NeuroImage最新文献

{"title":"Screening tools for subjective cognitive decline and mild cognitive impairment based on task-state prefrontal functional connectivity: a functional near-infrared spectroscopy study","authors":"Zhengping Pu ,&nbsp;Hongna Huang ,&nbsp;Man Li ,&nbsp;Hongyan Li ,&nbsp;Xiaoyan Shen ,&nbsp;Lizhao Du ,&nbsp;Qingfeng Wu ,&nbsp;Xiaomei Fang ,&nbsp;Xiang Meng ,&nbsp;Qin Ni ,&nbsp;Guorong Li ,&nbsp;Donghong Cui","doi":"10.1016/j.neuroimage.2025.121130","DOIUrl":"10.1016/j.neuroimage.2025.121130","url":null,"abstract":"<div><h3>Background</h3><div>Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) carry the risk of progression to dementia, and accurate screening methods for these conditions are urgently needed. Studies have suggested the potential ability of functional near-infrared spectroscopy (fNIRS) to identify MCI and SCD. The present fNIRS study aimed to develop an early screening method for SCD and MCI based on activated prefrontal functional connectivity (FC) during the performance of cognitive scales and subject-wise cross-validation via machine learning.</div></div><div><h3>Methods</h3><div>Activated prefrontal FC data measured by fNIRS were collected from 55 normal controls, 80 SCD patients, and 111 MCI patients. Differences in FC were analyzed among the groups, and FC strength and cognitive scale performance were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95 % confidence interval (CI) values.</div></div><div><h3>Results</h3><div>Statistical analysis revealed a trend toward more impaired prefrontal FC with declining cognitive function. Prediction models were built by combining features of prefrontal FC and cognitive scale performance and applying machine learning models, The models showed generally satisfactory abilities to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 92.0 % for MCI vs. NC, 80.0 % for MCI vs. SCD, and 76.1 % for SCD vs. NC were achieved, and the highest AUC values were 97.0 % (95 % CI: 94.6 %-99.3 %) for MCI vs. NC, 87.0 % (95 % CI: 81.5 %-92.5 %) for MCI vs. SCD, and 79.2 % (95 % CI: 71.0 %-87.3 %) for SCD vs. NC.</div></div><div><h3>Conclusion</h3><div>The developed screening method based on fNIRS and machine learning has the potential to predict early-stage cognitive impairment based on prefrontal FC data collected during cognitive scale-induced activation.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121130"},"PeriodicalIF":4.7,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586390","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}

{"title":"Fingerprint patterns of human brain activity reveal a dynamic mix of emotional responses during virtual intergroup encounters","authors":"Gloria Mendoza-Franco ,&nbsp;Inga Jasinskaja-Lahti ,&nbsp;Matthias B. Aulbach ,&nbsp;Ville J. Harjunen ,&nbsp;Anna Peltola ,&nbsp;J. Niklas Ravaja ,&nbsp;Matilde Tassinari ,&nbsp;Saana Vainio ,&nbsp;Iiro P. Jääskeläinen","doi":"10.1016/j.neuroimage.2025.121129","DOIUrl":"10.1016/j.neuroimage.2025.121129","url":null,"abstract":"<div><div>The Stereotype Content Model (SCM) states that different social groups elicit different emotions according to their perceived level of competence and warmth. Because of this relationship between stereotypes and emotional states and because emotions are highly predictive of intergroup behaviors, emotional evaluation is crucial for research on intergroup relations. However, emotional assessment heavily relies on self-reports, which are often compromised by social desirability and challenges in reporting immediate emotional appraisals. In this study, we used machine learning to identify emotional brain patterns using functional magnetic resonance imaging. Subsequently, those patterns were used to monitor emotional reactions during virtual intergroup encounters. Specifically, we showed Finnish majority group members 360-videos depicting members of their ethnic ingroup and immigrant outgroups approaching and entering participants’ personal space. All the groups showed different levels of perceived competence and warmth. In alignment with the SCM, our results showed that the groups perceived as low in competence and warmth evoked contempt and discomfort. Moreover, the ambivalent low-competent/high-warm group elicited both happiness and discomfort. Additionally, upon the protagonists’ approach into personal space, emotional reactions were modulated differently for each group. Taken together, our findings suggest that our method could be used to explore the temporal dynamics of emotional responses during intergroup encounters.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121129"},"PeriodicalIF":4.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579342","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}

