Most recent paper

Neurology. 2025 Apr 22;104(8):e213502. doi: 10.1212/WNL.0000000000213502. Epub 2025 Apr 1.

ABSTRACT

BACKGROUND AND OBJECTIVES: Pediatric concussion can disrupt functional brain network connectivity, but prospective longitudinal research is needed to clarify recovery and identify moderators of change. This study investigated network functional connectivity (FC) up to 6 months after pediatric concussion.

METHODS: This prospective longitudinal concurrent cohort observational study consecutively recruited children (aged 8 to 17 years) at 5 Canadian pediatric hospital emergency departments within 48 hours of sustaining a concussion or mild orthopaedic injury (OI). Children completed 3T MRI scanning postacutely (2 to 33 days) and at either 3 or 6 months after injury (randomly assigned at the postacute visit). Reliable change between retrospective preinjury (based on parent report) and 1-month postinjury symptom ratings based on parent and child report was used to classify concussion with or without persisting symptoms. Within-network and between-network FC was computed for 8 brain networks from resting-state fMRI scans and analyzed using linear mixed-effects models, with multiple comparison correction.

RESULTS: Groups (385 with concussion/198 with OI; 59% male; 69% White; age 12.42 ± 2.29 years) did not differ in within-network FC. Relative to OI, connectivity between the visual and ventral attention networks was lower after concussion, d (95% CI) = -0.46 (-0.79 to -0.14), across time. Connectivity between the visual and default mode networks was lower at 6 months after concussion, -0.60 (-0.92 to -0.27). At 3 months after concussion, connectivity between the frontoparietal and ventral attention networks was lower in younger children, -0.98 (-1.58 to -0.37), but higher in older children, 0.81 (0.20 to 1.42). For symptom groups based on parent report, connectivity between the dorsal and ventral attention networks was higher in female children at 3 months after concussion without persisting symptoms relative to concussion with persisting symptoms, 1.25 (2.05 to 0.46), and OI, 0.87 (0.25 to 1.49).

DISCUSSION: Time after injury, age at injury, biological sex, and persistent symptom status are important moderators of FC after pediatric concussion for children seen in emergency department settings. Postacute FC may not enhance clinical diagnosis. Although within-network connectivity is preserved, between-network connectivity differences emerge after most children clinically recover and persist up to 6 months after pediatric concussion, providing a potential objective biomarker for lasting changes in brain function.

PMID:40168632 | DOI:10.1212/WNL.0000000000213502

Radiology. 2025 Apr;315(1):e233264. doi: 10.1148/radiol.233264.

ABSTRACT

Background Special operations forces members often face multiple blast injuries and have a higher risk of traumatic brain injury. However, the relationship between neuroimaging markers, the cumulative severity of injury, and long-term symptoms has not previously been well-established in the literature. Purpose To determine the relationship between the frequency of blast injuries, persistent clinical symptoms, and related cortical volumetric and functional connectivity (FC) changes observed at brain MRI in special operations forces members. Materials and Methods A cohort of 220 service members from a prospective study between January 2021 and May 2023 with a history of repetitive blast exposure underwent psychodiagnostics and a comprehensive neuroimaging evaluation, including structural and resting-state functional MRI (fMRI). Of these, 212 met the inclusion criteria. Participants were split into two datasets for model development and validation, and each dataset was divided into high- and low-exposure groups based on participants' exposure to various explosives. Differences in FC were analyzed using a general linear model, and cortical gray matter volumes were compared using the Mann-Whitney U test. An external age- and sex-matched healthy control group of 212 participants was extracted from the SRPBS Multidisorder MRI Dataset for volumetric analyses. A multiple linear regression model was used to assess correlations between clinical scores and FC, while a logistic regression model was used to predict exposure group from fMRI scans. Results In the 212 participants (mean age, 43.0 years ± 8.6 [SD]; 160 male [99.5%]) divided into groups with low or high blast exposure, the high-exposure group had higher scores for the Neurobehavioral Symptom Inventory (NSI) (t = 3.16, P < .001) and Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PCL-5) (t = 2.72, P = .01). FC differences were identified in the bilateral superior and inferior lateral occipital cortex (LOC) (P value range, .001-.04), frontal medial cortex (P < .001), left superior frontal gyrus (P < .001), and precuneus (P value range, .02-.03). Clinical scores from NSI and PCL-5 were inversely correlated with FC in the LOC, superior parietal lobule, precuneus, and default mode networks (r = -0.163 to -0.384; P value range, <.001 to .04). The high-exposure group showed increased cortical volume in regions of the LOC compared with healthy controls and the low-exposure group (P value range, .01-.04). The predictive model helped accurately classify participants into high- and low-exposure groups based on fMRI data with 88.00 sensitivity (95% CI: 78.00, 98.00), 67% specificity (95% CI: 53.00, 81.00), and 73% accuracy (95% CI: 60.00, 86.00). Conclusion Repetitive blast exposure leads to distinct alterations in FC and cortical volume, which correlate with neurobehavioral symptoms. The predictive model suggests that even in the absence of observable anatomic changes, FC may indicate blast-related trauma. © RSNA, 2025 Supplemental material is available for this article.

