Speech-Based Digital Biomarkers for Alzheimer's Research.

Digital biomarkers are of growing interest in the field of Alzheimer's Disease (AD) research. Digital biomarker data arising from digital health tools holds various potential benefits: more objective and more accurate assessment of patients' symptoms and remote collection of signals in real-world scenarios but also multimodal variance for prediction models of individual disease progression. Speech can be collected at minimal patient burden and provides rich data for assessing multiple aspects of AD pathology including cognition. However, the operations around collecting, preparing, and validly interpreting speech data within the context of clinical research on AD remains complex and sometimes challenging. Through a dedicated pipeline of speech collection tools, preprocessing steps and algorithms, precise qualification and quantification of an AD patient's pathology can be achieved from their speech. The aim of this chapter is to describe the methods that are needed to create speech collection scenarios that result in valuable speech-based digital biomarkers for clinical research.

The Reliability and Clinical Validation of Automatically-Derived Verbal Memory Features of the Verbal Learning Test in Early Diagnostics of Cognitive Impairment.

BACKGROUND: Previous research has shown that verbal memory accurately measures cognitive decline in the early phases of neurocognitive impairment. Automatic speech recognition from the verbal learning task (VLT) can potentially be used to differentiate between people with and without cognitive impairment. OBJECTIVE: Investigate whether automatic speech recognition (ASR) of the VLT is reliable and able to differentiate between subjective cognitive decline (SCD) and mild cognitive impairment (MCI). METHODS: The VLT was recorded and processed via a mobile application. Following, verbal memory features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to distinguish between participants with SCD versus MCI/dementia. RESULTS: The ICC for inter-rater reliability between the clinical and automatically derived features was 0.87 for the total immediate recall and 0.94 for the delayed recall. The full model including the total immediate recall, delayed recall, recognition count, and the novel verbal memory features had an AUC of 0.79 for distinguishing between participants with SCD versus MCI/dementia. The ten best differentiating VLT features correlated low to moderate with other cognitive tests such as logical memory tasks, semantic verbal fluency, and executive functioning. CONCLUSIONS: The VLT with automatically derived verbal memory features showed in general high agreement with the clinical scoring and distinguished well between SCD and MCI/dementia participants. This might be of added value in screening for cognitive impairment.

Validation of an Automated Speech Analysis of Cognitive Tasks within a Semiautomated Phone Assessment.

Introduction: We studied the accuracy of the automatic speech recognition (ASR) software by comparing ASR scores with manual scores from a verbal learning test (VLT) and a semantic verbal fluency (SVF) task in a semiautomated phone assessment in a memory clinic population. Furthermore, we examined the differentiating value of these tests between participants with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). We also investigated whether the automatically calculated speech and linguistic features had an additional value compared to the commonly used total scores in a semiautomated phone assessment. Methods: We included 94 participants from the memory clinic of the Maastricht University Medical Center+ (SCD N = 56 and MCI N = 38). The test leader guided the participant through a semiautomated phone assessment. The VLT and SVF were audio recorded and processed via a mobile application. The recall count and speech and linguistic features were automatically extracted. The diagnostic groups were classified by training machine learning classifiers to differentiate SCD and MCI participants. Results: The intraclass correlation for inter-rater reliability between the manual and the ASR total word count was 0.89 (95% CI 0.09-0.97) for the VLT immediate recall, 0.94 (95% CI 0.68-0.98) for the VLT delayed recall, and 0.93 (95% CI 0.56-0.97) for the SVF. The full model including the total word count and speech and linguistic features had an area under the curve of 0.81 and 0.77 for the VLT immediate and delayed recall, respectively, and 0.61 for the SVF. Conclusion: There was a high agreement between the ASR and manual scores, keeping the broad confidence intervals in mind. The phone-based VLT was able to differentiate between SCD and MCI and can have opportunities for clinical trial screening.

Dissociating memory and executive function impairment through temporal features in a word list verbal learning task.

