Most recent paper

bioRxiv [Preprint]. 2026 Jun 4:2026.06.01.729443. doi: 10.64898/2026.06.01.729443.

ABSTRACT

Understanding the principles governing large-scale functional organization of the brain remains a central challenge in systems neuroscience. Despite convergent findings, substantial variability across analytical approaches suggests that functional networks may not admit a unique partitioning. Here, we propose that this variability reflects an intrinsic property of the connectome itself: its organization may be fundamentally multi-modal rather than singular.To test this hypothesis, we employ a Bayesian generative modeling framework based on stochastic block models, enabling principled comparison of competing organizational principles and characterization of the full posterior distribution over network partitions. Applying this framework to resting-state fMRI data in mice, we find that a non-degree-corrected hierarchical architecture provides the most parsimonious description of the functional connectome. Importantly, the inferred posterior landscape is not dominated by a single configuration, but instead comprises multiple distinct and co-dominant organizational schemes.At the mesoscale, these hierarchical communities are anatomically grounded yet systematically reorganize canonical resting-state networks: primary sensory systems remain cohesive, whereas higher-order association networks are fractionated into multiple interacting sub-circuits. This global structural variation is driven by structured variability at the community level, where integrative systems exhibit variable regional affiliations while sensory systems act as structurally stable anchors.Together, these findings suggest that the resting-state connectome is best described as a distribution over alternative, yet co-dominant, organizational configurations. This perspective reconciles inconsistencies across previous studies and supports a view of brain organization as inherently degenerate, providing a latent repertoire of network configurations that may underlie adaptive information routing and dynamic functional reconfiguration.

PMID:42282699 | PMC:PMC13251934 | DOI:10.64898/2026.06.01.729443

bioRxiv [Preprint]. 2026 Jun 3:2026.05.31.729141. doi: 10.64898/2026.05.31.729141.

ABSTRACT

What does the brain do during the continuous, varied experience of watching a story unfold? One account holds that the brain traverses a finite repertoire of recurring states, but whether that repertoire is a stable property of the individual or is reshaped by each new experience has not been tested across diverse naturalistic content within the same person. We characterized the dynamic brain-state repertoire in six individuals who watched the television series Friends across its six seasons during fMRI (up to ∼146 episodes, ∼54 hours per person). For each individual we fit a sticky hierarchical Dirichlet process hidden Markov model across all episodes, discovering brain states (recurring whole-brain activity patterns with characteristic coupling) without pre-specifying their number. Each individual's brain visited roughly forty-five states arrayed along a continuous recurrence gradient, from states active in nearly every episode to episode-specific ones, with no sharp division between them. The repertoire was heterogeneous in why its states recurred: a minority locked to scan-run structure, the majority remaining eligible for content. Transitions were organized by the functional-connectivity similarity between states (per-individual Spearman ρ = 0.33-0.55) and, in most individuals, respected resting-state network boundaries. Episode content was associated with which states the brain occupied moment to moment. The recurrence ordering discovered in Friends transferred to state occupancy during other social-narrative films (five of six individuals) and attenuated as stimuli departed from that class, weakening for visual-only reading and audio-only listening. Across diverse narrative experience, the dynamic repertoire is a property of the individual: content varies which states are visited and when, not which states exist.

PMID:42282603 | PMC:PMC13252121 | DOI:10.64898/2026.05.31.729141

Sports Med Health Sci. 2025 Aug 6;8(4):407-425. doi: 10.1016/j.smhs.2025.07.008. eCollection 2026 Jul.

ABSTRACT

PURPOSE: Neuroimaging studies show that the functional connectivity of the brain changes with age. Resting state functional connectivity (rsFC) in the brain appears to decrease with aging in key networks associated with higher order thinking and effective emotional regulation. Interventions that potentially preserversFC in the brain include 1) physical activity and 2) contemplative practice commonly referred to as mindfulness. The present narrative review aims to summarize the literature concerning the effect of interventions involving exercise, mindful movement, and purely mindfulness-based training on rsFC.

METHODS: Search terms focused on identifying multi-day exercise, mindfulness, or mindful-movement interventions in non-clinical adult populations that included a control group and both pre- and post-assessment of brain rsFC.

RESULTS: Thirty studies were reviewed. Assessed methodological factors that potentially impact findings included subject sample size, scan time length, brain regions targeted for analyses, intervention length and intensity, population characteristics, and differences in sleep quantity/quality. Most studies reported changes in rsFC related to interventions with most observed changes found within the default mode, executive control and salience networks of the brain. However, the largest and most methodologically rigorous study found minimal associations between rsFC and either exercise or mindfulness.

