Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-17T05:18:48.894Z Has data issue: false hasContentIssue false
  • English
  • Français

Fusing Functional MRI and Diffusion Tensor Imaging Measures of Brain Function and Structure to Predict Working Memory and Processing Speed Performance among Inter-episode Bipolar Patients

Published online by Cambridge University Press:  03 June 2015

Benjamin S. McKenna ,
Rebecca J. Theilmann ,
Ashley N. Sutherland  and
Lisa T. Eyler
Benjamin S. McKenna*
Affiliation:
VISN-22 Mental Illness Research, Education, and Clinical Center, Veterans Affairs Healthcare System, San Diego, California Department of Psychiatry, University of California, San Diego, California
Rebecca J. Theilmann
Affiliation:
Department of Radiology, University of California, San Diego, California
Ashley N. Sutherland
Affiliation:
Veterans Medical Research Foundation, San Diego, California
Lisa T. Eyler
Affiliation:
VISN-22 Mental Illness Research, Education, and Clinical Center, Veterans Affairs Healthcare System, San Diego, California Department of Psychiatry, University of California, San Diego, California
*
Correspondence and reprint requests to: Benjamin S McKenna, 3350 La Jolla Village Drive, San Diego, CA 92161 Mail Code: 151B. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Evidence for abnormal brain function as measured with diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI) and cognitive dysfunction have been observed in inter-episode bipolar disorder (BD) patients. We aimed to create a joint statistical model of white matter integrity and functional response measures in explaining differences in working memory and processing speed among BD patients. Medicated inter-episode BD (n=26; age=45.2±10.1 years) and healthy comparison (HC; n=36; age=46.3±11.5 years) participants completed 51-direction DTI and fMRI while performing a working memory task. Participants also completed a processing speed test. Tract-based spatial statistics identified common white matter tracts where fractional anisotropy was calculated from atlas-defined regions of interest. Brain responses within regions of interest activation clusters were also calculated. Least angle regression was used to fuse fMRI and DTI data to select the best joint neuroimaging predictors of cognitive performance for each group. While there was overlap between groups in which regions were most related to cognitive performance, some relationships differed between groups. For working memory accuracy, BD-specific predictors included bilateral dorsolateral prefrontal cortex from fMRI, splenium of the corpus callosum, left uncinate fasciculus, and bilateral superior longitudinal fasciculi from DTI. For processing speed, the genu and splenium of the corpus callosum and right superior longitudinal fasciculus from DTI were significant predictors of cognitive performance selectively for BD patients. BD patients demonstrated unique brain-cognition relationships compared to HC. These findings are a first step in discovering how interactions of structural and functional brain abnormalities contribute to cognitive impairments in BD. (JINS, 2015, 21, 330–341)

Keywords

Bipolar disorderFunctional magnetic resonance imagingDiffusion tensor imagingWorking memoryProcessing speedNeuropsychology
Type
Research Articles
Information
Journal of the International Neuropsychological Society , Volume 21 , Issue 5 , May 2015 , pp. 330 - 341
Copyright
Copyright © The International Neuropsychological Society 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR. 4th Text Revision. Washington, DC: American Psychiatric Association Press.Google Scholar
Andersson, J., Skare, S., & Ashburner, J. (2003). How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging. Neuroimage, 20, 870–888.CrossRefGoogle ScholarPubMed
Basser, P. (2006). Diffusion-tensor MR imaging fundamentals. In R.R. Edelman (Ed.), Clinical magnetic resonance imaging (pp. 320–332). Philadelphia: Elsevier.Google Scholar
Basser, P., & Jones, D. (2002). Diffusion-tensor MRI: Theory, experimental design and data analysis - A technical review. NMR in Biomedicine, 15, 456–467.CrossRefGoogle ScholarPubMed