The Rey Auditory Verbal Learning Test (RAVLT) is an established verbal learning test commonly used to quantify memory impairments due to Alzheimer's Disease (AD) both at a clinical dementia stage or prodromal stage of mild cognitive impairment (MCI). Focal memory impairment-as quantified e.g. by the RAVLT-at an MCI stage is referred to as amnestic MCI (aMCI) and is often regarded as the cognitive phenotype of prodromal AD. However, recent findings suggest that not only learning and memory but also other cognitive domains, especially executive functions (EF) and processing speed (PS), influence verbal learning performance. This research investigates whether additional temporal features extracted from audio recordings from a participant's RAVLT response can better dissociate memory and EF in such tasks and eventually help to better describe MCI subtypes. 675 age-matched participants from the H70 Swedish birth cohort were included in this analysis; 68 participants were classified as MCI (33 aMCI and 35 due to executive impairment). RAVLT performances were recorded and temporal features extracted. Novel temporal features were correlated with established neuropsychological tests measuring EF and PS. Lastly, the downstream diagnostic potential of temporal features was estimated using group differences and a machine learning (ML) classification scenario. Temporal features correlated moderately with measures of EF and PS. Performance of an ML classifier could be improved by adding temporal features to traditional counts. We conclude that RAVLT temporal features are in general related to EF and that they might be capable of dissociating memory and EF in a word list learning task.

PROSA-a multicenter prospective observational study to develop low-burden digital speech biomarkers in ALS and FTD.

Objective: There is a need for novel biomarkers that can indicate disease state, project disease progression, or assess response to treatment for amyotrophic lateral sclerosis (ALS) and associated neurodegenerative diseases such as frontotemporal dementia (FTD). Digital biomarkers are especially promising as they can be collected non-invasively and at low burden for patients. Speech biomarkers have the potential to objectively measure cognitive, motor as well as respiratory symptoms at low-cost and in a remote fashion using widely available technology such as telephone calls. Methods: The PROSA study aims to develop and evaluate low-burden frequent prognostic digital speech biomarkers. The main goal is to create a single, easy-to-perform battery that serves as a valid and reliable proxy for cognitive, respiratory, and motor domains in ALS and FTD. The study will be a multicenter 12-months observational study aiming to include 75 ALS and 75 FTD patients as well as 50 healthy controls and build on three established longitudinal cohorts: DANCER, DESCRIBE-ALS and DESCRIBE-FTD. In addition to the extensive clinical phenotyping in DESCRIBE, PROSA collects a comprehensive speech protocol in fully remote and automated fashion over the telephone at four time points. This longitudinal speech data, together with gold standard measures, will allow advanced speech analysis using artificial intelligence for the development of speech-based phenotypes of ALS and FTD patients measuring cognitive, motor and respiratory symptoms. Conclusion: Speech-based phenotypes can be used to develop diagnostic and prognostic models predicting clinical change. Results are expected to have implications for future clinical trial stratification as well as supporting innovative trial designs in ALS and FTD.

The Accuracy of Speech and Linguistic Analysis in Early Diagnostics of Neurocognitive Disorders in a Memory Clinic Setting.

OBJECTIVE: To investigate whether automatic analysis of the Semantic Verbal Fluency test (SVF) is reliable and can extract additional information that is of value for identifying neurocognitive disorders. In addition, the associations between the automatically derived speech and linguistic features and other cognitive domains were explored. METHOD: We included 135 participants from the memory clinic of the Maastricht University Medical Center+ (with Subjective Cognitive Decline [SCD; N = 69] and Mild Cognitive Impairment [MCI]/dementia [N = 66]). The SVF task (one minute, category animals) was recorded and processed via a mobile application, and speech and linguistic features were automatically extracted. The diagnostic performance of the automatically derived features was investigated by training machine learning classifiers to differentiate SCD and MCI/dementia participants. RESULTS: The intraclass correlation for interrater reliability between the clinical total score (golden standard) and automatically derived total word count was 0.84. The full model including the total word count and the automatically derived speech and linguistic features had an Area Under the Curve (AUC) of 0.85 for differentiating between people with SCD and MCI/dementia. The model with total word count only and the model with total word count corrected for age showed an AUC of 0.75 and 0.81, respectively. Semantic switching correlated moderately with memory as well as executive functioning. CONCLUSION: The one-minute SVF task with automatically derived speech and linguistic features was as reliable as the manual scoring and differentiated well between SCD and MCI/dementia. This can be considered as a valuable addition in the screening of neurocognitive disorders and in clinical practice.

