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

Babiloni, Claudio; Ferri, Raffaele; Moretti, Davide; Strambi, Andrea; Binetti, Giuliano; Forno, Gloria Dal; Ferreri, Florinda; Lanuzza, Bartolo; Bonato, Claudio; Nobili, Flavio; Rodriguez, Guido; Salinari, S.; Passero, S.; Rocchi, Raffaele; Stam, Cornelis J.; Rossini, Paolo Maria

doi:10.1111/j.0953-816x.2004.03333.x

Cited by 149 publications

(135 citation statements)

References 53 publications

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“…The ␦ rhythm has been implicated in slow-wave sleep (16) and in supporting normal awake physiological functions (37,38). For example, Babiloni et al (37) reported that interhemispheric and frontoparietal ␦ band synchronization is reduced in patients with mild Alzheimer's disease and vascular dementia compared with normal subjects. Animal studies have suggested a role for the ␦ rhythm in subcortical EEG synchronization (39,40).…”

Section: (Abbreviations As Inmentioning

confidence: 99%

Synchronized delta oscillations correlate with the resting-state functional MRI signal

Zuo

et al. 2007

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

388

344

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Synchronized low-frequency spontaneous fluctuations of the functional MRI (fMRI) signal have recently been applied to investigate large-scale neuronal networks of the brain in the absence of specific task instructions. However, the underlying neural mechanisms of these fluctuations remain largely unknown. To this end, electrophysiological recordings and resting-state fMRI measurements were conducted in ␣-chloralose-anesthetized rats. Using a seed-voxel analysis strategy, region-specific, anesthetic dosedependent fMRI resting-state functional connectivity was detected in bilateral primary somatosensory cortex (S1FL) of the resting brain. Cortical electroencephalographic signals were also recorded from bilateral S1FL; a visual cortex locus served as a control site. Results demonstrate that, unlike the evoked fMRI response that correlates with power changes in the ␥ bands, the resting-state fMRI signal correlates with the power coherence in low-frequency bands, particularly the ␦ band. These data indicate that hemodynamic fMRI signal differentially registers specific electrical oscillatory frequency band activity, suggesting that fMRI may be able to distinguish the ongoing from the evoked activity of the brain.electroencephalogram ͉ spontaneous fluctuations ͉ functional connectivity T he human brain is thought to be composed of multiple coherent neuronal networks of variable scales that support sensory, motor, and cognitive functions (1). The traditional approach to studying such networks has been to use specific tasks to probe neurobiological responses. In contrast, recent studies have demonstrated the existence of spontaneous, low-frequency (i.e., Ͻ0.1 Hz) fluctuations in the functional MRI (fMRI) signal of the resting brain that exhibit coherence patterns within specific neuronal networks in the absence of overt task performance or explicit attentional demands (2-4). Such precisely patterned spontaneous activity has been reported in both awake human and anesthetized nonhuman primates (5). Recently, ''resting-state'' fMRI has been applied to study alterations in brain networks under such pathological conditions as Alzheimer's disease (6), multiple sclerosis (7), and spatial neglect syndrome (8). These studies collectively suggest that, rather than simple physiological artifacts induced by cardiac pulsations or respiration, as was originally suspected, these widely distributed coherent low-frequency fMRI fluctuations have a direct neural basis (9, 10). However, more than a decade since they were first identified, the linkage between neuronal activity and restingstate fMRI signal remains largely unknown, underscoring the clear and critical need for well controlled animal models to investigate this phenomenon.Across various states of vigilance, the electrical activity of neuronal networks is known to oscillate at various frequencies and amplitudes, with high-frequency oscillations confined to local networks, whereas large networks are recruited during slow oscillations (11,12). Imposed tasks alter local field potent...

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Section: (Abbreviations As Inmentioning

confidence: 99%

Synchronized delta oscillations correlate with the resting-state functional MRI signal

Zuo

et al. 2007

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

388

344

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“…The following frequency bands were studied: delta (2-4Hz), theta (4 -8Hz), alpha 1 (8 -10.5Hz), alpha 2 (10.5-13Hz), beta 1 (13-20Hz), and beta 2 (20 -30Hz) in line with previous EEG studies on dementia 17,19,[71][72][73][74][75][76][77] Of note, sharing of a frequency bin by two contiguous bands is a widely accepted procedure. 19,73,-91 Choice of fixed EEG bands did not account for individual alpha frequency (IAF) peak, defined as the frequency associated with the strongest EEG power at the extended alpha range.…”

Section: Spectral Analysis Of the Electroencephalogram Datamentioning

confidence: 99%

Apolipoprotein E and alpha brain rhythms in mild cognitive impairment: A multicentric Electroencephalogram study

