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Aberrant topographical organization in gray matter structural network in late life depression: a graph theoretical analysis

Published online by Cambridge University Press:  07 October 2013

Hyun Kook Lim ,
Won Sang Jung  and
Howard J Aizenstein
Hyun Kook Lim
Affiliation:
Department of Psychiatry, The Saint Vincent Hospital, The Catholic University of Korea, Suwon, South Korean Departments of Psychiatry and Bioengineering, the University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Won Sang Jung
Affiliation:
Department of Radiology, The Saint Vincent Hospital, The Catholic University of Korea, Suwon, South Korean
Howard J Aizenstein*
Affiliation:
Departments of Psychiatry and Bioengineering, the University of Pittsburgh, Pittsburgh, Pennsylvania, USA
*
Correspondence should be addressed to: Dr Howard J Aizenstein, MD, PhD, Departments of Psychiatry and Bioengineering, the University of Pittsburgh, Pittsburgh, Pennsylvania, USA. Phone: +1-412-246-5464. Email: haizenstein@gmail.com.
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Abstract

Background:

Although previous studies on late life depression (LLD) have shown morphological abnormalities in frontal–striatal–temporal areas, alterations in coordinated patterns of structural brain networks in LLD are still poorly understood. The aim of this study was to investigate differences in gray matter structural brain network between LLD and healthy controls.

Methods:

We used gray matter volume measurement from magnetic resonance imaging to investigate large-scale structural brain networks in 37 LLD patients and 40 normal controls. Brain networks were constructed by thresholding gray matter volume correlation matrices of 90 regions and analyzed using graph theoretical approaches.

Results:

Although both LLD and control groups showed a small-world organization of group networks, there were no differences in the clustering coefficient, the path length, and the small-world index across a wide range of network density. Compared with controls, LLD patients showed decreased nodal betweenness in the medial orbitofrontal and angular gyrus regions. In addition, LLD patients showed hub regions in superior temporal gyrus and middle cingulate gyrus, and putamen. On the other hand, the control group showed hub regions in the medial orbitofrontal gyrus, middle cingulate gyrus, and cuneus.

Conclusion:

Our findings suggest that the gray matter structural networks are not globally but regionally altered in LLD patients. This multivariate structural analysis using graph theory might provide a more appropriate paradigm for understanding complicated neurobiological mechanism of LLD.

Keywords

brain networklate life depressiongraph theorygray matter volumes
Type
2013 IPA Junior Research Awards – Second Prize Winner
Information
International Psychogeriatrics , Volume 25 , Issue 12 , December 2013 , pp. 1929 - 1940
Copyright
Copyright © International Psychogeriatric Association 2013 