PMID:40167438 | DOI:10.1148/radiol.233264

ArXiv [Preprint]. 2025 Mar 18:arXiv:2503.14700v1.

ABSTRACT

In brain network analysis using resting-state fMRI, there is growing interest in modeling higher-order interactions beyond simple pairwise connectivity via persistent homology. Despite the promise of these advanced topological tools, robust and consistently observed higher-order interactions over time remain elusive. In this study, we investigate why conventional analyses often fail to reveal complex higher-order structures - such as interactions involving four or more nodes - and explore whether such interactions truly exist in functional brain networks. We utilize a simplicial complex framework often used in persistent homology to address this question.

PMID:40166738 | PMC:PMC11957234

Arthritis Res Ther. 2025 Mar 31;27(1):70. doi: 10.1186/s13075-025-03526-7.

ABSTRACT

BACKGROUND: Pain remains a principal complaint for people with psoriatic arthritis (PsA), despite successful mitigation of inflammation. This situation alludes to the co-existence of distinct pain mechanisms. Nociceptive and nociplastic mechanisms are clinically challenging to distinguish. Advances in brain functional magnetic resonance imaging (fMRI) have successfully characterised distinct pain mechanisms across several disorders, in particular implicating the insula. This is the first study to characterise neurobiological markers of pain mechanisms in PsA employing fMRI.

METHODS: PsA participants underwent a 6-minutes resting-state fMRI brain scan, and questionnaire assessments of nociplastic pain (2011 ACR fibromyalgia criteria) and body pain, assessed using the Numeric Rating Scale (NRS, 0-100). Functional connectivity between insula seeds (anterior, mid, posterior), and the whole brain was correlated with the above pain outcomes correcting for age and sex, and false discovery rate (FDR) for multiple comparisons.

RESULTS: A total of 46 participants were included (age 49 ± 11.2; 52% female; FM score 12.5 ± 5.7; overall pain 34.8 ± 23.5). PsA participants with higher fibromyalgia scores displayed increased connectivity between: (1) right anterior insula to DMN (P < 0.05), (2) right mid and left posterior insula to parahippocampal gyri (P < 0.01 FDR); and (3) right mid insula to left frontal pole (P = 0.001 FDR). Overall pain was correlated with connectivity of left posterior insula to classical nociceptive regions, including thalamus (P = 0.01 FDR) and brainstem (P = 0.002 FDR).

CONCLUSION: For the first time, we demonstrate objectively that nociceptive and nociplastic pain mechanisms co-exist in PsA. PsA pain cannot be assumed to be only nociceptive in origin and screening for nociplastic pain in the future will inform supplementary analgesic approaches.

PMID:40165287 | DOI:10.1186/s13075-025-03526-7

BMC Psychiatry. 2025 Mar 31;25(1):308. doi: 10.1186/s12888-025-06747-x.

ABSTRACT

BACKGROUND: Clinical high-risk (CHR) refers to prodromal phase before schizophrenia onset, characterized by attenuated psychotic symptoms and functional decline. They exhibit similar but milder cognitive impairments, brain abnormalities and eye movement change compared with first-episode schizophrenia (FSZ). These alterations may increase vulnerability to transitioning to the disease. This study explores cognitive-related functional connectivity (FC) and eye movement abnormalities to examine differences in the progression of schizophrenia.

METHODS: Thirty drug-naive FSZ, 28 CHR, and 30 healthy controls (HCs) were recruited to undergo resting-state functional magnetic resonance imaging (rs-fMRI). Connectome-based predictive modeling (CPM) was employed to extract cognitive-related brain regions, which were then selected as seeds to form FC networks. Support vector machine (SVM) was used to distinguish FSZ from CHR. Smooth pursuit eye-tracking tasks were conducted to assess eye movement features.

