We examine the influence of the noise correlation on aperiodic stochastic resonance. To this end, we compute the discharge rate of a FitzHugh-Nagumo neuron model in response to a slow sub-threshold aperiodic input in the presence of additive correlated noise. Aperiodic stochastic resonance is observed for each level of noise correlation; i.e., there exists a noise amplitude that maximizes the covariance between the input signal and the model’s discharge rate. Both the maximal covariance and the optimal noise level depend on the noise correlation. The former remains almost constant for low noise correlations, and steadily decreases at larger correlations. The latter displays a U-shaped curve when plotted against the noise correlation, indicating that aperiodic stochastic resonance occurs at lower noise amplitudes for an appropriate range of noise correlation. We show that the results are consistent with the evaluation of the discharge rate of the model through a quasistatic assumption. Our results suggest that the interplay of the noise correlation with the time scales of the neuron model can lead to an improvement of the aperiodic stochastic resonance effect, in that for an appropriate level of correlation maximal covariance can be attained with lower noise amplitude.

Background: This study aimed to clarify the neuropsychiatric symptoms of right-sided predominant semantic dementia (SD-R) by comparing them with those of behavioral variant frontotemporal dementia (bvFTD), left-sided predominant SD (SD-L), and Alzheimer’s disease (AD). This study also aimed to identify clinical factors related to caregiver burden for bvFTD, SD-R, and SD-L. Methods: The neuropsychiatric symptoms of 28 patients with bvFTD, 14 patients with SD-R, 24 patients with SD-L, and 43 patients with AD were evaluated using the Neuropsychiatric Inventory (NPI) and the Stereotypy Rating Inventory (SRI). Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Dementia severity was assessed using the Clinical Dementia Rating. Activities of daily living were assessed using the Lawton Instrument Activities of Daily Living (IADL) scale and the Physical Self-Maintenance Scale. We compared the NPI and SRI scores among the four groups using the Kruskal-Wallis test. In addition, clinical factors related to caregiver burden, represented by the Japanese version of the Zarit Burden Interview (J-ZBI), were analyzed using multiple regression analysis in the bvFTD, SD-R, and SD-L groups. Results: The NPI total score and the NPI subscale scores of apathy and disinhibition were significantly higher in the bvFTD group than in the SD-L and AD groups. The SD-R group scores were closer to those of the bvFTD group than the SD-L group. The SRI total score and SRI subscale scores for eating and cooking and speaking were significantly higher in the bvFTD, SD-R, and SD-L groups than in the AD group. The NPI total score was significantly associated with the J-ZBI score in the bvFTD group. The NPI total score and Lawton IADL scale score were independently associated with the J-ZBI score in the SD-R group. Furthermore, the NPI total score and MMSE score were independently associated with the J-ZBI score in the SD-L group. Conclusions: SD-R seemed to be a similar condition to bvFTD rather than SD-L regarding behavioral symptoms. Our results suggest that each frontotemporal dementia subgroup requires different approaches to reduce the caregiver burden.

Early detection methods for cognitive impairment are crucial for its effective treatment. Dual-task-based pipelines that rely on skeleton sequences can detect cognitive impairment reliably. Although such pipelines achieve state-of-the-art results by analyzing skeleton sequences of periodic stepping motion, we propose that their performance can be improved by decomposing the skeleton sequence into representative phase-aligned periods and focusing on them instead of the entire sequence. We present the phase-aligned periodic graph convolutional network, which is capable of processing phase-aligned periodic skeleton sequences. We trained it with a cross-modality feature fusion loss using a representative dataset of 392 samples annotated by medical professionals. As part of a dual-task cognitive impairment detection pipeline that relies on two-dimensional skeleton sequences extracted from RGB images to improve its general usability, our proposed method outperformed existing approaches and achieved a mean sensitivity of 0.9231 and specificity of 0.9398 in a four-fold cross-validation setup.

Background/Objectives: Dysregulated gamma oscillations are associated with cognitive dysfunction. Auditory stimulation at 40 Hz enhances neural activity in brain regions associated with learning, attention, and memory. This study assessed the safety and acceptability of 40 Hz amplitude-modulated auditory stimulation in healthy older people. Auditory stimuli were created using popular songs, where vocals and background music were separated and independently amplitude-modulated at 40 Hz with different modulation depths to generate periodic 40 Hz gamma waveforms. Methods: In this open-label, single-arm study, healthy participants aged ≥65 years received 40 Hz amplitude-modulated auditory stimulation daily via a smartphone for 28 days through earphones/headphones. Safety was assessed through adverse event (AE) monitoring and changes in clinical scores for depression, cognitive function, and hearing thresholds. Acceptability was evaluated by adherence rates, listening time, dropout reasons, volume levels, intent for future use, and subjective impressions of the sound source on a 7-point Likert scale. Results: Among 28 participants (mean age 69.1 years, 53.6% female), six reported 12 AEs, with six considered device-related (e.g., ear discomfort, jaw pain, musculoskeletal stiffness). The AEs observed were mild or moderate. Scores for cognitive function, depression, and hearing thresholds did not worsen during the study period. Adherence was observed in 96.4%, with 85.7% expressing interest in continuing. Most participants rated the sounds’ unnaturalness between 2 and 3 and discomfort between 1 and 3 on the 7-point Likert scale. Conclusions: The intervention was well tolerated and acceptable in study participants, with no major safety concerns identified. Auditory stimulation did not cause severe discomfort or reduce acceptability. Further studies should explore the long-term effects and broader clinical applications.

Objective: The aim of this research is to develop a simple automated screening approach for early-stage cognitive impairment with high performance. Method: Our approach is based on the dual-task paradigm, where individuals perform two tasks simultaneously, typically a physical task combined with a cognitive task. In this study, we developed a dual-task system capable of collecting gait data, cognitive scores, and EEG (electroencephalography) signals within a single five-minute measurement. EEG data were recorded while the individual performs both gait and cognitive tasks simultaneously. To improve overall accuracy, we introduced a novel fusion algorithm with an innovative loss function to integrate gait, cognitive scores, and EEG modalities. In our fusion model, we employ cross-attention mechanisms to capture implicit relationships among the three modalities. Furthermore, our proposed loss function minimizes the overlap between different modalities to maximize their complementary contributions. Results: The experimental results validate the effectiveness of both the proposed fusion model and the new loss function. The proposed approach outperforms existing methods in nearly all conditions, achieving an overall performance of AUC: 0.9845, sensitivity: 0.9659, and specificity: 0.9535. Furthermore, compared with other methods, our approach demonstrates a relatively superior cross-fold stability and sensitivity-specificity balance. Conclusion: This study presents an effective multi-modality fusion framework for early-stage cognitive impairment screening. The experimental results confirm the validity and robustness of the proposed algorithm. Significance: The proposed algorithm, supported by comprehensive analysis, facilitates early detection of cognitive impairment, ultimately contributing to the prevention and treatment of dementia.