Frontiers in Neuroscience最新文献

{"title":"Harmonization of the fastest and densest responses reflects humanlike reaction time in mice.","authors":"Chan Hee Kim","doi":"10.3389/fnins.2025.1501374","DOIUrl":"10.3389/fnins.2025.1501374","url":null,"abstract":"<p><strong>Introduction: </strong>Reaction time (RT) is important for evaluating delayed latency in behavior. Unlike humans, whose RT usually reflects a one-to-one stimulus-response relationship, the RT of animals can show two peaks representing the fastest and densest responses in the response distribution due to multiple responses per trial and can be further delayed depending on stimulus duration.</p><p><strong>Methods: </strong>Stimulus duration was controlled to investigate whether these two peak latencies align to form a single RT. Sound cues lasting 10, 5, and 2 s, each associated with a food reward of condensed milk, were tested in three groups of 24 mice using delay conditioning paradigm. The frequency and latency of responses, along with basic indices such as accuracy, were analyzed.</p><p><strong>Results: </strong>In delay conditioning experiments using sound cues of 10, 5, and 2 s, the 2 s group exhibited the strongest positive correlations between the two peaks, as well as between the number of responses and accuracy rate, suggesting a coupling of the fastest and densest responses and a one-to-one relationship between stimulus and response.</p><p><strong>Discussion: </strong>Based on these findings, I propose harmonizing the two peaks, elicited by stimuli that induce prompt and minimal responses, as a criterion for designing animal experiments to better mimic humanlike RT.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1501374"},"PeriodicalIF":3.2,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11813871/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143407104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered brain functional network connectivity and topology in type 2 diabetes mellitus.","authors":"Weiwei Ni, Weiyin Vivian Liu, Mingrui Li, Shouchao Wei, Xuanzi Xu, Shutong Huang, Lanhui Zhu, Jieru Wang, Fengling Wen, Hailing Zhou","doi":"10.3389/fnins.2025.1472010","DOIUrl":"10.3389/fnins.2025.1472010","url":null,"abstract":"<p><strong>Introduction: </strong>Type 2 diabetes mellitus (T2DM) accelerates brain aging and disrupts brain functional network connectivity, though the specific mechanisms remain unclear. This study aimed to investigate T2DM-driven alterations in brain functional network connectivity and topology.</p><p><strong>Methods: </strong>Eighty-five T2DM patients and 67 healthy controls (HCs) were included. All participants underwent clinical, neuropsychological, and laboratory tests, followed by MRI examinations, including resting-state functional magnetic resonance imaging (rs-fMRI) and three-dimensional high-resolution T1-weighted imaging (3D-T1WI) on a 3.0 T MRI scanner. Post-image preprocessing, brain functional networks were constructed using the Dosenbach atlas and analyzed with the DPABI-NET toolkit through graph theory.</p><p><strong>Results: </strong>In T2DM patients, functional connectivity within and between the default mode network (DMN), frontal parietal network (FPN), subcortical network (SCN), ventral attention network (VAN), somatosensory network (SMN), and visual network (VN) was significantly reduced compared to HCs. Conversely, two functional connections within the VN and between the DMN and SMN were significantly increased. Global network topology analysis showed an increased shortest path length and decreased clustering coefficient, global efficiency, and local efficiency in the T2DM group. MoCA scores were negatively correlated with the shortest path length and positively correlated with global and local efficiency in the T2DM group. Node network topology analysis indicated reduced clustering coefficient, degree centrality, eigenvector centrality, and nodal efficiency in multiple nodes in the T2DM group. MoCA scores positively correlated with clustering coefficient and nodal efficiency in the bilateral precentral gyrus in the T2DM group.</p><p><strong>Discussion: </strong>This study demonstrated significant abnormalities in connectivity and topology of large-scale brain functional networks in T2DM patients. These findings suggest that brain functional network connectivity and topology could serve as imaging biomarkers, providing insights into the underlying neuropathological processes associated with T2DM-related cognitive impairment.