RESULTS: FSZ displayed decreased cognitive-related FC between right posterior cingulate cortex and right superior frontal gyrus compared with HCs and between right amygdala and left inferior parietal gyrus (IPG) compared with CHR. SVM analysis indicated a combination of BACS-SC and CFT-A scores, and FC between right amygdala and left IPG could serve as a potential biomarker for distinguishing FSZ from CHR with high sensitivity. FSZ also exhibited a wide range of eye movement abnormalities compared with HCs, which were associated with alterations in cognitive-related FC.

CONCLUSIONS: FSZ and CHR exhibited different patterns of cognitive-related FC and eye movement alteration. Our findings illustrate potential neuroimaging and cognitive markers for early identification of psychosis that could help in the intervention of schizophrenia in high-risk groups.

PMID:40165149 | DOI:10.1186/s12888-025-06747-x

J Comput Assist Tomogr. 2025 Mar 14. doi: 10.1097/RCT.0000000000001740. Online ahead of print.

ABSTRACT

OBJECTIVE: Cognitive impairment is a prevalent complication among patients with diabetes mellitus. It tends to be more prominent in patients with diabetic retinopathy (DR) compared with patients with diabetes without DR (NDR). However, the functional connectivity (FC) between bilateral cerebral hemispheres in both remains poorly understood. This study aimed to investigate altered brain connectivity in patients with DR and NDR.

SUBJECTS AND METHODS: We selected 26 patients with DR, 30 patients with NDR, and 30 healthy controls (HCs) to participate in resting-state functional magnetic resonance imaging (rs-fMRI) and high-resolution T1-weighted structural scans. We employed the DPABI toolbox in MATLAB to preprocess the acquired images and applied voxel-mirrored homotopic connectivity (VMHC) and FC analysis methods to estimate differences among the 3 groups. The patients also underwent neuropsychological assessment scales. We utilized partial correlation analysis to explore the associations between aberrant connections and clinical variables as well as neuropsychological characteristics in patients with DR. Receiver operating characteristic (ROC) analysis was conducted to assess the diagnostic performance of VMHC values in distinct brain regions for differentiating DR patients from NDR patients.

RESULTS: The results showed significantly altered VMHC values across the 3 groups, including bilateral lingual gyrus (LING_B), superior temporal gyrus (STG_B), and postcentral gyrus (PoCG_B). Significant differences in FC values were found across the LING_B, right cuneus (CUN_R), STG_R, PoCG_B, right precentral gyrus (PreCG_R), right precuneus (PCUN_R), and middle temporal gyrus (MTG_L) among the 3 groups. Moreover, a negative correlation was noted between the VMHC values of LING_B and disease duration in patients with DR. Positive correlations were detected between FC values in PoCG_B and fasting blood glucose (FBG) levels. Furthermore, ROC analysis of the VMHC values demonstrated that combining all the differential regions achieved the highest area under the curve of 0.826.

CONCLUSIONS: Significant alterations in VMHC and FC may reflect the underlying neuropathology of cognitive dysfunction in DR and NDR. These altered connectivity patterns could serve as neuroimaging biomarkers, offering insights into the early diagnosis and intervention of cognitive impairments in DR patients.

PMID:40164961 | DOI:10.1097/RCT.0000000000001740

J Anesth. 2025 Mar 31. doi: 10.1007/s00540-025-03477-y. Online ahead of print.

ABSTRACT

BACKGROUND: In recent years, the effects of general anesthetics on the brain have been widely studied at the sedation level using resting-state functional magnetic resonance imaging (rs-fMRI). Most anesthesia protocols use a single agent, and changes in spontaneous brain activity are examined to show the characteristics of each anesthetic agent. However, no studies have used rs-fMRI to evaluate the effects of anesthesia during actual surgery. We examined the feasibility of evaluating the effects of general anesthesia with sevoflurane using rs-fMRI during neurosurgery.

METHODS: We enrolled 20 adult patients scheduled for transsphenoidal surgery. We compared differences between before and during general anesthesia in terms of brain functional connectivity of the thalamus by seed-to-voxel correlation analysis and local neural activity using fractional amplitude of low-frequency fluctuations (fALFF) analysis. An exclusion mask was applied to exclude brain areas showing intraoperative spatial artifacts and correct for differences in the magnitude of intra- and preoperative head movements.

RESULTS: We analyzed 16 patients. Functional connectivity of the thalamus to the contralateral thalamus, bilateral caudate nucleus and globus pallidus were significantly decreased during anesthesia. The precuneus and posterior cingulate cortex showed significantly decreased fALFF values during anesthesia.

CONCLUSION: These findings were consistent with previous studies and indicate the feasibility of intraoperative rs-fMRI during general anesthesia.

