Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T15:25:28.190Z Has data issue: false hasContentIssue false
  • English
  • Français

Structural covariance and cortical reorganisation in schizophrenia: a MRI-based morphometric study

Published online by Cambridge University Press:  06 May 2018

Lena Palaniyappan ,
Olha Hodgson ,
Vijender Balain ,
Sarina Iwabuchi ,
Penny Gowland  and
Peter Liddle
Lena Palaniyappan*
Affiliation:
Robarts Research Institute & The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada Department of Psychiatry, University of Western Ontario, London, Ontario, Canada Lawson Health Research Institute, London, Ontario, Canada
Olha Hodgson
Affiliation:
Translational Neuroimaging in Mental Health, University of Nottingham, UK
Vijender Balain
Affiliation:
Translational Neuroimaging in Mental Health, University of Nottingham, UK
Sarina Iwabuchi
Affiliation:
Translational Neuroimaging in Mental Health, University of Nottingham, UK
Penny Gowland
Affiliation:
Sir Peter Mansfield Imaging Center, University of Nottingham, Nottingham, UK
Peter Liddle
Affiliation:
Translational Neuroimaging in Mental Health, University of Nottingham, UK
*
Author for correspondence: Lena Palaniyappan, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

In patients with schizophrenia, distributed abnormalities are observed in grey matter volume. A recent hypothesis posits that these distributed changes are indicative of a plastic reorganisation process occurring in response to a functional defect in neuronal information transmission. We investigated the structural covariance across various brain regions in early-stage schizophrenia to determine if indeed the observed patterns of volumetric loss conform to a coordinated pattern of structural reorganisation.

Methods

Structural magnetic resonance imaging scans were obtained from 40 healthy adults and 41 age, gender and parental socioeconomic status matched patients with schizophrenia. Volumes of grey matter tissue were estimated at the regional level across 90 atlas-based parcellations. Group-level structural covariance was studied using a graph theoretical framework.

Results

Patients had distributed reduction in grey matter volume, with high degree of localised covariance (clustering) compared with controls. Patients with schizophrenia had reduced centrality of anterior cingulate and insula but increased centrality of the fusiform cortex, compared with controls. Simulating targeted removal of highly central nodes resulted in significant loss of the overall covariance patterns in patients compared with controls.

Conclusion

Regional volumetric deficits in schizophrenia are not a result of random, mutually independent processes. Our observations support the occurrence of a spatially interconnected reorganisation with the systematic de-escalation of conventional ‘hub’ regions. This raises the question of whether the morphological architecture in schizophrenia is primed for compensatory functions, albeit with a high risk of inefficiency.

Keywords

Cortical reorganisationgrey mattermorphometrypsychosisschizophrenia
Type
Original Articles
Information
Psychological Medicine , Volume 49 , Issue 3 , February 2019 , pp. 412 - 420
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.)

