Hostname: page-component-76fb5796d-dfsvx Total loading time: 0 Render date: 2024-04-27T04:10:14.479Z Has data issue: false hasContentIssue false
  • English
  • Français

Disrupted rich club organization and structural brain connectome in unmedicated bipolar disorder

Published online by Cambridge University Press:  08 May 2018

Ying Wang Open the ORCID record for Ying Wang [Opens in a new window] ,
Feng Deng ,
Yanbin Jia ,
Junjing Wang ,
Shuming Zhong ,
Huiyuan Huang ,
Lixiang Chen ,
Guanmao Chen ,
Huiqing Hu  and
Li Huang
...Show all authors
Ying Wang*
Affiliation:
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
Feng Deng
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
Yanbin Jia
Affiliation:
Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Junjing Wang
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
Shuming Zhong
Affiliation:
Department of Psychiatry, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Huiyuan Huang
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
Lixiang Chen
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
Guanmao Chen
Affiliation:
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China
Huiqing Hu
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
Li Huang
Affiliation:
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China
Ruiwang Huang*
Affiliation:
Center for the Study of Applied Psychology & MRI Center, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou 510631, China
*
Author for correspondence: Ying Wang, E-mail: johneil@vip.sina.com and Ruiwang Huang, E-mail: ruiwang.huang@gmail.com
Author for correspondence: Ying Wang, E-mail: johneil@vip.sina.com and Ruiwang Huang, E-mail: ruiwang.huang@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Bipolar disorder (BD) has been associated with altered brain structural and functional connectivity. However, little is known regarding alterations of the structural brain connectome in BD. The present study aimed to use diffusion-tensor imaging (DTI) and graph theory approaches to investigate the rich club organization and white matter structural connectome in BD.

Methods

Forty-two patients with unmedicated BD depression and 59 age-, sex- and handedness-matched healthy control participants underwent DTI. The whole-brain structural connectome was constructed by a deterministic fiber tracking approach. Graph theory analysis was used to examine the group-specific global and nodal topological properties, and rich club organizations, and then nonparametric permutation tests were used for group comparisons of network parameters.

Results

Compared with healthy control participants, the patients with BD showed abnormal global properties, including increased characteristic path length, and decreased global efficiency and local efficiency. Locally, the patients with BD showed abnormal nodal parameters (nodal strength, nodal efficiency, and nodal betweenness) predominantly in the parietal, orbitofrontal, occipital, and cerebellar regions. Moreover, the patients with BD showed decreased rich club and feeder connectivity density.

Conclusions

Our results may reflect the disrupted white matter topological organization in the whole-brain, and abnormal regional connectivity supporting cognitive and affective functioning in depressed BD, which, in part, be due to impaired rich club connectivity.

Keywords

Bipolar disorderdiffusion-tensor imaginggraph theoryrich club
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 3 , February 2019 , pp. 510 - 518
Copyright
Copyright © Cambridge University Press 2018 

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.)

Footnotes

*

These authors contributed equally to this work.

