Frontiers in Human Neuroscience最新文献

Introduction: Deep brain stimulation (DBS) is a promising treatment for refractory depression, utilizing surgically implanted electrodes to stimulate specific anatomical targets within the brain. However, limitations of patient-reported and clinician-administered mood assessments pose obstacles in evaluating DBS treatment efficacy. In this study, we investigated whether an affective bias task, which leverages the inherent negative interpretation bias seen in individuals with depression, could serve as a reliable measure of mood changes during DBS therapy in patients with treatment-resistant depression.

Methods: Two cohorts of patients (n = 8, n = 2) undergoing DBS for treatment-resistant depression at different academic medical centers completed an affective bias task at multiple time points before and after DBS implantation. The affective bias task involved rating the emotional content of a series of static photographic stimuli of facial expressions throughout their DBS treatment. Patients' ratings were compared with those of non-depressed controls to calculate affective bias scores. Linear mixed-effects modeling was used to assess changes in bias scores over time and their relationship with depression severity measured by the Hamilton Depression Rating Scale (HDRS-17).

Results: We observed significant improvements in total affective bias scores over the course of DBS treatment in both cohorts. Pre-DBS, patients exhibited a negative affective bias, which was nearly eliminated post-DBS, with total bias scores approaching those of non-depressed controls. Positive valence trials showed significant improvement post-DBS, while negative valence trials showed no notable change. A control analysis indicated that stimulation status did not significantly affect bias scores, and thus stimulation status was excluded from further modeling. Linear mixed-effects modeling revealed that more negative bias scores were associated with higher HDRS-17 scores, particularly for positive valence stimuli. Additionally, greater time elapsed since DBS implantation was associated with a decrease in HDRS-17 scores, indicating clinical improvement over time.

Discussion: Our findings demonstrate that the affective bias task leverages the inherent negative interpretation bias seen in individuals with depression, providing a standardized measure of how these biases change over time. Unlike traditional mood assessments, which rely on subjective introspection, the affective bias task consistently measures changes in mood, offering potential as a tool to monitor mood changes and evaluate the candidacy of DBS treatment in refractory depression.

Introduction: Anhedonic depression is a subtype of depression characterized by deficits in reward processing. This subtype of depression is associated with higher suicide risk and longer depressive episodes, underscoring the importance of effective treatments. Anhedonia has also been found to correlate with alterations in activity in several subcortical regions, including the caudate head and nucleus accumbens. Low intensity focused ultrasound pulsation (LIFUP) is an emerging technology that enables non-invasive stimulation of these subcortical regions, which were previously only accessible with surgically-implanted electrodes.

Methods: This double-blinded, sham-controlled study aims to investigate the effects of LIFUP to the left caudate head and right nucleus accumbens in participants with anhedonic depression. Participants in this protocol will undergo three sessions of LIFUP over the span of 5-9 days. To investigate LIFUP-related changes, this 7-week protocol collects continuous digital phenotyping data, an array of self-report measures of depression, anhedonia, and other psychopathology, and magnetic resonance imaging (MRI) before and after the LIFUP intervention. Primary self-report outcome measures include Ecological Momentary Assessment, the Positive Valence Systems Scale, and the Patient Health Questionnaire. Primary imaging measures include magnetic resonance spectroscopy and functional MRI during reward-based tasks and at rest. Digital phenotyping data is collected with an Apple Watch and participants' personal iPhones throughout the study, and includes information about sleep, heart rate, and physical activity.

Discussion: This study is the first to investigate the effects of LIFUP to the caudate head or nucleus accumbens in depressed subjects. Furthermore, the data collected for this protocol covers a wide array of potentially affected modalities. As a result, this protocol will help to elucidate potential impacts of LIFUP in individuals with anhedonic depression.

