Background Migraine is a prevalent neurological disorder that is frequently observed in clinical practice and is commonly comorbid with insomnia. Insomnia can exacerbate and precipitate migraine attacks, with both conditions exerting a reciprocal influence on one another. The cerebellar crus is significantly associated with the pathophysiology of migraine and insomnia. The relationship between cerebellar crus functional alterations and migraine-associated insomnia remains unclear. This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) to examine functional alterations in the cerebellar crus of patients with migraine and concurrent insomnia. Methods Participants underwent resting-state functional magnetic resonance imaging. Subsequently, the disparity in amplitude of low-frequency fluctuations (ALFF) values among groups was analyzed, followed by functional connectivity (FC) investigations employing the cerebellum crus as seed regions. Results Migraine patients frequently experience neuropsychological disorders and insomnia, which are interconnected. Both migraine with insomnia (MwI) and migraine without insomnia (MwoI) groups demonstrated elevated amplitude of low-frequency fluctuations (ALFF) in the left Crus I and II compared to the healthy controls (HC) group, with the MwI group exhibiting more pronounced alterations. Additionally, both patient groups showed decreased FC between the left Crus I and the right middle temporal gyrus (MTG) and inferior temporal gyrus (ITG) relative to the HC group. The MwoI group showed significantly lower FC compared to both the HC and MwI groups. A significant negative correlation was observed between ALFF in the left Crus I/II and Pittsburgh Sleep Quality Index (PSQI) scores in the MwoI group. Conversely, in the combined migraine cohort, FC between the left Crus I and the right MTG/ITG showed a positive correlation with PSQI scores. Conclusion This study identified a correlation between aberrant functional activity in the left Crus I/II and migraine comorbidity with insomnia. These findings provide fresh perspectives on the neural mechanisms underlying the migraine-insomnia relationship, thereby facilitating the identification of potential neuroimaging biomarkers and the exploration of targeted interventions for this patient subgroup.

Cognitive workload plays a vital role in tasks that demand dynamic decision-making, especially under high-risk and time-sensitive conditions. An excessive workload can lead to unexpected and disproportionate risks, whereas insufficient workload may cause disengagement, undermining task performance. This underscores the importance of maintaining an optimal level of mental focus in high-pressure situations to ensure successful task execution. This study leverages deep learning methods alongside functional connectivity measures to classify cognitive workload levels. Using the N-back EEG dataset, functional connectivity metrics such as Phase Locking Value (PLV), Phase Lagging Index (PLI), and Coherency are extracted after data pre-processing. These metrics, characterized as directed or non-directed, enable efficient computational analysis. A convolutional neural network (CNN) classifier is employed to categorize cognitive workload into three levels: low (0-back), medium (2-back), and high (3-back). The CNN-A architecture achieves peak performance with an accuracy of 93.75% using PLV, 87.5% using Coherency, and 68.75% using PLI.

To investigate the relationship between age-related brain volume loss and neural functional connectivity (FC), whole brain volume and mean FC were calculated in 75 healthy participants aged 20 to 86 years (39 women, 36 men; mean age: 59.31 years). Resting-state MEG with eyes closed and MRI were conducted. Correlations between age and whole brain volume, mean neural connectivity expressed as amplitude envelope correlation (AEC) in the alpha frequency band across 66 functional parcellations, and the standard deviation (SD) of AEC were analyzed. In seven brain regions showing significant age-related volume loss, mean AEC and SD of AEC with other regions were assessed. Whole brain volume decreased with age (r = -0.322, p = .00480), particularly in participants older than 75 years (p < .05, ANOVA). AEC values also declined with age (r = -0.359, p = .00153), with significant differences observed between generational subgroups under 45 and over 75 years (p < .05). The SD of AEC decreased across the brain with age (r = -0.326, p = .0043). However, seven brain regions with significant age-related volume loss did not consistently show significant differences in AEC or its SD between generational subgroups, in contrast to consistent volume differences observed. Overall, brain volume and neural FC declined with age, accompanied by reduced variability in FC across the brain. Nevertheless, regions exhibiting significant volume loss were not always associated with functional decline in FC or its variability, suggesting the brain may compensate for global decline through localized functional adaptations.