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1472010"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating fMRI spatial network dynamics and EEG spectral power: insights into resting state connectivity.","authors":"Souvik Phadikar, Krishna Pusuluri, Armin Iraji, Vince D Calhoun","doi":"10.3389/fnins.2025.1484954","DOIUrl":"10.3389/fnins.2025.1484954","url":null,"abstract":"<p><strong>Introduction: </strong>The Integration of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) has allowed for a novel exploration of the brain's spatial-temporal resolution. While functional brain networks show variations in both spatial and temporal dimensions, most studies focus on fixed spatial networks that change together over time.</p><p><strong>Methods: </strong>In this study, for the first time, we link spatially dynamic brain networks with EEG spectral properties recorded simultaneously, which allows us to concurrently capture high spatial and temporal resolutions offered by these complementary imaging modalities. We estimated time-resolved brain networks using sliding window-based spatially constrained independent component analysis (scICA), producing resting brain networks that evolved over time at the voxel level. Next, we assessed their coupling with four time-varying EEG spectral power (delta, theta, alpha, and beta).</p><p><strong>Results: </strong>Our analysis demonstrated how the networks' volumes and their voxel-level activities vary over time and revealed significant correlations with time-varying EEG spectral power. For instance, we found a strong association between increasing volume of the primary visual network and alpha band power, consistent with our hypothesis for eyes open resting state scan. Similarly, the alpha, theta, and delta power of the Pz electrode were localized to voxel-level activities of primary visual, cerebellum, and temporal networks, respectively. We also identified a strong correlation between the primary motor network and alpha (mu rhythm) and beta activity. This is consistent with motor tasks during rest, though this remains to be tested directly.</p><p><strong>Discussion: </strong>These association between space and frequency observed during rest offer insights into the brain's spatial-temporal characteristics and enhance our understanding of both spatially varying fMRI networks and EEG band power.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1484954"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11810936/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal channel and feature selection for automatic prediction of functional brain age of preterm infant based on EEG.","authors":"Ling Li, Jiahui Li, Hui Wu, Yanping Zhao, Qinmei Liu, Hairong Zhang, Wei Xu","doi":"10.3389/fnins.2025.1517141","DOIUrl":"10.3389/fnins.2025.1517141","url":null,"abstract":"<p><strong>Introduction: </strong>Approximately 15 million premature infants are born each year, many of whom face risks of neurological impairments. Accurate assessment of brain maturity is crucial for timely intervention and treatment planning. Electroencephalography (EEG) is a noninvasive method commonly used for this purpose. However, using all channels and features for brain maturity assessment can lead to high computational burden and overfitting, which can decrease the performance of the prediction system.</p><p><strong>Methods: </strong>In this study, we propose an automatic prediction framework based on EEG to predict functional brain age (FBA) for assessing brain maturity in preterm infants. To optimize channel selection, we combine Binary Particle Swarm Optimization (BPSO) with Forward Addition (FA) and Backward Elimination (BE) methods. For feature selection, we combine the Pearson Correlation Coefficient (PCC), Recursive Feature Elimination (RFE), and Support Vector Regression (SVR) model.</p><p><strong>Results: </strong>The proposed framework achieved a prediction accuracy of 76.71% within ±1 week and 94.52% within ±2 weeks. Effective channel and feature selection significantly improved model performance while reducing computational costs.</p><p><strong>Discussion: </strong>These results demonstrate that optimizing channel and feature selection can enhance the performance of FBA prediction in preterm infants, offering a more efficient and accurate tool for brain maturity assessment.