Elsevier

NeuroImage

Volume 59, Issue 4, 15 February 2012, Pages 3085-3093
NeuroImage

Disrupted modular brain dynamics reflect cognitive dysfunction in Alzheimer's disease

https://doi.org/10.1016/j.neuroimage.2011.11.055Get rights and content

Abstract

The relation between pathology and cognitive dysfunction in dementia is still poorly understood, although disturbed communication between different brain regions is almost certainly involved. In this study we combine magneto-encephalography (MEG) and network analysis to investigate the role of functional sub-networks (modules) in the brain with regard to cognitive failure in Alzheimer's disease. Whole-head resting-state (MEG) was performed in 18 Alzheimer patients (age 67 ± 9, 6 females, MMSE 23 ± 5) and 18 healthy controls (age 66 ± 9, 11 females, MMSE 29 ± 1). We constructed functional brain networks based on interregional synchronization measurements, and performed graph theoretical analysis with a focus on modular organization. The overall modular strength and the number of modules changed significantly in Alzheimer patients. The parietal cortex was the most highly connected network area, but showed the strongest intramodular losses. Nonetheless, weakening of intermodular connectivity was even more outspoken, and more strongly related to cognitive impairment. The results of this study demonstrate that particularly the loss of communication between different functional brain regions reflects cognitive decline in Alzheimer's disease. These findings imply the relevance of regarding dementia as a functional network disorder.

Highlights

► Network analysis applied to MEG data to study functional sub-networks (modules). ► In Alzheimer's disease, altered modular organization relates to cognitive symptoms. ► Intermodular connectivity is damaged most, parietal region has highest local damage.

Introduction

A theoretical framework to interpret the rapidly increasing amount of experimental data describing the complex organization of the human brain is highly desired. In recent years, graph theory has emerged as a promising candidate for this purpose (Bullmore et al., 2009, Rubinov and Sporns, 2010, Stam, 2010a, Stam, 2010b). Graph theory investigates the principles of network architecture, and the relation between network structure and function (Barabasi and Albert, 1999, Newman, 2010, Sporns, 2010, Watts and Strogatz, 1998). The application of graph theoretical analysis to neuroscientific data has revealed important organizational brain features such as an efficient ‘small-world’ architecture (combining good global and local connectivity) and the existence of highly connected network regions, called hubs (Achard et al., 2006, Eguiluz et al., 2005, He et al., 2008, Salvador et al., 2005, Stam et al., 2009, van den Heuvel and Hulshoff Pol, 2010). Changes in brain network topology have been related to normal cognitive development and aging as well as to a wide range of brain diseases, implying a close relation between connectivity and cognitive status (Achard and Bullmore, 2007, Bullmore and Sporns, 2009, Stam and Reijneveld, 2007).

In the most prevalent type of dementia, Alzheimer's disease (AD), cognitive functions that depend strongly on communication between different brain areas are particularly disturbed, and it has therefore been characterized as a ‘disconnection syndrome’ (Delbeuck et al., 2003, Geschwind, 1965). Graph theoretical studies of AD patient data have consistently revealed perturbations of brain network organization (de Haan et al., 2009, He et al., 2008, He et al., 2009, Stam and Reijneveld, 2007, Stam et al., 2009, Supekar et al., 2008). Interestingly, highly connected hub regions (e.g. the posterior cingulate gyrus and precuneus) seem most susceptible to AD pathology, which consists of amyloid deposition, hypometabolism and atrophy (Buckner et al., 2005, Celone et al., 2006, Greicius et al., 2004, Sperling et al., 2009). What causes this hub vulnerability in AD is unclear, but a more detailed description and understanding of hubs, or network clustering in general, could provide further clues.

A related network characteristic dealing with clustering is modularity, which expresses the extent to which networks can be decomposed into smaller functional sub-groups or modules (Boccaletti et al., 2006, Guimerà and Amaral, 2005, Newman, 2006, Newman and Girvan, 2004). Network nodes belonging to the same module have a higher level of inter-connectivity than with the rest of the network. In the brain, a high level of structural or functional connectivity among a group of regions implies a collective function or goal (Hilgetag et al., 2000, Salvador et al., 2005, Varela et al., 2001). Therefore, large-scale modular organization might be an appropriate level to examine cognitive processing and its impairment in brain disease. In this MEG study, we focus on functional modularity: the description of distinct sub-networks with intensive dynamical interaction, as expressed by levels of neuronal synchronization.