To tackle the disease-related process in early pre-dementia Lewy Body Dementia, we investigated the changes of functional brain networks and their cognitive relevance. A cohort of 38 Mild Cognitive Impairment with Lewy Bodies (MCI-LB) subjects and one of 24 healthy controls (HC) underwent neuropsychological assessment and resting state (RS) functional and structural MRI. Functional connectivity (FC) between ROIs belonging to a set of RS networks, including the Salience Network (SN), Fronto-Parietal (FPN), Default Mode (DMN), Dorsal and Ventral Attention (DAN and VAN), Somato-Motor (SMN), Visual (VN) and Language (LN) was estimated and compared between cohorts. Finally, neuropsychological scores were correlated with FC of MCI-LB and HC separately. Compared to HC, MCI-LB exhibited lower FC between DAN, FPN and LN. Higher inter-network FC was found between FPN and SN, FPN and DMN, SN and SMN and DAN and SMN. In MCI-LB the correlational analysis revealed significant positive and negative associations between cognitive performance and FC values between nodes. In conclusion, we found a possible compensation mechanism between nodes in SN and FPN, and FPN and DMN following disconnection between the control system of the FPN and the top down attention system. The complex compensatory mechanisms involving multiple networks may not be efficient to counteract the cognitive impairment in MCI-LB. Overall, in MCI-LB we found an aberrant engagement of the networks that are not primarily involved in the performance of specific tasks.

Cerebral small vessel disease (CSVD) significantly contributes to cognitive decline and poses potential risk factors for both dementia and acute cerebrovascular events. Tai Chi has been recognized as an effective mind-body intervention for enhancing cognitive function and promoting neural plasticity. However, the neurobiological mechanisms that underlie how Tai Chi exercises affect cognitive improvement in patients with CSVD remain unclear. We recruited 51 patients with CSVD, randomly assigning them to either a 24-week Tai Chi exercise group (n = 26) or a health education control group (n = 25). We collected and analyzed data on neuropsychological assessments, plasma homocysteine levels, and brain structural and functional connectivity (SC and FC). Additionally, we employed network-based statistics along with correlational and mediation effect analyses to analyze the intervention process. Patients in the intervention group demonstrated a significant improvement in cognitive function. Furthermore, they demonstrated an increased FC pattern in the frontal-parietal-occipital regions, along with a significant rise of structural-functional coupling within the frontal network and a reduction in the occipital network. The enhanced structural-functional coupling in the frontal network partially mediated the reduction in homocysteine levels and the improvement in cognitive function.

Cancer-related cognitive impairment (CRCI) is a common and debilitating complication among colorectal cancer survivors, even in those without chemotherapy exposure. To identify cancer-related neural changes, we investigated spontaneous brain activity and cognition in colorectal cancer survivors using cognitive assessments and resting-state functional magnetic resonance imaging (rsfMRI). Nineteen survivors (stages I-II, cancer diagnosis < 12 months, chemotherapy-naïve) and 18 healthy controls underwent a battery of objective/subjective cognitive tests and MRI. RsfMRI data was analyzed with fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity (FC). Statistical analysis was controlled for age, sex, education, depression, and anxiety, with multiple comparison correction. Compared to controls, survivors performed significantly worse on the Hopkins Verbal Learning Test (HVLT-R) Recognition Discrimination Index (RDI) (p = 0.03) and showed slower psychomotor speed on the Trail Making Test (TMT-A) (p = 0.02). RsfMRI analysis revealed increased fALFF in the right hippocampus and bilateral inferior/middle temporal, parahippocampal, and fusiform gyri, with decreased fALFF in the bilateral superior/middle frontal gyri and left inferior frontal gyrus. RDI was negatively correlated with fALFF in right temporal regions. Survivors also exhibited reduced FC within the default mode network (DMN) (p < 0.05). This cross-sectional study shows that colorectal cancer survivors display hyperactivity in the temporal regions and disrupted DMN connectivity associated with cognitive decline, suggesting a maladaptive neural response. Our study identified the functionally altered brain regions and networks associated with colorectal CRCI using MRI. This would provide potential biological targets for developing interventions such as neuromodulation for mitigating the adverse effects of colorectal CRCI.

Mentalizing, the ability to infer others' thoughts and intentions, relies on a network of brain regions whose functional connectivity changes across development. While prior research has focused on adults, little is known about task-based functional connectivity in this network during development. We use fMRI to examine mentalizing-related activation and task-based connectivity in 181 participants (80 children aged 6-14; 101 adults aged 20-61). Analyses assess age-related changes in activation and connectivity, and test whether connectivity mediates the relationship between age and mentalizing ability across neurofunctional groups. Adults outperform children in mentalizing accuracy, though children show age-related improvements. Activation patterns are largely overlapping across age groups, involving core regions such as the temporoparietal junction, precuneus, and medial prefrontal cortex. Connectivity analyses reveal that children show stronger local (frontal-frontal, posterior-posterior) connections, with increasing long-range (frontal-posterior) connectivity with age. Adults exhibit a more integrated network, though connectivity declines with age. Connectivity strength follows a quadratic trajectory, peaking in early adulthood (~ 32 years). Importantly, connectivity mediates the age-mentalizing relationship in children, but not in adults. These findings suggest a shift from local to distributed mentalizing network connectivity across development, followed by age-related decline, shedding light on lifespan changes in social cognition.