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1517141"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A bibliometric analysis of the top 100 most cited articles on corticospinal tract regeneration from 2004 to 2024.","authors":"Saijilafu, Ling-Chen Ye, Huanyi Li, Haokun Li, Xinyi Lin, Kehui Hu, Zekai Huang, Chimedragchaa Chimedtseren, Linjun Fang, Saijilahu, Ren-Jie Xu","doi":"10.3389/fnins.2024.1509850","DOIUrl":"10.3389/fnins.2024.1509850","url":null,"abstract":"<p><strong>Objective: </strong>Here, bibliometric and visual analytical techniques were employed to assess the key features of the 100 most cited publications concerning corticospinal tract (CST) regeneration.</p><p><strong>Methods: </strong>Research was conducted within the Web of Science Core Collection to pinpoint the 100 most cited publications on CST regeneration. From these, comprehensive data encompassing titles, authorship, key terms, publication venues, release timelines, geographic origins, and institutional affiliations were extracted, followed by an in-depth bibliometric examination.</p><p><strong>Results: </strong>The 100 most cited publications were all published between 2004 and 2024. These seminal papers amassed an aggregate of 18,321 citations, with individual citation counts ranging from 83 to 871 and a median of 136 citations per paper. Schwab M. E., stood out as the most prominent contributor, with significant authorship in 9 of the 100 papers. The United States dominated the geographical distribution, accounting for 49 of the articles. With 17 publications, the University of California System led the institutional rankings. A thorough keyword analysis revealed pivotal themes in the field, encompassing the optic nerve, gene expression, CST integrity and regeneration, diffusion tensor imaging, myelin-associated glycoproteins, inhibitors of neurite outgrowth, and methods of electrical and intracortical microstimulation.</p><p><strong>Conclusion: </strong>This investigation provides a bibliometric analysis of CST regeneration, underscoring the significant contribution of the United States to this field. Our findings unveiled the dynamics and trends within the field of CST regeneration, providing a scientific foundation for advancing clinical applications. Building on this analysis, the clinical application of CST regeneration should be optimized through interdisciplinary collaboration, enabling the exploration and validation of a variety of therapeutic approaches, including the use of neurotrophic factors, stem cell therapies, biomaterials, and electrical stimulation. Concurrently, additional clinical trials are necessary to test the safety and efficacy of these therapeutic methods and develop assessment tools for monitoring the recovery of patients. Furthermore, rehabilitation strategies should be refined, and professional education and training should be provided to enhance the understanding of CST regeneration treatments among both medical professionals and patients. The implementation of these strategies promises to enhance therapeutic outcomes and the quality of life of patients with spinal cord injury (SCI).</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"18 ","pages":"1509850"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11811756/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Women in neuroengineering and neurotechnologies.","authors":"Michela Chiappalone, Alessandra Pedrocchi, Suha Abuarqub, Chiara Bartolozzi, Valentina Benfenati, Silvia Giordani, Elvira Pirondini, Marianna Semprini","doi":"10.3389/fnins.2025.1542839","DOIUrl":"https://doi.org/10.3389/fnins.2025.1542839","url":null,"abstract":"","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1542839"},"PeriodicalIF":3.2,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11812058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RIPK1 expression and inhibition in tauopathies: implications for neuroinflammation and neuroprotection.","authors":"Ignacio Silva-Llanes, Enrique Madruga, Ana Martínez, Isabel Lastres-Becker","doi":"10.3389/fnins.2024.1530809","DOIUrl":"10.3389/fnins.2024.1530809","url":null,"abstract":"<p><p>Tauopathies are a group of neurodegenerative diseases characterized by the alteration/aggregation of TAU protein. One of the main challenges of these diseases is that they have neither biomarkers nor pharmacological targets to stop the neurodegenerative process. Apart from the neurodegenerative process, tauopathies are also characterized by a chronic low-grade neuroinflammation process, where the receptor-interacting protein kinase 1 (RIPK1) protein plays an essential role. Our research aimed to explore the role of RIPK1 in various tauopathies. We examined mouse models