References

Achard, S, et al. (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of neuroscience: the Official Journal of the Society for Neuroscience 26, 63–72.Google Scholar
Alexander-Bloch, A, Giedd, JN and Bullmore, E (2013) Imaging structural co-variance between human brain regions. Nature Reviews. Neuroscience 14, 322–336.Google Scholar
Alexander-Bloch, AF, et al. (2010) Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia. Frontiers in Systems Neuroscience 4, 147.Google Scholar
Ammons, RB and Ammons, CH (1962) The quick test (QT): provisional manual. Psychological Reports 11, 111–161.Google Scholar
Ashburner, J (2007) A fast diffeomorphic image registration algorithm. NeuroImage 38, 95–113.Google Scholar
Barabasi, AL (2009) Scale-free networks: a decade and beyond. Science 325, 412–413.Google Scholar
Bassett, DS, et al. (2008) Hierarchical organization of human cortical networks in health and schizophrenia. Journal of Neuroscience 28, 9239–9248.Google Scholar
Bullmore, E and Sporns, O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 10, 186–198.Google Scholar
Bullmore, E, et al. (2009) Generic aspects of complexity in brain imaging data and other biological systems. NeuroImage 47, 1125–1134.Google Scholar
Chan, RCK, et al. (2011) Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophrenia Bulletin 37, 177–188.Google Scholar
Chen, Z, et al. (2014) Extensive brain structural network abnormality in first-episode treatment-naive patients with schizophrenia: morphometrical and covariation study. Psychological Medicine 44, 2489–2501.Google Scholar
Crossley, NA, et al. (2014) The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain: a Journal of Neurology 137, 2382–2395.Google Scholar
Eack, SM, et al. (2010) Neuroprotective effects of cognitive enhancement therapy against gray matter loss in early schizophrenia: results from a 2-year randomized controlled trial. Archives of General Psychiatry 67, 674–682.Google Scholar
Ellison-Wright, I, et al. (2008) The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. American Journal of Psychiatry 165, 1015–1023.Google Scholar
Evans, AC (2013) Networks of anatomical covariance. NeuroImage 80, 489–504.Google Scholar
Fornito, A, Zalesky, A and Breakspear, M (2013) Graph analysis of the human connectome: promise, progress, and pitfalls. NeuroImage 80C, 426–444.Google Scholar
Fornito, A, et al. (2012) Schizophrenia, neuroimaging and connectomics. NeuroImage 62, 2296–2314.Google Scholar
Fusar-Poli, P and Meyer-Lindenberg, A (2016) Forty years of structural imaging in psychosis: promises and truth. Acta Psychiatrica Scandinavica 134, 207–224.Google Scholar
Glahn, DC, et al. (2008) Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biological Psychiatry 64, 774–781.Google Scholar
Griffa, A, et al. (2013) Structural connectomics in brain diseases. NeuroImage 80, 515–526.Google Scholar
Griffa, A, et al. (2015) Characterizing the connectome in schizophrenia with diffusion spectrum imaging. Human Brain Mapping 36, 354–366.Google Scholar
Ho, BC, et al. (2011) Long-term antipsychotic treatment and brain volumes: a longitudinal study of first-episode schizophrenia. Archives of General Psychiatry 68, 128–137.Google Scholar
Hoekzema, E, et al. (2011) Training-induced neuroanatomical plasticity in ADHD: a tensor-based morphometric study. Human Brain Mapping 32, 1741–1749.Google Scholar
Hosseini, SMH, Hoeft, F and Kesler, SR (2012) GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks. PLoS ONE 7, e40709.Google Scholar
Hosseini, SMH, et al. (2013) Topological properties of large-scale structural brain networks in children with familial risk for reading difficulties. NeuroImage 71, 260–274.Google Scholar
Humphries, MD and Gurney, K (2008) Network “small-world-ness”: a quantitative method for determining canonical network equivalence. PLoS ONE 3, e0002051.Google Scholar
Leckman, JF, et al. (1982) Best estimate of lifetime psychiatric diagnosis: a methodological study. Archives of General Psychiatry 39, 879–883.Google Scholar
Leung, M, et al. (2009) Gray matter in first-episode schizophrenia before and after antipsychotic drug treatment. Anatomical likelihood estimation meta-analyses with sample size weighting. Schizophrenia Bulletin 37, 199–211.Google Scholar
Li, X, et al. (2013) Age-related changes in brain structural covariance networks. Frontiers in Human Neuroscience 7, 98.Google Scholar
Liddle, PF, et al. (2002) Signs and symptoms of psychotic illness (SSPI): a rating scale. The British Journal of Psychiatry 180, 45–50.Google Scholar