Ischaemic stroke is one of the leading causes of disability and death worldwide, yet effective treatment options remain limited. Bilateral hemisphere theta burst stimulation (TBS), a non-invasive brain stimulation technique, has shown considerable therapeutic potential in stroke. However, relevant research is still limited, and their specific mechanisms remain unclear. This study utilized a middle cerebral artery occlusion (MCAO) model in non-human primates to explore the application of bilateral hemispheric TBS in stroke rehabilitation. By integrating behavioural assessments, electrophysiology, fMRI, DTI, and proteomics, the study evaluated the effects of TBS on neural repair, functional recovery, and brain region remodeling. TBS significantly improved upper limb function after MCAO, regulated cortical excitability imbalance, enhanced neural conduction efficiency, and showed notable improvements in functional connectivity and white matter repair across multiple brain regions. Proteomic analysis further revealed the potential roles of TBS in neurorepair, metabolic regulation, and anti-inflammatory effects. Additionally, large-scale genetic analysis indicated that ischaemic stroke may alter white matter structure by affecting blood proteins, and TBS intervention may help reverse these blood protein changes, thereby promoting white matter repair. These findings provide new insights for optimizing stroke treatment strategies. TBS promotes functional recovery by enhancing neuroplasticity.

We set out to explore the neural correlates of individual-specific experiences. We propose an approach through which we compute individual-specific dynamics of functional connectivity states. These dynamics do not require estimation of common states across individuals and can be directly related to dynamic behavioural ratings of subjective experience. To this end, we leverage a unique functional magnetic resonance imaging dataset where subjects listened to an engaging naturalistic story while awake and under different levels of anaesthesia, altering or abolishing conscious experience. We find that this method can detect correspondences between neural and subjective dynamics. We then show that the dynamics of the default mode network are more dissimilar between participants during awareness compared to unconsciousness and therefore may tend to underlie more personal experiences of the story. On the other hand, the auditory and posterior dorsal attention networks show higher inter-subject similarity in consciousness compared to unconsciousness and suggest that the dynamics of these networks support more "generalisable" experiences of the story. We further characterise individual-specific brain dynamics by showing that they are associated with higher complexity in consciousness, whilst conversely, brain dynamics underlying shared experience become less complex during the conscious experience of the story.

Abstract Context The effective delineation of habitat is crucial for understanding drivers of habitat loss and fragmentation, and their effects on biodiversity outcomes at local to global scales. The concept of the habitat patch is central to this process but presents both theoretical and methodological challenges related to the seemingly irreconcilable tendency of habitat to simultaneously exhibit characteristics of both gradation and aggregation. This apparent contradiction, recently described as the continuity-contiguity problem in landscape ecology, presents a problem of classification in which the associated ambivalence is analogous to that surrounding the fate of Schrödinger’s Cat. Objectives This is the first of a pair of papers that aim to address the theoretical and methodological challenges associated with the habitat patch concept. This first paper aims to (a) articulate the theoretical and practical limitations of working with the habitat patch concept and (b) set out a framework based on a functional definition of habitat that captures the tendency of resources to exhibit both discrete and continuous spatial characteristics. The second paper (Dennis et al. this issue) presents a demonstration of this framework applied to a real-world landscape, in which the impact of adopting alternative perspectives on habitat delineation on potential functional connectivity is revealed. Methods We present a new methodological approach that integrates alternative gradient and patch-based models of habitat in landscape ecology. We achieve this integration by leveraging the notion of geographical vagueness and the application of fuzzy set theory to land cover classification. We apply this approach to simulated landscapes that contain information on membership values to different land cover classes and their associated uncertainty. We then demonstrate the functional delineation of habitat from these landscapes based on the use of species-specific parameters, the leveraging of spatial kernels, and type-1 and type-2 fuzzy sets. The possibility of incorporating this approach into subsequent workflows is then described using estimates of between-patch distances and potential functional connectivity as examples. Results Our method provides a functional spatial delineation of habitat that reflects both resource-based and patch-based habitat perspectives and can be applied to any gradient or patch-based landscape modelling method. This approach achieves the integration of multiple resource types, habitat complementarity associated with neighbouring cover types, and negative edge effects. We refer to this measure of habitat as Functional Habitat so-called as it reflects the total availability of habitat accounting for the influence of all land cover types and positive and negative neighbourhood effects. Conclusion This paper describes a functional approach to habitat delineation and its integration into the computation of fragmentation-related metrics. This methodological framework achieves, for the first time, (1) a multivariate delineation of habitat based on type-1 fuzzy membership and the operationalising of neighbourhood effects and (2) the harnessing of uncertainty in land cover classification ( type-2 fuzzy membership) to achieve a distribution of possible outcomes that resolves the continuity-contiguity problem . This new methodology provides a long-awaited functional definition of habitat patches for those seeking to understand the role of habitat fragmentation in biodiversity outcomes.