of frontotemporal dementia (FTD), as well as brain tissue samples from patients with progressive supranuclear palsy (PSP), a primary form of 4R tauopathy, and Alzheimer's disease (AD), which is considered a secondary tauopathy. Our findings show elevated levels of <i>RIPK1</i> mRNA levels across various forms of tauopathies, in both mouse models and human tissue samples associated with primary and secondary TAU-related disorders. Furthermore, we investigated the potential of using a RIPK1 inhibitor, known as GSK2982772, in a mouse model as a novel treatment strategy for FTD. The data showed that GSK2982772 treatment effectively reduced the reactive astrocyte response triggered by TAU^P301L overexpression. However, this RIPK1 inhibitor failed to protect against the neurodegeneration caused by elevated TAU^P301L levels in the hippocampal region. These results suggest that although inhibiting RIPK1 activity may help reduce TAU-related astrogliosis in the brain, the complexity of the inflammatory pathways involved could explain the absence of neuroprotective effects against TAU-induced neurodegeneration.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"18 ","pages":"1530809"},"PeriodicalIF":3.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11808139/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143390711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusion of EEG and EMG signals for detecting pre-movement intention of sitting and standing in healthy individuals and patients with spinal cord injury.","authors":"Chenyang Li, Yuchen Xu, Tao Feng, Minmin Wang, Xiaomei Zhang, Li Zhang, Ruidong Cheng, Weihai Chen, Weidong Chen, Shaomin Zhang","doi":"10.3389/fnins.2025.1532099","DOIUrl":"10.3389/fnins.2025.1532099","url":null,"abstract":"<p><strong>Introduction: </strong>Rehabilitation devices assist individuals with movement disorders by supporting daily activities and facilitating effective rehabilitation training. Accurate and early motor intention detection is vital for real-time device applications. However, traditional methods of motor intention detection often rely on single-mode signals, such as EEG or EMG alone, which can be limited by low signal quality and reduced stability. This study proposes a multimodal fusion method based on EEG-EMG functional connectivity to detect sitting and standing intentions before movement execution, enabling timely intervention and reducing latency in rehabilitation devices.</p><p><strong>Methods: </strong>Eight healthy subjects and five spinal cord injury (SCI) patients performed cue-based sit-to-stand and stand-to-sit transition tasks while EEG and EMG data were recorded simultaneously. We constructed EEG-EMG functional connectivity networks using data epochs from the 1.5-s period prior to movement onset. Pairwise spatial filters were then designed to extract discriminative spatial network topologies. Each filter paired with a support vector machine classifier to classify future movements into one of three classes: sit-to-stand, stand-to-sit, or rest. The final prediction was determined using a majority voting scheme.</p><p><strong>Results: </strong>Among the three functional connectivity methods investigated-coherence, Pearson correlation coefficient and mutual information (MI)-the MI-based EEG-EMG network showed the highest decoding performance (94.33%), outperforming both EEG (73.89%) and EMG (89.16%). The robustness of the fusion method was further validated through a fatigue training experiment with healthy subjects. The fusion method achieved 92.87% accuracy during the post-fatigue stage, with no significant difference compared to the pre-fatigue stage (<i>p</i> > 0.05). Additionally, the proposed method using pre-movement windows achieved accuracy comparable to trans-movement windows (<i>p</i> > 0.05 for both pre- and post-fatigue stages). For the SCI patients, the fusion method showed improved accuracy, achieving 87.54% compared to single- modality methods (EEG: 83.03%, EMG: 84.13%), suggesting that the fusion method could be promising for practical rehabilitation applications.</p><p><strong>Conclusion: </strong>Our results demonstrated that the proposed multimodal fusion method significantly enhances the performance of detecting human motor intentions. By enabling early detection of sitting and standing intentions, this method holds the potential to offer more accurate and timely interventions within rehabilitation systems.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"19 ","pages":"1532099"},"PeriodicalIF":3.