NeuroImage最新文献

{"title":"Electroencephalography estimates brain age in infants with high precision: Leveraging advanced machine learning in healthcare","authors":"Saeideh Davoudi ,&nbsp;Gabriela Lopez Arango ,&nbsp;Florence Deguire ,&nbsp;Inga Sophie Knoth ,&nbsp;Fanny Thebault-Dagher ,&nbsp;Rebecca Reh ,&nbsp;Laurel Trainor ,&nbsp;Janet Werker ,&nbsp;Sarah Lippé","doi":"10.1016/j.neuroimage.2025.121200","DOIUrl":"10.1016/j.neuroimage.2025.121200","url":null,"abstract":"<div><div>Changes in the pace of neurodevelopment are key indicators of atypical maturation during early life. Unfortunately, reliable prognostic tools rely on assessments of cognitive and behavioral skills that develop towards the second year of life and after. Early assessment of brain maturation using electroencephalography (EEG) is crucial for clinical intervention and care planning. We developed a reliable methodology using conventional machine learning (ML) and novel deep learning (DL) networks to efficiently quantify the difference between chronological and biological age, so-called brain age gap (BAG) as a marker of accelerated/decelerated biological brain development. In this cross-sectional study, EEG from 219 typically-developing infants aged from three to 14-months was used. For DL networks, the input samples were increased to 2628 recordings. We further validated the BAG tool in a population at clinical risk with abnormal brain growth (macrocephaly) to capture deviation from normal aging. Our results indicate that DL networks outperform conventional ML models, capturing complex non-monotonic EEG characteristics and predicting the biological age with a mean absolute error of only one month (MAE = 1 month, 95 %CI:0.88–1.15, r = 0.82, 95 %CI:0.78–0.85). Additionally, the developing brain follows a trajectory characterized by increased non-linearity and complexity in which alpha rhythm plays an important role. BAG could detect group-level maturational delays between typically-developing and macrocephaly <span><math><mrow><mo>(</mo><mrow><mi>p</mi><mi>v</mi><mi>a</mi><mi>l</mi><mi>u</mi><mi>e</mi><mo>=</mo><mn>0.009</mn></mrow><mo>)</mo></mrow></math></span>. In macrocephaly, BAG negatively correlated with the general adaptive composite of the ABAS-II (<span><math><mrow><mi>p</mi><mi>v</mi><mi>a</mi><mi>l</mi><mi>u</mi><mi>e</mi><mo>=</mo><mn>0.04</mn></mrow></math></span>) at 18-months and the information processing speed scale of the WPSSI-IV at age four (<span><math><mrow><mi>p</mi><mi>v</mi><mi>a</mi><mi>l</mi><mi>u</mi><mi>e</mi><mo>=</mo><mn>0.006</mn></mrow></math></span>). The EEG-based BAG score offers a reliable non-invasive measure of brain maturation, with significant advantages and implications for developmental neuroscience and clinical practice.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"312 ","pages":"Article 121200"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-based gene expression and corresponding behavioural relevance of risk genes for broad antisocial behaviour","authors":"Jaroslav Rokicki ,&nbsp;Megan L. Campbell ,&nbsp;Dennis van der Meer ,&nbsp;Alina I. Sartorius ,&nbsp;Natalia Tesli ,&nbsp;Piotr Jahołkowski ,&nbsp;Alexey Shadrin ,&nbsp;Ole Andreassen ,&nbsp;Lars T. Westlye ,&nbsp;Daniel S. Quintana ,&nbsp;Unn K. Haukvik","doi":"10.1016/j.neuroimage.2025.121198","DOIUrl":"10.1016/j.neuroimage.2025.121198","url":null,"abstract":"<div><div>Antisocial behaviour (ASB) involves persistent irresponsible, delinquent activities violating rights and safety of others. A meta-analysis of genome-wide association studies revealed significant genetic associations with ASB, yet their brain expression patterns and behavioural relevance remain unclear. Our investigation of fifteen genes associated with ASB examined their biological role and distribution across tissues, integrating post-mortem brain sample data from the Allen-Human-Brain Atlas and the Genotype-Tissue Expression project. We found that these genes were differentially expressed in the brain, particularly in regions like the cerebellum, putamen, and caudate, and were notably downregulated in the pancreas. Single cell type expression analysis revealed that ASB-associated genes had strong correlations with ductal and endothelial cells in the pancreas, indicating a possible metabolic influence on ASB. Certain genes like <em>NTN1, SMAD5, NCAM2</em>, and <em>CDC42EP3</em> displayed specificity for cognitive terms including chronic pain, heart rate, and aphasia. These expression patterns aligned with neurocognitive domains related to thinking, and learning, distress, motor skills, as determined by fMRI analysis. This study connects specific brain gene expression with potential genetic and metabolic factors in ASB, offering novel insights into its biological basis and possible interdisciplinary approaches to understanding and addressing aggressive behaviours.