Elsevier

NeuroImage

Volume 32, Issue 3, September 2006, Pages 1335-1344
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2006.05.033Get rights and content

Abstract

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimer's disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimer's disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.

Introduction

The neurophysiological mechanisms that underlie cognitive and behavioral dysfunction in Alzheimer's disease (AD) are still incompletely understood. Despite an enormous increase in knowledge about the cellular, molecular, vascular (chronical cerebral hypoperfusion) and genetic processes involved in AD pathology, the relationship between these fundamental changes and abnormal functioning of large scale brain networks remains unclear.

One approach to this problem has concentrated on the idea that AD pathology at the cellular and molecular level could give rise to impaired activation of specific brain regions or a slowing down of local electrophysiological oscillatory activity. Evidence for such local abnormalities has been found with fMRI studies showing impaired activation, in particular, of the hippocampus and related areas during memory tasks (Rombouts et al., 2000). Neurophysiological techniques such as EEG and more recently MEG have also been used to identify local physiological abnormalities (for a review, see Jeong, 2004). EEG studies have demonstrated a slowing of the dominant rhythms, in particular, over the posterior temporal parietal and occipital brain areas (Boerman et al., 1994, Jeong, 2004, Jonkman, 1997). This EEG slowing has been correlated with brain atrophy, APOE genotype and low central cholinergic activity (Lehtovirta et al., 1996, Riekkinen et al., 1991). MEG studies have confirmed the notion of a slowing of brain rhythms and have also suggested an anterior displacement of the sources of these rhythms (Berendse et al., 2000, Fernandez et al., 2002, Fernandez et al., 2003, Fernandez et al., 2006, Maestu et al., 2001, Maestu et al., 2003, Maestu et al., 2004, Maestu et al., 2005, Osipova et al., 2005). However, a limitation of these approaches is that it is unclear how these local abnormalities influence the functioning of the brain as an integrated system.

A promising alternative approach focuses on connections rather than on local dysfunction. A central problem in cognitive neuroscience is the question how different, widely distributed and specialized brain areas integrate their activity. It is widely believed that such large scale functional integration is crucial for higher cognitive and behavioral functioning (Fuster, 2003, Mesulam, 1990, Mesulam, 1998, Tononi et al., 1998). One candidate mechanism for large scale functional integration is the phenomenon of synchronization or temporal correlations between neural activity in different brain regions (Le van Quyen, 2003, Varela et al., 2001). Synchronization of brain regions can be studied by measuring statistical interdependencies (functional connectivity) between physiological signals such as fMRI BOLD, EEG or MEG from different brain regions either during a resting state or during a task (Lee et al., 2003, Fingelkurts et al., 2005, Pereda et al., 2005, Stam, 2005). Studies of functional connectivity have revealed the existence of synchronized neural networks in different frequency bands and involving different brain regions. For instance, working memory is associated with long distance interactions in the theta band, while gamma synchronization may be related to perception and consciousness (Rodriguez et al., 1999, Sarnthein et al., 1998, Stam et al., 2002a, Micheloyannis et al., 2005). Large scale low frequency synchronization has been associated with a context of cognition, while smaller scale high frequency synchronization might be related to content (Palva et al., 2005).

This raises the question whether AD is perhaps better characterized by abnormalities at the network level in addition to, or instead of, the well-known local disturbances. Disturbed functional connectivity would support a ‘disconnection hypothesis’ of cognitive dysfunction in AD (Delbeuck et al., 2003). Several EEG studies have demonstrated a lower coherence, a linear measure of functional connectivity, of EEG, especially in the alpha band, in AD (Adler et al., 2003, Babiloni et al., 2004a, Besthorn et al., 1994, Dunkin et al., 1994, Hogan et al., 2003, Jelic et al., 1996, Jiang, 2005, Koenig et al., 2005, Knott et al., 2000, Leuchter et al., 1992, Locatelli et al., 1998, Pogarell et al., 2005, Stevens et al., 2001). Changes in coherence outside the alpha band have been reported less frequently, and controversy exists about the question whether delta and theta band coherence are decreased or increased in AD.