{"title":"Augmenting brain-computer interfaces with ART: An artifact removal transformer for reconstructing multichannel EEG signals","authors":"Chun-Hsiang Chuang ,&nbsp;Kong-Yi Chang ,&nbsp;Chih-Sheng Huang ,&nbsp;Anne-Mei Bessas","doi":"10.1016/j.neuroimage.2025.121123","DOIUrl":"10.1016/j.neuroimage.2025.121123","url":null,"abstract":"<div><div>Artifact removal in electroencephalography (EEG) is a longstanding challenge that significantly impacts neuroscientific analysis and brain–computer interface (BCI) performance. Tackling this problem demands advanced algorithms, extensive noisy-clean training data, and thorough evaluation strategies. This study presents the Artifact Removal Transformer (ART), an innovative EEG denoising model employing transformer architecture to adeptly capture the transient millisecond-scale dynamics characteristic of EEG signals. Our approach offers a holistic, end-to-end denoising solution that simultaneously addresses multiple artifact types in multichannel EEG data. We enhanced the generation of noisy-clean EEG data pairs using an independent component analysis, thus fortifying the training scenarios critical for effective supervised learning. We performed comprehensive validations using a wide range of open datasets from various BCI applications, employing metrics like mean squared error and signal-to-noise ratio, as well as sophisticated techniques such as source localization and EEG component classification. Our evaluations confirm that ART surpasses other deep-learning-based artifact removal methods, setting a new benchmark in EEG signal processing. This advancement not only boosts the accuracy and reliability of artifact removal but also promises to catalyze further innovations in the field, facilitating the study of brain dynamics in naturalistic environments.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121123"},"PeriodicalIF":4.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586383","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}

{"title":"Depression is marked by differences in structural covariance between deep-brain nuclei and sensorimotor cortex","authors":"Siraj Lyons ,&nbsp;Isak Beck ,&nbsp;Brendan E. Depue","doi":"10.1016/j.neuroimage.2025.121127","DOIUrl":"10.1016/j.neuroimage.2025.121127","url":null,"abstract":"<div><h3>Background</h3><div>Depression impacts nearly 3% of the global adult population. Symptomatology is likely related to regions encompassing frontoparietal, somatosensory, and salience networks. Questions regarding deep brain nuclei (DBN), including the substantia nigra (STN), subthalamic nucleus (STN), and red nucleus (RN) remain unanswered.</div></div><div><h3>Methods</h3><div>Using an existing structural neuroimaging dataset including 86 individuals (Baranger et al., 2021; <em>n</em>_DEP = 39), frequentist and Bayesian logistic regressions assessed whether DBN volumes predict diagnosis, then structural covariance analyses in FreeSurfer tested diagnostic differences in deep brain volume and cortical morphometry covariance. Exploratory correlations tested relationships between implicated cortical regions and Hamilton Depression Rating Scale (HAM-D) scores.</div></div><div><h3>Results</h3><div>Group differences emerged in deep brain/cortical covariance. Right RN volume covaried with left parietal operculum volume and central sulcus thickness, while left RN and right STN volumes covaried with right occipital pole volume. Positive relationships were observed within the unaffected group and negative relationships among those with depression. These cortical areas did not correlate with HAM-D scores. Simple DBN volumes did not predict diagnostic group.</div></div><div><h3>Conclusion</h3><div>Structural codependence between DBN and cortical regions may be important in depression, potentially for sensorimotor features. Future work should focus on causal mechanisms of DBN involvement with sensory integration.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121127"},"PeriodicalIF":4.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586385","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}