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121198"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the distribution of neural and non-neural components in multi-echo EPI data across echo times based on tensor-ICA","authors":"Tengfei Feng ,&nbsp;Halim Ibrahim Baqapuri ,&nbsp;Jana Zweerings ,&nbsp;Huanjie Li ,&nbsp;Fengyu Cong ,&nbsp;Klaus Mathiak","doi":"10.1016/j.neuroimage.2025.121199","DOIUrl":"10.1016/j.neuroimage.2025.121199","url":null,"abstract":"<div><div>Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121199"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI","authors":"Bo Li,&nbsp;Qi Zeng,&nbsp;Simon K. Warfield,&nbsp;Davood Karimi","doi":"10.1016/j.neuroimage.2025.121190","DOIUrl":"10.1016/j.neuroimage.2025.121190","url":null,"abstract":"<div><div>Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages centered between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at <span><span>https://gitlab.com/blibli/fetdtialign</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121190"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Functional divisions of the left anterior and posterior temporoparietal junction for phonological and semantic processing in Chinese character reading","authors":"Aqian Li ,&nbsp;Chuansheng Chen ,&nbsp;Yuan Feng ,&nbsp;Rui Hu ,&nbsp;Xiaoxue Feng ,&nbsp;Jingyu Yang ,&nbsp;Xingying Lin ,&nbsp;Leilei Mei","doi":"10.1016/j.neuroimage.2025.121201","DOIUrl":"10.1016/j.neuroimage.2025.121201","url":null,"abstract":"<div><div>Previous studies have shown that the left temporoparietal junction (TPJ) plays a critical role in word reading. Nevertheless, there is still controversy surrounding the phonological and semantic functions of the left TPJ. The parietal unified connectivity-biased computation (PUCC) model posits that the function of the left TPJ depends on both the neurocomputation of this local area and its long-range connectivity. To clarify the specific roles of different TPJ subregions in phonological and semantic processing of Chinese characters, the present study used connectivity-based clustering to identify seven subdivisions within the left TPJ, and conducted comprehensive analyses including functional and structural connectivity, univariate and multivariate analyses (i.e., representational similarity analysis, RSA) on multimodal imaging data (task-state fMRI, resting-state fMRI, and diffusion-weighted imaging [DWI]). Functional and structural connectivity analyses revealed that the left anterior TPJ had stronger connections with the phonological network, while the left posterior TPJ had stronger connections with the semantic network. RSA revealed that the left anterior and posterior TPJ represented phonological and semantic information of Chinese characters, respectively. More importantly, the phonological and semantic representations of the left TPJ were respectively correlated with its functional connectivity to the phonological and semantic networks. Altogether, our results provide a more elaborate perspective on the functional dissociation of the left anterior and posterior TPJ in phonological and semantic processing of Chinese characters, and support the PUCC model.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121201"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The self-awareness brain network: Construction, characterization, and alterations in schizophrenia and major depressive disorder","authors":"Xiaoluan Xia ,&nbsp;Fei Gao ,&nbsp;Shiyang Xu ,&nbsp;Kaixin Li ,&nbsp;Qingxia Zhu ,&nbsp;Yuwen He ,&nbsp;Xinglin Zeng ,&nbsp;Lin Hua ,&nbsp;Shaohui Huang ,&nbsp;Zhen Yuan","doi":"10.1016/j.neuroimage.2025.121205","DOIUrl":"10.1016/j.neuroimage.2025.121205","url":null,"abstract":"<div><div>Self-awareness (SA) research is crucial for understanding cognition, social behavior, mental health, and education, but SA's underlying network architecture, particularly connectivity patterns, remains largely uncharted. We integrated meta-analytic findings with connectivity-behavior correlation analyses to systematically identify SA-related regions and connections in healthy adults. Edge-weighted networks capturing public, private, and composite SA dimensions were established, where weights represented correlation strengths between tractography-derived structural connectivities and SA levels quantified through behavioral assessments. Then, multilevel SA networks were extracted