Bearden, C.E., Hoffman, K.M., & Cannon, T.D. (2001). The neuropsychology and neuroanatomy of bipolar affective disorder: A critical review. Bipolar Disorders, 3, 106–150.CrossRefGoogle ScholarPubMed
Bearden, C.E., Shih, V., Green, M., Gitlin, M., Sokolski, K., Levander, E., & Altshuler, L.L. (2011). The impact of neurocognitive impairment on occupational recovery of clinically stable patients with bipolar disorder: A prospective study. Bipolar Disorders, 13, 323–333.CrossRefGoogle ScholarPubMed
Bearden, C.E., Woogen, M., & Glahn, D.C. (2010). Neurocognitive and neuroimaging predictors of clinical outcome in bipolar disorder. Current Psychiatry Reports, 12, 499–504.CrossRefGoogle ScholarPubMed
Biesbroek, J., Kuijf, H., van der Graaf, Y., Vincken, K., Postma, A., Mali, W., … SMART Study Group (2013). Association between subcortical vascular lesion location and cognition: A voxel-based and tract-based lesion-symptom mapping study. The SMART-MR study. PLoS One, 8, e60541.CrossRefGoogle Scholar
Brebion, G., Stephan-Otto, C., Huerta-Ramos, E., Usall, J., Perez del Olmo, M., Contel, M., & Ochoa, S. (2014). Decreased processing speed might account for working memory span deficit in schizophrenia, and might mediate the associations between working memory span and clinical symptoms. European Psychiatry, 29, 473–478.CrossRefGoogle ScholarPubMed
Bunea, F., She, Y., Ombao, H., Gongvatana, A., Devlin, K., & Cohen, R. (2011). Penalized least squares regression methods and applications to neuroimaging. Neuroimage, 55, 1519–1527.CrossRefGoogle Scholar
Calhoun, V., Adali, T., Giuliani, N., Pekar, J., Kiehl, K., & Pearlson, G. (2006). Method for multimodal analysis of independent source differences in schizophrenia: Combining gray matter structural and auditory oddball functional data. Human Brain Mapping, 27, 47–62.CrossRefGoogle ScholarPubMed
Chen, C.-H., Suckling, J., Lennox, B., Ooi, C., & Bullmore, E. (2011). A quantitative meta-analysis of fMRI studies in bipolar disorder. Bipolar Disorders, 13, 1–15.CrossRefGoogle ScholarPubMed
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162–173.CrossRefGoogle ScholarPubMed
Delaloye, C., Moy, G., Baudois, S., de Bilbao, F., Remund, C., Hofer, F., & Giannakopoulos, P. (2009). Cognitive features in euthymic bipolar patients in old age. Bipolar Disorders, 11, 735–743.CrossRefGoogle ScholarPubMed
Delis, D., Kaplan, E., & Kramer, J. (2001). Delis Kaplan Executive Function System. San Antonio: The Psychological Corporation.Google Scholar
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222.CrossRefGoogle ScholarPubMed
Dux, P., Ivanoff, J., Asplund, C., & Marois, R. (2006). Isolation of a central bottleneck of information processing with time-resolved FMRI. Neuron, 52, 1109–1120.CrossRefGoogle ScholarPubMed
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407–499.CrossRefGoogle Scholar
Emsell, L., Chaddock, C., Forde, N., Van Hecke, W., Barker, G., Leemans, A., & McDonald, C. (2013). White matter microstructural abnormalities in families multiply affected with bipolar I disorder: A diffusion tensor tractography study. Psychological Medicine [Epub ahead of print] doi:10.1017/S0033291713002845 Google ScholarPubMed
Eyler, L., Sherzai, A., Kaup, A., & Jeste, D. (2011). A review of functional brain imaging correlates of successful cognitive aging. Biological Psychiatry, 70, 115–122.CrossRefGoogle ScholarPubMed
Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., & Noll, D.C. (1995). Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): Use of a cluster-size threshold. Magnetic Resonance in Medicine, 33, 636–647.CrossRefGoogle ScholarPubMed
Ha, T., Her, J., Kim, J., Chang, J., Cho, H., & Ha, K. (2011). Similarities and differences of white matter connectivity and water diffusivity in bipolar I and II disorder. Neuroscience Letters, 505, 150–154.CrossRefGoogle ScholarPubMed
Hafeman, D., Chang, K., Garrett, A., Sanders, E., & Phillips, M. (2012). Effects of medication on neuroimaging findings in bipolar disorder: An updated review. Bipolar Disorders, 14, 375–410.CrossRefGoogle ScholarPubMed
Hassel, S., Almeida, J., Kerr, N., Nau, S., Ladouceur, C., Fissell, K., & Phillips, M. (2008). Elevated striatal and decreased dorsolateral prefrontal cortical activity in response to emotional stimuli in euthymic bipolar disorder: No associations with psychotropic medication load. Bipolar Disorders, 10, 916–927.CrossRefGoogle ScholarPubMed