PMID:40164844 | DOI:10.1007/s00540-025-03477-y

Mol Psychiatry. 2025 Mar 31. doi: 10.1038/s41380-025-02974-6. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) presents a substantial health burden with low treatment response rates. Predicting antidepressant efficacy is challenging due to MDD's complex and varied neuropathology. Identifying biomarkers for antidepressant treatment requires thorough analysis of clinical trial data. Multimodal neuroimaging, combined with advanced data-driven methods, can enhance our understanding of the neurobiological processes influencing treatment outcomes. To address this, we analyzed resting-state fMRI and EEG connectivity data from 130 patients treated with sertraline and 135 patients with placebo from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study. A deep learning framework was developed using graph neural networks to integrate data-augmented connectivity and cross-modality correlation, aiming to predict individual symptom changes by revealing multimodal brain network signatures. The results showed that our model demonstrated promising prediction accuracy, with an R2 value of 0.24 for sertraline and 0.20 for placebo. It also exhibited potential in transferring predictions using only EEG. Key brain regions identified for predicting sertraline response included the inferior temporal gyrus (fMRI) and posterior cingulate cortex (EEG), while for placebo response, the precuneus (fMRI) and supplementary motor area (EEG) were critical. Additionally, both modalities identified the superior temporal gyrus and posterior cingulate cortex as significant for sertraline response, while the anterior cingulate cortex and postcentral gyrus were common predictors in the placebo arm. Additionally, variations in the frontoparietal control, ventral attention, dorsal attention, and limbic networks were notably associated with MDD treatment. By integrating fMRI and EEG, our study established novel multimodal brain network signatures to predict individual responses to sertraline and placebo in MDD, providing interpretable neural circuit patterns that may guide future targeted interventions. Trial Registration: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) ClinicalTrials.gov Identifier: NCT#01407094.

PMID:40164695 | DOI:10.1038/s41380-025-02974-6

Sci Data. 2025 Mar 31;12(1):538. doi: 10.1038/s41597-025-04901-4.

ABSTRACT

Fake news poses significant societal risks by spreading rapidly on social media. While existing research predominantly examines its propagation patterns and psychological drivers, the neural underpinnings remain insufficiently understood. Moreover, current studies often focus on Western political contexts, overlooking cultural variations where social-lifestyle fake news may be more prevalent, such as in China. In this paper, we introduce a multimodal dataset that combines neuroimaging, behavioral data, and standardized Chinese social-lifestyle fake and true news materials. The dataset includes T1 structural, resting-state, and task-based fMRI data from 43 college students, capturing brain activity during tasks involving sharing news and assessing its accuracy. Additionally, participants' trait and rating data were collected to explore individual differences in brain structure, intrinsic functional states, and responses to fake and true news. This dataset could inform future studies on misinformation, offering deeper insights into the neural and psychological aspects of fake news. An overview of the data acquisition, cleaning, and sharing procedures is presented.

PMID:40164637 | DOI:10.1038/s41597-025-04901-4

Brain Imaging Behav. 2025 Mar 31. doi: 10.1007/s11682-025-00999-7. Online ahead of print.

ABSTRACT

Anhedonia is a core feature of depression. It contains a consummatory and a motivational aspect. Whilst much neuroimaging research in patients with depression focused on the consummatory aspect of anhedonia, less is known about its motivational aspect. This study aimed to explore the neurobiology of networks related to motivational anhedonia. Thirty-eight patients with major depressive disorder (MDD) and 19 healthy controls underwent diffusion-weighted and resting state functional magnetic resonance imaging (rs-fMRI). For assessment of motivational anhedonia, we summed the values of the CORE non-interactiveness score, and the items 1 (hopelessness) and 7 (work and activities) of the Hamilton Depression Rating Scale. Whole-brain voxel-wise statistical analysis of fractional anisotropy (FA) data was performed using Tract-Based Spatial Statistics (TBSS). Additionally, we performed a whole-brain comparison of integrated local correlation of rs-fMRI signal (LCOR), to investigate regional functional differences between patients and healthy controls. Whole brain correlations between motivational anhedonia and measures of structural and functional connectivity (FA, and LCOR) were calculated. TBSS-analyses revealed reduced FA in the left superior longitudinal fasciculus (SLF) in patients with MDD. LCOR was reduced in patients with depression in an adjacent cluster localized in bilateral precunei. Within patients, there was a positive correlation between motivational anhedonia and LCOR in the precunei and a negative correlation in bilateral sensorimotor areas. FA-values did not show significant correlations. These findings suggest that motivational anhedonia in depression is linked to alterations of functional connectivity within bilateral precunei. Observed white matter microstructural alterations in the SLF do not show such an association.

