Serial Reaction Time Task Performance in Older Adults with Neuropsychologically Defined Mild Cognitive Impairment

Quantification of Motor Learning in Hand Adjustability Movements: An Evaluation Variable for Discriminant Cognitive Decline

OBJECTIVE

Mild cognitive impairment (MCI) is characterized by early symptoms of attentional decline and may be distinguished through motor learning results. A relationship was reported between dexterous hand movements and cognitive function in older adults. Therefore, this study focuses on motor learning involving dexterous hand movements. As motor learning engages two distinct types of attention, external and internal, we aimed to develop an evaluation method that separates these attentional functions within motor learning. The objective of this study was to develop and verify the effectiveness of this evaluation method. The effectiveness was assessed by comparing two motor learning variables between a normal cognitive (NC) and MCI groups.

METHOD

To evaluate motor learning through dexterous hand movements, we utilized the iWakka device. Two types of visual tracking tasks, repeat and random, were designed to evaluate motor learning from different aspects. The tracking errors in both tasks were quantitatively measured, and the initial and final improvement rates during motor learning were defined as the evaluation variables. The study included 28 MCI participants and 40 NC participants, and the effectiveness of the proposed method was verified by comparing results between the groups.

RESULTS

The repeat task revealed a significantly lower learning rate in MCI participants (p <0.01). In contrast, no significant difference was observed between MCI and NC participants in the random task (p =0.67).

CONCLUSION

The evaluation method proposed in this study demonstrated the possibility of obtaining evaluation variables that indicate the characteristics of MCI.

CLINICAL IMPACT

The methods proposed in this work are clinically relevant because the proposed evaluation system can make evaluation variables for discriminating cognitive decline in MCI. That it, the proposed approach can also be used to provide discrimination for cognitive decline in MCI.

Early-Stage Alzheimer's Disease Affects Fast But Not Slow Adaptive Processes in Motor Learning

Alzheimer's disease (AD) is characterized by an initial decline in declarative memory, while nondeclarative memory processing remains relatively intact. Error-based motor adaptation is traditionally seen as a form of nondeclarative memory, but recent findings suggest that it involves both fast, declarative, and slow, nondeclarative adaptive processes. If the declarative memory system shares resources with the fast process in motor adaptation, it can be hypothesized that the fast, but not the slow, process is disturbed in AD patients. To test this, we studied 20 early-stage AD patients and 21 age-matched controls of both sexes using a reach adaptation paradigm that relies on spontaneous recovery after sequential exposure to opposing force fields. Adaptation was measured using error clamps and expressed as an adaptation index (AI). Although patients with AD showed slightly lower adaptation to the force field than the controls, both groups demonstrated effects of spontaneous recovery. The time course of the AI was fitted by a hierarchical Bayesian two-state model in which each dynamic state is characterized by a retention and learning rate. Compared to controls, the retention rate of the fast process was the only parameter that was significantly different (lower) in the AD patients, confirming that the memory of the declarative, fast process is disturbed by AD. The slow adaptive process was virtually unaffected. Since the slow process learns only weakly from an error, our results provide neurocomputational evidence for the clinical practice of errorless learning of everyday tasks in people with dementia.

Neural Underpinnings of Learning in Dementia Populations: A Review of Motor Learning Studies Combined with Neuroimaging

The intent of this review article is to serve as an overview of current research regarding the neural characteristics of motor learning in Alzheimer disease (AD) as well as prodromal phases of AD: at-risk populations, and mild cognitive impairment. This review seeks to provide a cognitive framework to compare various motor tasks. We will highlight the neural characteristics related to cognitive domains that, through imaging, display functional or structural changes because of AD progression. In turn, this motivates the use of motor learning paradigms as possible screening techniques for AD and will build upon our current understanding of learning abilities in AD populations.

Performance Differences of a Touch-Based Serial Reaction Time Task in Healthy Older Participants and Older Participants With Cognitive Impairment on a Tablet: Experimental Study

BACKGROUND

Digital neuropsychological tools for diagnosing neurodegenerative diseases in the older population are becoming more relevant and widely adopted because of their diagnostic capabilities. In this context, explicit memory is mainly examined. The assessment of implicit memory occurs to a lesser extent. A common measure for this assessment is the serial reaction time task (SRTT).