Screening for Mild Cognitive Impairment Using a Machine Learning Classifier and the Remote Speech Biomarker for Cognition: Evidence from Two Clinically Relevant Cohorts.

BACKGROUND: Modern prodromal Alzheimer's disease (AD) clinical trials might extend outreach to a general population, causing high screen-out rates and thereby increasing study time and costs. Thus, screening tools that cost-effectively detect mild cognitive impairment (MCI) at scale are needed. OBJECTIVE: Develop a screening algorithm that can differentiate between healthy and MCI participants in different clinically relevant populations. METHODS: Two screening algorithms based on the remote ki:e speech biomarker for cognition (ki:e SB-C) were designed on a Dutch memory clinic cohort (N = 121) and a Swedish birth cohort (N = 404). MCI classification was each evaluated on the training cohort as well as on the unrelated validation cohort. RESULTS: The algorithms achieved a performance of AUC  0.73 and AUC  0.77 in the respective training cohorts and AUC  0.81 in the unseen validation cohorts. CONCLUSION: The results indicate that a ki:e SB-C based algorithm robustly detects MCI across different cohorts and languages, which has the potential to make current trials more efficient and improve future primary health care.

Validation of the Remote Automated ki:e Speech Biomarker for Cognition in Mild Cognitive Impairment: Verification and Validation following DiME V3 Framework.

Introduction: Progressive cognitive decline is the cardinal behavioral symptom in most dementia-causing diseases such as Alzheimer's disease. While most well-established measures for cognition might not fit tomorrow's decentralized remote clinical trials, digital cognitive assessments will gain importance. We present the evaluation of a novel digital speech biomarker for cognition (SB-C) following the Digital Medicine Society's V3 framework: verification, analytical validation, and clinical validation. Methods: Evaluation was done in two independent clinical samples: the Dutch DeepSpA (N = 69 subjective cognitive impairment [SCI], N = 52 mild cognitive impairment [MCI], and N = 13 dementia) and the Scottish SPeAk datasets (N = 25, healthy controls). For validation, two anchor scores were used: the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating (CDR) scale. Results: Verification: The SB-C could be reliably extracted for both languages using an automatic speech processing pipeline. Analytical Validation: In both languages, the SB-C was strongly correlated with MMSE scores. Clinical Validation: The SB-C significantly differed between clinical groups (including MCI and dementia), was strongly correlated with the CDR, and could track the clinically meaningful decline. Conclusion: Our results suggest that the ki:e SB-C is an objective, scalable, and reliable indicator of cognitive decline, fit for purpose as a remote assessment in clinical early dementia trials.

Detecting subtle signs of depression with automated speech analysis in a non-clinical sample.