Lo, CYZ, et al. (2015) Randomization and resilience of brain functional networks as systems-level endophenotypes of schizophrenia. Proceedings of the National Academy of Sciences of the United States of America 112, 9123–9128.Google Scholar
Luders, E, et al. (2012) The unique brain anatomy of meditation practitioners: alterations in cortical gyrification. Frontiers in Human Neuroscience 6, 34.Google Scholar
Lynall, ME, et al. (2010) Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience 30, 9477–9487.Google Scholar
May, A, et al. (2007) Structural brain alterations following 5 days of intervention: dynamic aspects of neuroplasticity. Cerebral Cortex 17, 205–210.Google Scholar
Mechelli, A, et al. (2005) Structural covariance in the human cortex. Journal of Neuroscience 25, 8303–8310.Google Scholar
Modinos, G, et al. (2009) Structural covariance in the hallucinating brain: a voxel-based morphometry study. Journal of Psychiatry & Neuroscience 34, 465–469.Google Scholar
Montembeault, M, et al. (2012) The impact of aging on gray matter structural covariance networks. NeuroImage 63, 754–759.Google Scholar
Newman, MEJ (2006) Finding community structure in networks using the eigenvectors of matrices. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics 74, 36104.Google Scholar
Pajonk, FG, et al. (2010) Hippocampal plasticity in response to exercise in schizophrenia. Archives of General Psychiatry 67, 133–143.Google Scholar
Palaniyappan, L (2017) Progressive cortical reorganisation: a framework for investigating structural changes in schizophrenia. Neuroscience & Biobehavioral Reviews 79, 1–13.Google Scholar
Palaniyappan, L and Liddle, PF (2012) Differential effects of surface area, gyrification and cortical thickness on voxel based morphometric deficits in schizophrenia. NeuroImage 60, 693–699.Google Scholar
Palaniyappan, L and Liddle, PF (2013) Diagnostic discontinuity in psychosis: a combined study of cortical gyrification and functional connectivity. Schizophrenia Bulletin 40, 675–684.Google Scholar
Palaniyappan, L, et al. (2014) Abnormalities in structural covariance of cortical gyrification in schizophrenia. Brain Structure & Function 220, 2059–2071.Google Scholar
Palaniyappan, L, et al. (2016) Globally efficient brain organization and treatment response in psychosis: a connectomic study of gyrification. Schizophrenia Bulletin 42, 1446–1456.Google Scholar
Ramsay, JO and Dalzell, CJ (1991) Some tools for functional data analysis. Journal of the Royal Statistical Society. Series B. Methodological 53, 539–572.Google Scholar
Rapp, C, et al. (2012) Effects of Cannabis use on human brain structure in psychosis: a systematic review combining In vivo structural neuroimaging and post mortem studies. Current Pharmaceutical Design 18, 5070–5080.Google Scholar
Rose, D and Pevalin, DJ (2003) A Researcher's Guide to the National Statistics Socio-Economic Classification. London: Sage Publications.Google Scholar
Rubinov, M and Sporns, O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52, 1059–1069.Google Scholar
Singh, MK, et al. (2013) Anomalous gray matter structural networks in major depressive disorder. Biological Psychiatry 74, 777–785.Google Scholar
Stam, C and Reijneveld, J (2007) Graph theoretical analysis of complex networks in the brain. Nonlinear Biomedical Physics 1, 3.Google Scholar
Tandon, R, Keshavan, MS and Nasrallah, HA (2008) Schizophrenia, ‘Just the Facts’: what we know in 2008 part 1: overview. Schizophrenia Research 100, 4–19.Google Scholar
van den Heuvel, MP and Sporns, O (2011) Rich-club organization of the human connectome. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 31, 15775–15786.Google Scholar
van den Heuvel, MP, et al. (2010) Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 30, 15915–15926.Google Scholar
Wang, Q, et al. (2012) Anatomical insights into disrupted small-world networks in schizophrenia. NeuroImage 59, 1085–1093.Google Scholar
WHO Collaborating Centre for Drug Statistics and Methodology (2003) Guidelines for ATC Classification and DDD Assignment. WHO Collaborating Centre for Drug Statistics and Methodology.Google Scholar
Xia, M, Wang, J and He, Y (2013) Brainnet viewer: a network visualization tool for human brain connectomics. PLoS ONE 8, e68910.Google Scholar
Zalesky, A, et al. (n.d.) Whole-brain anatomical networks: does the choice of nodesmatter? NeuroImage 50, 970–983.Google Scholar
Zhang, Y, et al. (2012) Abnormal topological organization of structural brain networks in schizophrenia. Schizophrenia Research 141, 109–118.Google Scholar
Supplementary material: File

Palaniyappan et al. supplementary material

Palaniyappan et al. supplementary material 1

Download Palaniyappan et al. supplementary material(File)
File 2.7 MB