Abstract
The objective of this study was to characterize network-level changes in nonfluent/agrammatic Primary Progressive Aphasia (agPPA) and Primary Progressive Apraxia of Speech (PPAOS) with graph theory (GT) measures derived from scalp electroencephalography (EEG) recordings. EEGs of 15 agPPA and 7 PPAOS patients were collected during relaxed wakefulness with eyes closed (21 electrodes, 10–20 positions, 256 Hz sampling rate, 1–200 Hz bandpass filter). Eight artifact-free, non-overlapping 1024-point epochs were selected. Via Brainwave software, GT weighted connectivity and minimum spanning tree (MST) measures were calculated for theta and upper and lower alpha frequency bands. Differences in GT and MST measures between agPPA and PPAOS were assessed with Wilcoxon rank-sum tests. Of greatest interest, Spearman correlations were computed between behavioral and network measures in all frequency bands across all patients. There were no statistically significant differences in GT or MST measures between agPPA and PPAOS. There were significant correlations between several network and behavioral variables. The correlations demonstrate a relationship between reduced global efficiency and clinical symptom severity (e.g., parkinsonism, AOS). This preliminary, exploratory study demonstrates potential for EEG GT measures to quantify network changes associated with degenerative speech–language disorders.
Highlights
Past research has demonstrated that clinical EEG is sensitive to dementia associated with Alzheimer’s (AD) [1] and Parkinson’s diseases (PD) [2], and nonfluent/agrammatic Primary Progressive Aphasia [3], but not Primary Progressive Apraxia of Speech (PPAOS; patients who present with isolated apraxia of speech (AOS)) [4]
Consistent with the diagnoses, indices of language functioning (e.g., Northwestern Anagram Test (NAT) and WAB-AQ) were lower in agrammatic Primary Progressive Aphasia (agPPA) compared to PPAOS
Scores on the index of general cognition were lower and ratings of parkinsonism were slightly higher in agPPA compared to PPAOS
Summary
Graph theory is a branch of mathematics that is central to much of the modern “network neuroscience.”. It is premised on representing a system or network as a collection of nodes, with the interaction among them represented by edges. Edge, subgraph, and global metrics can be calculated and compared between groups or to a behavioral measure. Most real-world networks balance integration, or a high level of connectivity between nodes, and segregation, reflecting distinct modules in a network. In EEG studies, the nodes are represented by the electrodes and the edges by a measure of coherence within a selected frequency band [5]
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