Training Response Artificial Intelligence Network (TRAIN)

Amnestic Mild Cognitive Impairment Clinical Trial

Official title: Using Artificial Intelligence to Predict Cognitive Training Response in Amnestic Mild Cognitive Impairment

Status Recruiting

Clinical Trial Summary

This study will train a machine learning tool to predict response to a cognitive training intervention using baseline brain MRI sequences from older adults with amnestic mild cognitive impairment.

Clinical Trial Description

This randomized controlled clinical trial proposes to test a well-supported Cognitive Training intervention approach for enhancing cognition in older adults who meet criteria for amnestic mild cognitive impairment (aMCI). The trial will recruit participants recently enrolled in a larger multi-site clinical trial (NCT04171323) at the University of Florida site based on meeting inclusion criteria and willingness to participate in the additional procedures (baseline MRI) required. The intervention will consist of a take-home, iPad-based, adaptive cognitive training (Posit Science Brain HQ Suite) program which participants will be trained to self-administer 2-3 days/week for 60-minutes per session over the course of 16 weeks with a goal of completing 40 training sessions total. Participants randomized to one of four arms of cognitive training or an active control condition. All cognitive training arms will include training on a measure of rapid decision making (Double Decision, similar to the Useful Field of View [UFOV] task) in addition to a combination of other subtests to determine which combination best facilitates cognitive gains.

Outcomes: The investigators plan to test the hypotheses that 1) Compared to the active control condition, CT will result in greater than 1.0SD cognitive improvement on several trained tasks (proximal transfer) and untrained cognitive skills (near transfer) for 20% of the sample, while another 20% will continue to experience cognitive decline equivalent to 1.0SD or greater despite adequate adherence to the intervention, and 2) baseline factors related to cognitive function, brain structure, and resting-state brain function can ultimately predict which individuals will show improvement after the 16-week intervention. These baseline factors will be measured by a well-established neuropsychological battery, the useful field of view (UFOV) task, a detailed set of MRI sequences, and their associations with the outcomes will be determined by a type of machine learning known as multi-modal support vector machine (SVM). This type of analysis will allow the integration of a myriad of data points and MRI features to combine into a highly accurate predictive model for cognitive functioning improvement after intervention. The primary outcome measure that will be predicted by the SVM is the UFOV composite score change, which is a combination of post-minus-pre intervention change across UFOV processing speed, selective attention, and divided attention scores. The secondary outcome measure that will be predicted by the SVM is the Posit Composite score, which is a fixed difficulty assessment of proximal transfer effects of the trained tasks. Assessments will occur at baseline (month 1) and at the end of the 16-week intervention (Month 4). Brain imaging will only be performed at baseline. The primary aim is to further establish the efficacy of this well-supported cognitive training paradigm in a population of patients with amnestic Mild Cognitive Impairment, and the secondary aim is to distinguish intervention responders from non-responders using baseline cognitive, functional-neural, and demographic data alone. ;

Related Conditions & MeSH terms

• Amnestic Mild Cognitive Impairment
• Cognitive Dysfunction

• NCT number: NCT05903573
• Study type: Interventional
• Source: University of Florida
• Contact: Joseph Gullett, Ph.D.
• Phone: 352-294-8631
• Email: gullettj@phhp.ufl.edu
• Status: Recruiting
• Phase: N/A
• Start date: October 9, 2023
• Completion date: April 30, 2028