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References

Achard, S. and Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3, e17–17.CrossRefGoogle ScholarPubMed
Achard, S., Salvador, R., Whitcher, B., Suckling, J. and Bullmore, E. (2006). A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 26, 63–72.CrossRefGoogle ScholarPubMed
Alberti, K. G., Zimmet, P. and Shaw, J. (2005). The metabolic syndrome a new worldwide definition. Lancet, 366, 1059–1062.CrossRefGoogle ScholarPubMed
Ballmaier, M.et al. (2004). Anterior cingulate, gyrus rectus, and orbitofrontal abnormalities in elderly depressed patients: an MRI-based parcellation of the prefrontal cortex. American Journal of Psychiatry, 161, 99–108.CrossRefGoogle ScholarPubMed
Bassett, D. and Bullmore, E. (2006). Small-world brain networks. Neuroscientist, 12, 512–523.CrossRefGoogle ScholarPubMed
Bassett, D., Bullmore, E., Verchinski, B., Mattay, V., Weinberger, D. and Meyer Lindenberg, A. (2008). Hierarchical organization of human cortical networks in health and schizophrenia. Journal of Neuroscience, 28, 9239–9248.CrossRefGoogle ScholarPubMed
Bernhardt, B.et al. (2008). Mapping limbic network organization in temporal lobe epilepsy using morphometric correlations: insights on the relation between mesiotemporal connectivity and cortical atrophy. NeuroImage, 42, 515–524.CrossRefGoogle ScholarPubMed
Bernhardt, B., Chen, Z., He, Y., Evans, A. and Bernasconi, N. (2011). Graph-theoretical analysis reveals disrupted small-world organization of cortical thickness correlation networks in temporal lobe epilepsy. Cerebral Cortex, 21, 2147–2157.CrossRefGoogle ScholarPubMed
Butters, M.et al. (2008). Pathways linking late-life depression to persistent cognitive impairment and dementia. Dialogues in Clinical Neuroscience, 10, 345–357.CrossRefGoogle ScholarPubMed
Chen, Z., He, Y., Rosa Neto, P., Germann, J. and Evans, A. (2008). Revealing modular architecture of human brain structural networks by using cortical thickness from MRI. Cerebral Cortex, 18, 2374–2381.CrossRefGoogle ScholarPubMed
Chobanian, A. V.et al. (2013). The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA, 289, 2560–2572.CrossRefGoogle Scholar
Crocco, E. A., Castro, K. and Loewenstein, D. A. (2010). How late-life depression affects cognition: neural mechanisms. Current Psychiatry Reports, 12, 34–38.CrossRefGoogle ScholarPubMed
Duman, R. S. and Monteggia, L. M. (2006). A neurotrophic model for stress-related mood disorders. Biological Psychiatry, 59, 1116–1127.CrossRefGoogle ScholarPubMed
Edelman, G. M. and Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of National Academy of Sciences of United States of America, 98, 13763–13768.CrossRefGoogle ScholarPubMed
Gong, G.et al. (2009). Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex, 19, 524–536.CrossRefGoogle ScholarPubMed
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J. and Frackowiak, R. S. (2001). A voxel-based morphometric study of aging in 465 normal adult human brains. NeuroImage, 14, 21–36.CrossRefGoogle ScholarPubMed
Grundy, S. M.et al. (2005). Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation, 112, 2735–2752.CrossRefGoogle ScholarPubMed
Hagmann, P.et al. (2008). Mapping the structural core of human cerebral cortex. PLoS Biology, 6, e159–e159.CrossRefGoogle ScholarPubMed
Hamilton, M. (1967). Development of a rating scale for primary depressive illness. British Journal of Social and Clinical Psychology, 6, 278–296.CrossRefGoogle ScholarPubMed
He, Y., Chen, Z. and Evans, A. (2007). Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cerebral Cortex, 17, 2407–2419.CrossRefGoogle ScholarPubMed
He, Y., Chen, Z. and Evans, A. (2008). Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. Journal of Neuroscience, 28, 4756–4766.CrossRefGoogle ScholarPubMed
Hosseini, S. M. H., Hoeft, F. and Kesler, S. (2012). GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS ONE, 7, e40709–e40709.CrossRefGoogle Scholar
Huang, J., Friedland, R. P. and Auchus, A. P. (2007). Diffusion tensor imaging of normal-appearing white matter in mild cognitive impairment and early Alzheimer's disease: preliminary evidence of axonal degeneration in the temporal lobe. American Journal of Neuroradiology, 28, 1943–1948.CrossRefGoogle ScholarPubMed
Hutton, C., Draganski, B., Ashburner, J. and Weiskopf, N. (2009). A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. NeuroImage, 48, 371–380.CrossRefGoogle ScholarPubMed
Kaiser, M. and Hilgetag, C. (2006). Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Computational Biology, 2, e95. doi:10.1371/journal.pcbi.0020095.CrossRefGoogle ScholarPubMed
Lavretsky, H., Roybal, D. J., Ballmaier, M., Toga, A. W. and Kumar, A. (2005). Antidepressant exposure may protect against decrement in frontal gray matter volumes in geriatric depression. Journal of Clinical Psychiatry, 66, 964–967.CrossRefGoogle ScholarPubMed
Lerch, J.et al. (2006). Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. NeuroImage, 31, 993–1003.CrossRefGoogle ScholarPubMed
Lim, H. K.et al. (2012). Regional cortical thickness and subcortical volume changes are associated with cognitive impairments in the drug-naive patients with late-onset depression. Neuropsychopharmacology, 37, 838–849.CrossRefGoogle ScholarPubMed
Maslov, S. and Sneppen, K. (2002). Specificity and stability in topology of protein networks. Science, 296, 910–913.CrossRefGoogle ScholarPubMed
Mitelman, S., Buchsbaum, M., Brickman, A. and Shihabuddin, L. (2005). Cortical intercorrelations of frontal area volumes in schizophrenia. NeuroImage, 27, 753–770.CrossRefGoogle ScholarPubMed
Morris, J. C. (1993). The Clinical Dementia Rating (CDR): current version and scoring rules. Neurology, 43, 2412–2414.CrossRefGoogle ScholarPubMed
Ongr, D. and Price, J. L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cerebral Cortex, 10, 206–219.CrossRefGoogle Scholar
Park, J. and Kwon, Y. (1990). Modification of the Mini-Mental State Examination for use in the elderly in a non-western society: part I. Development of Korean version of Mini-Mental State Examination. International Journal of Geriatric Psychiatry, 5, 381–387.CrossRefGoogle Scholar
Rubinov, M. and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52, 1059–1069.CrossRefGoogle ScholarPubMed
Seghier, M. (2013). The angular gyrus: multiple functions and multiple subdivisions. Neuroscientist, 19, 43–61.CrossRefGoogle ScholarPubMed
Sheehan, D. V.et al. (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 (Suppl 20), 22–33; quiz 34–57.Google ScholarPubMed
Sporns, O. (2011). Networks of the Brain. Camrbridge, MA: MIT Press.Google Scholar
Sporns, O. and Zwi, J. (2004). The small world of the cerebral cortex. Neuroinformatics, 2, 145–162.CrossRefGoogle ScholarPubMed
Tononi, G., Sporns, O. and Edelman, G. M. (1999). Measures of degeneracy and redundancy in biological networks. Proceedings of National Academy of Sciences of United States of America, 96, 3257–3262.CrossRefGoogle ScholarPubMed
Tzourio Mazoyer, N.et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.CrossRefGoogle ScholarPubMed
Yao, Z., Zhang, Y., Lin, L., Zhou, Y., Xu, C. and Jiang, T. (2010). Abnormal cortical networks in mild cognitive impairment and Alzheimer's disease. PLoS Computational Biology, 6, e1001006.CrossRefGoogle ScholarPubMed
Zhang, K. and Sejnowski, T. J. (2000). A universal scaling law between gray matter and white matter of cerebral cortex. Proceedings of National Academy of Sciences of United States of America, 97, 5621–5626.CrossRefGoogle ScholarPubMed