Modern machine learning and linguistic analysis will be increasingly useful in assessment and clustering of suspected AD.

Studies of working memory load effects on human EEG power have indicated divergent effects in different frequency bands. Although gamma power typically increases with load, the load dependency of the lower frequency theta and alpha bands is uncertain. We obtained intracranial electroencephalography measurements from 1453 electrode sites in 14 epilepsy patients performing a Sternberg task, in order to characterize the anatomical distribution of load-related changes across the frequency spectrum. Gamma power increases occurred throughout the brain, but were most common in the occipital lobe. In the theta and alpha bands, both increases and decreases were observed, but with different anatomical distributions. Increases in theta and alpha power were most prevalent in frontal midline cortex. Decreases were most commonly observed in occipital cortex, colocalized with increases in the gamma range, but were also detected in lateral frontal and parietal regions. Spatial overlap with group functional magnetic resonance imaging results was minimal except in the precentral gyrus. These findings suggest that power in any given frequency band is not a unitary phenomenon; rather, reactivity in the same frequency band varies in different brain regions, and may relate to the engagement or inhibition of a given area in a cognitive task.

In the early stages of neurodegenerative disorders, individuals may exhibit a decline in language abilities that is difficult to quantify with standardized tests. Careful analysis of connected speech can provide valuable information about a patient's language capacities. To date, this type of analysis has been limited by its time-consuming nature. In this study, we present a method for evaluating and classifying connected speech in primary progressive aphasia using computational techniques. Syntactic and semantic features were automatically extracted from transcriptions of narrative speech for three groups: semantic dementia (SD), progressive nonfluent aphasia (PNFA), and healthy controls. Features that varied significantly between the groups were used to train machine learning classifiers, which were then tested on held-out data. We achieved accuracies well above baseline on the three binary classification tasks. An analysis of the influential features showed that in contrast with controls, both patient groups tended to use words which were higher in frequency (especially nouns for SD, and verbs for PNFA). The SD patients also tended to use words (especially nouns) that were higher in familiarity, and they produced fewer nouns, but more demonstratives and adverbs, than controls. The speech of the PNFA group tended to be slower and incorporate shorter words than controls. The patient groups were distinguished from each other by the SD patients' relatively increased use of words which are high in frequency and/or familiarity.

Positive BOLD responses and increased EEG oscillations do not necessarily arise in the same regions. Negative BOLD responses may also relate to cognitive activity, as traditionally indexed by increased EEG power in the theta band.

EEG studies employing time-frequency analysis have revealed changes in theta and alpha power in a variety of language and memory tasks. Semantic and syntactic violations embedded in sentences evoke well-known ERPs, but little is known about the oscillatory responses to these violations. We investigated oscillatory responses to both kinds of violations, while monolingual and bilingual participants performed an acceptability judgment task. Both violations elicited power decreases (event-related desynchronization, ERD) in the 8-30 Hz frequency range, but with different scalp topographies. In addition, semantic anomalies elicited power increases (event-related synchronization, ERS) in the 1-5 Hz frequency band. The 1-5 Hz ERS was strongly phase-locked to stimulus onset and highly correlated with time domain averages, whereas the 8-30 Hz ERD response varied independently of these. In addition, the results showed that language expertise modulated 8-30 Hz ERD for syntactic violations as a function of the executive demands of the task. When the executive function demands were increased using a grammaticality judgment task, bilinguals but not monolinguals demonstrated reduced 8-30 Hz ERD for syntactic violations. These findings suggest a putative role of the 8-30 Hz ERD response as a marker of linguistic processing that likely represents a separate neural process from those underlying ERPs.