BACKGROUND: Automated speech analysis has gained increasing attention to help diagnosing depression. Most previous studies, however, focused on comparing speech in patients with major depressive disorder to that in healthy volunteers. An alternative may be to associate speech with depressive symptoms in a non-clinical sample as this may help to find early and sensitive markers in those at risk of depression. METHODS: We included n = 118 healthy young adults (mean age: 23.5 ± 3.7 years; 77% women) and asked them to talk about a positive and a negative event in their life. Then, we assessed the level of depressive symptoms with a self-report questionnaire, with scores ranging from 0-60. We transcribed speech data and extracted acoustic as well as linguistic features. Then, we tested whether individuals below or above the cut-off of clinically relevant depressive symptoms differed in speech features. Next, we predicted whether someone would be below or above that cut-off as well as the individual scores on the depression questionnaire. Since depression is associated with cognitive slowing or attentional deficits, we finally correlated depression scores with performance in the Trail Making Test. RESULTS: In our sample, n = 93 individuals scored below and n = 25 scored above cut-off for clinically relevant depressive symptoms. Most speech features did not differ significantly between both groups, but individuals above cut-off spoke more than those below that cut-off in the positive and the negative story. In addition, higher depression scores in that group were associated with slower completion time of the Trail Making Test. We were able to predict with 93% accuracy who would be below or above cut-off. In addition, we were able to predict the individual depression scores with low mean absolute error (3.90), with best performance achieved by a support vector machine. CONCLUSIONS: Our results indicate that even in a sample without a clinical diagnosis of depression, changes in speech relate to higher depression scores. This should be investigated in more detail in the future. In a longitudinal study, it may be tested whether speech features found in our study represent early and sensitive markers for subsequent depression in individuals at risk.

Validation of a digital, tablet-based version of the Trail Making Test in the ∆elta platform.

Using digital technology for neuropsychological assessment is gaining popularity in both clinical and research settings. Digital neuropsychology offers many benefits over the traditional paper-pencil assessments; however, their comparability requires further validation. The aim of this study was to compare a digital, tablet-based Trail Making Test to the standard paper version. In a within-subject design, 108 healthy adults completed both digital and paper Trail Making Test in a counterbalanced order. Each participant also performed other tasks measuring core executive abilities (inhibition, working memory, and flexibility) on the tablet. Our findings indicated that the Trail Making Test performance on the two different modalities correlated significantly. Furthermore, correlations of Trail Making Test performance with other cognitive tasks revealed that digital Trail Making Test is comparable with the paper version. However, the modality had a significant effect on Trail Making Test performance; that is, participants were generally faster on the digital platform. Taken together, our findings suggest that with new normative data, traditional Trail Making Test can be adapted successfully to a digital platform.

Patients with amnestic MCI Fail to Adapt Executive Control When Repeatedly Tested with Semantic Verbal Fluency Tasks.

OBJECTIVE: Semantic verbal fluency (SVF) tasks require individuals to name items from a specified category within a fixed time. An impaired SVF performance is well documented in patients with amnestic Mild Cognitive Impairment (aMCI). The two leading theoretical views suggest either loss of semantic knowledge or impaired executive control to be responsible. METHOD: We assessed SVF 3 times on 2 consecutive days in 29 healthy controls (HC) and 29 patients with aMCI with the aim to answer the question which of the two views holds true. RESULTS: When doing the task for the first time, patients with aMCI produced fewer and more common words with a shorter mean response latency. When tested repeatedly, only healthy volunteers increased performance. Likewise, only the performance of HC indicated two distinct retrieval processes: a prompt retrieval of readily available items at the beginning of the task and an active search through semantic space towards the end. With repeated assessment, the pool of readily available items became larger in HC, but not patients with aMCI. CONCLUSION: The production of fewer and more common words in aMCI points to a smaller search set and supports the loss of semantic knowledge view. The failure to improve performance as well as the lack of distinct retrieval processes point to an additional impairment in executive control. Our data did not clearly favour one theoretical view over the other, but rather indicates that the impairment of patients with aMCI in SVF is due to a combination of both.

Measuring neuropsychiatric symptoms in patients with early cognitive decline using speech analysis.

