Most recent paper

Front Neurosci. 2025 Mar 14;19:1549172. doi: 10.3389/fnins.2025.1549172. eCollection 2025.

ABSTRACT

This study aimed to directly compare electroencephalography (EEG) whole-brain patterns of neural dynamics with concurrently measured fMRI BOLD data. To achieve this, we aim to derive EEG patterns based on a spatio-spectral decomposition of band-limited EEG power in the source-reconstructed space. In a large dataset of 72 subjects undergoing resting-state hdEEG-fMRI, we demonstrated that the proposed approach is reliable in terms of both the extracted patterns as well as their spatial BOLD signatures. The five most robust EEG spatio-spectral patterns not only include the well-known occipital alpha power dynamics, ensuring consistency with established findings, but also reveal additional patterns, uncovering new insights into brain activity. We report and interpret the most reproducible source-space EEG-fMRI patterns, along with the corresponding EEG electrode-space patterns, which are better known from the literature. The EEG spatio-spectral patterns show weak, yet statistically significant spatial similarity to their functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signatures, particularly in the patterns that exhibit stronger temporal synchronization with BOLD. However, we did not observe a statistically significant relationship between the EEG spatio-spectral patterns and the classical fMRI BOLD resting-state networks (as identified through independent component analysis), tested as the similarity between their temporal synchronization and spatial overlap. This provides evidence that both EEG (frequency-specific) power and the BOLD signal capture reproducible spatio-temporal patterns of neural dynamics. Instead of being mutually redundant, these only partially overlap, providing largely complementary information regarding the underlying low-frequency dynamics.

PMID:40161575 | PMC:PMC11949981 | DOI:10.3389/fnins.2025.1549172

Front Neural Circuits. 2025 Mar 14;19:1500227. doi: 10.3389/fncir.2025.1500227. eCollection 2025.

ABSTRACT

The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences.

PMID:40160867 | PMC:PMC11949950 | DOI:10.3389/fncir.2025.1500227

Quant Imaging Med Surg. 2025 Mar 3;15(3):1834-1844. doi: 10.21037/qims-24-1684. Epub 2025 Feb 26.

ABSTRACT

BACKGROUND: Alopecia areata (AA) is a nonscarring chronic inflammatory hair loss disease with a complex etiology. Psychological stress and dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis have been strongly linked to the etiology of AA, but the associated changes in intrinsic brain activity remain unknown. We hypothesized that patients with AA exhibit altered hypothalamic activity that is linked to psychological stress. This study aimed to characterize the altered hypothalamic activity in patients with AA and its relationship to psychological stress.

METHODS: A total of 102 patients with AA and 84 age- and sex-matched healthy controls (HCs) were recruited. All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) to assess brain activity and completed neuropsychological evaluations, including the Hamilton Anxiety Rating Scale (HAM-A) score and the Hamilton Depression Rating Scale (HAM-D). Additionally, patients with AA were assessed using the Dermatology Life Quality Index (DLQI), and blood samples were obtained to measure total serum immunoglobulin E (IgE) levels. We chose the hypothalamus as the region of interest (ROI) to compare alterations in hypothalamic of amplitude of low-frequency fluctuation (ALFF) and whole-brain functional connectivity (FC) between patients with AA and HCs. Analyses of the correlation of brain activity and clinical data were conducted, including neuropsychological tests, DLQI, and blood samples.

RESULTS: The HAM-A score, the HAM-D score, and the altered ALFF in the hypothalamus showed a statistically significant difference between patients with AA and HCs (P<0.05). Patients with AA exhibited increased FC between the hypothalamus, the left postcentral gyrus, and right inferior temporal gyrus (Gaussian random field-corrected: voxel <0.001 and cluster <0.05). Moreover, increased FC between the hypothalamus and left postcentral gyrus was positively correlated with HAM-D score (r=0.296; P=0.020), while increased FC between the hypothalamus and the right inferior temporal gyrus was negatively correlated with both DLQI (r=-0.256; P=0.012) and total serum IgE (r=-0.203; P=0.048).

CONCLUSIONS: Patients with AA exhibited altered hypothalamus activity and connectivity. These alterations may underlie the neurophysiological basis of psychological stress experienced by patients with AA.

PMID:40160661 | PMC:PMC11948389 | DOI:10.21037/qims-24-1684

Quant Imaging Med Surg. 2025 Mar 3;15(3):2362-2375. doi: 10.21037/qims-24-2062. Epub 2025 Feb 26.

