Reduced Capacity of Cognitive Control in Older Adults with Mild Cognitive Impairment

Attention control training and transfer effects on cognitive tasks

Attention control is the common element underlying different executive functions. The backward Masking Majority Function Task (MFT-M) requires intensive attention control, and represents a diverse situation where attentional resources need to be allocated dynamically and flexibly to reduce uncertainty. Aiming to train attention control using MFT-M and examine the training transfer effects in various executive functions, we recruited healthy young adults (n = 84) and then equally randomized them into two groups trained with either MFT-M or a sham program for seven consecutive days. Cognitive evaluations were conducted before and after the training, and the electroencephalograph (EEG) signals were recorded for the revised Attention Network Test (ANT-R), N-back, and Task-switching (TS) tasks. Compared to the control group, the training group performed better on the congruent condition of Flanker and the double-congruency condition of Flanker and Location in the ANT-R task, and on the learning trials in the verbal memory test. The training group also showed a larger P2 amplitude decrease and P3 amplitude increase in the 2-back task and a larger P3 amplitude increase in the TS task's repeat condition than the control group, indicating improved neural efficiency in two tasks' attentional processes. Introversion moderated the transfer effects of training, as indicated by the significant group*introversion interactions on the post-training 1-back efficiency and TS switching cost. Our results suggested that attention control training with the MFT-M showed a broad transfer scope, and the transfer effect was influenced by the form of training task. Introversion facilitated the transfer to working memory and hindered the transfer to flexibility.

Associations between capacity of cognitive control and sleep quality: a two-wave longitudinal study

This longitudinal study explored the impact of the upper limit of cognitive control on the sleep quality of high school students. We collected data in two waves to examine four main variables: capacity of cognitive control (CCC), trait mindfulness, emotional distress and sleep quality. At the first time point (T1), trait mindfulness and emotional distress were measured by rating scales, and the CCC was evaluated by revised backward masking majority function task. Sleep quality was rated 5 months later (T2). The results indicated that: (1) the CCC was negatively correlated with trait mindfulness, and trait mindfulness was negatively correlated with emotional stress; (2) there was no simple mediation of either trait mindfulness or emotional distress in the relationship between CCC and sleep quality; (3) instead, the CCC was associated with poor sleep quality in a sequential mediation through trait mindfulness and then emotional stress. The research highlights the importance of trait mindfulness and emotional distress for addressing sleep problems in adolescents.

Aggressive behavior and prognosis in patients with mild cognitive impairment

The diagnosis of mild cognitive impairment (MCI) is associated with an increased risk of developing dementia. When evaluating the further prognosis of MCI, the occurrence of neuropsychiatric symptoms, particularly aggressive and impulsive behavior, may play an important role.

Objective

The aim of this study was to evaluate the relationship between aggressive behavior and cognitive dysfunction in patients diagnosed with MCI.

Methods

The results are based on a 7-year prospective study. At the time of inclusion in the study, participants, recruited from an outpatient clinic, were assessed with Mini-Mental State Examination (MMSE) and the Cohen-Mansfield Agitation Inventory (CMAI). A reassessment was performed after 1 year using the MMSE scale in all patients. The time of next MMSE administration was depended on the clinical condition of patients took place at the end of follow-up, that is, at the time of diagnosis of the dementia or after 7 years from inclusion when the criteria for dementia were not met.

Results

Of the 193 patients enrolled in the study, 75 were included in the final analysis. Patients who converted to dementia during the observation period exhibited a greater severity of symptoms in each of the assessed CMAI categories. Moreover, there was a significant correlation between the global result of CMAI and the results of the physical nonaggressive and verbal aggressive subscales with cognitive decline during the first year of observation.

Conclusions

Despite several limitations to the study, aggressive and impulsive behaviors seem to be an unfavorable prognostic factor in the course of MCI.

Cognitive Control Deficits in Children With Subthreshold Attention-Deficit/Hyperactivity Disorder

Subthreshold Attention-Deficit/Hyperactivity Disorder (ADHD) is defined as a neurobiological condition with some core inattentive or hyperactive/impulsive symptoms of ADHD which do not meet the full diagnosis clinically. Although it has been well documented that deficits in cognitive control, a high-level cognitive construct closely related to attention, are frequently found among children with ADHD, whether subthreshold ADHD is also associated with similar deficits remains unclear. In this study, we examined the attention functions and the cognitive control capacity (CCC) in children with ADHD (n = 39), those with subthreshold ADHD (n = 34), and typically developing peers (TD, n = 36). The results showed that the ADHD and subthreshold ADHD groups exhibited similar patterns of the impaired executive function of attention (revealed as an augment in flanker conflict effect) and reduced cognitive control capacity, and no significant difference was found between the two groups. These findings suggest that although children with subthreshold ADHD have not met the full criteria of ADHD, they showed reduced efficiency in cognitive control and attention function, similar to children with ADHD.