PMID:40163222 | DOI:10.1007/s11682-025-00999-7

J Mood Anxiety Disord. 2025 Mar;9:100096. doi: 10.1016/j.xjmad.2024.100096. Epub 2024 Dec 9.

ABSTRACT

Self-judgment is a trans-diagnostic symptom among various psychological disorders, therefore can be a therapeutic target for many common psychiatric conditions. Self-judgment often arises among those who experienced childhood maltreatment, which increases the risk for developing comorbid psychiatric disorders that are resistant to traditional pharmacological and psychological interventions. Understanding the neural correlates of the therapeutic effect of behavioral interventions for reducing self-judgment is key for developing and refining evidence-based intervention programs. This single arm pilot study (N = 24) explored the neural correlates of reduction in self-judgment after an eight-week mindful self-compassion (MSC) intervention program for a sample of adult patients with either anxiety or depressive disorders, with 83 % having more than one diagnoses. The results demonstrated significant reduction of self-judgment after the intervention (p < 0.001, d = -1.04) along with increased self-compassion (p < 0.001, d =1.20); in particular, participants with above median score on the Childhood Trauma Questionnaire had significantly more improvement than those with below median scores (p < 0.05). Resting state fMRI was used to study neural correlates and showed that reduced self-judgment was associated with increased posterior cingulate cortex functional connectivity with dorsal lateral prefrontal cortex, inferior frontal gyrus, and dorsal medial prefrontal cortex, accompanied by reduced posterior cingulate cortex functional connectivity with the amygdala-hippocampal complex. These findings suggest reduced self-judgment after MSC training was substantiated by reduced fear circuitry influences on self-referential processes along with enhanced frontal regulation from the executive network and language network.

PMID:40162192 | PMC:PMC11952680 | DOI:10.1016/j.xjmad.2024.100096

Netw Neurosci. 2025 Mar 3;9(1):61-76. doi: 10.1162/netn_a_00421. eCollection 2025.

ABSTRACT

Brain function can be modeled as dynamic interactions between functional sources at different spatial scales, and each spatial scale can contain its functional sources with unique information, thus using a single scale may provide an incomplete view of brain function. This paper introduces a novel approach, termed "telescopic independent component analysis (TICA)," designed to construct spatial functional hierarchies and estimate functional sources across multiple spatial scales using fMRI data. The method employs a recursive independent component analysis (ICA) strategy, leveraging information from a larger network to guide the extraction of information about smaller networks. We apply our model to the default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). We investigate further on the DMN by evaluating the difference between healthy people and individuals with schizophrenia. We show that the TICA approach can detect the spatial hierarchy of the DMN, VN, and RFPN. In addition, the TICA revealed DMN-associated group differences between cohorts that may not be captured if we focus on a single-scale ICA. In sum, our proposed approach represents a promising new tool for studying functional sources.

PMID:40161992 | PMC:PMC11949590 | DOI:10.1162/netn_a_00421

Netw Neurosci. 2025 Mar 20;9(1):392-420. doi: 10.1162/netn_a_00438. eCollection 2025.

ABSTRACT

Despite significant research, discovering causal relationships from fMRI remains a challenge. Popular methods such as Granger causality and dynamic causal modeling fall short in handling contemporaneous effects and latent common causes. Methods from causal structure learning literature can address these limitations but often scale poorly with network size and need acyclicity. In this study, we first provide a taxonomy of existing methods and compare their accuracy and efficiency on simulated fMRI from simple topologies. This analysis demonstrates a pressing need for more accurate and scalable methods, motivating the design of Causal discovery for Large-scale Low-resolution Time-series with Feedback (CaLLTiF). CaLLTiF is a constraint-based method that uses conditional independence between contemporaneous and lagged variables to extract causal relationships. On simulated fMRI from the macaque connectome, CaLLTiF achieves significantly higher accuracy and scalability than all tested alternatives. From resting-state human fMRI, CaLLTiF learns causal connectomes that are highly consistent across individuals, show clear top-down flow of causal effect from attention and default mode to sensorimotor networks, exhibit Euclidean distance dependence in causal interactions, and are highly dominated by contemporaneous effects. Overall, this work takes a major step in enhancing causal discovery from whole-brain fMRI and defines a new standard for future investigations.

PMID:40161986 | PMC:PMC11949584 | DOI:10.1162/netn_a_00438

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