OBJECTIVE

This study aims to develop and empirically test a digital tablet-based SRTT in older participants with cognitive impairment (CoI) and healthy control (HC) participants. On the basis of the parameters of response accuracy, reaction time, and learning curve, we measure implicit learning and compare the HC and CoI groups.

METHODS

A total of 45 individuals (n=27, 60% HCs and n=18, 40% participants with CoI-diagnosed by an interdisciplinary team) completed a tablet-based SRTT. They were presented with 4 blocks of stimuli in sequence and a fifth block that consisted of stimuli appearing in random order. Statistical and machine learning modeling approaches were used to investigate how healthy individuals and individuals with CoI differed in their task performance and implicit learning.

RESULTS

Linear mixed-effects models showed that individuals with CoI had significantly higher error rates (b=-3.64, SE 0.86; z=-4.25; P<.001); higher reaction times (F1,41=22.32; P<.001); and lower implicit learning, measured via the response increase between sequence blocks and the random block (β=-0.34; SE 0.12; t=-2.81; P=.007). Furthermore, machine learning models based on these findings were able to reliably and accurately predict whether an individual was in the HC or CoI group, with an average prediction accuracy of 77.13% (95% CI 74.67%-81.33%).

CONCLUSIONS

Our results showed that the HC and CoI groups differed substantially in their performance in the SRTT. This highlights the promising potential of implicit learning paradigms in the detection of CoI. The short testing paradigm based on these results is easy to use in clinical practice.

Does transcranial direct current stimulation of the primary motor cortex improve implicit motor sequence learning in Parkinson's disease?

Implicit motor sequence learning (IMSL) is a cognitive function that is known to be associated with impaired motor function in Parkinson's disease (PD). We previously reported positive effects of transcranial direct current stimulation (tDCS) over the primary motor cortex (M1) on IMSL in 11 individuals with PD with mild cognitive impairments (MCI), with the largest effects occurring during reacquisition. In the present study, we included 35 individuals with PD, with (n = 15) and without MCI (n = 20), and 35 age- and sex-matched controls without PD, with (n = 13) and without MCI (n = 22). We used mixed-effects models to analyze anodal M1 tDCS effects on acquisition (during tDCS), short-term (five minutes post-tDCS) and long-term reacquisition (one-week post-tDCS) of general and sequence-specific learning skills, as measured by the serial reaction time task. At long-term reacquisition, anodal tDCS resulted in smaller general learning effects compared to sham, only in the PD group, p = .018, possibly due to floor effects. Anodal tDCS facilitated the acquisition of sequence-specific learning (M = 54.26 ms) compared to sham (M = 38.98 ms), p = .003, regardless of group (PD/controls). Further analyses revealed that this positive effect was the largest in the PD-MCI group (anodal: M = 69.07 ms; sham: M = 24.33 ms), p < .001. Although the observed effect did not exceed the stimulation period, this single-session tDCS study confirms the potential of tDCS to enhance IMSL, with the largest effects observed in patients with lower cognitive status. These findings add to the body of evidence that anodal tDCS can beneficially modulate the abnormal basal ganglia network activity that occurs in PD.

Procedural learning and retention relative to explicit learning and retention in mild cognitive impairment and Alzheimer's disease using a modification of the trail making test

Amnestic mild cognitive impairment (aMCI) and Alzheimer's disease (AD) dementia are characterized by pathological changes to the medial temporal lobes, resulting in explicit learning and retention reductions. Studies demonstrate that implicit/procedural memory processes are relatively intact in these populations, supporting different anatomical substrates for differing memory systems. This study examined differences between explicit and procedural learning and retention in individuals with aMCI and AD dementia relative to matched healthy controls. We also examined anatomical substrates using volumetric MRI. Results revealed expected difficulties with explicit learning and retention in individuals with aMCI and AD with relatively preserved procedural memory. Explicit verbal retention was associated with medial temporal cortex volumes. However, procedural retention was not related to medial temporal or basal ganglia volumes. Overall, this study confirms the dissociation between explicit relative to procedural learning and retention in aMCI and AD dementia and supports differing anatomical substrates.

