Correlating natural language processing and automated speech analysis with clinician assessment to quantify speech-language changes in mild cognitive impairment and Alzheimer's dementia.

BACKGROUND: Language impairment is an important marker of neurodegenerative disorders. Despite this, there is no universal system of terminology used to describe these impairments and large inter-rater variability can exist between clinicians assessing language. The use of natural language processing (NLP) and automated speech analysis (ASA) is emerging as a novel and potentially more objective method to assess language in individuals with mild cognitive impairment (MCI) and Alzheimer's dementia (AD). No studies have analyzed how variables extracted through NLP and ASA might also be correlated to language impairments identified by a clinician. METHODS: Audio recordings (n=30) from participants with AD, MCI, and controls were rated by clinicians for word-finding difficulty, incoherence, perseveration, and errors in speech. Speech recordings were also transcribed, and linguistic and acoustic variables were extracted through NLP and ASA. Correlations between clinician-rated speech characteristics and the variables were compared using Spearman's correlation. Exploratory factor analysis was applied to find common factors between variables for each speech characteristic. RESULTS: Clinician agreement was high in three of the four speech characteristics: word-finding difficulty (ICC = 0.92, p<0.001), incoherence (ICC = 0.91, p<0.001), and perseveration (ICC = 0.88, p<0.001). Word-finding difficulty and incoherence were useful constructs at distinguishing MCI and AD from controls, while perseveration and speech errors were less relevant. Word-finding difficulty as a construct was explained by three factors, including number and duration of pauses, word duration, and syntactic complexity. Incoherence was explained by two factors, including increased average word duration, use of past tense, and changes in age of acquisition, and more negative valence. CONCLUSIONS: Variables extracted through automated acoustic and linguistic analysis of MCI and AD speech were significantly correlated with clinician ratings of speech and language characteristics. Our results suggest that correlating NLP and ASA with clinician observations is an objective and novel approach to measuring speech and language changes in neurodegenerative disorders.

Dexmedetomidine Prevents Excessive γ-Aminobutyric Acid Type A Receptor Function after Anesthesia.

WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Postoperative delirium is associated with poor long-term outcomes and increased mortality. General anesthetic drugs may contribute to delirium because they increase cell-surface expression and function of α5 subunit-containing γ-aminobutyric acid type A receptors, an effect that persists long after the drugs have been eliminated. Dexmedetomidine, an α2 adrenergic receptor agonist, prevents delirium in patients and reduces cognitive deficits in animals. Thus, it was postulated that dexmedetomidine prevents excessive function of α5 γ-aminobutyric acid type A receptors. METHODS: Injectable (etomidate) and inhaled (sevoflurane) anesthetic drugs were studied using cultured murine hippocampal neurons, cultured murine and human cortical astrocytes, and ex vivo murine hippocampal slices. γ-Aminobutyric acid type A receptor function and cell-signaling pathways were studied using electrophysiologic and biochemical methods. Memory and problem-solving behaviors were also studied. RESULTS: The etomidate-induced sustained increase in α5 γ-aminobutyric acid type A receptor cell-surface expression was reduced by dexmedetomidine (mean ± SD, etomidate: 146.4 ± 51.6% vs. etomidate + dexmedetomidine: 118.4 ± 39.1% of control, n = 8 each). Dexmedetomidine also reduced the persistent increase in tonic inhibitory current in hippocampal neurons (etomidate: 1.44 ± 0.33 pA/pF, n = 10; etomidate + dexmedetomidine: 1.01 ± 0.45 pA/pF, n = 9). Similarly, dexmedetomidine prevented a sevoflurane-induced increase in the tonic current. Dexmedetomidine stimulated astrocytes to release brain-derived neurotrophic factor, which acted as a paracrine factor to reduce excessive α5 γ-aminobutyric acid type A receptor function in neurons. Finally, dexmedetomidine attenuated memory and problem-solving deficits after anesthesia. CONCLUSIONS: Dexmedetomidine prevented excessive α5 γ-aminobutyric acid type A receptor function after anesthesia. This novel α2 adrenergic receptor- and brain-derived neurotrophic factor-dependent pathway may be targeted to prevent delirium.

The order of complexity of visuomotor learning.

BACKGROUND: Learning algorithms come in three orders of complexity: zeroth-order (perturbation), first-order (gradient descent), and second-order (e.g., quasi-Newton). But which of these are used in the brain? We trained 12 people to shoot targets, and compared them to simulated subjects that learned the same task using various algorithms. RESULTS: Humans learned significantly faster than optimized zeroth-order algorithms, but slower than second-order ones. CONCLUSIONS: Human visuomotor learning is too fast to be explained by zeroth-order processes alone, and must involve first or second-order mechanisms.