Turner, R. (1996). Movement-related effects in fMRI time-series. Magnetic Resonance in Medicine, 35, 346–355.Google Scholar
Glahn, D.C., Ragland, J.D., Abramoff, A., Barrett, J., Laird, A.R., Bearden, C.E., & Velligan, D.I. (2005). Beyond hypofrontality: A quantitative meta-analysis of functional neuroimaging studies of working memory in schizophrenia. Human Brain Mapping, 25, 60–69.Google Scholar
Gupta, A., Elheis, M., & Pansari, K. (2004). Imaging in psychiatric illnesses. International Journal of Clinical Practice, 58, 850–858.Google Scholar
Hajnal, J.V., Myers, R., Oatridge, A., Schwieso, J.E., Young, I.R., & Bydder, G.M. (1994). Artifacts due to stimulus correlated motion in functional imaging of the brain. Magnetic Resonance in Medicine, 31, 283–291.Google Scholar
Johnstone, T., Ores Walsh, K.S., Greischar, L.L., Alexander, A.L., Fox, A.S., Davidson, R.J., & Oakes, T.R. (2006). Motion correction and the use of motion covariates in multiple-subject fMRI analysis. Human Brain Mapping, 27, 779–788.Google Scholar
Kim, B., Boes, J.L., Bland, P.H., Chenevert, T.L., & Meyer, C.R. (1999). Motion correction in fMRI via registration of individual slices into an anatomical volume. Magnetic Resonance in Medicine, 41, 964–972.Google Scholar
Kindermann, S.S., Brown, G.G., Zorrilla, L.E., Olsen, R.K., & Jeste, D.V. (2004). Spatial working memory among middle-aged and older patients with schizophrenia and volunteers using fMRI. Schizophrenia Research, 68, 203–216.Google Scholar
Lund, T.E., Norgaard, M.D., Rostrup, E., Rowe, J.B., & Paulson, O.B. (2005). Motion or activity: Their role in intra- and inter-subject variation in fMRI. Neuroimage, 26, 960–964.Google Scholar
Manoach, D.S., Halpern, E.F., Kramer, T.S., Chang, Y., Goff, D.C., Rauch, S.L., Kennedy, D.N., & Gollub, R.L. (2001). Test-retest reliability of a functional MRI working memory paradigm in normal and schizophrenic subjects. The American Journal of Psychiatry, 158, 955–958.Google Scholar
McDowell, J.E., Brown, G.G., Paulus, M., Martinez, A., Stewart, S.E., Dubowitz, D.J., & Braff, D.L. (2002). Neural correlates of refixation saccades and antisaccades in normal and schizophrenia subjects. Biological Psychiatry, 51, 216–223.Google Scholar
Oakes, T.R., Johnstone, T., Ores Walsh, K.S., Greischar, L.L., Alexander, A.L., Fox, A.S., & Davidson, R.J. (2005). Comparison of fMRI motion correction software tools. Neuroimage, 28, 529–543.Google Scholar
Oldfield, R.C. (1971). The assessment and analysis of handedness: The Edinburgh inventory. Cognitive Neuropsychology, 9, 97–113.Google Scholar
Quintana, J., Davidson, T., Kovalik, E., Marder, S.R., & Mazziotta, J.C. (2001). A compensatory mirror cortical mechanism for facial affect processing in schizophrenia. Neuropsychopharmacology, 25, 915–924.Google Scholar
Rowe, J.B. & Passingham, R.E. (2001). Working memory for location and time: Activity in prefrontal area 46 relates to selection rather than maintenance in memory. Neuroimage, 14, 77–86.Google Scholar
Salek-haddadi, A., Lemieux, L., Merschhemke, M., Friston, K.J., Duncan, J.S., & Fish, D.R. (2003). Functional magnetic resonance imaging of human absence seizures. Annals of Neurology, 53, 663–667.Google Scholar
Seto, E., Sela, G., McIlroy, W.E., Black, S.E., Staines, W.R., Bronskill, M.J., McIntosh, A.R., & Graham, S.J. (2001). Quantifying head motion associated with motor tasks used in fMRI. Neuroimage, 14, 284–297.Google Scholar
Speck, O., Hennig, J., & Zaitsev, M. (2006). Prospective real-time slice-by-slice motion correction for fMRI in freely moving subjects. Magnetic Resonance Materials in Physics, Biology and Medicine, 19, 55–61.Google Scholar
Talairach, J. & Tournoux, P. (1988). Co-planar stereotaxic atlas of the human brain. New York: Thieme.
Tremblay, M., Tam, F., & Graham, S.J. (2005). Retrospective coregistration of functional magnetic resonance imaging data using external monitoring. Magnetic Resonance in Medicine, 53, 141–149.Google Scholar
Weinberger, D.R., Mattay, V., Callicott, J., Kotrla, K., Santha, A., van Gelderen, P., Duyn, J., Moonen, C., & Frank, J. (1996). fMRI applications in schizophrenia research. Neuroimage, 4, S118–S126.Google Scholar
Yang, S., Ross, T.J., Zhang, Y., Stein, E.A., & Yang, Y. (2005). Head motion suppression using real-time feedback of motion information and its effects on task performance in fMRI. Neuroimage, 27, 153–162.Google Scholar
Yoo, S.S., Choi, B.G., Juh, R., Pae, C.U., & Lee, C.U. (2005). Head motion analysis during cognitive fMRI examination: Application in patients with schizophrenia. Neuroscience Research, 53, 84–90.Google Scholar