Evidence map›Paper›PMID 40661328›Full record

ArticleBrain communications2025

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

Luis A Sanchez, Surya Pandiaraju, Autumn O Williams, Amir H Daraie, Chiadi U Onyike, Sridevi V Sarma

Abstract read
In one paragraph

Article in Brain communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Luis A SanchezDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.ORCID https://orcid.org/0000-0003-1724-7491
Surya PandiarajuDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Autumn O WilliamsDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Amir H DaraieDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Chiadi U OnyikeDepartment of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
Sridevi V SarmaDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.

Funding

EEG Biomarkers Derived from Dynamical Network Models Enable Rapid Paths to Accurate Diagnosis and Effective Treatment of EpilepsyR35NS132228 · NINDS · JOHNS HOPKINS UNIVERSITY · PI Sridevi V. Sarma · 2023 to 2026
$2.6M
NINDS NIH HHS R35 NS132228
6 · The paper itself

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.

Indexed as

Alzheimerdynamical systemsEEG markerfrontotemporal dementiasink-Index

Identifiers

PMID40661328
PMCPMC12256813

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