ABSTRACT

BACKGROUND: Nonarteritic anterior ischemic optic neuropathy (NAION) is often accompanied by degeneration of optic nerve axons and ganglion cell apoptosis, but the mechanism of its effects on the cerebral cortex and visual centers is not clear. Graph theory analysis, as a quantitative tool for complex networks, has made it possible to characterize the topological alterations of brain networks in patients with NAION. The objective of this pilot study was to investigate the topological characteristics of functional brain networks in patients with NAION and to analyze their potential correlation with visual dysfunction.

METHODS: This prospective, cross-sectional study recruited 25 patients with NAION and 24 matched healthy controls (HCs) from Dongfang Hospital, Beijing University of Chinese Medicine. Following resting-state functional magnetic resonance imaging (rs-fMRI) scans, large-scale functional connectivity matrices of 90 regions were constructed. Graph theory was then used to compare global and local network parameters. Subsequently, network-based statistics (NBS) analysis was employed to detect differences in functional connectivity across the brain. Finally, correlations were assessed between the network topological properties and clinical variables.

RESULTS: Individuals with NAION, as compared to controls, exhibited significant decreases in normalized clustering coefficient (gamma; P=0.021), small-worldness (sigma; P=0.043), and local efficiency (Eloc; P=0.030), as well as a significant increase in the size of the largest connected component (LCC; P=0.039) of the network. Additionally, the LCC showed a negative association with gamma, sigma and global efficiency (Eg) but a positive correlation with the normalized characteristic path length (lambda) of the two groups (all P values <0.05). Regionally, patients exhibited changes in nodal centralities, particularly affecting the attention, visual, and salience networks. NBS analysis identified an interconnected subnetwork consisting of 49 nodes and 77 edges (P<0.001, NBS-corrected) that showed significantly higher connectivity in patients with NAION. The mean connectivity of this subnetwork was negatively correlated with the global topological parameters gamma, sigma, and Eg in the NAION group and gamma and sigma in the HCs but positively correlated with the LCC in both groups (all P values <0.05). Moreover, the nodal betweenness centrality of the left dorsolateral superior frontal gyrus exhibited a significant positive correlation with the visual field (VF) mean deviation (MD) in the NAION group (P<0.001).

CONCLUSIONS: This study initially identified aberrant topological and connectivity changes in the functional brain networks associated with visual impairment in patients with NAION, thus expanding our existing understanding of the neurobiological mechanisms of NAION.

PMID:40160619 | PMC:PMC11948378 | DOI:10.21037/qims-24-2062

Mach Learn Clin Neuroimaging (2024). 2025;15266:145-154. doi: 10.1007/978-3-031-78761-4_14. Epub 2024 Dec 6.

ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that encompasses a wide variety of symptoms and degrees of impairment, which makes the diagnosis and treatment challenging. Functional magnetic resonance imaging (fMRI) has been extensively used to study brain activity in ASD, and machine learning methods have been applied to analyze resting state fMRI (rs-fMRI) data. However, fewer studies have explored the recent transformer-based models on rs-fMRI data. Given the superiority of transformer models in capturing long-range dependencies in sequence data, we have developed a transformer-based self-supervised framework that directly analyzes time-series fMRI data without computing functional connectivity. To address over-fitting in small datasets and enhance the model performance, we propose self-supervised pre-training tasks to reconstruct the randomly masked fMRI time-series data, investigating the effects of various masking strategies. We then fine-tune the model for the ASD classification task and evaluate it using two public datasets and five-fold cross-validation with different amounts of training data. The experiments show that randomly masking entire ROIs gives better model performance than randomly masking time points in the pre-training step, resulting in an average improvement of 10.8% for AUC and 9.3% for subject accuracy compared with the transformer model trained from scratch across different levels of training data availability. Our code is available on GitHub .

PMID:40160559 | PMC:PMC11951341 | DOI:10.1007/978-3-031-78761-4_14

Soc Cogn Affect Neurosci. 2025 Mar 31:nsaf025. doi: 10.1093/scan/nsaf025. Online ahead of print.

