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