BACKGROUND: Certain neuropsychiatric symptoms (NPS), namely apathy, depression, and anxiety demonstrated great value in predicting dementia progression, representing eventually an opportunity window for timely diagnosis and treatment. However, sensitive and objective markers of these symptoms are still missing. Therefore, the present study aims to investigate the association between automatically extracted speech features and NPS in patients with mild neurocognitive disorders. METHODS: Speech of 141 patients aged 65 or older with neurocognitive disorder was recorded while performing two short narrative speech tasks. NPS were assessed by the neuropsychiatric inventory. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, correlated with NPS. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. RESULTS: Different speech variables are associated with specific NPS; apathy correlates with temporal aspects, and anxiety with voice quality-and this was mostly consistent between male and female after correction for cognitive impairment. Machine learning regressors are able to extract information from speech features and perform above baseline in predicting anxiety, apathy, and depression scores. CONCLUSIONS: Different NPS seem to be characterized by distinct speech features, which are easily extractable automatically from short vocal tasks. These findings support the use of speech analysis for detecting subtypes of NPS in patients with cognitive impairment. This could have great implications for the design of future clinical trials as this cost-effective method could allow more continuous and even remote monitoring of symptoms.

Language Impairment in Alzheimer's Disease-Robust and Explainable Evidence for AD-Related Deterioration of Spontaneous Speech Through Multilingual Machine Learning.

Alzheimer's disease (AD) is a pervasive neurodegenerative disease that affects millions worldwide and is most prominently associated with broad cognitive decline, including language impairment. Picture description tasks are routinely used to monitor language impairment in AD. Due to the high amount of manual resources needed for an in-depth analysis of thereby-produced spontaneous speech, advanced natural language processing (NLP) combined with machine learning (ML) represents a promising opportunity. In this applied research field though, NLP and ML methodology do not necessarily ensure robust clinically actionable insights into cognitive language impairment in AD and additional precautions must be taken to ensure clinical-validity and generalizability of results. In this study, we add generalizability through multilingual feature statistics to computational approaches for the detection of language impairment in AD. We include 154 participants (78 healthy subjects, 76 patients with AD) from two different languages (106 English speaking and 47 French speaking). Each participant completed a picture description task, in addition to a battery of neuropsychological tests. Each response was recorded and manually transcribed. From this, task-specific, semantic, syntactic and paralinguistic features are extracted using NLP resources. Using inferential statistics, we determined language features, excluding task specific features, that are significant in both languages and therefore represent "generalizable" signs for cognitive language impairment in AD. In a second step, we evaluated all features as well as the generalizable ones for English, French and both languages in a binary discrimination ML scenario (AD vs. healthy) using a variety of classifiers. The generalizable language feature set outperforms the all language feature set in English, French and the multilingual scenarios. Semantic features are the most generalizable while paralinguistic features show no overlap between languages. The multilingual model shows an equal distribution of error in both English and French. By leveraging multilingual statistics combined with a theory-driven approach, we identify AD-related language impairment that generalizes beyond a single corpus or language to model language impairment as a clinically-relevant cognitive symptom. We find a primary impairment in semantics in addition to mild syntactic impairment, possibly confounded by additional impaired cognitive functions.

Autoimmune Global Amnesia as Manifestation of AMPAR Encephalitis and Neuropathologic Findings.

OBJECTIVE: To report an unusual clinical phenotype of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) encephalitis and describe associated neuropathologic findings. METHODS: We retrospectively investigated 3 AMPAR encephalitis patients with autoimmune global hippocampal amnesia using comprehensive cognitive and neuropsychologic assessment, antibody testing by in-house tissue-based and cell-based assays, and neuropathologic analysis of brain autopsy tissue including histology and immunohistochemistry. RESULTS: Three patients presented with acute-to-subacute global amnesia without affection of cognitive performance, attention, concentration, or verbal function. None of the patients had epileptic seizures, change of behavior, personality changes, or psychiatric symptoms. The MRI was normal in 1 patient and showed increased fluid-attenuated inversion recovery/T2 signal in the hippocampus in the other 2 patients. Two patients showed complete remission after immunotherapy. The one patient who did not improve had an underlying adenocarcinoma of the lung and died 3.5 months after disease onset because of tumor progression. Neuropathologic analysis of the brain autopsy revealed unilateral hippocampal sclerosis accompanied by mild inflammatory infiltrates, predominantly composed of T lymphocytes, and decrease of AMPAR immunoreactivity. CONCLUSION: AMPAR antibodies usually associate with limbic encephalitis but may also present with immune responsive, acute-to-subacute, isolated hippocampal dysfunction without overt inflammatory CSF or MRI changes.