References

Abe, C et al. (2016) Cortical thickness, volume and surface area in patients with bipolar disorder types I and II. Journal of Psychiatry Neuroscience 41, 240–250.Google Scholar
Alonso-Lana, S et al. (2016) Brain functional changes in first-degree relatives of patients with bipolar disorder: evidence for default mode network dysfunction. Psychological Medicine 46, 2513–2521.Google Scholar
Bai, F et al. (2012) Topologically convergent and divergent structural connectivity patterns between patients with remitted geriatric depression and amnestic mild cognitive impairment. Journal of Neuroscience 32, 4307–4318.Google Scholar
Bassett, DS et al. (2009) Cognitive fitness of cost-efficient brain functional networks. Proceedings of the National Academy of Sciences of the United States of America 106, 11747–11752.Google Scholar
Bellani, M et al. (2016) DTI and myelin plasticity in bipolar disorder: integrating neuroimaging and neuropathological findings. Frontiers in Psychiatry 7, 21.Google Scholar
Belleau, EL, Taubitz, LE and Larson, CL (2015) Imbalance of default mode and regulatory networks during externally focused processing in depression. Social Cognitive and Affective Neuroscience 10, 744–751.Google Scholar
Calhoun, VD et al. (2011) Exploring the psychosis functional connectome: aberrant intrinsic networks in schizophrenia and bipolar disorder. Frontiers in Psychiatry 2, 75.Google Scholar
Cao, Q et al. (2013) Probabilistic diffusion tractography and graph theory analysis reveal abnormal white matter structural connectivity networks in drug-naive boys with attention deficit/hyperactivity disorder. Journal of Neuroscience 33, 10676–10687.Google Scholar
Collin, G et al. (2014) Impaired rich club connectivity in unaffected siblings of schizophrenia patients. Schizophrenia Bulletin 40, 438–448.Google Scholar
Collin, G et al. (2016) Brain network analysis reveals affected connectome structure in bipolar I disorder. Human Brain Mapping 37, 122–134.Google Scholar
Cui, Z et al. (2013) PANDA: a pipeline toolbox for analyzing brain diffusion images. Frontiers in Human Neuroscience 7, 42.Google Scholar
Daianu, M et al. (2016) Disrupted rich club network in behavioral variant frontotemporal dementia and early-onset Alzheimer's disease. Human Brain Mapping 37, 868–883.Google Scholar
Fisher, AC et al. (2015) Neurophysiological correlates of dysregulated emotional arousal in severe traumatic brain injury. Clinical Neurophysiology 126, 314–324.Google Scholar
Goya-Maldonado, R et al. (2016) Differentiating unipolar and bipolar depression by alterations in large-scale brain networks. Human Brain Mapping 37, 808–818.Google Scholar
Grande, I et al. (2016) Bipolar disorder. Lancet 387, 1561–1572.Google Scholar
Griffa, A et al. (2015) Characterizing the connectome in schizophrenia with diffusion spectrum imaging. Human Brain Mapping 36, 354–366.Google Scholar
Hafeman, DM et al. (2012) Effects of medication on neuroimaging findings in bipolar disorder: an updated review. Bipolar Disorders 14, 375–410.Google Scholar
Hanford, LC et al. (2016) Cortical thickness in bipolar disorder: a systematic review. Bipolar Disorders 18, 4–18.Google Scholar
Jung, WH et al. (2017) Altered functional network architecture in orbitofronto-striato-thalamic circuit of unmedicated patients with obsessive-compulsive disorder. Human Brain Mapping 38, 109–119.Google Scholar
Kaiser, RH et al. (2015) Large-scale network dysfunction in major depressive disorder: a meta-analysis of resting-state functional connectivity. JAMA Psychiatry 72, 603–611.Google Scholar
Kim, DJ et al. (2013) Disturbed resting state EEG synchronization in bipolar disorder: a graph-theoretic analysis. NeuroImage: Clinical 2, 414–423.Google Scholar
Klauser, P et al. (2017) White matter disruptions in schizophrenia Are spatially widespread and topologically converge on brain network hubs. Schizophrenia Bulletin 43, 425–435.Google Scholar
Korgaonkar, MS et al. (2014) Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biological Psychiatry 76, 567–574.Google Scholar
Leow, A et al. (2013) Impaired inter-hemispheric integration in bipolar disorder revealed with brain network analyses. Biological Psychiatry 73, 183–193.Google Scholar
Liang, X et al. (2017) The rich-club organization in Rat functional brain network to balance between communication cost and efficiency. Cerebral Cortex 28, 924–935.Google Scholar
Lo, CY et al. (2010) Diffusion tensor tractography reveals abnormal topological organization in structural cortical networks in Alzheimer's disease. Journal of Neuroscience 30, 16876–16885.Google Scholar
Lois, G et al. (2017) Large-scale network functional interactions during distraction and reappraisal in remitted bipolar and unipolar patients. Bipolar Disorders 19, 487–495.Google Scholar
Lynall, ME et al. (2010) Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience 30, 9477–9487.Google Scholar
Meng, C et al. (2014) Aberrant topology of striatum's connectivity is associated with the number of episodes in depression. Brain 137, 598–609.Google Scholar