Perioperative abstinent smokers experience heightened pain sensitivity and increased postoperative analgesic requirements, likely due to nicotine withdrawal-induced hyperalgesia. However, the underlying neural mechanisms in humans remain unclear. To address this issue, this study enrolled 60 male patients (30 abstinent smokers and 30 nonsmokers) undergoing partial hepatectomy, collecting clinical data, smoking history, pain-related measures, and resting-state functional magnetic resonance imaging. Compared with nonsmokers, abstinent smokers showed lower pain threshold and higher postoperative analgesic requirements. Neuroimaging revealed altered brain function in abstinent smokers, including reduced fractional amplitude of low-frequency fluctuations (0.01-0.1 Hz) in the ventromedial prefrontal cortex (vmPFC), increased regional homogeneity in the left middle occipital gyrus, and decreased functional connectivity between the vmPFC to both the bilateral middle temporal gyrus and precuneus. Preoperative pain threshold was positively correlated with abstinence duration and specific regional brain activities and connectivity. Furthermore, the observed association between abstinent time and pain threshold was mediated by the calcarine and posterior cingulate cortex activity. The dysfunction in vmPFC and the left anterior cingulate cortex was totally mediated by the association between withdrawal symptoms and postoperative analgesic requirements. These findings suggest that nicotine withdrawal might alter brain functional activity and contribute to hyperalgesia for the abstinent smokers. This study provided novel insights into the supraspinal neurobiological mechanisms underlying nicotine withdrawal-induced hyperalgesia and potential therapeutic targets for postoperative pain in abstinent smokers.

Breast cancer patients undergoing adjuvant hormone therapy commonly report adverse effects that can lead to lower quality of life and treatment nonadherence. How hormone therapy, independent of other systemic therapies, may impact patient functioning is a relatively new area of research with few neuroimaging studies delineating the effects. Prior nonspecific neuropsychological findings and the multifaceted role of estrogen in the brain suggest potentially diffuse effects of hormone therapy. The current study examined intrinsic neural functional organization and cognitive correlates unique to breast cancer patients undergoing hormone therapy. Resting state functional magnetic resonance imaging was acquired from 24 breast cancer patients undergoing hormone therapy and 32healthy controls. Resting-state functional connectivity (rsFC) was calculated between brain regions. Fractional amplitude of low frequency fluctuations (fALFF) was computed within a rsFC-derived mask to describe the regional properties within sites of dysconnectivity. Objective measures of cognition were obtained using neuropsychological tests and correlated with rsFC. Patients demonstrated extensive dysconnectivity relative to controls, largely characterized by parietal-occipital hypoconnectivity. Reduced rsFC occurred primarily between regions with increased fALFF. A modest relationship between rsFC and visual working memory was observed in breast cancer patients but not in controls. This study is the first to examine whole-brain rsFC in breast cancer patients undergoing hormone therapy. We found robust hypoconnectivity in patients, which demonstrated modest relationships with cognition. Identifying the pattern by which breast cancer and hormone therapy affect brain networks may aid in the development of therapeutic options for patients experiencing negative effects of hormone therapy, thus improving quality of life for cancer survivors. Further, the detection of abnormal brain function may help characterize treatment-associated neural changes that are not captured by standard cognitive measures.