EEG theta and alpha oscillations in early versus late mild cognitive impairment during a semantic Go/NoGo task

Amnestic mild cognitive impairment (aMCI) is marked by episodic memory deficits, which can be used to classify individuals into early MCI (EMCI) and late MCI (LMCI). Although mounting evidence suggests that individuals with aMCI have additional cognitive alterations including deficits in cognitive control, few have examined if EMCI and LMCI differ on processes other than episodic memory. Using a semantic Go/NoGo task, we examined differences in cognitive control between EMCI and LMCI on behavioral (accuracy and reaction time) and neural (scalp-recorded event-related oscillations in theta and alpha band) measures. Although no behavioral differences were observed between the EMCI and LMCI groups, differences in neural oscillations were observed. The LMCI group had higher theta synchronization on Go trials at central electrodes compared to the EMCI group. In addition, the EMCI group showed differences in theta power at central electrodes and alpha power at central and centro-parietal electrodes between Go and NoGo trials, while the LMCI group did not exhibit such differences. These findings suggest that while behavioral differences may not be observable, neural changes underlying cognitive control processes may differentiate EMCI and LMCI stages and may be useful to understand the trajectory of aMCI in future studies.

Decline in the integration of top-down and bottom-up attentional control in older adults with mild cognitive impairment

Individuals with mild cognitive impairment (MCI) have deficits in goal-directed top-down and stimulus-driven bottom-up attentional control. However, it remains unclear whether and how the interaction between the two processes is altered in individuals with MCI. We collected electroencephalography (EEG) data from 30 older adults with MCI and 30 demographically matched healthy controls (HCs) when they were performing a perceptual decision-making task, in which we manipulated the cognitive load involved in task-relevant top-down processing and the surprise level involved in task-irrelevant bottom-up processing. We found the significant group difference in the interaction between top-down and bottom-up processes. HCs showed enlarged P3 and strengthened event-related microstate C on high (vs. low) surprise level trials under high cognitive load, while there was no such surprise effect suggesting distraction under low cognitive load. In contrast, participants with MCI showed increased P2 and P3 amplitudes and strengthened microstates C and D on high (vs. low) surprise level trials under low cognitive load yet no surprise effect under high load. These results suggested that participants with MCI were distracted by task-irrelevant information under low cognitive load, while under high load, they might experience a passive inhibition on the task-irrelevant bottom-up processing because of the exhaustion of attentional resources; in addition, this altered interaction observed in the MCI group occurred at the stages of selective attention and uncertainty reduction.

Beyond information rate, the capacity of cognitive control predicts response criteria in perceptual decision-making

Recent studies indicate that higher capacity of cognitive control (CCC) represents higher processing efficiency (i.e., high accuracy with fast speed). However, the speed-accuracy tradeoff (SAT) exists ubiquitously in decision-making, and little is known about whether and how the CCC is associated with SAT and whether the CCC-SAT relationship would be affected by changes in information entropy. In this study, fifty-nine college students performed a majority function task in which accuracy and response speed were equally emphasized. A Bayesian-based hierarchical drift diffusion modeling method was used to estimate three parameters of boundary separation, drift rate, and nondecision time for each participant in this task. In addition, the CCC of each participant was estimated. The results showed that the CCC was positively correlated with the SAT represented by jointly increasing accuracy and reaction time (RT), which was modulated by the change in task-relevant information entropy. Multiple mediation analyses indicated that drift rate served as the key mediator in the positive CCC-accuracy relationship while boundary separation played the major mediating role in the positive CCC-RT relationship. These findings suggest that the CCC reflects not only the rate of information processing but also decision strategies for achieving current goals.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach

BACKGROUND

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.

OBJECTIVE

This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.

METHODS

In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.

RESULTS

There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.

CONCLUSION

The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease

Introduction: The goal of this study was to investigate and compare the classification performance of machine learning with behavioral data from standard neuropsychological tests, a cognitive task, or both.

Methods: A neuropsychological battery and a simple 5-min cognitive task were administered to eight individuals with mild cognitive impairment (MCI), eight individuals with mild Alzheimer's disease (AD), and 41 demographically match controls (CN). A fully connected multilayer perceptron (MLP) network and four supervised traditional machine learning algorithms were used.

Results: Traditional machine learning algorithms achieved similar classification performances with neuropsychological or cognitive data. MLP outperformed traditional algorithms with the cognitive data (either alone or together with neuropsychological data), but not neuropsychological data. In particularly, MLP with a combination of summarized scores from neuropsychological tests and the cognitive task achieved ~90% sensitivity and ~90% specificity. Applying the models to an independent dataset, in which the participants were demographically different from the ones in the main dataset, a high specificity was maintained (100%), but the sensitivity was dropped to 66.67%.

Discussion: Deep learning with data from specific cognitive task(s) holds promise for assisting in the early diagnosis of Alzheimer's disease, but future work with a large and diverse sample is necessary to validate and to improve this approach.