Skip to main content Accessibility help
×
Home

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

  • Benjamin S. McKenna (a1) (a2), Rebecca J. Theilmann (a3), Ashley N. Sutherland (a4) and Lisa T. Eyler (a1) (a2)

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)

Copyright

Corresponding author

Correspondence and reprint requests to: Benjamin S McKenna, 3350 La Jolla Village Drive, San Diego, CA 92161 Mail Code: 151B. E-mail: bmckenna@ucsd.edu

References

Hide All
American Psychiatric Association (2000). Diagnostic and statistical manual of mental disorders: DSM-IV-TR. 4th Text Revision. Washington, DC: American Psychiatric Association Press.
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, 870888.
Basser, P. (2006). Diffusion-tensor MR imaging fundamentals. In R.R. Edelman (Ed.), Clinical magnetic resonance imaging (pp. 320332). Philadelphia: Elsevier.
Basser, P., & Jones, D. (2002). Diffusion-tensor MRI: Theory, experimental design and data analysis - A technical review. NMR in Biomedicine, 15, 456467.
Bearden, C.E., Hoffman, K.M., & Cannon, T.D. (2001). The neuropsychology and neuroanatomy of bipolar affective disorder: A critical review. Bipolar Disorders, 3, 106150.
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, 323333.
Bearden, C.E., Woogen, M., & Glahn, D.C. (2010). Neurocognitive and neuroimaging predictors of clinical outcome in bipolar disorder. Current Psychiatry Reports, 12, 499504.
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.
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, 473478.
Bunea, F., She, Y., Ombao, H., Gongvatana, A., Devlin, K., & Cohen, R. (2011). Penalized least squares regression methods and applications to neuroimaging. Neuroimage, 55, 15191527.
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, 4762.
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, 115.
Cox, R.W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29, 162173.
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, 735743.
Delis, D., Kaplan, E., & Kramer, J. (2001). Delis Kaplan Executive Function System. San Antonio: The Psychological Corporation.
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193222.
Dux, P., Ivanoff, J., Asplund, C., & Marois, R. (2006). Isolation of a central bottleneck of information processing with time-resolved FMRI. Neuron, 52, 11091120.
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004). Least angle regression. Annals of Statistics, 32, 407499.
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
Eyler, L., Sherzai, A., Kaup, A., & Jeste, D. (2011). A review of functional brain imaging correlates of successful cognitive aging. Biological Psychiatry, 70, 115122.
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, 636647.
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, 150154.
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, 375410.
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, 916927.
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, 588598.
Heng, S., Song, A., & Sim, K. (2010). White matter abnormalities in bipolar disorder: Insights from diffusion tensor imaging studies. Journal of Neural Transmission, 117, 639654.
Holland, D., Kuperman, J., & Dale, A. (2010). Efficient correction of inhomogeneous static magnetic field-induced distortion in Echo Planar Imaging. Neuroimage, 50, 175183.
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, 336347.
Kay, S., Fiszbein, A., & Opler, L. (1987). The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin, 13, 261276.
Kurtz, M., & Gerraty, R. (2009). A meta-analytic investigation of neurocognitive deficits in bipolar illness: Profile and effects of clinical state. Neuropsychology, 5, 551562.
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, 183193.
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, 908916.
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, 334342.
Meinshausen, N., & Yu, B. (2009). Lassoy-type recovery of sparse representations for highdimensional data. Annals of Statistics, 720, 246270.
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, 5760.
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, 243255.
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, 289299.
McNab, F., & Klingberg, T. (2008). Prefrontal cortex and basal ganglia control access to working memory. Nature Neuroscience, 11, 103107.
Noppeney, U., Friston, K.J., & Price, C.J. (2004). Degenerate neuronal systems sustaining cognitive functions. Journal of Anatomy, 205, 433442.
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, 4659.
Phillips, M., Drevets, W., Rauch, S., & Lane, R. (2003). Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biological Psychiatry, 54, 515528.
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, 833857.
Phillips, M., Travis, M., Fagiolini, A., & Kupfer, D. (2008). Medication effects in neuroimaging studies of bipolar disorder. American Journal of Psychiatry, 165, 313320.
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, 829843.
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, 11561165.
R Development Core Team (2009). A language and environment for statistical computing. Austria: R Foundation for Statistical Computing.
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, 65586563.
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, 839848.
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, 113.
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, 2233.
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, 14871505.
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, 208219.
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.
Strakowski, S., DelBello, M., & Adler, C. (2005). The functional neuroanatomy of bipolar disorder: A review of neuroimaging findings. Molecular Psychiatry, 10, 105116.
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, 313325.
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, 839855.
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, 34663473.
Talairach, J., & Tournoux, P. (1988). Co-Planar stereotaxic atlas of the human brain. New York: Thieme Medical.
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, 2229.
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, 19.
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, 516521.
Young, R., Biggs, J., Ziegler, V., & Meyer, D. (1978). A rating scale for mania: Reliability, validity and sensitivity. British Journal of Psychiatry, 133, 429435.
Zhang, C., & Huang, J. (2008). The sparsity and bias of the LASSO selection in high dimensional linear regression. Annals of Statistics, 36, 15671594.

Keywords

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

  • Benjamin S. McKenna (a1) (a2), Rebecca J. Theilmann (a3), Ashley N. Sutherland (a4) and Lisa T. Eyler (a1) (a2)

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed.