Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment

Abstract

The present study aimed to explore the effect of computerized multi-domain cognitive training (MDCT) on brain gray matter volume and neuropsychological performance in patients with amnestic mild cognitive impairment (amnestic MCI). Twenty-one patients with amnestic MCI participated in a computerized MDCT program. The program targeted a broad set of cognitive domains via programs focused on reasoning, memory, visuospatial, language, calculation, and attention. Seventeen Participants completed the intervention and all completed a battery of neuropsychological tests to evaluate cognitive function while 12 out of 17 underwent 3 T MRI scanning before and after the intervention to measure gray matter (GM) volume. We examined correlations between the changes in neuropsychological scores and GM volumes across participants after the intervention. After training, we observed significant increases in GM volume in the right angular gyrus (AG) and other parietal subareas near the intraparietal sulcus (p < 0.05, FWE-corrected, 10000 permutations). However, we found no significant changes in neuropsychological test scores (p > 0.05). A correlation analysis revealed positive correlations between the changes in GM volume in the right AG and scores in the immediate recall component of the Hopkins Verbal Learning Test-Revised (HVLT-R) (r = 0.64, p = 0.024) and the Brief Visuospatial Memory Test–Revised (BVMT-R) (r = 0.67, p = 0.016). Our findings indicate that a computerized MDCT program may protect patients with amnestic MCI against brain GM volume loss and has potential in preserving general cognition. Thus, our non-pharmacological intervention may slow the rate of disease progression.

A new Graph Gaussian embedding method for analyzing the effects of cognitive training

“…In addition to the within-subject analysis quantifying network alterations after intervention, we also analyzed statistically network alterations at the group-level by defining a new measure, the reorganization index (RI). In this case too, we found that a large number of ROIs were affected after intervention (Fig 8), and these changes at the system/community level (Fig 9) were comparable to the ones we obtained with the within-subject analysis in Fig 7B. Taken together, our results using two different approaches (MG2G and node2vec) and two different methods of analysis (top-15 ROI and t-test) showed consistency in the regions affected by the MDCT intervention, with details of each region presented in S2 Table in Supporting Information. In addition to fMRI networks, in previous work [4] we have investigated the MDCT intervention effects on structural MRI data and found significant increases in gray matter volume in the right angular gyrus and other subareas following the MDCT intervention. In the current study, we further investigated the underlying MDCT intervention effects at both ROI-level and community-level on the fMRI networks.…”

“…Hence, non-pharmacological cognitive intervention for patients at early stages of AD has received a lot of attention due to its non-invasive manner, safety and scalability. Recent studies show that non-pharmacological cognitive intervention can play a positive role in delaying the process or even reducing the cognitive decline for both healthy controls [3] and amnestic mild cognitive impairment (aMCI) patients [4]. In particular, aMCI is a vital prodromal state of AD harboring memory impairment and has a high risk to progress into AD [5].…”

“…In the present study, we focused on functional brain network analysis for aMCI patients, who completed a multi-domain cognitive training (MDCT) intervention that was designed at the PKU-sixth hospital of China. For each of 12 patients, resting-state functional MRI scans and cognitive assessment scores (MMSE [21] and MOCA [22]) were collected before and immediately after a 12-week intervention [4]. The new method we propose enables mapping of brain networks into multivariate probabilistic Gaussian distributions so as to detect the underlying link changes of functional brain connectomes after the MDCT intervention.…”

Harnessing CURATE.AI as a Digital Therapeutics Platform by Identifying N‐of‐1 Learning Trajectory Profiles

“…Additionally, the quadratic surfaces comprising the core of CURATE.AI have been implemented in drug combination discovery platforms to aid in narrowing expansive drug libraries, find the most efficacious combinations from a large parameter space (e.g., dose ranges), and even elucidate and understand the relationships of underlying mechanisms within in vivo and in vitro systems of interest representative of various oncological and infectious disease indications, such as multiple myeloma and tuberculosis. Multiple studies have shown the efficacy of cognitive training delivered in a digital form, that is, to assess dementia state, [44] slow down progression of amnestic mild cognitive impairment, [45] and eventually remediate agerelated deficits in cognitive control. CURATE.AI does not rely on synergy prediction between the various inputs in order to globally optimize intervention.…”

Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

“…In the present study, we focused on functional brain network analysis for aMCI patients, who completed a multi-domain cognitive training (MDCT) intervention that was designed at the PKU-sixth hospital of China. For each of 12 patients, resting-state functional MRI scans and cognitive assessment scores (MMSE 10 and MOCA 11 ) were collected before and immediately after a 12-week intervention 4 . Aiming to investigate quantitatively the underlying functional brain network changes associated with the MDCT intervention, we propose a new approach based on an unsupervised Gaussian embedding-based functional brain network analysis for resting state fMRI data.…”