Spontaneous intrapersonal coordination is the unintentional coordination of periodic behaviors within an individual. Spontaneous interlimb coordination involving finger-, arm-, foot-, leg- and orofacial muscle movements may be weaker between finger-tapping and walking than between finger-tapping and vocalizing. This could be due to the additional attentional cost of walking, which may be more complex than other periodic movements. Here we compared the coordination stability of simultaneous finger-tapping and walking against simultaneous finger-tapping and repetitive vocalization. We also tested the coordination stability of tapping-walking and tapping-vocalizing under additional cognitive load imposed through concurrent cognitive tasks. Two experiments conceptually replicated spontaneous intrapersonal coordination between the pairs of periodic tasks as well as the effect of concurrent cognitive tasks on coordination stability. To assess coordination, we compared the phase coherence of two periodic tasks, tapping with walking (Experiment 1) or tapping with vocalization (Experiment 2), when produced separately (single task) versus simultaneously (dual task). In the first experiment, participants regularly tapped a microphone while walking, either with no concurrent cognitive task or with concurrent backward counting. In the second experiment, participants tapped while repeating the word "tick," again either with no concurrent cognitive task, or with concurrent visual pattern-matching. Higher spontaneous intrapersonal coordination was evident between periodic tasks when performed simultaneously compared to separately, and lower task coordination stability was evident with a concurrent cognitive task compared to without. These results were in line with past findings. Coordination stability between tapping and walking was lower than that between tapping and ticking overall. This finding supports the categorization of walking as a more complex cognitive task compared to other periodic tasks, as the additional attentional load involved in walking could have resulted in lower coordination stability between tapping and walking. Spontaneous intrapersonal coordination appears sensitive to the attentional costs of performing periodic activities and achieving / maintaining coordination between them.

This pilot study attempted to identify a relationship between dance and mirror neuron activity in people with Parkinson's disease by investigating Mu rhythm desynchronization in electroencephalographic (EEG) data before and after regular participation in a program of dance classes. During the EEG recordings, the participants observed a sequence of videos showing either choreographic (complex) or daily (simple) movements, each preceded by a baseline image (dark screen) and a control video (moving blocks). The results showed a statistically significant increase in Mu rhythm desynchronization in the alpha 1 band at the central channels after 6 months of dance classes. Control comparisons with occipital channels showed no such increase. Mu rhythm suppression has been demonstrated to reflect the activity of the human mirror neuron system, respond to variations in motor expertise, and seem to be impaired in Parkinson's disease. The Mu wave desynchronization increase shown here, after 6 months of dance classes, is an objective measurement of the benefits of such practice for people with Parkinson's disease (PD).

Prosthetic vision systems aim to restore functional sight for visually impaired individuals by replicating visual perception by inducing phosphenes through electrical stimulation in the visual cortex, yet there remain challenges in visual representation strategies such as including gaze information and task-dependent optimization. In this paper, we introduce Point-SPV, an end-to-end deep learning model designed to enhance object recognition in simulated prosthetic vision. Point-SPV takes an initial step toward gaze-based optimization by simulating viewing points, representing potential gaze locations, and training the model on patches surrounding these points. Our approach prioritizes task-oriented representation, aligning visual outputs with object recognition needs. A behavioral gaze-contingent object discrimination experiment demonstrated that Point-SPV outperformed a conventional edge detection method, by facilitating observers to gain a higher recognition accuracy, faster reaction times, and a more efficient visual exploration. Our work highlights how task-specific optimization may enhance representations in prosthetic vision, offering a foundation for future exploration and application.

Introduction: Combining many types of imaging data-especially structural MRI (sMRI) and functional MRI (fMRI)-may greatly assist in the diagnosis and treatment of brain disorders like Alzheimer's. Current approaches are less helpful for forecasting, however, as they do not always blend spatial and temporal patterns from different sources properly. This work presents a novel mixed deep learning (DL) method combining data from many sources using CNN, GRU, and attention techniques. This work introduces a novel hybrid deep learning method combining CNN, GRU, and a Dynamic Cross-Modality Attention Module to help more efficiently blend spatial and temporal brain data. Through working around issues with current multimodal fusion techniques, our approach increases the accuracy and readability of diagnoses.

Methods: Utilizing CNNs and models of temporal dynamics from fMRI connection measures utilizing GRUs, the proposed approach extracts spatial characteristics from sMRI. Strong multimodal integration is made possible by including an attention mechanism to give diagnostically important features top priority. Training and evaluation of the model took place using the Human Connectome Project (HCP) dataset including behavioral data, fMRI, and sMRI. Measures include accuracy, recall, precision and F1-score used to evaluate performance.

Results: It was correct 96.79% of the time using the combined structure. Regarding the identification of brain disorders, the proposed model was more successful than existing ones.

Discussion: These findings indicate that the hybrid strategy makes sense for using complimentary information from several kinds of photos. Attention to detail helped one choose which aspects to concentrate on, thereby enhancing the readability and diagnostic accuracy.

