A Computerized Cognitive Test Battery for Detection of Dementia and Mild Cognitive Impairment: Instrument Validation Study.

BACKGROUND: Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. OBJECTIVE: The aim of this study was to evaluate a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. METHODS: A total of 99 participants diagnosed with dementia, mild cognitive impairment (MCI), or normal cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. RESULTS: BrainCheck battery performance showed significant differences between the NC, MCI, and dementia groups, achieving 88% or higher sensitivity and specificity (ie, true positive and true negative rates) for separating dementia from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and dementia groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. CONCLUSIONS: BrainCheck was able to distinguish between diagnoses of dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

Assessing the Utility of Language and Voice Biomarkers to Predict Cognitive Impairment in the Framingham Heart Study Cognitive Aging Cohort Data.

BACKGROUND: There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE: To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS: This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS: Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION: Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

Investigating voice as a biomarker: Deep phenotyping methods for early detection of Parkinson's disease.

Voice technology has grown tremendously in recent years and using voice as a biomarker has also been gaining evidence. We demonstrate the potential of voice in serving as a deep phenotype for Parkinson's Disease (PD), the second most common neurodegenerative disorder worldwide, by presenting methodology for voice signal processing for clinical analysis. Detection of PD symptoms typically requires an exam by a movement disorder specialist and can be hard to access and inconsistent in findings. A vocal digital biomarker could supplement the cumbersome existing manual exam by detecting and quantifying symptoms to guide treatment. Specifically, vocal biomarkers of PD are a potentially effective method of assessing symptoms and severity in daily life, which is the focus of the current research. We analyzed a database of PD patient and non-PD subjects containing voice recordings that were used to extract paralinguistic features, which served as inputs to machine learning models to predict PD severity. The results are presented here and the limitations are discussed given the nature of the recordings. We note that our methodology only advances biomarker research and is not cleared for clinical use. Specifically, we demonstrate that conventional machine learning models applied to voice signals can be used to differentiate participants with PD who exhibit little to no symptoms from healthy controls. This work highlights the potential of voice to be used for early detection of PD and indicates that voice may serve as a deep phenotype for PD, enabling precision medicine by improving the speed, accuracy, accessibility, and cost of PD management.

By the numbers: ratings and utilization of behavioral health mobile applications.

Although >10,000 behavioral health applications ("apps") are currently available on the Apple and Google Play marketplaces, they have been minimally evaluated or regulated and little is known about "real world" usage patterns. This investigation combined data from online behavioral health app rating frameworks and a mobile health market research firm to identify the most downloaded apps as well as determine rating and ranking concordance between frameworks. Findings demonstrated that the most commonly downloaded apps focus on relaxation, mindfulness, and meditation skills and that they often have notably discordant reviews across rating frameworks. Our results suggest that there is a growing need for: (1) standardized behavioral health app quality and effectiveness measures, (2) up-to-date behavioral health app guidance for clinicians and consumers, and (3) evidence-based apps that incorporate revealed consumer preferences.

Schizophrenia and the gut-brain axis.

Several risk factors for the development of schizophrenia can be linked through a common pathway in the intestinal tract. It is now increasingly recognized that bidirectional communication exists between the brain and the gut that uses neural, hormonal, and immunological routes. An increased incidence of gastrointestinal (GI) barrier dysfunction, food antigen sensitivity, inflammation, and the metabolic syndrome is seen in schizophrenia. These findings may be influenced by the composition of the gut microbiota. A significant subgroup of patients may benefit from the initiation of a gluten and casein-free diet. Antimicrobials and probiotics have therapeutic potential for reducing the metabolic dysfunction and immune dysregulation seen in patients with schizophrenia.

Modular MR-compatible lower leg exercise device for whole-body scanners.

PURPOSE: To develop a modular MR-compatible lower leg exercise device for muscle testing using a clinical 3 T MR scanner. MATERIALS AND METHODS: An exercise device to provide isotonic resistance to plantar- or dorsiflexion was constructed from nonferrous materials and designed for easy setup and use in a clinical environment. Validation tests were performed during dynamic MR acquisitions. For this purpose, the device was tested on the posterior lower leg musculature of five subjects during 3 min of exercise at 30% of maximum voluntary plantarflexion during 31-phosphorus MR spectroscopy ((31)P-MRS). Measures of muscle phosphocreatine (PCr), inorganic phosphate (Pi), and pH were obtained before, during, and after the exercise protocol. RESULTS: At the end of exercise regimen, muscle PCr showed a 28% decrease from resting levels (to 21.8 ± 3.9 from 30.4 ± 3.0 mM) and the average PCr recovery rate was 35.3 ± 8.3 s. Muscle Pi concentrations increased 123% (to 14.6 ± 4.7 from 6.5 ± 3.3 mM) and pH decreased 1.5% (to 7.06 ± 0.14 from 7.17 ± 0.07) from resting levels. CONCLUSION: The described MR-compatible lower leg exercise was an effective tool for data acquisition during dynamic MR acquisitions of the calf muscles. The modular design allows for adaptation to other whole-body MR scanners and incorporation of custom-built mechanical or electronic interfaces and can be used for any MR protocol requiring dynamic evaluation of calf muscles.