Theoretically, there are several advantages of a modular brain network structure. It offers an elegant solution for balancing the opposing demands that are placed on many dynamical systems: a high level of local specialization, while maintaining tight global integration (Sporns et al., 2004). In a modular network, hubs can have different roles; connector hubs form bridges between different modules, while provincial hubs are central nodes within modules. Graph theoretical measures that quantify inter- and intra-modular connectivity and are able to classify (hub) nodes accordingly have been developed (Guimerà and Amaral, 2005) and incorporated in the present study.

Using graph theoretical methods, several previous studies have demonstrated the presence of modular organization in the brain (Chen et al., 2008, Hagmann et al., 2008, Hilgetag et al., 2000, Kaiser et al., 2007, Leise, 1990). Moreover, modularity seems to develop during infancy and to degrade with age, suggesting a relation with cognitive abilities (Fair et al., 2009, Fan et al., 2011, Ferrarini et al., 2009, Meunier et al., 2009a, Meunier et al., 2009b, Schwarz et al., 2008, van den Heuvel and Hulshoff Pol, 2010). Consequently, the progressive impairment of specific cognitive domains in AD might well be reflected by changes in functional modularity.

In this study we explore functional modularity in resting-state MEG data of AD patients and healthy controls using a well-known graph theoretical modularity algorithm (Newman and Girvan, 2004). Our main aim is to examine whether and to what extent modular organization of spontaneous brain activity changes in AD, and if these changes are related to cognitive performance. Our hypothesis is that cognitive impairment in AD will be primarily reflected by impaired communication between functional modules, based on the notions that cognition requires intensive distributed processing and that vulnerable hub regions in AD are mainly located in association cortex areas (that integrate information from multiple modalities). In network terms, we expect AD to be a ‘connector hub disease’.

Section snippets

Patients and controls

The study involved 18 patients with a diagnosis of probable AD according to the NINCDS-ADRDA criteria (McKhann et al., 1984) and 18 healthy controls who were all recruited from the Alzheimer Center of the VU University Medical Center. Controls were often spouses of the patients. AD patients were assessed according to a standard clinical protocol, which involved history taking, physical and neurological examination, an interview with a spouse or close family member, blood tests, MRI of the brain

Modularity — descriptive results

To get a first impression of modular organization, individual network modules were visualized. Comparing several different resting-state MEG epochs of the same person, modular structure was generally consistent. Often, three or four strongly clustered frontal or parietal modules were found, along with several weaker temporal and occipital ones. Modules were usually localized clusters of adjacent cortical areas, but also showed long-distance fragments. Inter-hemispheric modules were a frequent

Discussion

The main message of this study is that the modular organization of large-scale spontaneous brain activity networks is disrupted in AD. Graph theoretical modularity analysis demonstrates weakening links within and, especially, between functional modules, correlating with cognitive dysfunction. Moreover, the vulnerability of the parietal region in AD is confirmed by regional analyses. In the following paragraphs we will relate our findings to current literature and discuss methodological issues.

Conclusion

It becomes more and more evident that disruption of structural and functional brain connectivity plays a pivotal role in the onset of dementia (Stam, 2010a, Stam, 2010b). Graph theory allows us to go beyond classifying AD as a disconnection syndrome, providing more detail and meaning. Functional modules are theoretically plausible representations of cognitive (sub-)processes, and therefore modularity analysis of MEG data seems a method with an appropriate spatiotemporal resolution to examine

Acknowledgments

The authors thank Nicole Sistermans, Ellemarije Altena, Annelies van der Vlies and Sofie Boom for neuropsychological assessments, and Karin Plugge and Ndedi Sijsma for performing the MEG recordings.

References (81)

  • E. Pereda et al.

