Neuropsychological Evaluation and Quantitative EEG in Patients with Frontotemporal Dementia, Alzheimer's Disease, and Mild Cognitive Impairment.
Letteria TomaselloLeonardo CarlucciAngelina LaganàSanti GallettaChiara Valeria MarinelliMassimo RaffaelePierluigi ZoccolottiPublished in: Brain sciences (2023)
This study analyzed the efficacy of EEG resting state and neuropsychological performances in discriminating patients with different forms of dementia, or mild cognitive impairment (MCI), compared with control subjects. Forty-four patients with dementia (nineteen patients with AD, and seven with FTD), eighteen with MCI, and nineteen healthy subjects, matched for age and gender, underwent an extensive neuropsychological test battery and an EEG resting state recording. Results showed greater theta activation in posterior areas in the Alzheimer's disease (AD) and Fronto-Temporal Dementia (FTD) groups compared with the MCI and control groups. AD patients also showed more delta band activity in the temporal-occipital areas than controls and MCI patients. By contrast, the alpha and beta bands did not discriminate among groups. A hierarchical clustering analysis based on neuropsychological and EEG data yielded a three-factor solution. The clusters differed for several neuropsychological measures, as well as for beta and theta bands. Neuropsychological tests were most sensitive in capturing an initial cognitive decline, while increased theta activity was uniquely associated with a substantial worsening of the clinical picture, representing a negative prognostic factor. In line with the Research Domains Framework (RDoC) perspective, the joint use of cognitive and neurophysiological data may provide converging evidence to document the evolution of cognitive skills in at-risk individuals.
Keyphrases
- mild cognitive impairment
- cognitive decline
- resting state
- functional connectivity
- prognostic factors
- working memory
- newly diagnosed
- end stage renal disease
- ejection fraction
- transcranial magnetic stimulation
- electronic health record
- mental health
- magnetic resonance
- mass spectrometry
- deep learning
- patient reported outcomes