Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging

Prichep, Leslie S.; John, E. Roy; Ferris, Steven; Rausch, Leonie; Fang, Ziyan; Cancro, Robert; Torossian, Carol; Reisberg, Barry

doi:10.1016/j.neurobiolaging.2005.07.021

Cited by 242 publications

(226 citation statements)

References 60 publications

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“…Furthermore, the correlation of relative theta and beta, and mean peak frequency seen in our study, has previously been reported in mild AD and has been shown to predict AD-progression in MCI cases [11,43].…”

Section: Discussionsupporting

confidence: 86%

Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years

Stomrud

Hansson

Minthon

et al. 2010

Neurobiology of Aging

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Section: Discussionsupporting

confidence: 86%

Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years

Stomrud

Hansson

Minthon

et al. 2010

Neurobiology of Aging

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“…The most stable pattern of EEG activity in MCI patients is defined by an increase in theta accompanied by a decrease of alpha and beta power, which correlates with APOE genotype, and hippocampal volumes (Babiloni et al 2006b(Babiloni et al , c, 2009Grunwald et al 2002;Prichep et al 2006). When the progression to AD was investigated, alpha and theta relative power in left temporooccipital derivation correctly classified 85 % of MCI subjects who would suffer dementia .…”

Section: Introductionmentioning

confidence: 99%

MEG spectral analysis in subtypes of mild cognitive impairment

López

Cuesta

Garcés

et al. 2014

AGE

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Mild cognitive impairment (MCI) has been described as an intermediate stage between normal aging and dementia. Previous studies characterized the alterations of brain oscillatory activity at this stage, but little is known about the differences between single and multidomain amnestic MCI patients. In order to study the patterns of oscillatory magnetic activity in amnestic MCI subtypes, a total of 105 subjects underwent an eyes-closed resting-state magnetoencephalographic recording: 36 healthy controls, 33 amnestic single domain MCIs (a-sd-MCI), and 36 amnestic multidomain MCIs (a-md-MCI). Relative power values were calculated and AGE (2014) compared among groups. Subsequently, relative power values were correlated with neuropsychological tests scores and hippocampal volumes. Both MCI groups showed an increase in relative power in lower frequency bands (delta and theta frequency ranges) and a decrease in power values in higher frequency bands (alpha and beta frequency ranges), as compared with the control group. More importantly, clear differences emerged from the comparison between the two amnestic MCI subtypes. The a-md-MCI group showed a significant power increase within delta and theta ranges and reduced relative power within alpha and beta ranges. Such pattern correlated with the neuropsychological performance, indicating that the a-md-MCI subtype is associated not only with a "slowing" of the spectrum but also with a poorer cognitive status. These results suggest that a-md-MCI patients are characterized by a brain activity profile that is closer to that observed in Alzheimer disease. Therefore, it might be hypothesized that the likelihood of conversion to dementia would be higher within this subtype.

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“…The quantitative analysis of electroencephalographic (EEG) rhythms in resting subjects is a low-cost but still powerful approach to the study of elderly subjects in normal aging, MCI and dementia (Gueguen et al, 1991;Maurer and Dierks, 1992;Leuchter et al, 1993;Schreiter-Gasser et al, 1993;Zappoli et al, 1995;Jelic et al, 1996Jelic et al, , 2000Huang et al, 2000;Bennys et al, 2001;Koenig et al, 2005;Babiloni et al, 2006a,b;Prichep et al, 2006;Rossini et al, 2006). The anchor point for a quantitative analysis of the EEG is the a rhythm, which dominates the EEG power spectrum in resting, awake and healthy subjects (Elul, 1972;Lopes da Silva et al, 1976Steriade and Llinas, 1988;Singer, 1993;Destexhe and Sejnowski, 1996;Klimesch, 1997Klimesch, , 1999Pfurtscheller and Lopes da Silva, 1999;Nunez et al, 2001;Suffczynski et al, 2001;Klimesch et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Vascular damage and EEG markers in subjects with mild cognitive impairment

Moretti

Miniussi

Frisoni

et al. 2007

Clinical Neurophysiology

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Objective: We evaluated the changes induced by cerebrovascular (CV) damage on brain rhythmicity recorded by electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI). Methods: We enrolled 99 MCI subjects (Mini-Mental State Examination [MMSE] mean score 26.6). All subjects underwent EEG recording and magnetic resonance imaging (MRI). EEGs were recorded at rest. Individual EEG frequencies were indexed by the h/a transition frequency (TF) and by the individual a frequency (IAF) with power peak in the extended a range (5-14 Hz). Relative power was separately computed for d, h, a1, a2, and a3 frequency bands on the basis of the TF and IAF values. Subsequently, we divided the cohort in four sub-groups based on subcortical CV damage as scored by the age-related white matter changes scale (ARWMC). Results: CV damage was associated with 'slowing' of TF proportional to its severity. In the spectral bandpower the severity of vascular damage was associated with increased d power and decreased a2 power. No association of vascular damage was observed with IAF and a3 power. Moreover, the h/a1 ratio could be a reliable index for the estimation of the individual extent of CV damage. Conclusions: EEG analysis may show physiological markers sensitive to CV damage. The appropriate use of this EEG index may help the differential diagnosis of different forms of cognitive decline, namely primary degenerative and secondary to CV damage. Significance: The EEG neurophysiological approach, together with anatomical features from imaging, could be helpful in the understanding of the functional substrate of dementing disorders.

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Prediction of longitudinal cognitive decline in normal elderly with subjective complaints using electrophysiological imaging

Cited by 242 publications

References 60 publications

Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years

Slowing of EEG correlates with CSF biomarkers and reduced cognitive speed in elderly with normal cognition over 4 years

MEG spectral analysis in subtypes of mild cognitive impairment

Vascular damage and EEG markers in subjects with mild cognitive impairment

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