Numerous studies have shown sex differences in mental rotation (MR) tasks, but few have explored the internal neural influences through functional connectivity outcomes. To investigate neuro-activity influences on sex differences, this study conducted a revised MR task, examining low-scoring individuals via behavioral and electrophysiological measures. It obtained event-related potential (ERP) components from fronto-central channels and explored the functional connectivity of different frequency bands. The results showed males outperformed females, consistent with prior research. There were significant differences between the two sexes when completing the task successfully. Males had shorter response times, smaller ERP amplitudes, and stronger beta and gamma functional connectivity than females. Compared to females, males showed better behavioral performance, weaker fronto-parietal ERP activity, required less mental effort, and had more effective internal regulation in connectivity. This helps clarify the fundamental neural activities in MR between different sex groups.

Abstract Developmental dyscalculia (DD), a learning disorder that affects one’s ability to work with numerical information and perform calculations, presents significant challenges for children acquiring mathematical skills. Recent research has provided a robust understanding of the behavioral and cognitive profile of DD, but its neurobiological underpinnings remain poorly understood. In this study, we use resting-state functional connectivity (rsFC) to investigate the neural differences between third graders with DD and typically achieving (TA) peers (DD = 30, TA = 37, mean age = 9.04 y). We employed two complementary analytical approaches: 1) a seed-based functional connectivity analysis to assess connectivity between a priori regions of interest (ROIs)—subregions of the intraparietal sulcus (IPS), angular gyrus, and the hippocampus—and the rest of the brain, and 2) a modified whole-brain, connectome-based predictive modeling approach to detect DD based on brain connectivity patterns. The seed-based connectivity analysis revealed greater functional connectivity for the TA group between the bilateral IPS and left hippocampal ROIs and frontal structures. Our whole-brain classification approach achieved a mean accuracy of 0.671 and an AUC of 0.816 and identified 14 brain connections that consistently classified the TA and DD groups. Our findings point to network-level differences underlying DD in brain regions previously implicated in mathematical cognition and offer a novel, data-driven approach to identifying differences in brain connectivity associated with DD.

Abstract Resting state fMRI time series are punctuated by spontaneous moments of high-amplitude activity lasting mere seconds. Previous research has demonstrated that such moments may contain a disproportionate amount of information and can be used to recapitulate maps of distributed brain activity or to recreate spatial functional connectivity patterns. Ultimately, this body of work has established that modeling neurovascular activity as a succession of spontaneous, punctuated moments is an effective approach for understanding cortex-wide brain activity. Here, we expand on this line of work by focusing our attention on the spatiotemporal properties of such punctuated moments, particularly on their duration. For this, we turn to an edge time series approach to resolve the dynamics of functional connectivity, identify moments of prominent synchrony, and record their duration. This procedure allows us to differentiate such punctuated moments by the timescales at which they unfold. By mapping moment duration to the cortex, we find that connectivity emanating from brain’s primary sensory areas transpires with the longest durations. We further construct spatial patterns of connectivity unfolding over distinct durations, demonstrating how timescales differentially relate to traditionally constructed functional connectivity. Finally, we show how the longest connectivity moments could convey information about fluctuations in subjects’ vigilance. Overall, the information that we have gleaned about prominent connectivity moments and their duration would otherwise be largely obscured when using other prevalent methods. Here we highlight an additional feature of functional connectivity to further our characterization of the brain’s spatiotemporal organization.

Abstract Functional connectivity (FC) patterns in the human brain form a reproducible, individual-specific “fingerprint” that allows reliable identification of the same participant across scans acquired over different sessions. While brain fingerprinting is robust across healthy individuals and neuroimaging modalities, little is known about whether the fingerprinting principle extends beyond the brain. Here, we used multiple functional magnetic resonance imaging (fMRI) datasets acquired at different sites to examine whether a fingerprint can be revealed from FCs of the cervical region of the human spinal cord. Our results demonstrate that the functional organisation of this region also exhibits individual-specific properties, suggesting the potential existence of a spine-print within the same acquisition session. Although the spine-print scores are not directly comparable to those observed in the brain, this discrepancy may in part reflect the intrinsic limitations of imaging this region with fMRI, where where the signals are more susceptible to noise and effective resolution relative to structure size, and tSNR are markedly lower than in the brain.This study provides the first evidence of a spinal cord connectivity fingerprint, underscoring the importance of considering a more comprehensive view of the entire central nervous system. Eventually, these spine-specific signatures could contribute to identifying individualized biomarkers of neuronal connectivity, with potential clinical applications in neurology and neurosurgery.