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802573/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of deconvolutional networks for feature interpretability in epilepsy detection.","authors":"Sihao Shao, Yu Zhou, Ruiheng Wu, Aiping Yang, Qiang Li","doi":"10.3389/fnins.2024.1539580","DOIUrl":"10.3389/fnins.2024.1539580","url":null,"abstract":"<p><strong>Introduction: </strong>Scalp electroencephalography (EEG) is commonly used to assist in epilepsy detection. Even automated detection algorithms are already available to assist clinicians in reviewing EEG data, many algorithms used for seizure detection in epilepsy fail to account for the contributions of different channels. The Fully Convolutional Network (FCN) can provide the model's interpretability but has not been applied in seizure detection.</p><p><strong>Methods: </strong>To address these challenges, a novel convolutional neural network (CNN) model, combining SE (Squeeze-and-Excitation) modules, was proposed on top of the FCN. The epilepsy detection performance for patient-independent was evaluated on the CHB-MIT dataset. Then, the SE module was removed from the model and integrated the model with Inception, ResNet, and CBAM modules separately.</p><p><strong>Results: </strong>The method showed superior advancement, stability, and reliability compared to the other three methods. The method demonstrated a G-Mean of 82.7% for sensitivity (SEN) and specificity (SPE) on the CHB-MIT dataset. In addition, The contributions of each channel to the seizure detection task have also been quantified, which led us to find that the FZ, CZ, PZ, FT9, FT10, and T8 brain regions have a more pronounced impact on epileptic seizures.</p><p><strong>Discussion: </strong>This article presents a novel algorithm for epilepsy detection that accurately identifies seizures in different patients and enhances the model's interpretability.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"18 ","pages":"1539580"},"PeriodicalIF":3.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11802560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractional amplitude of low-frequency fluctuations during music-evoked autobiographical memories in neurotypical older adults.","authors":"Teresa Lesiuk, Kaitlyn Dillon, Giulia Ripani, Ioannis Iliadis, Gabriel Perez, Bonnie Levin, Xiaoyan Sun, Roger McIntosh","doi":"10.3389/fnins.2024.1479150","DOIUrl":"10.3389/fnins.2024.1479150","url":null,"abstract":"<p><strong>Introduction: </strong>Researchers have shown that music-evoked autobiographical memories (MEAMs) can stimulate long-term memory mechanisms while requiring little retrieval effort and may therefore be used in promising non-pharmacological interventions to mitigate memory deficits. Despite an increasing number of studies on MEAMs, few researchers have explored how MEAMs are bound in the brain.</p><p><strong>Methods: </strong>In the current study activation indexed by fractional amplitude of low frequency fluctuations (fALFF) during familiar and unfamiliar MEAM retrieval was compared in a sample of 24 healthy older adults. Additionally, we aimed to investigate the impact of age-related gray matter volume (GMV) reduction in key regions associated with MEAM-related activation. In addition to a T1 structural scan, neuroimaging data were collected while participants listened to familiar music (MEAM retrieval) versus unfamiliar music.</p><p><strong>Results: </strong>When listening to familiar compared to unfamiliar music, greater fALFF activation patterns were observed in the right parahippocampal gyrus, controlling for age and GMV. The current findings for the familiar (MEAM) condition have implications for cognitive aging as persons experiencing age-related memory decline are particularly susceptible to volumetric reduction in the parahippocampal cortex. <i>Post-hoc</i> analyses to explore correlations between brain activity and the content of MEAMs were performed using the text analysis program Linguistic Inquiry and Word Count.</p><p><strong>Discussion: </strong>Our findings suggest that MEAM-related activation of the parahippocampal cortex is evident in normative older adults. However, it is yet to be determined whether such brain states are attainable in older adult populations diagnosed with mild cognitive impairment and/or prodromal Alzheimer's disease.</p>","PeriodicalId":12639,"journal":{"name":"Frontiers in Neuroscience","volume":"18 ","pages":"1479150"},"PeriodicalIF":3.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}