Clinical trial NCT05159661: Intelligent Digital Tools for Screening of Brain Connectivity and Dementia Risk Estimation in People Affected by Mild Cognitive Impairment
Official title: Intelligent Digital Tools for Screening of Brain Connectivity and Dementia Risk Estimation in People Affected by Mild Cognitive Impairment
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Cognitive Dysfunction
Dementia
Mild Cognitive Impairment
Keywords: MCI
AI
EEG
Alzheimer
Dementia
Functional brain connectivity
Deep learning
Machine learning
Lead sponsor: Oslo University Hospital
Sponsor class: OTHER
Collaborators: name: Aalto University
name: Accelopment AG
name: Alzheimer Europe
name: BrainSymph AS
name: Det Norske Veritas
name: Helsinki University Central Hospital
name: IRCCS San Raffaele Roma
name: Lurtis Rules S.L.
name: Neuroconnect Srl
name: Oslo Metropolitan University
name: Radboud University Medical Center
name: Tallinna Ülikool
name: Universidad Complutense de Madrid
name: Catholic University of the Sacred Heart
First posted: 2021-12-16
Enrollment: 1000
Sex: ALL
Minimum age (years): 60
Maximum age (years): 75
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Approximately 1000 MCI participants will be screened and consecutively enrolled at AI-MINDs by the clinical criteria and assessments. All patients will be enrolled by SOA procedures being referred to neurological and memory clinics due to their mild cognitive impaired symptoms.

Subjects that fulfil the AI-Mind criteria for MCI based on the acknowledged criteria by the National Institute on Aging-Alzheimer's Association (NIA-AA), and meet the projects inclusion criteria listed below, will be offered to participate in the AI-Mind clinical study. The clinical identification of MCI is defined using specific assessment tools known to accurately identify cognitive status and progression of cognitive decline. The assessment tools include the use of Mini Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Instrumental activities of daily living IADL and Petersen/Winblad criteria with 1.5 SD in 1 or more cognitive domains.

Brief summary: Every three seconds someone in the world develops dementia. There are over 50 million people worldwide living with dementia and by 2030 this figure is expected to reach 82 million. Besides time-consuming patient investigations with low discriminative power for dementia risk, current treatment options focus on late symptom management. By screening brain connectivity and dementia risk estimation in people affected by mild cognitive impairment, the European Union (EU) funded AI-Mind project will open the door to extending the 'dementia-free' period by offering proper diagnosis and early intervention. AI-Mind will develop two artificial intelligence-based digital tools that will identify dysfunctional brain networks and assess dementia risk. Personalised patient reports will be generated, potentially opening new windows for intervention possibilities.

Detailed description: The aim of this study is to validate an AI based risk assessment tool for new clinical neurological data management in five clinical centres (Oslo OUS, Helsinki HUH, Madrid UCM, Rome IRCCS and Rome UCSC)). Today, around 50% of patients with mild cognitive impairment (MCI) are at risk to develop dementia, and that early risk signs include brain network disturbances as an expression of beginning synaptic dysfunction in the course of dementia development. This synaptic dysfunction can be registered by electrophysiological brain signals. The AI-Mind Connector will identify such disturbed brain network based on EEG technology. Brain networks patterns are identified among other mathematical possibilities by Graph theory. Classical machine learning and deep learning approaches of artificial intelligence will be used in automating these brain network identification processes in existing M/EEG data.

The secondly developed tool, the AI-Mind Predictor, will serve as an enriched Connector, a multimodal prediction method for risk estimation of dementia in MCI patients. In addition to Connector data, cognitive test results, genetic apolipoprotein E (APOE) allele and P-Tau-protein level information are integrated in the AI-Mind Predictor. The AI-Mind Predictor will discriminate between people at risk for further dementia development and non-at-risk. The anticipated high specific and sensitive AI-Mind Predictor results will be compared to state-of-the-art (SOA) approaches.

The cutting-edge AI-Mind model development and testing will be done by available anonymised and prospective pseudo-anonymised data collected at the 5 included clinical centres. Final adaptation, validation, and prototype development will be conducted by the hereby described collection of prospective data of a total 1000 MCI subjects, based on standardized clinical inclusion/exclusion criteria listed below. All patients will sign an informed consent before entering the study.

The patients will follow the AI-Mind protocol for a 2-year period in parallel with the SOA follow-up procedures at each hospital and country. The protocol includes repetitive M/EEG measurements, digitalised cognitive testing, and at the first visit a blood sample for APOE allele and p-Tau 181 analyses. At two of our clinical centres (HUH and UCM) clinical MEG is additionally offered for specific feature extraction for modelling by new EEG based AI-Mind Connector technology.

Importantly, AI-Mind's new data handling procedure will only use existing well-established, globally accessible and low-cost SOA technologies. With AI-Mind's new data processing approach the goal is to increase today's low predictive value (\<0.5) of SOA clinical dementia prediction, and proactively select, with higher accuracy than before, MCI patients at risk to be able to receive earlier clinical intervention. Thereby, AI-Mind wishes to contribute to delaying dementia development by detecting the risk already at the first visit when symptoms occur.

Eligibility criteria: Inclusion Criteria:

* Male and female aged between 60 and 75 years
* MCI diagnosis with a MMSE \>= 25
* or MCI diagnosis with MoCa \>= 17

Exclusion Criteria:

* Confirmed dementia
* History of cerebrovascular disease (i.e. stroke episodes)
* Alcohol Use Disorder Identification Test (AUDIT) score positive
* Severe medical disorders associated with cognitive impairment (organ insufficiencies, chronic infections, endocrinological disorders)
* Severe head trauma with structural brain lesion and/or previous brain surgery;
* Severe mental disorders; Schizophrenia, known Major depression or bipolar disorder
* Neuroimaging evidence of other potential causes of cognitive decline (e.g. subdural haematoma, malignancy)
* History of malignancy \< 5 years;
* Recent use of psychotropic drugs including AChEI and Memantine (\< 3 months);
* Participation in trials with experimental drugs.

Primary outcomes:
measure: AI-Mind Connector with estimated specificity and sensitivity value > 0.9
timeFrame: 2026
measure: AI-Mind Predictor with estimated specificity and sensitivity value > 0.9.
timeFrame: 2026
Secondary outcomes:
measure: Clinical utility of Digital Cognitive Test compared to classical Neuropsychological test
timeFrame: 2026
measure: M/EEG connectivity measures
timeFrame: 2026
Other outcomes:
Locations:
city: Helsinki
country: Finland
facility: Helsinki University Hospital
city: Roma
country: Italy
facility: Università Cattolica del Sacro Cuore Campus di Roma
city: Roma
country: Italy
facility: Scientific Institute for Research, Hospitalization and Healthcare
city: Oslo
country: Norway
facility: Oslo University Hospital
city: Madrid
country: Spain
facility: Universidad Complutense de Madrid
Study officials:
affiliation: Oslo University Hospital
name: Ira Haraldsen, PhD, MD
role: PRINCIPAL_INVESTIGATOR
References: