From "invisible" to "audible": Features extracted during simple speech tasks classify patient-reported fatigue in multiple sclerosis.

Background: Fatigue is a major "invisible" symptom in people with multiple sclerosis (PwMS), which may affect speech. Automated speech analysis is an objective, rapid tool to capture digital speech biomarkers linked to functional outcomes.

Objective: To use automated speech analysis to assess multiple sclerosis (MS) fatigue metrics.

Corrigendum: An automatic measure for speech intelligibility in dysarthrias-validation across multiple languages and neurological disorders.

[This corrects the article DOI: 10.3389/fdgth.2024.

The voice of depression: speech features as biomarkers for major depressive disorder.

Background: Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures.

Detecting fatigue in multiple sclerosis through automatic speech analysis.

Multiple sclerosis (MS) is a chronic neuroinflammatory disease characterized by central nervous system demyelination and axonal degeneration. Fatigue affects a major portion of MS patients, significantly impairing their daily activities and quality of life. Despite its prevalence, the mechanisms underlying fatigue in MS are poorly understood, and measuring fatigue remains a challenging task.

User Experience of a (Semi-) Automated Cognitive Phone-Based Assessment Within a Memory Clinic Population.

Objective: We examined the user experience in different modalities (face-to-face, semi-automated phone-based, and fully automated phone-based) of cognitive testing in people with subjective cognitive decline and mild cognitive impairment.

Method: A total of 67 participants from the memory clinic of the Maastricht University Medical Center+ participated in the study. The study consisted of cognitive tests in different modalities, namely, face-to-face, semi-automated phone-based guided by a researcher, and fully automated phone-based without the involvement of a researcher.

An automatic measure for speech intelligibility in dysarthrias-validation across multiple languages and neurological disorders.

Introduction: Dysarthria, a motor speech disorder caused by muscle weakness or paralysis, severely impacts speech intelligibility and quality of life. The condition is prevalent in motor speech disorders such as Parkinson's disease (PD), atypical parkinsonism such as progressive supranuclear palsy (PSP), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). Improving intelligibility is not only an outcome that matters to patients but can also play a critical role as an endpoint in clinical research and drug development.

Remote data collection speech analysis in people at risk for Alzheimer's disease dementia: usability and acceptability results.

Introduction: Digital cognitive assessments are gathering importance for the decentralized remote clinical trials of the future. Before including such assessments in clinical trials, they must be tested to confirm feasibility and acceptability with the intended participant group. This study presents usability and acceptability data from the Speech on the Phone Assessment (SPeAk) study.

Modern scores for traditional tests - Review of the diagnostic potential of scores derived from word list learning tests in mild cognitive impairment and early Alzheimer's Disease.

Episodic memory impairment is one of the early hallmarks in Alzheimer's Disease. In the clinical diagnosis and research, episodic memory impairment is typically assessed using word lists that are repeatedly presented to and recalled by the participant across several trials. Until recently, total learning scores, which consist of the total number of words that are recalled by participants, were almost exclusively used for diagnostic purposes.

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.

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).

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.

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.

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

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.

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]).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.