Sink-index: a network-based EEG marker for frontotemporal dementia and Alzheimer’s disease

TL;DR

A new EEG-based marker called Sink-Index can help distinguish between Alzheimer’s disease, Frontotemporal dementia, and healthy individuals.

Contribution

The Sink-Index is a novel EEG-derived network marker for differential diagnosis of dementia syndromes.

Findings

The Sink-Index differentiates Frontotemporal dementia from healthy controls with high accuracy.

Alzheimer’s disease shows distinct Sink-Index values compared to Frontotemporal dementia and controls.

EEG-based Sink-Index offers a non-invasive and cost-effective alternative for early dementia diagnosis.

Abstract

Frontotemporal dementia is a complex neurodegenerative illness characterized by a progressive deterioration in temperament, judgement, behaviour, and communication. Misdiagnosis and late diagnosis occur frequently due to the complexity of the phenotypes, overlaps of features with other neurodegenerative syndromes and psychiatric disorders, and ill-defined preclinical phases of the illness. Diagnosis relies on structural or functional brain imaging to show characteristic atrophy, hypoperfusion or hypometabolism profiles. The sensitivity of neuroimaging is lower in the earliest phases of the illness, and there are few alternatives. Scalp electroencephalography (EEG) is a widely available, low-cost technology, but its utility in the differential diagnosis of dementia will require EEG indices of high sensitivity and discriminatory value. We have used scalp EEG to develop subject-specific Dynamic Network Models, from which we summarize the reciprocal relationships between the nodes (defined by the EEG channel). This index, the ‘Sink-Index’, characterizes how activity in each node (or channel) responds to activity in other nodes in the network. In this context, ‘sources’ are nodes that exert significant influence on the activity of different regions but are not themselves influenced, whereas ‘sinks’ represent influenced regions that do not affect activity in others. We hypothesized that brain regions associated with Frontotemporal dementia and Alzheimer's disease syndromes behave as sinks and have higher sink indices than healthy brain regions. This hypothesis was tested in a cohort of 88 subjects: 23 with frontotemporal dementia, 36 with Alzheimer's disease, and 29 healthy controls. The Sink-Index of nodes in the frontal–temporal and central–parietal–occipital brain regions differed between Frontotemporal dementia (1.3389 ± 0.0895 versus 0.8444 ± 0.0651), Alzheimer’s disease (0.6015 ± 0.0188 versus 0.7766 ± 0.0158), and healthy controls (0.8978 ± 0.0453 versus 0.9116 ± 0.0457). These findings suggest the Sink-Index is an EEG marker with utility for the differential diagnosis of dementia syndromes.

Sanchez et al. report that the novel Sink-Index, an EEG marker derived from dynamic network models, distinguishes Alzheimer’s disease and Frontotemporal Dementia from Healthy Controls with high accuracy. The study highlights the Sink-Index’s potential as a cost-effective, non-invasive diagnostic tool, enhancing differential diagnosis and understanding of neurodegenerative network pathophysiology.

Graphical Abstract

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