Heaton, R., Akshoomoff, N., Tulsky, D., Mungas, D., Weintraub, S., Dikmen, S., & Gershon, R. (2014). Reliability and validity of composite scores from the NIH Toolbox Cognition Battery in Adults. Journal of the International Neuropsychological Society, 20, 588–598.CrossRefGoogle ScholarPubMed
Heng, S., Song, A., & Sim, K. (2010). White matter abnormalities in bipolar disorder: Insights from diffusion tensor imaging studies. Journal of Neural Transmission, 117, 639–654.CrossRefGoogle ScholarPubMed
Holland, D., Kuperman, J., & Dale, A. (2010). Efficient correction of inhomogeneous static magnetic field-induced distortion in Echo Planar Imaging. Neuroimage, 50, 175–183.CrossRefGoogle ScholarPubMed
Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D., & Mori, S. (2008). Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification. Neuroimage, 39, 336–347.CrossRefGoogle ScholarPubMed
Kay, S., Fiszbein, A., & Opler, L. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13, 261–276.CrossRefGoogle ScholarPubMed
Kurtz, M., & Gerraty, R. (2009). A meta-analytic investigation of neurocognitive deficits in bipolar illness: Profile and effects of clinical state. Neuropsychology, 5, 551–562.CrossRefGoogle Scholar
Leow, A., Ajilore, O., Zhan, L., Arienzo, D., GadElkarim, J., Zhang, A., & Altshuler, L. (2013). Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses. Biological Psychiatry, 73, 183–193.CrossRefGoogle ScholarPubMed
Linke, J., King, A., Poupon, C., Hennerici, M., Gass, A., & Wessa, M. (2013). Impaired anatomical connectivity and related executive functions: Differentiating vulnerability and disease marker in bipolar disorder. Biological Psychiatry, 74, 908–916.CrossRefGoogle ScholarPubMed
Mann-Wrobel, M., Carreno, J., & Dickinson, D. (2011). Meta-analysis of neuropsychological functioning in euthymic bipolar disorder: An update and investigation of moderator variables. Bipolar Disorders, 13, 334–342.CrossRefGoogle ScholarPubMed
Meinshausen, N., & Yu, B. (2009). Lassoy-type recovery of sparse representations for highdimensional data. Annals of Statistics, 720, 246–270.Google Scholar
McCarley, R., Nakamura, M., Shenton, M., & Salisbury, D. (2008). Combining ERP and structural MRI information in first episode schizophrenia and bipolar disorder. Clinical EEG and Neuroscience, 39, 57–60.CrossRefGoogle ScholarPubMed
McKenna, B.S., Brown, G., Drummond, S.P.A., Turner, T., & Mano, Q. (2013). Linking mathematical modeling with human neuroimaging to segregate verbal working memory maintenance processes from stimulus encoding. Neuropsychology, 27, 243–255.CrossRefGoogle ScholarPubMed
McKenna, B.S., Sutherland, A., Legenkaya, A., & Eyler, L. (2014). Abnormalities of brain response during encoding in verbal working memory among euthymic patients with bipolar disorder. Bipolar Disorders, 16, 289–299.CrossRefGoogle ScholarPubMed
McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11, 103–107.CrossRefGoogle ScholarPubMed
Noppeney, U., Friston, K.J., & Price, C.J. (2004). Degenerate neuronal systems sustaining cognitive functions. Journal of Anatomy, 205, 433–442.CrossRefGoogle ScholarPubMed
Owens, A., McMillan, K., Laird, A., & Bullmore, E. (2005). N-back working memory paradigm: A meta-analysis of normative functional neuroimaging studies. Human Brain Mapping, 25, 46–59.CrossRefGoogle Scholar
Phillips, M., Drevets, W., Rauch, S., & Lane, R. (2003). Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biological Psychiatry, 54, 515–528.CrossRefGoogle ScholarPubMed
Phillips, M., Ladouceur, C., & Drevets, W. (2008). A neural model of voluntary and automatic emotion regulation: Implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Molecular Psychiatry, 13, 833–857.CrossRefGoogle ScholarPubMed
Phillips, M., Travis, M., Fagiolini, A., & Kupfer, D. (2008). Medication effects in neuroimaging studies of bipolar disorder. American Journal of Psychiatry, 165, 313–320.CrossRefGoogle ScholarPubMed