CONCLUSION: Given the inconsistent results found in this review, caution is warranted in the interpretation of changes in rsFC attributable to exercise and mindfulness. This review highlights key factors likely to contribute to differences in reported outcomes. Methodological consistency in fMRI acquisition, data preparation, and analytical approaches are crucial to improve reproducibility and allow for comparison and aggregation.

PMID:42282450 | PMC:PMC13250210 | DOI:10.1016/j.smhs.2025.07.008

Res Sq [Preprint]. 2026 Jun 4:rs.3.rs-9900613. doi: 10.21203/rs.3.rs-9900613/v1.

ABSTRACT

Understanding the functional organization of the primate cortex requires metrics that capture both the temporal and topological dimensions of functional connectivity. Here we propose the spatial connectivity for local homogeneity in cortex (SoHo), a vertex-wise, continuous metric that quantifies the degree to which a cortical vertex and its immediate neighbors share similar spatial profiles of whole-brain functional connectivity. We validated SoHo using large-scale wakeful resting-state fMRI datasets from the Human Connectome Project (HCP) and the NIH Marmoset Brain Mapping Project. In humans, SoHo values showed a striking correspondence with the parcellation boundaries of the HCP multimodal atlas, with low-value regions consistently aligning with areal boundaries. Higher-order association areas exhibited lower SoHo values (functional diversity), while primary sensorimotor areas demonstrated higher values (functional uniformity). Cross-species SoHo mapping revealed that this primary-to-association gradient is evolutionarily conserved across primates, alongside species-specific adaptations in frontoparietal and motor regions. By capturing the local concordance of spatial fingerprints of whole-brain connectivity, SoHo bridges discrete parcellation schemes and continuous models of brain function, offering new insights into primate brain organization and evolution.

PMID:42281994 | PMC:PMC13252542 | DOI:10.21203/rs.3.rs-9900613/v1

Res Sq [Preprint]. 2026 Jun 7:rs.3.rs-9683033. doi: 10.21203/rs.3.rs-9683033/v1.

ABSTRACT

While stable patterns of fMRI task-evoked brain activity and functional connectivity (FC) exist at the population level, a growing body of research emphasizes that variability exists across individuals. These differences define the critical idiosyncrasies in cognition and behavior across individuals that make individuals unique. Resting-state fMRI data (60 minutes) were examined from 1012 participants from the HCP dataset of healthy adults between the ages of 22 and 37. Functional connectivity was estimated between 360 regions, and variability was defined by each individual's mean correlational distance (MCD) to all other participants. High MCD indicated a more 'idiosyncratic' connectivity pattern deviating from the group pattern. Hierarchical regression was used to determine predictors of variability in FC. The base model (demographics, sleep, sex, brain volume) explained 9.22% of the variance in heterogeneity in functional connectivity. Increased variance was explained by cognition, squared cognition, and NEO personality scores, while emotional scores and fitness explained no additional variance. The final model explained 11.9% of the variance in MCD. Low MCD (i.e., being closer to average) was associated with higher BMI, greater crystalized cognitive scores, more positive emotional valence, and NEO Agreeableness. Greater variability was associated with age, brain volume (potentially a sex difference), and NEO Extroversion. The model underestimated variability in the highest MCD participants, suggesting unexplained factors in highly variable individuals. Differences were observed between males and females, and monozygotic twins showed similar variability, suggesting a genetic component. These results suggest benefits for a connectivity pattern being more similar to the group average.

PMID:42281972 | PMC:PMC13252532 | DOI:10.21203/rs.3.rs-9683033/v1

Nutrients. 2026 May 23;18(11):1670. doi: 10.3390/nu18111670.

ABSTRACT

Background/Objectives: Intentional inhibition reflects voluntary control abilities and is assumed to be an indicator of overweight. The medial frontal cortex is an important brain region associated with intentional inhibition. Nevertheless, it is uncertain whether being overweight is connected to impaired food-related intentional inhibition (FII), and if so, what its underlying neural correlates are. The present study therefore aims to provide increased support for overweight due to impairment of FII. Methods: Firstly, 55 overweight and 45 normal-weight college students (Sample 1) were instructed to perform a go/no-go/choose task, which included a resting-state fMRI. Neural correlates of FII were examined using regional homogeneity (ReHo) analyses. Subsequently, an additional 180 undergraduates (87 overweight and 93 normal-weight; Sample 2) were examined to ascertain the differences in resting-state functional connectivity (rsFC) between overweight and normal-weight participants. The study also investigated whether restrained eating mediated the effect of rsFCs on one-year body index changes. Results: FII demonstrated a positive correlation with the cerebellum, inferior temporal gyrus, orbitofrontal cortex, inferior frontal gyrus, and cingulate gyrus. Additionally, in comparison with participants with normal weight, overweight participants demonstrated diminished rsFC between the FII-related areas and the postcentral gyrus, while heightened rsFC strengths were found between these areas and the middle temporal gyrus and precuneus. Furthermore, mediation analyses demonstrated that cingulate-precuneus connectivity is linked to fat mass index change a year later through restrained eating. Conclusions: FII was associated with connectivity between brain regions involved in inhibitory control and maladaptive eating. Furthermore, we investigated how these connectivity patterns could potentially affect future body fat loss through restrained eating.

PMID:42280314 | DOI:10.3390/nu18111670

Diagnostics (Basel). 2026 May 25;16(11):1619. doi: 10.3390/diagnostics16111619.

ABSTRACT

Background: Chemotherapy-induced peripheral neuropathy (CIPN) is a common, disabling toxicity with no validated biomarkers. MRI-based functional neuroimaging could offer insight into central pain processing and may reveal reproducible brain signatures of CIPN. Methods: Following PRISMA 2020 (PROSPERO: CRD420251132102), we systematically reviewed whole-brain MRI studies in adult cancer patients with CIPN. Eligible MRI techniques included task-based fMRI, resting-state fMRI, perfusion MRI, and structural MRI. Data were synthesized through voxelwise activation likelihood estimation (ALE), systems-level region-of-interest (ROI) mapping, and proportion meta-analysis of regional involvement. Results: Of 2488 screened records, five observational studies were included. The voxelwise ALE analysis did not identify clusters surviving correction, but dispersed foci appeared within the default mode network (DMN), prefrontal executive cortex, and primary sensorimotor regions, suggesting the engagement of these pain-processing networks. ROI synthesis confirmed consistent alterations in the DMN and executive prefrontal and sensorimotor cortices in CIPN patients compared with controls, while the brainstem/periaqueductal gray and cerebellum were rarely implicated. Proportion meta-analysis further quantified these differences: CIPN patients showed altered involvement in 30% (95% CI 0.16-0.48) of contrasts, with the highest frequencies in the DMN (50%), sensorimotor (33%), and executive prefrontal regions (33%). By contrast, control-higher contrasts were less frequent (10%, 95% CI 0.03-0.27), highlighting CIPN-related increases particularly in self-referential and somatosensory networks. Conclusions: Across analytic approaches, CIPN is characterized by reproducible alterations in the DMN and executive prefrontal and sensorimotor networks. These central pain signatures represent promising MRI-based biomarkers for identifying and monitoring CIPN in oncology.

PMID:42279487 | DOI:10.3390/diagnostics16111619

J Clin Med. 2026 Jun 2;15(11):4306. doi: 10.3390/jcm15114306.

ABSTRACT

Introduction: Schizophrenia is characterised by distributed abnormalities in electrophysiological dynamics and large-scale brain networks, yet unimodal EEG or MRI alone cannot fully explain how fast neural computations relate to spatially organised circuit dysfunction. Multimodal EEG-MRI approaches offer a bridge across temporal and anatomical scales by explicitly modelling cross-modal coupling. Methods: Following PRISMA 2020 guidance, we conducted a systematic, mechanistic review of human studies (adults ≥ 18 years) comparing schizophrenia-spectrum groups with healthy controls using EEG combined with at least one MRI modality (fMRI, structural MRI, and/or diffusion MRI) and explicit EEG-MRI integration (e.g., EEG-informed fMRI, joint ICA, mCCA/MCCA, coupled matrix-tensor factorisation, DCM-based fusion). Searches were performed in PubMed/MEDLINE, Embase, Web of Science, Scopus, PsycINFO, IEEE Xplore, ResearchGate, and Google Scholar for January 2000-December 2025, supplemented by citation tracking. Risk of bias was assessed with ROBINS-I, and due to heterogeneity, results were synthesised narratively by integration of families. Results: From 148 records, 23 studies met the inclusion criteria. Studies used mainly simultaneous EEG-fMRI at 3T and spanned resting-state designs and task paradigms dominated by auditory processing (oddball, MMN/N100-P200, ASSR/aeGBR), with additional work in affective context, working memory, semantic processing (N400), sensory gating, and pharmacologic challenge. Across tasks, the most reproducible multimodal signature was disrupted coupling between electrophysiological markers and the recruitment of large-scale networks, rather than isolated changes in EEG or fMRI metrics. Target detection/oddball paradigms converged on reduced late ERP responses (especially P300, sometimes N2) alongside reduced expression or loss of coupling to salience/ventral attention and control circuitry (including ACC/anterior insula/TPJ). Resting-state studies most consistently indicated altered "coupling rules" (frequency specificity, timing/lag structure, and directionality), including abnormalities detectable even when unimodal summaries were weak. Extended multimodal studies (adding sMRI/DTI and/or classification) suggested that combining modalities can improve discrimination, though performance was sensitive to sample size, demographic imbalance, and feature-selection/validation choices. Conclusions: Multimodal EEG-MRI studies support schizophrenia as a disorder involving persistent structural and circuit-level abnormalities whose functional expression varies dynamically across cognitive states and task demands. Future progress will depend on harmonised acquisition/artefact-control practices for simultaneous EEG-fMRI, larger and more diverse samples (including early/CHR and longitudinal designs), and cross-site replication of mechanistically interpretable coupling biomarkers.