Use of nonlinear measures has also suggested a loss of functional connectivity in AD, especially in the alpha and beta bands (Babiloni et al., 2004a,b; Jeong et al., 2001, Pijnenburg et al., 2004, Stam et al., 2003a). MEG may be more suitable than EEG to assess functional connectivity since MEG does not require the use of a reference and is more sensitive to nonlinear correlations (Stam et al., 2003b). In a pilot study, Berendse et al. showed a lower coherence in all frequency bands in AD patients (Berendse et al., 2000). More recently, we used the synchronization likelihood, a measure of generalized synchronization, to study functional connectivity in a larger group of AD subjects and controls (Stam and van Dijk, 2002, Stam et al., 2002b). This study revealed a lower level of synchronization in the upper alpha band, the beta and the gamma band in AD (Stam et al., 2002b). However, lower levels of functional connectivity per se may not yet explain why the large scale brain networks are functioning abnormally. Recently, we found that in AD abnormal topographic organization of large scale brain networks was present, with loss of so called ‘small-world’ features which correlated with MMSE scores (Stam et al., 2006). This points to the possibility that in AD a specific loss of certain long or short distance connections occurs, involving brain regions at risk in AD.

The present study was undertaken to study in more detail resting-state functional connectivity changes in AD. In particular, we addressed the question whether AD might be associated with a specific loss of either long distance or short distance interactions in particular regions and frequency bands. To this end, MEG was recorded during an eyes-closed no-task state in 18 AD patients and 18 healthy controls. The synchronization likelihood and coherence were computed between all pairs of sensors for signal filtered in delta, theta, alpha1, alpha2, beta and gamma bands. SL and coherence values were averaged for long distance (intra- and interhemispheric) and short distance local sensor pairs.

Section snippets

Subjects

The study involved 18 patients (mean age 72.1 years, SD 5.6; 11 males; mean MMSE 19.2, range: 13–25) with a diagnosis of probable AD according to the NINCDS-ADRDA criteria (McKhann et al., 1984) and 18 healthy control subjects (mean age 69.1 years, SD 6.8; 7 males; mean MMSE 29, range: 27–30), mostly spouses of the patients. Patients and control subjects were recruited from the Alzheimer Center of the VU University Medical Center. Subjects were assessed according to a clinical protocol, which

Nonlinear analysis

The delta band showed no significant effects involving the factor Group. In the theta band, a significant Group × Region interaction (F[9,306] = 2.604; P = 0.029) was found for short distances. This interaction effect is illustrated in Fig. 2. Inspection of Fig. 2 shows that the SL was higher in AD patients compared to controls in the right and left parietal and to a lesser extent central regions. This difference was significant for the right parietal region (two-sided t test, P = 0.037) In the

Discussion

This study demonstrated a specific pattern of changes in resting-state functional connectivity in AD patients. SL was increased in the theta band over the central and parietal areas and in the beta band over the parietal and occipital areas. Coherence showed a similar pattern of parieto-occipital increase in AD in alpha2, beta and gamma bands. In contrast, SL was decreased in the alpha1 band for long distance intrahemispheric sensor pairs, and both SL and coherence (and crosscorrelation) were

Acknowledgments

The study was financially supported by a grant from Alzheimer Nederland. T.M. is the recipient of a Praxis XXI doctoral fellowship from FCT, Ministry of Science, Portugal.

References (78)

  • J. Jeong

    EEG dynamics in patients with Alzheimer's disease

    Clin. Neurophysiol.

    (2004)
  • J. Jeong et al.

    Mutual information analysis of the EEG in patients with Alzheimer's disease

    Clin. Neurophysiol.

    (2001)
  • E.J. Jonkman

    The role of the electroencephalogram in the diagnosis of dementia of the Alzheimer type: an attempt at technology assessment

    Neurophysiol. Clin.

    (1997)
  • G.B. Karas et al.

    Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease

    NeuroImage

    (2004)
  • T. Koenig et al.

    Decreased EEG synchronization in Alzheimer's disease and mild cognitive impairment

    Neurobiol. Aging

    (2005)
  • L. Lee et al.

    A report of the functional connectivity workshop, Dusseldorf 2002

    NeuroImage

    (2003)
  • M. Lehtovirta et al.

    Spectral analysis of EEG in Alzheimer's disease: relation to Apolipoprotein E polymorphism

    Neurobiol. Aging

    (1996)
  • T. Locatelli et al.

    EEG coherence in Alzheimer's disease

    Electroencephalogr. Clin. Neurophysiol.

    (1998)
  • F. Maestu et al.

    Medial temporal lobe neuromagnetic hypoactivation and risk for developing cognitive decline in elderly population: a 2-year follow-up study

    Neurobiol. Aging

    (2006)
  • S. Micheloyannis et al.

    Neural networks involved in mathematical thinking: evidence for linear and non-linear analysis of electroencephalographic activity

    Neurosci. Lett.

    (2005)
  • G. Nolte et al.

    Identifying true brain interaction from EEG data using the imaginary part of coherency

    Clin. Neurophysiol.

    (2004)
  • P.L. Nunez et al.

    EEG coherency I: statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales

    Electroencephalogr. Clin. Neurophysiol.

    (1997)
  • D. Osipova et al.

    Effects of scopolamine on MEG spectral power and coherence in elderly subjects

    Clin. Neurophysiol.

    (2003)
  • D. Osipova et al.

    Altered generation of spontaneous oscillations in Alzheimer's disease

    NeuroImage

    (2005)
  • E. Pereda et al.

    Nonlinear multivariate analysis of neurophysiological signals

    Prog. Neurobiol.

    (2005)
  • Y.A.L. Pijnenburg et al.

    EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task

    Clin. Neurophysiol.

    (2004)
  • P. Riekkinen et al.

    The cholinergic system and EEG slow waves

    Electroencephalogr. Clin. Neurophysiol.

    (1991)
  • C.J. Stam

    Nonlinear dynamical analysis of EEG and MEG: review of an emerging field

    Clin. Neurophysiol.

    (2005)
  • C.J. Stam et al.

    Variability of EEG synchronization during a working memory task in healthy subjects

    Int. J. Psychophysiol.

    (2002)
  • G. Tononi et al.

    Complexity and coherency: integrating information in the brain

    TICS

    (1998)
  • A-M. Van Cappellen van Walsum et al.

    A neural complexity measure applied to MEG data in Alzheimer's disease

    Clin. Neurophysiol.

    (2003)
  • L.A. Wheaton et al.

    Synchronization of parietal and premotor areas during preparation an execution of praxis hand movement

    Clin. Neurophysiol.

    (2005)
  • G. Adler et al.

    Short-term rivastigmine treatment reduces EEG slow-wave power in Alzheimer patients

    Neuropsychobiology

    (2001)
  • G. Adler et al.

    EEG coherence in Alzheimer's dementia

    J. Neural Transm.

    (2003)
  • C. Babiloni et al.

    Cortical networks generating movement-related EEG rhythms in Alzheimer's disease: an EEG coherence study

    Behav. Neurosci.

    (2004)
  • C. Babiloni et al.

    Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer's disease: a multicentric EEG study

    Eur. J. Neurosci.

    (2004)
  • F. Bartolomei et al.

    How do brain tumors alter functional connectivity? A magnetoencephalography study

    Ann. Neurol.

    (2006)
  • O. David et al.

    Estimation of neural dynamics from MEG/EEG cortical current density maps: application to the reconstruction of large-scale cortical synchrony

    IEEE Trans. Biomed. Eng.

    (2002)
  • X. Delbeuck et al.

    Alzheimer's disease as a disconnection syndrome?

    Neuropyschol. Rev.

    (2003)
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