ABSTRACT

Previous work suggests blood pressure (BP) relates to social algesia, where those with higher BP are more tolerant of social pain. The neural correlates of this association, however, are unknown. Based on findings suggesting neural regions involved in physical pain are activated during social pain, the current study explores whether BP relates to subjective and neural responses to social pain, apart from emotional responding. BP was measured, after which participants completed emotional processing and social exclusion fMRI paradigms. Results replicated previous findings, with higher systolic BP related to lower trait sensitivity to social pain. However, there were no associations between BP and reported social pain sensitivity during social exclusion. Moreover, after accounting for adiposity, we found no association between BP and anterior insula (AI) or dorsal anterior cingulate cortex (dACC) activity to exclusion. Finally, there were no reliable associations between BP and reported valence or arousal, or AI and dACC activity to emotional images. Findings partly replicate and extend prior findings on BP and emotional responding to social pain; however, they appear inconsistent with predictions at the neural level. Future experimental manipulation of BP may allow for causal inferences and adjudication of conceptual perspectives on cardiovascular contributions to social algesia.

PMID:40160022 | DOI:10.1093/scan/nsaf025

Sci Rep. 2025 Mar 29;15(1):10888. doi: 10.1038/s41598-025-95190-9.

ABSTRACT

Network modeling are widely using in resting-state functional magnetic resonance imaging (rs-fMRI) for Alzheimer's disease (AD) research. Typically, Pearson correlation coefficient (PCC) was widely applied to construct brain connectivity network from BOLD signals of regions of interest. However, it often results in significant intra-group variability and complicates the identification of disease-specific functional connectivity patterns. To address this issue, we propose a novel brain network construction strategy, called SNBG, which uses aggregated information from the control group to derive a single-sample network. We compare SNBG and the PCC based method on a dataset from an Alzheimer's Disease Neuroimaging Initiative (ADNI) study. SNBG method captures more stable connections between regions of interest (ROIs) and increases classification accuracy from 89.24% of PCC based method to 97.13%. In addition, in AD-related local networks, such as default mode network (DMN), medial frontal network (MFN) and frontoparietal network (FPN), SNBG demonstrates lower intra-group heterogeneity than the PCC based method.

PMID:40157941 | DOI:10.1038/s41598-025-95190-9

Neuroimage. 2025 Mar 27:121177. doi: 10.1016/j.neuroimage.2025.121177. Online ahead of print.

ABSTRACT

Naturalistic stimuli have become an effective tool to uncover the dynamic functional brain networks triggered by cognitive and emotional real-life experiences through multimodal and dynamic stimuli. However, current research predominantly focused on exploring dynamic functional connectivity generated via chosen templates under resting-state paradigm, with relatively limited investigation into the dynamic functional interactions among large-scale brain networks. Moreover, these studies might overlook the longer time-scale adaptability and information transmission that occur over extended periods during naturalistic stimuli. In this study, we introduced an unsupervised deep recursive autoencoder (DRAE) model combined with a sliding window approach, effectively capturing the brain's long-term temporal dependencies, as measured in functional magnetic resonance imaging (fMRI), when subjects viewing a long-duration and emotional film. The experimental results revealed that naturalistic stimuli can induce dynamic large-scale brain networks, of which functional interactions covary with the development of the film's narrative. Furthermore, the dynamic interactions among brain networks were temporally synchronized with specific features of the movie, especially with the emotional arousal and valence. Our study provided novel insight to the underlying neural mechanisms of dynamic functional interactions among brain regions in an ecologically valid sensory experience.

PMID:40157466 | DOI:10.1016/j.neuroimage.2025.121177

Psychiatry Res Neuroimaging. 2025 Mar 25;349:111986. doi: 10.1016/j.pscychresns.2025.111986. Online ahead of print.

ABSTRACT

Numerous studies have revealed that patients with major depressive disorder (MDD) suffer from insomnia symptoms. However, the dysfunction pattern in specific insomnia symptoms in patients with MDD remains unclear. The present study aimed to examine the regional brain neuroimaging activity features of difficulty falling asleep (DFA) and early awakening (EA) in patients with MDD. The resting-fMRI by applying the amplitude of low-frequency fluctuation (ALFF) method was estimated in 50 MDD patients with DFA, 36 patients with EA, 46 patients without insomnia symptoms, and 60 matched healthy controls. The Pearson correlation analysis was used among the ALFF with significant difference brain regions, the 17-item Hamilton Depression Rating Scale factor scores, and the Pittsburgh Sleep Quality Index scores. Patients with DFA showed lower ALFF values in the left precentral gyrus than those with EA and higher ALFF values in the left insula than those without insomnia symptoms. Patients with EA showed higher ALFF values in the left precentral gyrus than those without insomnia symptoms. This study revealed distinct neural mechanisms underlying specific insomnia symptoms, identifying the left insula as a potential pathological region in DFA patients and the left precentral gyrus as a characteristic neuropathological region in EA patients.