Conclusion: The proposed method offers a fresh benchmark for multimodal neuroimaging analysis and has great potential for use in real-world brain assessment and prediction. Researchers will investigate future applications of this technique to new picture kinds and clinical data.

Background: Bipolar disorder is associated with impairments in cognition and psychosocial functioning. Although these impairments occur frequently, often persist during euthymic times, and worsen quality of life, the impact of cognitive abilities on functioning has not yet been fully elucidated.

Methods: The current study investigated the effects of cognitive domains (attention/psychomotor speed, verbal learning/memory, executive function) on psychosocial functioning cross-sectionally. Data from 210 euthymic individuals with bipolar disorder [101 female, 109 male; M _(age) = 44.47; SD _(age) = 14.25] were included into the analysis. A neurocognitive test battery was administered and the Global Assessment of Functioning was used to depict psychosocial functioning. Correlation analyses were conducted to observe the associations between functioning and the cognitive domains. Moreover, three hierarchical regression analyses were applied to predict functioning by each of the cognitive domains, while considering age, sex, and education as control variables.

Results: Correlation analyses revealed that functioning was positively associated with attention/psychomotor speed and verbal learning/memory. However, the consecutive hierarchical regression analyses found that none of the cognitive domains were able predict functioning beyond the control variables age, sex, and education.

Conclusion: Our findings indicate that greater abilities in the domains of attention/psychomotor speed and verbal learning/memory are associated with better functioning. However, this association can be explained by other relevant variables such as age or education, indicating that cognitive abilities are not the sole contributor of psychosocial functioning. Investigating other measurements of functioning or cognitive abilities could lead to different results. Nevertheless, promoting cognitive abilities and autonomy in daily life remains an important aspect of therapy in bipolar disorder.

Introduction: Sudden road conditions can trigger drivers' psychological stress, increasing the risk of traffic accidents. Music, as an emotion regulation tool, effectively alleviates stress and enhances psychological health. However, the effects of different genres of music on drivers' stress remain understudied.

Methods: To address this, the present study collected 120 EEG recordings from 60 drivers in a standardized simulated driving environment and developed a classification model based on EEG signals to recognize emotions. By integrating time-frequency domain features (mean, variance, skewness, kurtosis, and power spectral density) with classification algorithms, the model accurately identified slight, moderate, and severe stress states in drivers, achieving an accuracy of 90%.

Results: Furthermore, the study evaluated the intervention effects of four types of music (joyful, sorrowful, exhilarating, and gentle) on stress using EEG signals and subjective stress ratings. The results showed that gentle music had the best stress-relieving effect in both slight and severe stress states, reducing stress by 41.67% and 45%, respectively, whereas joyful music was most effective in relieving moderate stress, reducing moderate stress by 50%. In contrast, exhilarating and sorrowful music had weaker effects. Additionally, the asymmetry of frontal pole EEG signals was found to be significantly negatively correlated with stress levels.

Discussion: This finding further supports the accuracy of the emotion recognition model and the potential effectiveness of the music intervention strategy. The study demonstrates that personalized music intervention strategies can help alleviate drivers' stress, thereby improving psychological health, enhancing driving safety, and increasing driving comfort.

This bibliometric analysis comprehensively investigates the literature on acupuncture for stroke rehabilitation, aiming to identify key research hotspots, map the underlying knowledge structure, and examine developmental trends. The central hypothesis posits that acupuncture plays a pivotal role in enhancing neurological and motor function recovery in stroke patients, particularly when combined with complementary therapeutic modalities. A total of 2,217 relevant publications, spanning from database inception through 2024, were selected following stringent data screening and cleaning protocols. Utilizing advanced bibliometric tools such as CiteSpace and VOSviewer, we analyzed publication trends, leading authors, influential institutions, and citation networks. Our findings reveal a consistent and significant increase in research activity, with China emerging as the predominant contributor to this field. The analysis strongly emphasizes neurological recovery, motor function improvement, language rehabilitation, and the integration of acupuncture with other therapeutic strategies. Prominent keywords, including "acupuncture treatment," "scalp acupuncture," "electroacupuncture," and "rehabilitation therapy," reflect the evolving priorities within this domain. This study provides valuable evidence-based insights to guide future research on acupuncture for stroke rehabilitation, offering a solid framework for experimental investigations. By delineating the knowledge landscape, it contributes to refining research hypotheses and optimizing the clinical application of acupuncture in stroke recovery.

Systematic review registration: https://inplasy.com/, INPLASY202530038.