Characteristics of Resting State EEG Power in 80+-Year-Olds of Different Cognitive Status

Light-based gamma entrainment with novel invisible spectral flicker stimuli

Light-based gamma entrainment using sensory stimuli (GENUS) shows considerable potential for the treatment of Alzheimer's disease (AD) in both animal and human models. While the clinical efficacy of GENUS for AD is paramount, its effectiveness will eventually also rely on the barrier to treatment adherence imposed by the discomfort of gazing at luminance flickering (LF) light. Currently, there have been few attempts to improve the comfort of GENUS. Here we investigate if Invisible spectral flicker (ISF), a novel type of light-based 40 Hz GENUS for which the flicker is almost imperceptible, can be used as a more comfortable option. We found that whereas ISF, LF, and chromatic flicker (CF) all produce a 40 Hz steady-state visually evoked potential (SSVEP), ISF scores significantly better on measures of comfort and perceived flicker. We also demonstrate that, while there is a trend towards a lower SSVEP response, reducing the stimulation brightness has no significant effect on the 40 Hz SSVEP or perceived flicker, though it significantly improves comfort. Finally, there is a slight decrease in the 40 Hz SSVEP response when stimulating with ISF from increasingly peripheral angles. This may ease the discomfort of GENUS treatment by freeing patients from gazing directly at the light.

Force Fluctuations During Role-Differentiated Bimanual Movements Reflect Cognitive Impairments in Older Adults: A Cohort Sequential Study

During role-differentiated bimanual movements (RDBM), an object is typically stabilized with 1 hand and manipulated with the other. RDBM require coupling both hands for coordinated action (achieved through interhemispheric connections), but also inhibition of crosstalk to avoid involuntary movements in the stabilizing hand. We investigated how healthy cognitive aging and mild cognitive impairments (MCI) affect force stabilization during an RDBM in a cohort sequential study design with up to 4 measurement points over 32 months. In total, 132 older adults (>80 years) participated in this study, 77 were cognitively healthy individuals (CHI) and 55 presented with MCI. Participants performed a visuomotor bimanual force-tracking task. They either produced a constant force with both hands (bimanual constant) or a constant force with 1 and an alternating force with the other hand (role-differentiated). We investigated force fluctuations of constant force production using the coefficient of variation (CV), detrended fluctuation analysis (DFA), and sample entropy (SEn). Results showed higher CV and less complex variability structure (higher DFA and lower SEn) during the role-differentiated compared to the bimanual constant task. Furthermore, CHI displayed a more complex variability structure during the bimanual constant, but a less complex structure during the role-differentiated task than MCI. Interestingly, this complexity reduction was more pronounced in CHI than MCI individuals, suggesting different changes in the control mechanisms. Although understanding these changes requires further research, potential causes might be structural deteriorations leading to less efficient (intra- and interhemispheric) networks because of MCI, or an inability to appropriately divert the focus of attention.

Examining the influence of self-care practices on brain activity in healthy older adults

Introduction

Studies on the aging brain often occur in active settings, but comparatively few investigate brain activity in resting states. However, exploring brain activity in a resting state offers valuable insights into spontaneous neural processes unaffected by task-specific influences. Objective: To investigate the relationship between self-care practices, cognitive function, and patterns of brain activity in healthy older adults, taking into account predictions from aging brain models.

Methodology

77 older adults aged 61 to 87 completing a self-care practices questionnaire, neuropsychological tests, and resting-state electroencephalogram (EEG) recordings. Participants were classified into two groups according to their self-care practices: traditional self-care (T-SC) and developmental self-care (D-SC).

Results

Although neuropsychological tests did not yield significant differences between the D-SC and T-SC groups, patterns of brain activity revealed distinct behaviors. The T-SC group demonstrated patterns more consistent with established aging brain models, contrasting with the D-SC group, which exhibited brain activity akin to that observed in younger adults. Specifically, the T-SC group displayed hyperactivation related to memory and executive function performance, alongside heightened alpha power in posterior regions. Furthermore, bilateral frontal activation in the beta band was evident.

Conclusions

The findings suggest a nuanced relationship between self-care practices and brain activity in older adults. While the T-SC group demonstrated brain activity patterns consistent with conservative aging, indicating the preservation of typical aging characteristics, the D-SC group displayed activity suggestive of a potential protective effect. This effect may be linked to self-care strategies that foster development and resilience in cognitive aging.

Resting-state electroencephalographic characteristics related to mild cognitive impairments

Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.

Sex-dependent performance differences in curvilinear aiming arm movements in octogenarians

In an aging society, it is necessary to detect the cognitive decline of individuals at an early stage using simple measurement methods. This makes early health care possible for those affected. The aim of the study was to develop a classifier for cognitive state in older adults with and without mild cognitive impairment (MCI) based on kinematic parameters of linear and curvilinear aiming arm movements. In a group of 224 older adults over 80 years of age (cognitively healthy and MCI), the movement duration and intersegment intervals of linear and curvilinear arm movements of 20 cm were recorded. Movement duration was significantly longer in the curvilinear condition than in the straight movement, and MCI participants required significantly more time than cognitively healthy participants. Post-hoc analysis on the fluidity of movement in the curvilinear condition showed that MCI men had significantly longer inter-segmental intervals than non-MCI men. No difference was found in women. Based on the inter-segmental intervals, a simple classifier could be developed that correctly classified 63% of the men. In summary, aiming arm movements are only conditionally suitable as a classifier for cognitive states. For the construction of an ideal classifier, age-related degeneration of cortical and subcortical motor areas should be considered.

Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan

The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime.

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer’s Disease Recognition

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity. Because the original EEG has the characteristics of weak amplitude, strong background noise and randomness, the research on intelligent AD recognition based on machine learning is still in the exploratory stage. This paper proposes the discriminant subspace low-rank representation (DSLRR) algorithm for EEG-based AD and mild cognitive impairment (MCI) recognition. The subspace learning and low-rank representation are flexibly integrated into a feature representation model. On the one hand, based on the low-rank representation, the graph discriminant embedding is introduced to constrain the representation coefficients, so that the robust representation coefficients can preserve the local manifold structure of the EEG data. On the other hand, the least squares regression, principle component analysis, and global graph embedding are introduced into the subspace learning, to make the model more discriminative. The objective function of DSLRR is solved by the inexact augmented Lagrange multiplier method. The experimental results show that the DSLRR algorithm has good classification performance, which is helpful for in-depth research on AD and MCI recognition.

Finger Tapping as a Biomarker to Classify Cognitive Status in 80+-Year-Olds

This study examined the association between finger tapping and cognitive function in a group of 225 elderly participants (116 males; age 79–92 years; M = 82.5; SD = 2.4). Finger tapping was assessed in two conditions: self-selected pace and fast pace. Based on cognitive assessments, including the MoCA and CERA-NP test battery, participants were classified as cognitively healthy individuals (CHI), participants with mild cognitive impairments (MCI), and those with possible MCI (pMCI). Results of the analyses show significant differences between groups, sex and the group × sex interaction in four parameters for the self-selected pace condition and eight parameters for the fast pace condition. These parameters were used for classification by means of linear discriminant analysis (LDA). The first LDA component showed significant differences between CHI and pMCI and between CHI and MCI. Furthermore, the second LDA component showed significant differences between CHI and pMCI as well as between pMCI and MCI. Nevertheless, the algorithm correctly classified only 50% of participants, regardless of group, suggesting that tapping parameters are only partially useful for classification in early stages of dementia. We discuss these findings in terms of the diadochokinetic nature of finger tapping as associated with the age-related degeneration of cortical and subcortical motor areas.