Introduction Amnestic mild cognitive impairment (aMCI), serving as a clinical precursor to Alzheimer's Disease (AD), assumes a pivotal role in the early stages of AD prevention. The longitudinal collection of data in aMCI is imperative for monitoring disease progression and guiding clinical interventions. Methods Utilizing a prospective cohort design, we recruited aMCI individuals and conducted a one-year follow-up study. During this period, electroencephalogram (EEG) signals were systematically collected at regular intervals, resulting in four time points for each participant. Based on the follow-up outcomes, participants were stratified into progressive mild cognitive impairment (PMCI) and stable mild cognitive impairment (SMCI) groups. We extracted spectral, nonlinear, and functional connectivity features from the EEG data at three cross-sectional time points in the initial nine months and constructed longitudinal features between these cross-sectional assessments. The longitudinal features were fed into machine learning classifiers to predict one-year follow-up outcomes. Results The dynamic trends of EEG features in SMCI and PMCI patients exhibited inconsistency. Utilizing the selected longitudinal features, the support vector machine (SVM) demonstrated the best prediction performance, achieving an accuracy of 94.92%, an area under the curve of 93.25%, a sensitivity of 90.20%, a specificity of 98.80%, a positive predictive value of 98.70%, and an F1-score of 93.65%. Discussion By capturing trend information associated with disease progression, longitudinal EEG features contributed to enhancing prediction performance in machine learning models.

Distinct functional connectivity patterns in myalgic encephalomyelitis and long COVID patients during cognitive fatigue: a 7 Tesla task-fMRI study.

Background: Approximately 20–30% of ultra-high risk (UHR) individuals transition to psychosis within 2–3 years. Neurobiological markers predicting conversion remain critical for precision prevention strategies. Objective: To systematically identify and evaluate structural and functional neuroimaging biomarkers at UHR baseline that predict subsequent conversion to psychosis. Methods: Following PRISMA 2020 guidelines, we searched five databases from January 2000 to February 2025. Two independent reviewers screened studies and assessed quality using the Newcastle–Ottawa Scale. Eligible studies examined baseline neuroimaging measures (structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy) as predictors of psychosis conversion in UHR cohorts. Results: Twenty-five studies comprising 2436 UHR individuals (627 converters, 25.7%) were included (80.0% high quality). Reduced baseline gray matter volume in medial temporal structures (hippocampus: Cohen’s d = −0.45 to −0.68; parahippocampal gyrus: d = −0.52 to −0.71) and prefrontal cortex (d = −0.41 to −0.68) consistently predicted conversion. Progressive gray matter loss in superior temporal gyrus distinguished converters (d = −0.72). Reduced prefrontal–temporal functional connectivity predicted conversion (AUC = 0.73–0.82). Compromised white matter integrity in uncinate fasciculus (fractional anisotropy: d = −0.47 to −0.71) and superior longitudinal fasciculus predicted transition. Elevated striatal glutamate predicted conversion (d = 0.52–0.76). Thalamocortical dysconnectivity showed large effects (Hedges’ g = 0.66–0.88). Multimodal imaging models achieved 78–85% classification accuracy. Conclusions: Neuroimaging biomarkers, particularly medial temporal and prefrontal structural alterations, functional dysconnectivity, and white matter abnormalities, demonstrate moderate-to-large effect sizes in predicting UHR conversion. Multimodal approaches combining structural, functional, and neurochemical measures show promise for individualized risk prediction and early intervention targeting in precision prevention strategies.

This study aimed to investigate the efficacy of intermittent theta burst stimulation (iTBS) combined with neuromuscular electrical stimulation (NMES) in the treatment of dysphagia after stroke and the changes of brain function.55 participants were randomly assigned to iTBS combined with NMES group (n = 18),iTBS group (n = 18) or NMES group(n = 19). All groups received two-week conventional swallowing therapy.On this basis, the iTBS combined with NMES group received a two-week iTBS combined with NMES treatment, the iTBS group received iTBS combined with sham NMES treatment, the NMES group received sham iTBS combined with NMES treatment. Before and after treatment, Standard Swallowing Function Assessment Scale (SSA), Penetration-Aspiration Scale(PAS), Functional Oral Intake Scale(FOIS), Yale Pharyngeal Residual Severity Scale(YPR-SRS) and swallowing quality of life questionnaire (SWAL-QOL) were used to assess swallowing function. In addition, functional near-infrared spectroscopy was used to investigate participants' brain function.After treatment, compared with NMES or iTBS group, iTBS combined with NMES group showed significant improvement in SSA, SWAL-QOL, FOIS, PAS, and YPR-SRS scores, increased the activation of Broca (P = 0.033, with FDR corrected) and temporopolar cortex (P = 0.009, with FDR corrected), and improved the functional connectivity between rois.