Deep brain stimulation of the ventrointermediate nucleus of the thalamus to treat essential tremor improves motor sequence learning

The network of brain structures engaged in motor sequence learning comprises the same structures as those involved in tremor, including basal ganglia, cerebellum, thalamus, and motor cortex. Deep brain stimulation (DBS) of the ventrointermediate nucleus of the thalamus (VIM) reduces tremor, but the effects on motor sequence learning are unknown. We investigated whether VIM stimulation has an impact on motor sequence learning and hypothesized that stimulation effects depend on the laterality of electrode location. Twenty patients (age: 38-81 years; 12 female) with VIM electrodes implanted to treat essential tremor (ET) successfully performed a serial reaction time task, varying whether the stimuli followed a repeating pattern or were selected at random, during which VIM-DBS was either on or off. Analyses of variance were applied to evaluate motor sequence learning performance according to reaction times (RTs) and accuracy. An interaction was observed between whether the sequence was repeated or random and whether VIM-DBS was on or off (F[1,18] = 7.89, p = .012). Motor sequence learning, reflected by reduced RTs for repeated sequences, was greater with DBS on than off (T[19] = 2.34, p = .031). Stimulation location correlated with the degree of motor learning, with greater motor learning when stimulation targeted the lateral VIM (n = 23, ρ = 0.46; p = .027). These results demonstrate the beneficial effects of VIM-DBS on motor sequence learning in ET patients, particularly with lateral VIM electrode location, and provide evidence for a role for the VIM in motor sequence learning.

Diagnostic accuracy of CompCog: reaction time as a screening measure for mild cognitive impairment

BACKGROUND

Reaction time is affected under different neurological conditions but has not been much investigated considering all types of mild cognitive impairment (MCI).

OBJECTIVE

This study investigated the diagnostic accuracy of CompCog, a computerized cognitive screening battery focusing on reaction time measurements.

METHODS

A sample of 52 older adults underwent neuropsychological assessments, including CompCog, and medical appointments, to be classified as a control group or be diagnosed with MCI. The accuracy of CompCog for distinguishing between the two groups was calculated.

RESULTS

The results from diagnostic accuracy analyses showed that the AUCs of ROC curves were as high as 0.915 (CI 0.837-0.993). The subtest with the highest sensitivity and specificity (choice reaction time subtest) had 91.7% sensitivity and 89.3% specificity. The logistic regression final model correctly classified 92.3% of individuals, with 92.9% specificity and 91.7% sensitivity, and included only four variables from different subtests.

CONCLUSIONS

In summary, the study showed that reaction time assessed through CompCog is a good screening measure to differentiate between normal aging and MCI. Reaction time measurements in milliseconds were more accurate than correct answers. This test can form part of routine clinical tests to achieve the objectives of screening for MCI, indicating further procedures for investigation and diagnosis and planning interventions.

Motor Sequence Learning Deficits in Idiopathic Parkinson's Disease Are Associated With Increased Substantia Nigra Activity

Previous studies have shown that persons with Parkinson’s disease (pwPD) share specific deficits in learning new sequential movements, but the neural substrates of this impairment remain unclear. In addition, the degree to which striatal dopaminergic denervation in PD affects the cortico-striato-thalamo-cerebellar motor learning network remains unknown. We aimed to answer these questions using fMRI in 16 pwPD and 16 healthy age-matched control subjects while they performed an implicit motor sequence learning task. While learning was absent in both pwPD and controls assessed with reaction time differences between sequential and random trials, larger error-rates during the latter suggest that at least some of the complex sequence was encoded. Moreover, we found that while healthy controls could improve general task performance indexed by decreased reaction times across both sequence and random blocks, pwPD could not, suggesting disease-specific deficits in learning of stimulus-response associations. Using fMRI, we found that this effect in pwPD was correlated with decreased activity in the hippocampus over time. Importantly, activity in the substantia nigra (SN) and adjacent bilateral midbrain was specifically increased during sequence learning in pwPD compared to healthy controls, and significantly correlated with sequence-specific learning deficits. As increased SN activity was also associated (on trend) with higher doses of dopaminergic medication as well as disease duration, the results suggest that learning deficits in PD are associated with disease progression, indexing an increased drive to recruit dopaminergic neurons in the SN, however, unsuccessfully. Finally, there were no differences between pwPD and controls in task modulation of the cortico-striato-thalamo-cerebellar network. However, a restricted nigral-striatal model showed that negative modulation of SN to putamen connection was larger in pwPD compared to controls during random trials, while no differences between the groups were found during sequence learning. We speculate that learning-specific SN recruitment leads to a relative increase in SN- > putamen connectivity, which returns to a pathological reduced state when no learning takes place.

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.