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Attention-related potentials allow for a highly accurate discrimination of mild cognitive impairment subtypes
Pascal Missonnier1, François R Herrmann, Jonas Richiardi
1Clinical Neurophysiology and Neuroimaging Unit, Division of Neuropsychiatry, Department of Psychiatry, University Hospitals of Geneva, Chene-Bourg, Switzerland. Pascal.Missonnier-Evrard@hcuge.ch
Abstract:
The three most frequent forms of mild cognitive impairment (MCI) are single-domain amnestic MCI (sd-aMCI), single-domain dysexecutive MCI (sd-dMCI) and multiple-domain amnestic MCI (md-aMCI). Brain imaging differences among single domain subgroups of MCI were recently reported supporting the idea that electroencephalography (EEG) functional hallmarks can be used to differentiate these subgroups. We performed event-related potential (ERP) measures and independent component analysis in 18 sd-aMCI, 13 sd-dMCI and 35 md-aMCI cases during the successful performance of the Attentional Network Test. Sensitivity and specificity analyses of ERP for the discrimination of MCI subgroups were also made. In center-cue and spatial-cue warning stimuli, contingent negative variation (CNV) was elicited in all MCI subgroups. Two independent components (ICA1 and 2) were superimposed in the time range on the CNV. The ICA2 was strongly reduced in sd-dMCI compared to sd-aMCI and md-aMCI (4.3 vs. 7.5% and 10.9% of the CNV component). The parietal P300 ERP latency increased significantly in sd-dMCI compared to md-aMCI and sd-aMCI for both congruent and incongruent conditions. This latency for incongruent targets allowed for a highly accurate separation of sd-dMCI from both sd-aMCI and md-aMCI with correct classification rates of 90 and 81%, respectively. This EEG parameter alone performed much better than neuropsychological testing in distinguishing sd-dMCI from md-aMCI. Our data reveal qualitative changes in the composition of the neural generators of CNV in sd-dMCI. In addition, they document an increased latency of the executive P300 component that may represent a highly accurate hallmark for the discrimination of this MCI subgroup in routine clinical settings.
Insights
Electroencephalography (EEG) measures can differentiate subtypes of mild cognitive impairment (MCI). Increased P300 latency in event-related potentials (ERPs) accurately distinguishes single-domain dysexecutive MCI (sd-dMCI) from other forms.
Area of Science:
- Neuroscience
- Cognitive Science
- Biomedical Engineering
Background:
- Mild cognitive impairment (MCI) presents in various forms, including single-domain amnestic (sd-aMCI), single-domain dysexecutive (sd-dMCI), and multiple-domain amnestic (md-aMCI).
- Distinguishing between these MCI subtypes is crucial for targeted interventions and understanding disease progression.
- Previous research suggests that electroencephalography (EEG) functional hallmarks may aid in differentiating MCI subgroups.
Purpose of the Study:
- To investigate the utility of event-related potential (ERP) measures and independent component analysis (ICA) in discriminating between sd-aMCI, sd-dMCI, and md-aMCI.
- To identify specific EEG parameters that can accurately classify these MCI subtypes.
Main Methods:
- The study involved 18 sd-aMCI, 13 sd-dMCI, and 35 md-aMCI participants performing the Attentional Network Test.
- Event-related potentials (ERPs) and independent component analysis (ICA) were employed to analyze EEG data.
- Sensitivity and specificity analyses were conducted to evaluate the discriminative power of ERP measures.
Main Results:
- A reduction in the ICA2 component of the contingent negative variation (CNV) was observed in sd-dMCI compared to sd-aMCI and md-aMCI.
- A significant increase in parietal P300 ERP latency was found in sd-dMCI compared to both sd-aMCI and md-aMCI.
- P300 latency for incongruent targets achieved high classification rates (90% for sd-aMCI, 81% for md-aMCI), outperforming neuropsychological testing.
Conclusions:
- EEG functional hallmarks, particularly P300 ERP latency, can accurately discriminate between MCI subtypes.
- Increased P300 latency serves as a reliable marker for identifying sd-dMCI.
- These findings suggest the potential for using EEG in routine clinical settings for precise MCI subtyping.
