Perspectives on the diagnosis and management of functional cognitive disorder: An international Delphi study

BACKGROUND

Current proposed criteria for functional cognitive disorder (FCD) have not been externally validated. We sought to analyse the current perspectives of cognitive specialists in the diagnosis and management of FCD in comparison with neurodegenerative conditions.

METHODS

International experts in cognitive disorders were invited to assess seven illustrative clinical vignettes containing history and bedside characteristics alone. Participants assigned a probable diagnosis and selected the appropriate investigation and treatment. Qualitative, quantitative and inter-rater agreement analyses were undertaken.

RESULTS

Eighteen diagnostic terminologies were assigned by 45 cognitive experts from 12 countries with a median of 13 years of experience, across the seven scenarios. Accurate discrimination between FCD and neurodegeneration was observed, independently of background and years of experience: 100% of the neurodegenerative vignettes were correctly classified and 75%-88% of the FCD diagnoses were attributed to non-neurodegenerative causes. There was <50% agreement in the terminology used for FCD, in comparison with 87%-92% agreement for neurodegenerative syndromes. Blood tests and neuropsychological evaluation were the leading diagnostic modalities for FCD. Diagnostic communication, psychotherapy and psychiatry referral were the main suggested management strategies in FCD.

CONCLUSIONS

Our study demonstrates the feasibility of distinguishing between FCD and neurodegeneration based on relevant patient characteristics and history details. These characteristics need further validation and operationalisation. Heterogeneous labelling and framing pose clinical and research challenges reflecting a lack of agreement in the field. Careful consideration of FCD diagnosis is advised, particularly in the presence of comorbidities. This study informs future research on diagnostic tools and evidence-based interventions.

Speech patterns in responses to questions asked by an intelligent virtual agent can help to distinguish between people with early stage neurodegenerative disorders and healthy controls

Previous research has provided strong evidence that speech patterns can help to distinguish between people with early stage neurodegenerative disorders (ND) and healthy controls. This study examined speech patterns in responses to questions asked by an intelligent virtual agent (IVA): a talking head on a computer which asks pre-recorded questions. The study investigated whether measures of response length, speech rate and pausing in responses to questions asked by an IVA help to distinguish between healthy control participants and people diagnosed with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD). The study also considered whether those measures can further help to distinguish between people with MCI, people with AD, and healthy control participants (HC). There were 38 people with ND (31 people with MCI, 7 people with AD) and 26 HC. All interactions took place in English. People with MCI spoke fewer words compared to HC, and people with AD and people with MCI spoke for less time than HC. People with AD spoke at a slower rate than people with MCI and HC. There were significant differences across all three groups for the proportion of time spent pausing and the average pause duration: silent pauses make up the greatest proportion of responses from people with AD, who also have the longest average silent pause duration, followed by people with MCI then HC. Therefore, the study demonstrates the potential of an IVA as a method for collecting data showing patterns which can help to distinguish between diagnostic groups.

Multimodal Capture of Patient Behaviour for Improved Detection of Early Dementia: Clinical Feasibility and Preliminary Results

Non-invasive automatic screening for Alzheimer’s disease has the potential to improve diagnostic accuracy while lowering healthcare costs. Previous research has shown that patterns in speech, language, gaze, and drawing can help detect early signs of cognitive decline. In this paper, we describe a highly multimodal system for unobtrusively capturing data during real clinical interviews conducted as part of cognitive assessments for Alzheimer’s disease. The system uses nine different sensor devices (smartphones, a tablet, an eye tracker, a microphone array, and a wristband) to record interaction data during a specialist’s first clinical interview with a patient, and is currently in use at Karolinska University Hospital in Stockholm, Sweden. Furthermore, complementary information in the form of brain imaging, psychological tests, speech therapist assessment, and clinical meta-data is also available for each patient. We detail our data-collection and analysis procedure and present preliminary findings that relate measures extracted from the multimodal recordings to clinical assessments and established biomarkers, based on data from 25 patients gathered thus far. Our findings demonstrate feasibility for our proposed methodology and indicate that the collected data can be used to improve clinical assessments of early dementia.

Intelligent agent-assisted organizational memory in knowledge management systems

Purpose

The purpose of this paper is to examine the application of intelligent agents (IAs) in organizational memory systems within the larger schema of knowledge management (KM) strategies. This includes targeted roles of IAs in relation to institutional memory approaches.

Design/methodology/approach

A conceptual exploration of related sections of the Grundspenkis seven-layer intelligent enterprise memory framework that serves to identify, retain, deliver and reuse information for future utilization is conducted. Applications of IAs in multiple industries are presented to illustrate the conceptual model in practice.

Findings

This paper identifies arising roles that IAs perform in information search, retrieval and analysis in the organizational memory formation process and extensions that have emerged in a non-linear bi-directional form. These layered roles include obtaining and reapplying important information as part of extended human–machine cognition.

Research limitations/implications

While exploratory and conceptual in nature, this research paper discusses IAs as possible components in the advancement of organization memory.

Practical implications

By analyzing the application of IAs in different industries, across select layers of a KM structure, groundwork is laid for both descriptive research (i.e. where and how artificial intelligence is being used in those industries) and prescriptive practice (i.e. how other industries can benefit from such assistance and what patterns of implementation to expect).

Originality/value

This study explores the role IAs play in helping knowledge workers gather, retain and find relevant information and how KM strategies may assist organizational memory.