across a spectrum of correlation thresholds. Robust full-threshold analyses revealed their hierarchical continuum encompassing distinct lateralization patterns, topological transitions, and characteristic hourglass-like architectures. Pathological analysis demonstrated SA connectivity disruptions in schizophrenia (SZ) and major depressive disorder (MDD): approximately 40 % of SA-related connectivities were altered in SZ and 20 % in MDD, with 90 % of MDD alterations overlapping with SZ. While disease-specific and shared alterations were also observed in network-level topological properties, the core SA connectivity framework remained preserved in both disorders. Collectively, these findings significantly advanced our understanding of SA's neurobiological substrates and their pathological deviations.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121205"},"PeriodicalIF":4.7,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Age-Related Differences in Speech and Gray Matter Volume: The Modulating Role of Multilingualism” [NeuroImage, 310 (2025) 121149]","authors":"Hanxiang Yu ,&nbsp;Keyi Kang ,&nbsp;Christos Pliatsikas ,&nbsp;Yushen Zhou ,&nbsp;Haoyun Zhang","doi":"10.1016/j.neuroimage.2025.121191","DOIUrl":"10.1016/j.neuroimage.2025.121191","url":null,"abstract":"","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121191"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tracking neural activity patterns during rapid high-altitude transitions","authors":"Ji-Yu Xie ,&nbsp;Yi Zhang ,&nbsp;Wei Shen ,&nbsp;Liying Wu ,&nbsp;Quanhao Yu ,&nbsp;Zhen Lyu ,&nbsp;Liangyuan Song ,&nbsp;Rui Yang ,&nbsp;Shuyi Ning ,&nbsp;Wenwen Duan ,&nbsp;Ying Li ,&nbsp;Yimeng Liu ,&nbsp;Xuemin Wang ,&nbsp;Liping Chen ,&nbsp;Jie Weng ,&nbsp;Yonglan Du ,&nbsp;Xiao Li ,&nbsp;Taicheng Huang ,&nbsp;Hailin Ma ,&nbsp;Quansheng Gao ,&nbsp;Ti-Fei Yuan","doi":"10.1016/j.neuroimage.2025.121197","DOIUrl":"10.1016/j.neuroimage.2025.121197","url":null,"abstract":"<div><div>Rapid adaptation to dynamic changes in the environment is critical for human survival. Extensive studies have observed human behavior and brain activity in a stable environment, but there is still a lack of understanding of how our brain's functional activity drives behavioral changes when the natural environment changes. Here, we used a virtual environment platform named the hypobaric hypoxia chamber to investigate how human neural oscillations and related behaviors are affected by changes in barometric pressure and oxygen levels at different altitudes. We found that physiological compensations occurred in the hypobaric hypoxic environment followed by an increase in altitude, resulting in faster response times in working memory tasks. High-density EEG analysis revealed a significant decrease in the alpha band at high altitudes, while delta band activity gradually increased with altitude. Moreover, a predictive model based on differences in brain regions across frequency bands identified the left supramarginal gyrus and left lingual gyrus as two hub regions strongly associated with hypoxia-related behavioral changes, and activations in the pallidum and amygdala could effectively decode the specific altitude at which humans are located. Our study underscores the potential of hypobaric hypoxia chambers as a powerful tool for dynamic high-altitude research and provides novel insights into how altitude-related changes shape human cognition and brain activity.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121197"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How to measure functional connectivity using resting-state fMRI? A comprehensive empirical exploration of different connectivity metrics","authors":"Lukas Roell ,&nbsp;Stephan Wunderlich ,&nbsp;David Roell ,&nbsp;Florian Raabe ,&nbsp;Elias Wagner ,&nbsp;Zhuanghua Shi ,&nbsp;Andrea Schmitt ,&nbsp;Peter Falkai ,&nbsp;Sophia Stoecklein ,&nbsp;Daniel Keeser","doi":"10.1016/j.neuroimage.2025.121195","DOIUrl":"10.1016/j.neuroimage.2025.121195","url":null,"abstract":"<div><h3>Background</h3><div>Functional connectivity in the context of functional magnetic resonance imaging is typically quantified by Pearson´s or partial correlation between regional time series of the blood oxygenation level dependent signal. However, a recent interdisciplinary methodological work proposes &gt;230 different metrics to measure similarity between different types of time series.</div></div><div><h3>Objective</h3><div>Hence, we systematically evaluated how the results of typical research approaches in functional neuroimaging vary depending on the functional connectivity metric of choice. We further explored which metrics most accurately detect presumed reductions in connectivity related to age and malignant brain tumors, aiming to initiate a debate on the best approaches for assessing brain connectivity in functional neuroimaging research.