Menon, V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends in Cognitive Sciences 15, 483–506.Google Scholar
Mori, S and Zijl, P (2002) Fiber tracking: principles and strategies – a technical review. NMR in Biomedicine 15, 468–480.Google Scholar
O'Donoghue, S et al. (2015) Applying neuroimaging to detect neuroanatomical dysconnectivity in psychosis. Epidemiology and Psychiatric Sciences 24, 298–302.Google Scholar
O'Donoghue, S et al. (2017 a) Anatomical dysconnectivity in bipolar disorder compared with schizophrenia: a selective review of structural network analyses using diffusion MRI. Journal of Affective Disorders 209, 217–228.Google Scholar
O'Donoghue, S et al. (2017 b) Anatomical integration and rich-club connectivity in euthymic bipolar disorder. Psychological Medicine 47, 1609–1623.Google Scholar
Poldrack, RA, Mumford, JA and Nichols, TE (2011) Handbook of Functional MRI Data Analysis. New York, NY: Cambridge University Press.Google Scholar
Puetz, VB et al. (2017) Altered brain network integrity after childhood maltreatment: a structural connectomic DTI-study. Human Brain Mapping 38, 855–868.Google Scholar
Rolls, ET, Joliot, M and Tzourio-Mazoyer, N (2015) Implementation of a new parcellation of the orbitofrontal cortex in the automated anatomical labeling atlas. Neuroimage 122, 1–5.Google Scholar
Rubinov, M and Sporns, O (2009) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059–1069.Google Scholar
Serpa, MH et al. (2017) State-dependent microstructural white matter changes in drug-naive patients with first-episode psychosis. Psychological Medicine 47, 2613–2627.Google Scholar
Sexton, CE, Mackay, CE and Ebmeier, KP (2009) A systematic review of diffusion tensor imaging studies in affective disorders. Biological Psychiatry 66, 814–823.Google Scholar
Shu, N et al. (2011) Diffusion tensor tractography reveals disrupted topological efficiency in white matter structural networks in multiple sclerosis. Cerebral Cortex 21, 2565–2577.Google Scholar
Shu, N et al. (2012) Disrupted topological organization in white matter structural networks in amnestic mild cognitive impairment: relationship to subtype. Radiology 265, 518–527.Google Scholar
Spielberg, JM et al. (2016) Resting state brain network disturbances related to hypomania and depression in medication-free bipolar disorder. Neuropsychopharmacology 41, 3016–3024.Google Scholar
Sporns, O (2011) The human connectome: a complex network. Annals of the New York Academy of Sciences 1224, 109–125.Google Scholar
Sporns, O, Tononi, G and Kotter, R (2005) The human connectome: a structural description of the human brain. PLoS Computational Biology 1, e42.Google Scholar
Taira, U et al. (2014) Efficiency of a ‘small-world’ brain network depends on consciousness level: a resting-state FMRI study. Cerebral Cortex 24, 1529–1539.Google Scholar
Tuladhar, AM et al. (2017) Disruption of rich club organisation in cerebral small vessel disease. Human Brain Mapping 38, 1751–1766.Google Scholar
van den Heuvel, MP and Sporns, O (2011) Rich-club organization of the human connectome. Journal of Neuroscience 31, 15775–15786.Google Scholar
van den Heuvel, MP et al. (2012) High-cost, high-capacity backbone for global brain communication. Proceedings of the National Academy of Sciences of the United States of America 109, 11372–11377.Google Scholar
van den Heuvel, MP et al. (2013) Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry 70, 783–792.Google Scholar
Vargas, C, Lopez-Jaramillo, C and Vieta, E (2013) A systematic literature review of resting state network–functional MRI in bipolar disorder. Journal of Affective Disorders 150, 727–735.Google Scholar
Vederine, FE et al. (2011) A meta-analysis of whole-brain diffusion tensor imaging studies in bipolar disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry 35, 1820–1826.Google Scholar
Wang, Y et al. (2016) Disrupted resting-state functional connectivity in nonmedicated bipolar disorder. Radiology 280, 529–536.Google Scholar
Wang, Y et al. (2017 a) Topologically convergent and divergent functional connectivity patterns in unmedicated unipolar depression and bipolar disorder. Translational Psychiatry 7, e1165.Google Scholar
Wang, Y et al. (2017 b) Altered cerebellar functional connectivity in remitted bipolar disorder: a resting-state functional magnetic resonance imaging study. Australian & New Zealand Journal of Psychiatry. doi: 10.1177/0004867417745996.Google Scholar
Wise, T et al. (2016) Voxel-Based meta-analytical evidence of structural disconnectivity in major depression and bipolar disorder. Biological Psychiatry 79, 293–302.Google Scholar
Zalesky, A et al. (2016) Connectome sensitivity or specificity: which is more important? Neuroimage 142, 407–420.Google Scholar
Zanetti, MV et al. (2009) State-dependent microstructural white matter changes in bipolar I depression. European Archives of Psychiatry and Clinical Neuroscience 259, 316–328.Google Scholar
Supplementary material: File

Wang et al. supplementary material

Wang et al. supplementary material 1

Download Wang et al. supplementary material(File)
File 446 KB