CH and NO planar laser-induced fluorescence and Rayleigh-scattering in turbulent flames using a multimode optical parametric oscillator.

An optical parametric oscillator (OPO) is developed and characterized for the simultaneous generation of ultraviolet (UV) and near-UV nanosecond laser pulses for the single-shot Rayleigh scattering and planar laser-induced-fluorescence (PLIF) imaging of methylidyne (CH) and nitric oxide (NO) in turbulent flames. The OPO is pumped by a multichannel, 8-pulse Nd:YAG laser cluster that produces up to 225 mJ/pulse at 355 nm with pulse spacing of 100 µs. The pulsed OPO has a conversion efficiency of 9.6% to the signal wavelength of ∼430nm when pumped by the multimode laser. Second harmonic conversion of the signal, with 3.8% efficiency, is used for the electronic excitation of the A-X (1,0) band of NO at ∼215nm, while the residual signal at 430 nm is used for direct excitation of the A-X (0,0) band of the CH radical and elastic Rayleigh scattering. The section of the OPO signal wavelength for simultaneous CH and NO PLIF imaging is performed with consideration of the pulse energy, interference from the reactant and product species, and the fluorescence signal intensity. The excitation wavelengths of 430.7 nm and 215.35 nm are studied in a laminar, premixed CH4-H2-NH3-air flame. Single-shot CH and NO PLIF and Rayleigh scatter imaging is demonstrated in a turbulent CH4-H2-NH3 diffusion flame using a high-speed intensified CMOS camera. Analysis of the complementary Rayleigh scattering and CH and NO PLIF enables identification and quantification of the high-temperature flame layers, the combustion product zones, and the fuel-jet core. Considerations for extension to simultaneous, 10-kHz-rate acquisition are discussed.

Detecting Apathy in Older Adults with Cognitive Disorders Using Automatic Speech Analysis.

BACKGROUND: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. OBJECTIVE: This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterization and detection of apathy. METHODS: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source, and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. RESULTS: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AUC = 0.88 for males and AUC = 0.77 for females. CONCLUSIONS: These findings reinforce the usability of speech as a reliable biomarker in the detection and assessment of apathy.

Exploitation vs. exploration-computational temporal and semantic analysis explains semantic verbal fluency impairment in Alzheimer's disease.

Impaired Semantic Verbal Fluency (SVF) in dementia due to Alzheimer's Disease (AD) and its precursor Mild Cognitive Impairment (MCI) is well known. Yet, it remains open whether this impairment mirrors the breakdown of semantic memory retrieval processes or executive control processes. Therefore, qualitative analysis of the SVF has been proposed but is limited in terms of methodology and feasibility in clinical practice. Consequently, research draws no conclusive picture which of these afore-mentioned processes drives the SVF impairment in AD and MCI. This study uses a qualitative computational approach-combining temporal and semantic information-to investigate exploitation and exploration patterns as indicators for semantic memory retrieval and executive control processes. Audio SVF recordings of 20 controls (C, 66-81 years), 55 MCI (57-94 years) and 20 AD subjects (66-82 years) were assessed while groups were matched according to age and education. All groups produced, on average, the same amount of semantically related items in rapid succession within word clusters. Conversely, towards AD, there was a clear decline in semantic as well as temporal exploration patterns between clusters. Results strongly point towards preserved exploitation-semantic memory retrieval processes-and hampered exploration-executive control processes-in AD and potentially in MCI.