{"title":"Development of a multichannel hand-adaptive tactile stimulation device for somatotopic map of human hand in somatosensory cortex with fMRI","authors":"Yutong Wang ,&nbsp;Di Luo ,&nbsp;Lihua Ma ,&nbsp;Luyao Wang ,&nbsp;Jinglong Wu ,&nbsp;Jian Zhang ,&nbsp;Tianyi Yan","doi":"10.1016/j.neuroimage.2025.121126","DOIUrl":"10.1016/j.neuroimage.2025.121126","url":null,"abstract":"<div><div>The 7T functional magnetic resonance imaging (fMRI) can provide a detailed somatotopic map. However, due to the constraints of MR-compatible applications, current tactile stimulation devices for the human hand are insufficient for precise somatotopic mapping experiments. In this study, we developed a novel 23-channel, hand-adaptive tactile stimulation device with high temporal and spatial resolution. The device consisted of an execution module and a control module. The device's output performance was measured using a laser displacement sensor. We investigated the somatotopic map of the non-dominant hand in the primary somatosensory cortex (S1) using the Bayesian population receptive field (pRF) model. The activation patterns, relative volumes, and activation center locations on S1 were assessed in somatotopic mapping experiments involving traveling wave stimulus paradigms with three stimulus orders (forward, backward, and random) in two dimensions (between-digit and within-digit). The percussive stimulation provided by the tactile stimulation device exhibited a stable displacement (2.58 mm) and a minimal output delay (4.45 milliseconds) across a wide range of vibration frequencies (0–30 Hz). The representation of digits and the palm in the between-digit dimension showed consistent somatotopic organization (D1-D2-D3-D4-D5-palm along the postcentral gyrus (poCG) from ventral to dorsal) across all three stimulation orders. Additionally, the relative volume of D1 in the random paradigm was significantly larger than in the forward and backward paradigms. The relative volume of the palm in the random paradigm was significantly larger than in the backward paradigm. The representation of the phalanges and palm in the within-digit dimension exhibited different activation patterns across different stimulation orders. These results provide new insights into the neural mechanisms in S1 and validate that the developed stimulation device can contribute to exploring the somatotopic map of the human hand.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121126"},"PeriodicalIF":4.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579251","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}

{"title":"How do the resting EEG preprocessing states affect the outcomes of postprocessing?","authors":"Shiang Hu ,&nbsp;Jie Ruan ,&nbsp;Pedro Antonio Valdes-Sosa ,&nbsp;Zhao Lv","doi":"10.1016/j.neuroimage.2025.121122","DOIUrl":"10.1016/j.neuroimage.2025.121122","url":null,"abstract":"<div><div>Plenty of artifact removal tools and pipelines have been developed to correct the resting EEG waves and discover scientific values behind. Without expertised visual inspection, it is susceptible to derive improper preprocessing, resulting in either insufficient preprocessed EEG (IPE) or excessive preprocessed EEG (EPE). However, little is known about the impacts of IPE or EPE on postprocessing in the temporal, frequency, and spatial domains, particularly as to the spectra and the functional connectivity analysis. Here, the clean EEG (CE) with linear and quasi-stationary assumption was synthesized as ground truth based on the New-York head model and the multivariate autoregressive model. Later, IPE and EPE were simulated by injecting Gaussian noise and losing brain components, respectively. Spectral homogeneities of all EEGs were evaluated by the proposed Parallel LOg Spectra index (PaLOSi). Then, the impacts on postprocessing were quantified by the IPE/EPE deviation from CE as to the temporal statistics, multichannel power, cross spectra, scalp EEG network properties, and source dispersion. Lastly, the association between PaLOSi and varying trends of postprocessing outcomes was analyzed with evolutionary preprocessing states. We found that compared with CE: 1) IPE (EPE) temporal statistics deviated more greatly with more noise injected (brain activities discarded); 2) IPE (EPE) power was higher (lower), and IPE power was almost parallel to that of CE across frequencies, while EPE power deviation decreased with higher frequencies; IPE cross spectra deviated more greatly than EPE, except for β band; 3) derived from 7 coupling measures, IPE (EPE) network had lower (higher) transmission efficiency and worse (better) integration ability; 4) IPE sources distributed more dispersedly with greater strength while EPE sources activated more focally with lower amplitudes; 5) PaLOSi was consistently correlated with varying trends of investigated postprocessing for both simulated and real data. This study shed light on how the postprocessing outcomes are affected by the preprocessing states and PaLOSi is a promising quality control metric for creating normative EEG databases.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121122"},"PeriodicalIF":4.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573168","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}