Babiloni

Benussi

Binetti

et al. 2005

Annals of Neurology

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Objective: Relationships between the apolipoprotein E 4 allele and electroencephalographic (EEG) rhythmicity have been demonstrated in Alzheimer's disease (AD) patients but not in the preclinical stage prodromic to it, namely, mild cognitive impairment (MCI). The present multicentric EEG study tested the hypothesis that presence of 4 affects sources of resting EEG rhythms in both MCI and AD subjects. Methods: We enrolled 89 MCI subjects (34.8% with 4) and 103 AD patients (50.4% with 4). Resting eyes-closed EEG data were recorded for all subjects. EEG rhythms of interest were delta (2-4Hz), theta (4 -8Hz), alpha 1 (8 -10.5Hz), alpha 2 (10.5-13Hz), beta 1 (13-20Hz), and beta 2 (20 -30Hz). EEG cortical sources were estimated by low-resolution brain electromagnetic tomography. Results: Results showed that amplitude of alpha 1 and 2 sources in occipital, temporal, and limbic areas was lower in subjects carrying the 4 allele than in those not carrying the 4 allele (p < 0.01). This was true for both MCI and AD. For the first time to our knowledge, a relationship was shown between ApoE genotype and global neurophysiological phenotype (ie, cortical alpha rhythmicity) in a preclinical AD condition, MCI, in addition to clinically manifest AD. Interpretation: Such a demonstration motivates future genotype-EEG phenotype studies for the early prediction of AD conversion in individual MCI subjects. Neurol 2006;59:323-334 Mild cognitive impairment (MCI) is characterized by selective memory impairment insufficient to meet criteria for a diagnosis of dementia. Ann1-3 This condition is considered as a prodromic stage of Alzheimer's disease (AD), 4 -6 because a high rate of progression to AD has been clearly shown. 3,7,8 Annual conversion rate to AD is 0.2 to 3.9% in normal aging (with no MCI symptoms) and 6 to 25% in MCI subjects. 3,9 At the end of 6 years of observation, approximately 80% of MCI subjects develop AD.10 Taken together, these data suggest the hypothesis that in most (yet not all) of cases, MCI is a transition state on a linear progression toward AD. According to such a hypothesis, early identification of MCI patients might be clinically crucial. 11,12 In mild AD, electroencephalographic (EEG) rhythms differ from normal elderly (Nold) and vascular dementia subjects, AD patients being characterized by higher delta (0 -3Hz) and lower parietooccipital alpha (8 -12Hz).13-17 Similarly, MCI subjects have shown increase of theta (4 -7Hz) power 18 -20 as well as decrease of alpha power, 15,18 -22 when compared with Nold subjects. These EEG parameters have presented an intermediate magnitude in MCI with respect to Nold and dementia patients. 15,23,24 Despite the converging evidence of abnormal cortical rhythms in MCI and AD, EEG analysis alone is unable to predict conversion of MCI to dementia. It is reasonable that additional biological parameters are needed for this purpose. In this regard, several studies have shown a strict relationship between apolipoproFrom the

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“…In most of these studies a consistent decrease of coherence in the alpha and beta band was reported, whereas results for other bands were more variable. Abnormalities of functional connectivity have also been demonstrated with nonlinear synchronization methods (Jeong et al, 2001;Stam et al, 2002Stam et al, , 2006Pijnenburg et al, 2004;Babiloni et al, 2004). While these studies in general support the hypothesis of a disconnection syndrome in Alzheimer's disease, two problems need further attention: (i) assessment of functional connectivity with EEG and MEG can be biased by volume conduction, which may yield spurious correlations between nearby sensors and hence render interpretation unreliable; (ii) connectivity studies in Alzheimer's disease are generally very descriptive and lack a more robust framework to discriminate between normal and abnormal networks in the brain.…”

Section: Introductionmentioning

confidence: 98%

Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease

Stam¹,

Haan²,

Daffertshofer³

et al. 2008

Brain

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714

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In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimer's disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimer's disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimer's disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimer's disease patients. Network changes in the lower alpha band were better explained by a 'Targeted Attack' model than by a 'Random Failure' model. Thus, Alzheimer's disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimer's disease is more random. The modelling results suggest that highly connected neural network 'hubs' may be especially at risk in Alzheimer's disease.

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

Cited by 149 publications

References 53 publications

Synchronized delta oscillations correlate with the resting-state functional MRI signal

Synchronized delta oscillations correlate with the resting-state functional MRI signal

Apolipoprotein E and alpha brain rhythms in mild cognitive impairment: A multicentric Electroencephalogram study

Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer's disease

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