Fully Automatic Speech-Based Analysis of the Semantic Verbal Fluency Task.

BACKGROUND: Semantic verbal fluency (SVF) tests are routinely used in screening for mild cognitive impairment (MCI). In this task, participants name as many items as possible of a semantic category under a time constraint. Clinicians measure task performance manually by summing the number of correct words and errors. More fine-grained variables add valuable information to clinical assessment, but are time-consuming. Therefore, the aim of this study is to investigate whether automatic analysis of the SVF could provide these as accurate as manual and thus, support qualitative screening of neurocognitive impairment. METHODS: SVF data were collected from 95 older people with MCI (n = 47), Alzheimer's or related dementias (ADRD; n = 24), and healthy controls (HC; n = 24). All data were annotated manually and automatically with clusters and switches. The obtained metrics were validated using a classifier to distinguish HC, MCI, and ADRD. RESULTS: Automatically extracted clusters and switches were highly correlated (r = 0.9) with manually established values, and performed as well on the classification task separating HC from persons with ADRD (area under curve [AUC] = 0.939) and MCI (AUC = 0.758). CONCLUSION: The results show that it is possible to automate fine-grained analyses of SVF data for the assessment of cognitive decline.

Three-color vibrational CARS thermometry of fuel-rich ethylene/air flames using a potassium gadolinium tungstate Raman-active crystal as a source of narrowband probe radiation.

Three-color broadband vibrational coherent anti-Stokes Raman scattering (CARS) temperature measurements were carried out in laminar fuel-rich sooting ethylene/air flames. Stimulated Raman scattering (SRS) of a picosecond pump laser pulse in a Raman-active potassium gadolinium tungstate [KGd(WO4)2] crystal was employed as a source of narrowband probe radiation. In the three-color CARS experiment, this wavelength-shifted radiation enables N2-based vibrational CARS temperature measurements in sooting flames free of the signal interference with the absorption/emission bands of the flame intermediate radicals C2. Spatial temperature profiles for different fuel-rich atmospheric pressure ethylene/air flames are presented in comparison with the results of two-color broadband vibrational and dual-broadband pure rotational CARS temperature measurements. The comparison shows the suitability of the three-color CARS measurement technique employing the KGd(WO4)2 crystal for accurate, C2 interference-free, temperature measurements in sooting flames.

Recognition memory for low- and high-frequency-filtered emotional faces: Low spatial frequencies drive emotional memory enhancement, whereas high spatial frequencies drive the emotion-induced recognition bias.

This article deals with two well-documented phenomena regarding emotional stimuli: emotional memory enhancement-that is, better long-term memory for emotional than for neutral stimuli-and the emotion-induced recognition bias-that is, a more liberal response criterion for emotional than for neutral stimuli. Studies on visual emotion perception and attention suggest that emotion-related processes can be modulated by means of spatial-frequency filtering of the presented emotional stimuli. Specifically, low spatial frequencies are assumed to play a primary role for the influence of emotion on attention and judgment. Given this theoretical background, we investigated whether spatial-frequency filtering also impacts (1) the memory advantage for emotional faces and (2) the emotion-induced recognition bias, in a series of old/new recognition experiments. Participants completed incidental-learning tasks with high- (HSF) and low- (LSF) spatial-frequency-filtered emotional and neutral faces. The results of the surprise recognition tests showed a clear memory advantage for emotional stimuli. Most importantly, the emotional memory enhancement was significantly larger for face images containing only low-frequency information (LSF faces) than for HSF faces across all experiments, suggesting that LSF information plays a critical role in this effect, whereas the emotion-induced recognition bias was found only for HSF stimuli. We discuss our findings in terms of both the traditional account of different processing pathways for HSF and LSF information and a stimulus features account. The double dissociation in the results favors the latter account-that is, an explanation in terms of differences in the characteristics of HSF and LSF stimuli.