Phillips, M., & Swartz, H. (2014). A critical appraisal of neuroimaging studies of bipolar disorder: Toward a new conceptualization of underlying neural circuitry and a road map for future research. American Journal of Psychiatry, 171, 829–843.CrossRefGoogle Scholar
Plis, S., Weisend, M., Damaraju, E., Eichele, T., Mayer, A., Clark, V., & Calhoun, V.D. (2011). Effective connectivity analysis of fMRI and MEG data collected under identical paradigms. Computers in Biology and Medicine, 41, 1156–1165.CrossRefGoogle ScholarPubMed
R Development Core Team (2009). A language and environment for statistical computing. Austria: R Foundation for Statistical Computing.Google Scholar
Rypma, B., & D’Esposito, M. (1999). The roles of prefrontal brain regions in components of working memory: Effects of memory load and individual differences. Proceedings of the National Academy of Sciences of the United States of America, 96, 6558–6563.CrossRefGoogle ScholarPubMed
Saad, Z.S., Glen, D.R., Chen, G., Beauchamp, M.S., Desai, R., & Cox, R.W. (2009). A new method for improving functional-to-structural MRI alignment using local Pearson correlation. Neuroimage, 44, 839–848.CrossRefGoogle ScholarPubMed
Sasson, E., Doniger, G., Pasternak, O., Tarrasch, R., & Assaf, Y. (2013). White matter correlates of cognitive domains in normal aging with diffusion tensor imaging. Frontiers in Neuroscience, 7, 1–13.CrossRefGoogle ScholarPubMed
Sheehan, D., Lecrubier, Y., Sheehan, K., Amorim, P., Janavs, J., Weiller, E., & Dunbar, G.C. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59, 22–33.Google ScholarPubMed
Smith, S., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T., Mackay, C., & Behrens, T.E. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. Neuroimage, 31, 1487–1505.CrossRefGoogle ScholarPubMed
Smith, S., Jenkinson, M., Woolrich, M., Beckmann, C., Behrens, T., Johansen-Berg, H., & Matthews, P.M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage, 23, 208–219.CrossRefGoogle ScholarPubMed
Spitzer, R., Williams, J., Gibbon, M., & First, M. (1995). Structured clinical interview for DSM-IV Patient Version (SCIDI/P, Version 2.0). New York: New York State Psychiatric Institute.Google Scholar
Strakowski, S., DelBello, M., & Adler, C. (2005). The functional neuroanatomy of bipolar disorder: A review of neuroimaging findings. Molecular Psychiatry, 10, 105–116.CrossRefGoogle ScholarPubMed
Strakowski, S., Adler, C., Almeida, J., Altshuler, L., Blumberg, H., Chang, K., & Townsend, J. (2012). The functional neuroanatomy of bipolar disorder: A consensus model. Bipolar Disorders, 14, 313–325.CrossRefGoogle ScholarPubMed
Sui, J., Pearlson, G., Caprihan, A., Adali, T., Kiehl, K., Liu, J., & Calhoun, V.D. (2011). Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model. Neuroimage, 57, 839–855.CrossRefGoogle Scholar
Takeuchi, H., Taki, Y., Sassa, Y., Hashizume, H., Sekiguchi, A., Fukushima, A., & Kawashima, R. (2011). Verbal working memory performance correlates with regional white matter structures in the frontoparietal regions. Neuropsychologia, 49, 3466–3473.CrossRefGoogle ScholarPubMed
Talairach, J., & Tournoux, P. (1988). Co-Planar stereotaxic atlas of the human brain. New York: Thieme Medical.Google Scholar
Townsend, J., Bookheimer, S., Foland-Ross, L., Sugar, C., & Altshuler, L. (2010). fMRI abnormalities in dorsolateral prefrontal cortex during a working memory task in manic, euthymic and depressed bipolar subjects. Psychiatric Research, 182, 22–29.CrossRefGoogle ScholarPubMed
Trajković, G., Starčević, V., Latas, M., Leštarević, M., Ille, T., Bukumirić, Z., & Marinković, J. (2011). Reliability of the Hamilton Rating Scale for Depression: A meta-analysis over a period of 49 years. Psychiatric Research, 189, 1–9.CrossRefGoogle Scholar
Wang, F., Kalmar, J., He, Y., Jackowski, M., Chepenik, L., Edmiston, E., & Blumberg, H.P. (2009). Functional and structural connectivity between the perigenual anterior cingulate and amygdala in bipolar disorder. Biological Psychiatry, 66, 516–521.CrossRefGoogle ScholarPubMed
Young, R., Biggs, J., Ziegler, V., & Meyer, D. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133, 429–435.CrossRefGoogle ScholarPubMed
Zhang, C., & Huang, J. (2008). The sparsity and bias of the LASSO selection in high dimensional linear regression. Annals of Statistics, 36, 1567–1594.CrossRefGoogle Scholar