An observational study in Cognitive Dysfunction, Dementia and Mild Cognitive Impairment, sponsored by Oslo University Hospital. Status unknown at 5 sites in 4 countries. Open to participants aged 60 Years to 75 Years. Per ClinicalTrials.gov, last updated 2024-04-02.
Sponsored by Oslo University Hospital · Observational
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.
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.
3,932 studies on the registry are indexed under Cognitive Dysfunction; 1,114 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 150 across 989 observational studies indexed under Cognitive Dysfunction.
Browse Cognitive Dysfunction studies →Oslo University Hospital is the lead sponsor of 830 studies on the registry; 146 are open to participants now.
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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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
AI-Mind Connector with estimated specificity and sensitivity value > 0.9
To validate the AI-Mind Connector as a biomarker for early brain network connectivity disturbance in at-risk patients.
Time frame: 2026
AI-Mind Predictor with estimated specificity and sensitivity value > 0.9.
To validate the AI-Mind Predictor as a diagnostic support tool for dementia risk evaluation. AI-Mind Predictor with higher prediction value for early dementia risk than current clinical practice.
Time frame: 2026
Clinical utility of Digital Cognitive Test compared to classical Neuropsychological test
Report on the clinical utility of digital cognitive assessment tool for evaluation of cognitive function in people suspected of MCI and compare it to paper pencil classical neuropsychological test (NPT). The specific test (and domains) to be applied are Motor Screening Task (Attention and psychomotor speed), Delayed Matching to Sample (Memory), One Touch Stockings of Cambridge (Executive function), Paired Associates Learning (Memory), Pattern Recognition Memory (Memory), Reaction Time (Attention and psychomotor speed), Rapid Visual Information Processing (Attention and psychomotor speed), Spatial Working Memory (Executive function) and Match to Sample Visual Search (Attention and psychomotor speed). The score of the test domains of the digital cognitive test will be compared to equal domains measured through classical NPT.
Time frame: 2026
M/EEG connectivity measures
To validate the accuracy of different M/EEG (connectivity) data features for predicting the risk of dementia
Time frame: 2026
This study is status unknown, as verified in Apr 2024. You cannot join it, but the record below documents what was studied.
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