{"title":"Anatomical abnormalities suggest a compensatory role of the cerebellum in early Parkinson's disease","authors":"Juyoung Jenna Yun ,&nbsp;Anastasia Gailly de Taurines ,&nbsp;Yen F Tai ,&nbsp;Shlomi Haar","doi":"10.1016/j.neuroimage.2025.121121","DOIUrl":"10.1016/j.neuroimage.2025.121121","url":null,"abstract":"<div><div>Brain atrophy is detected in early Parkinson's disease (PD) and accelerates over the first few years post-diagnosis. This was captured by multiple cross-sectional studies and a few longitudinal studies in early PD. Yet only a longitudinal study with a control group can capture accelerated atrophy in early PD and differentiate it from healthy ageing. Accordingly, we performed a multicohort longitudinal analysis between PD and healthy ageing, examining subcortical regions implicated in PD pathology, including the basal ganglia, thalamus, corpus callosum (CC), and cerebellum. Longitudinal volumetric analysis was performed on 56 early PD patients and 53 matched controls, with scans collected 2–3 years apart. At baseline, the PD group showed a greater volume in the pallidum, thalamus, and cerebellar white matter (WM), suggesting potential compensatory mechanisms in prodromal and early PD. After 2–3 years, accelerated atrophy in PD was observed in the putamen and cerebellar WM. Interestingly, healthy controls – but not PD patients – demonstrated a significant decline in Total Intracranial Volume (TIV), and atrophy in the thalamus and mid-CC. Between-group analysis revealed more severe atrophy in the right striatum and cerebellar WM in PD, and in the mid-posterior CC in controls. Using CEREbellum Segmentation (CERES) for lobule segmentation on the longitudinal PD cohort, we found a significant decline in the WM of non-motor regions in the cerebellum, specifically Crus I and lobule IX. Our results highlight an initial increase in cerebellar WM volume during prodromal PD, followed by significant degeneration over the first few years post-diagnosis.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121121"},"PeriodicalIF":4.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579341","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}

{"title":"Effector general representation of movement goals in human frontal and parietal cortex","authors":"Ziyi Duan ,&nbsp;Marissa H. Evans ,&nbsp;Bonnie Lawrence ,&nbsp;Clayton E. Curtis","doi":"10.1016/j.neuroimage.2025.121124","DOIUrl":"10.1016/j.neuroimage.2025.121124","url":null,"abstract":"<div><div>In the nonhuman primate, discrete parts of premotor frontal and parietal cortex appear to code for movements of different effectors. However, the evidence regarding homologous effector selectivity within the human brain remains inconclusive. Here, we measured neural activity in the human brain using functional magnetic resonance imaging while participants remembered a target location and planned either saccades or reaches that matched the rich kinematics used in seminal monkey studies. We compared activity patterns during the planning period and used assumption-free multivariate searchlight analysis to identify brain regions that could decode the spatial goals of planned movements. Critically, we performed two types of decoding analyses to determine if the spatial information embedded in activation patterns was effector-specific or effector-general. For effector-specific spatial coding, we compared brain regions that could decode target locations within each effector. However, we did not identify areas that coded spatial information in one effector but not the other. For effector-general spatial coding, we performed spatial decoding using trials across effectors and conducted cross-effector decoding. Both analyses identified several areas in the frontal and parietal regions that encoded spatial information for both effectors, including precentral sulcus, superior parietal lobe, and intraparietal sulcus. Our results indicate that premotor frontal and parietal cortex encode the spatial metrics of movement goals that can be read out and converted into effector-specific motor metrics for saccades and reaches.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121124"},"PeriodicalIF":4.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579411","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}

{"title":"Resting-state EEG network variability predicts individual working memory behavior","authors":"Chunli Chen ,&nbsp;Shiyun Xu ,&nbsp;Jixuan Zhou ,&nbsp;Chanlin Yi ,&nbsp;Liang Yu ,&nbsp;Dezhong Yao ,&nbsp;Yangsong Zhang ,&nbsp;Fali Li ,&nbsp;Peng Xu","doi":"10.1016/j.neuroimage.2025.121120","DOIUrl":"10.1016/j.neuroimage.2025.121120","url":null,"abstract":"<div><div>Even during periods of rest, the brain exhibits spontaneous activity that dynamically fluctuates across spatially distributed regions in a globally coordinated manner, which has significant cognitive implications. However, the relationship between the temporal variability of resting-state networks and working memory (WM) remains largely unexplored. This study aims to address this gap by employing an EEG-based protocol combined with fuzzy entropy. First, we identified both flexible and robust patterns of dynamic resting-state networks. Subsequently, we observed a significant positive correlation between WM performance and network variability, particularly in connections associated with the frontal, right central, and right parietal lobes. Moreover, we found that the temporal variability of network properties was positively and significantly associated with WM performance. Additionally, distinct patterns of network variability were delineated, contributing to inter-individual differences in WM abilities, with these distinctions becoming more pronounced as task demands increased. Finally, using a multivariable predictive model based on these variability metrics, we effectively predicted individual WM performances. Notably, analogous analyses conducted in the source space validated the reproducibility of the temporal variability of resting-state networks in predicting individual WM behavior at higher spatial resolution, providing more precise anatomical localization of key brain regions. These results suggest that the temporal variability of resting-state networks reflects intrinsic dynamic changes in brain organization supporting WM and can serve as an objective predictor for individual WM behaviors.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121120"},"PeriodicalIF":4.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586386","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}

{"title":"Time- and sex-dependent effects of juvenile social isolation on mouse brain morphology","authors":"Tatiana Sazhina ,&nbsp;Tomokazu Tsurugizawa ,&nbsp;Yuki Mochizuki ,&nbsp;Aika Saito ,&nbsp;Asuka Joji-Nishino ,&nbsp;Kazuya Ouchi ,&nbsp;Sho Yagishita ,&nbsp;Kazuo Emoto ,&nbsp;Akira Uematsu","doi":"10.1016/j.neuroimage.2025.121117","DOIUrl":"10.1016/j.neuroimage.2025.121117","url":null,"abstract":"<div><div>During early life stages, social isolation disrupts the proper brain growth and brain circuit formation, which is associated with the risk of mental disorders and cognitive deficits in adulthood. Nevertheless, the impact of juvenile social isolation on brain development, particularly regarding variations across age and sex, remains poorly understood. Here, we investigate the effects of social isolation stress (SIS) during early (3-5 weeks old) or late (5-7 weeks old) juvenile period on brain morphology in adult male and female mice using ultra high-field MRI (11.7 T). We found that both early and late SIS in female mice led to volumetric increases in multiple brain regions, such as the medial prefrontal cortex (mPFC) and hippocampus. Correlation tractography revealed that the fiber tracts in the right corpus callosum and right amygdala were positively correlated with SIS in female mice. In male mice, early SIS resulted in small volumetric increases in the isocortex, whereas late SIS led to reductions in the isocortex and hypothalamus. Furthermore, early SIS caused a negative correlation, while late SIS exhibited a positive correlation, with fiber tracts in the corpus callosum and amygdala in male mice. Using a Random Forest classifier, we achieved effective discrimination between socially isolated and control conditions in the brain volume of female mice, with the limbic areas playing a key role in the model's accuracy. Finally, we discovered that SIS led to context fear generalization in a sex-dependent manner. Our findings highlight the importance of considering both the time- and sex-dependent effects of juvenile SIS on brain development and emotional processing, providing new insights into its long-term consequences.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"310 ","pages":"Article 121117"},"PeriodicalIF":4.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143573322","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}