PMID:40156942 | DOI:10.1016/j.pscychresns.2025.111986

Sleep. 2025 Mar 29:zsaf088. doi: 10.1093/sleep/zsaf088. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: Adolescents often do not sleep as much as recommended by most national guidelines, which may impact their brain development. The current study aims to evaluate the relationship between objective assessment of sleep duration measured with actigraphy, and brain network connectivity on functional magnetic resonance imaging (fMRI).

METHODS: We used data from the two-year follow-up of the Adolescent Brain Cognitive Development (ABCD) study comprising 3,799 adolescents, ages 10 to 13 years old, to assess the relationship between sleep duration, measured by two weeks of Fitbit-derived actigraphy, and brain network connectivity derived from resting-state fMRI, using linear regression models. Linear regression analysis was also used to investigate the interaction between participant sex and sleep duration on brain network connectivity.

RESULTS: We identified both positive and negative correlations between mean sleep duration and 6 within brain network and 30 between-network pairs. These included networks involved in attention (Dorsal and Ventral Attention networks), executive control (Cingulo-Opercular and Default Mode networks), memory (Retrosplenial Temporal network), and sensory function (Auditory and Sensorimotor networks). We also identified sex-specific effects in three network pairs (Auditory - Retrosplenial Temporal, Retrosplenial Temporal - Sensorimotor, and Visual - Visual) and sex differences in functional connectivity across 23 distinct within- and between-network connections.

CONCLUSIONS: Sleep duration is associated with the functional network connectivity in attentional, executive control, memory, and sensory networks during early adolescence. The identification of sex-specific effects in select network pairs underscores the importance of sex as a biological variable in studies of adolescent sleep and brain development.

PMID:40156904 | DOI:10.1093/sleep/zsaf088

J Headache Pain. 2025 Mar 28;26(1):60. doi: 10.1186/s10194-025-02004-4.

ABSTRACT

BACKGROUND: Sleep deprivation can trigger acute headache attacks in individuals with migraine; however, the underlying mechanism remains poorly understood. The aim of this study was to investigate the effects of acute sleep deprivation (ASD) on brain function in individuals with migraine without aura (MWoA) via functional magnetic resonance imaging (fMRI).

METHODS: Twenty three MWoA individuals and 23 healthy controls (HCs) were fairly included in this study. All participants underwent two MRI scans: one at baseline (prior to sleep deprivation) and another following 24 h of ASD. Images were obtained with blood-oxygen-level-dependent and T1-weighted sequences on a Siemens 7.0 T MRI scanner. We conducted analyses of changes in the low-frequency fluctuations (ALFF) values and functional connectivity (FC) between brain networks and within network before and after ASD in both MWoA group and HC group. Additionally, we investigated the relationship between the changes in ALFF before and after ASD and the clinical features (VAS and monthly headache days).

RESULTS: In the HC group, ASD led to a significant increase in ALFF values in the left parahippocampal gyrus compared to baseline (p-FDR = 0.01). In the MWoA group, ALFF values were significantly greater in 64 brain regions after ASD than at baseline. The most significant change in ALFF before and after ASD in the MWoA group was detected in the right medial pulvinar of the thalamus (p-FDR = 0.017), which showed a significant negative correlation with monthly headache days. Moreover, seed-based connectivity (SBC) analysis using the right medial pulvinar of the thalamus as the seed point revealed significantly increased connectivity with the cerebellar vermis (p-FWE = 0.035) after ASD in individuals with MWoA, whereas connectivity with the right postcentral gyrus was significantly decreased (p-FWE = 0.048). Furthermore, we performed analyses of between-network connectivity (BNC) and within-network connectivity across 17 brain networks, utilizing the Yeo-17 atlas. Both MWoA individuals and HCs showed no significant changes in BNC after ASD compared to baseline. However, our analysis in within-network revealed that MWoA individuals exhibited a reduced within-network FC in dorsal attention network (DAN) after ASD compared to baseline (p-FDR = 0.031), whereas HCs showed no significant differences in within-network FC across all networks before and after ASD.

CONCLUSIONS: In comparison to HCs, MWoA individuals exhibited significant alterations in brain function after ASD, particularly within the thalamus, and MWoA individuals exhibited a reduced within-network FC in DAN after ASD compared to baseline. Brain regions and networks in MWoA individuals were more susceptible to the effects of ASD.

PMID:40155843 | DOI:10.1186/s10194-025-02004-4