    Nonlinear multivariate analysis of neurophysiological signals

    Prog. Neurobiol.

    (2005)
  • M. Pievani et al.

    Functional network disruption in the degenerative dementias

    Lancet Neurol.

    (2011)
  • M. Rubinov et al.

    Complex network measures of brain connectivity: uses and interpretations

    Neuroimage

    (2010)
  • R. Salvador et al.

    A simple view of the brain through a frequency-specific functional connectivity measure

    Neuroimage

    (2008)
  • A.J. Schwarz et al.

    Community structure and modularity in networks of correlated brain activity

    Magn. Reson. Imaging

    (2008)
  • R.A. Sperling et al.

    Amyloid deposition is associated with impaired default network function in older persons without dementia

    Neuron

    (2009)
  • O. Sporns et al.

    Organization, development and function of complex brain networks

    Trends Cogn. Sci.

    (2004)
  • C.J. Stam

    Characterization of anatomical and functional connectivity in the brain: a complex networks perspective

    Int. J. Psychophysiol.

    (2010)
  • C.J. Stam

    Use of magnetoencephalography (MEG) to study functional brain networks in neurodegenerative disorders

    J. Neurol. Sci.

    (2010)
  • C.J. Stam et al.

    Synchronization likelihood: an unbiased measure of generalized synchronization in multivariate data sets

    Physica D: Nonlinear Phenomena.

    (2002)
  • C.J. Stam et al.

    Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

    Neuroimage

    (2006)
  • M.P. van den Heuvel et al.

    Exploring the brain network: a review on resting-state fMRI functional connectivity

    Eur. Neuropsychopharmacol.

    (2010)
  • M.P. van den Heuvel et al.

    Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

    Neuroimage

    (2008)
  • S. Achard et al.

    Efficiency and cost of economical brain functional networks

    PLoS Comput. Biol.

    (2007)
  • S. Achard et al.

    A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs

    J. Neurosci.

    (2006)
  • R. Albert et al.

    Error and attack tolerance of complex networks

    Nature

    (2000)
  • A.F. Alexander-Bloch et al.

    Disrupted modularity and local connectivity of brain functional networks in childhood-onset schizophrenia

    Front. Syst. Neurosci.

    (2010)
  • J. Alstott et al.

    Modeling the impact of lesions in the human brain

    PLoS Comput. Biol.

    (2009)
  • A.L. Barabasi et al.

    Emergence of scaling in random networks

    Science

    (1999)
  • R.L. Buckner et al.

    Molecular, structural, and functional characterization of Alzheimer's disease: evidence for a relationship between default activity, amyloid, and memory

    J. Neurosci.

    (2005)
  • R.L. Buckner et al.

    Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease

    J. Neurosci.

    (2009)
  • J.M. Buldú et al.

    Reorganization of functional networks in mild cognitive impairment

    PLoS One

    (2011)
  • E. Bullmore et al.

    Complex brain networks: graph theoretical analysis of structural and functional systems

    Nat. Rev. Neurosci.

    (2009)
  • M. Catani et al.

    The rises and falls of disconnection syndromes

    Brain

    (2005)
  • K.A. Celone et al.

    Alterations in memory networks in mild cognitive impairment and Alzheimer's disease: an independent component analysis

    J. Neurosci.

    (2006)
  • M. Chavez et al.

    Functional modularity of background activities in normal and epileptic brain networks

    Phys. Rev. Lett.

    (2010)
  • Z.J. Chen et al.

    Revealing modular architecture of human brain structural networks by using cortical thickness from MRI

    Cereb. Cortex

    (2008)
  • J.S. Damoiseaux et al.

    Consistent resting-state networks across healthy subjects

    Proc. Natl. Acad. Sci. U. S. A.

    (2006)
  • W. de Haan et al.

    Functional neural network analysis in frontotemporal dementia and Alzheimer's disease using EEG and graph theory

    BMC Neurosci.

    (2009)
  • X. Delbeuck et al.

    Alzheimer's disease as a disconnection syndrome?

    Neuropsychol. Rev.

    (2003)
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