</div></div><div><h3>Methods</h3><div>We addressed both research questions using four independent neuroimaging datasets, comprising multimodal data from a total of 1187 individuals. We analyzed resting-state functional sequences to calculate functional connectivity using 20 representative metrics from four distinct mathematical domains. We further used T1- and T2-weighted images to compute regional brain volumes, diffusion-weighted imaging data to build structural connectomes, and pseudo-continuous arterial spin labeling to measure regional brain perfusion.</div></div><div><h3>Results</h3><div>First, our findings demonstrate that the results of typical functional neuroimaging approaches differ fundamentally depending on the functional connectivity metric of choice. Second, we show that correlational and distance metrics are most appropriate to cover reductions in connectivity linked to aging. In this context, partial correlation performs worse than other correlational metrics. Third, our findings suggest that the FC metric of choice depends on the utilized scanning parameters, the regions of interest, and the individual investigated. Lastly, beyond the major objective of this study, we provide evidence in favor of brain perfusion measured via pseudo-continuous arterial spin labeling as a robust neural entity mirroring age-related neural and cognitive decline.</div></div><div><h3>Conclusion</h3><div>Our empirical evaluation supports a recent theoretical functional connectivity framework. Future functional imaging studies need to comprehensively define the study-specific theoretical property of interest, the methodological property to assess the theoretical property, and the confounding property that may bias the conclusions.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"312 ","pages":"Article 121195"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The changes in neural complexity and connectivity in thalamocortical and cortico-cortical systems after propofol-induced unconsciousness in different temporal scales","authors":"Zhenhu Liang ,&nbsp;Luxin Fan ,&nbsp;Bin Zhang ,&nbsp;Wei Shu ,&nbsp;Duan Li ,&nbsp;Xiaoli Li ,&nbsp;Tao Yu","doi":"10.1016/j.neuroimage.2025.121193","DOIUrl":"10.1016/j.neuroimage.2025.121193","url":null,"abstract":"<div><div>Existing studies have indicated neural activity across diverse temporal and spatial scales. However, the alterations in complexity, functional connectivity, and directional connectivity within the thalamocortical and corticocortical systems across various scales during propofol-induced unconsciousness remain uncertain. We analyzed the stereo-electroencephalography (SEEG) from wakefulness to unconsciousness among the brain regions of the prefrontal cortex, temporal lobe, and anterior nucleus of the thalamus. The complexity (examined by permutation entropy (PE)), functional connectivity (permutation mutual information (PMI)), and directional connectivity (symbolic conditional mutual information (SCMI) and directionality index (DI)) were calculated across various scales. In the lower-band frequency (0.1–45 Hz) SEEG, after the loss of consciousness, PE significantly decreased (<em>p</em> &lt; 0.001) in all regions and scales, except for the thalamus, which remained relatively unchanged at large scales (<em>τ</em>=32 ms). Following the loss of consciousness, inter-regional PMI either significantly increased or remained stable across different scales (<em>τ</em>=4 ms to 32 ms). During the unconscious state, SCMI between brain regions exhibited inconsistent changes across scales. In the late unconscious stage, the inter-regional DI across all scales indicated a shift from a balanced state of information flow between brain regions to a pattern where the prefrontal cortex and thalamus drive the temporal lobe. Our findings demonstrate that propofol-induced unconsciousness is associated with reduced cortical complexity, diverse functional connectivity, and a disrupted balance of information integration among thalamocortical and cortico-cortical systems. This study enhances the theoretical understanding of anesthetic-induced loss of consciousness by elucidating the scale- and region-specific effects of propofol on thalamocortical and cortico-cortical systems.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"311 ","pages":"Article 121193"},"PeriodicalIF":4.7,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}