PMID:42279166 | DOI:10.3390/jcm15114306

Neuroimage. 2026 Jun 11:122044. doi: 10.1016/j.neuroimage.2026.122044. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) has been associated with atypical large-scale brain organization, yet most functional magnetic resonance imaging (fMRI) studies rely on static connectivity measures that do not explicitly characterize temporal dynamics. Here, we applied dynamic mode decomposition (DMD), a data-driven method that captures recurrent spatiotemporal patterns in terms of temporal persistence and oscillatory timing, to resting-state fMRI data from the Autism Brain Imaging Data Exchange (ABIDE; N = 849). Using a group-level DMD framework with subject-level mode estimation, we identified dynamic modes whose temporal properties differed between individuals with ASD and typically developing (TD) controls. In particular, ASD showed altered oscillatory timing in a posterior visual-parietal-temporal mode, and age-related associations with DMD features differed between ASD and TD groups, suggesting atypical developmental trajectories of large-scale temporal organization. Within the ASD group, DMD features were additionally associated with individual differences in IQ, indicating that temporal brain dynamics partially reflect cognitive heterogeneity in ASD. Spatiotemporal reconstruction and Neurosynth-based spatial correspondence analyses provided descriptive functional context for the extracted modes. Together, these findings suggest that DMD offers a compact framework for characterizing temporal organization of intrinsic brain activity and may capture dimensions of ASD-related neurocognitive variability beyond static connectivity alone. Oscillatory timing here refers to recurrence properties of low-frequency BOLD-derived dynamic modes rather than electrophysiological oscillations measured directly from neural signals.

PMID:42276437 | DOI:10.1016/j.neuroimage.2026.122044

Behav Brain Res. 2026 Jun 11:116334. doi: 10.1016/j.bbr.2026.116334. Online ahead of print.

ABSTRACT

OBJECTIVE: To explore brain network topological properties in treatment-resistant schizophrenia patients and their healthy siblings, identify genetic susceptibility markers and protective factors of the disease, and assist clinical diagnosis and treatment.

METHODS: 28 treatment-resistant schizophrenia patients (after excluding two for head motion), 25 healthy siblings, and 38 healthy controls were enrolled. Resting-state functional brain networks were constructed using functional magnetic resonance imaging (fMRI) data. Topological properties were analyzed via graph theory, followed by statistical and correlation analyses with clinical symptom scales.

RESULTS: 1. No significant difference in small-world properties among the three groups. 2. No difference in global efficiency. 3. No difference in local efficiency. 4. In the sibling group, nodal efficiency and degree centrality of the bilateral amygdala and right hippocampus were lower than those in healthy controls, while those of the right thalamus were lower than in patients. 5. Right thalamus properties in patients negatively correlated with PANSS scores and positively correlated with WAIS digit symbol scores.

CONCLUSION: Healthy siblings show brain network abnormalities, while patients have higher right thalamic metrics than siblings but do not differ from controls. The right thalamus correlates negatively with negative symptoms; this is observational, not a prognostic indicator. Whether this reflects compensation (e.g., thalamic compensation) remains speculative. Brain network abnormalities might tentatively suggest genetic susceptibility, and siblings could have protective factors. All findings are preliminary and require validation.

PMID:42276286 | DOI:10.1016/j.bbr.2026.116334

Alzheimers Dement. 2026 Jun;22(6):e71547. doi: 10.1002/alz.71547.

ABSTRACT

INTRODUCTION: The brain is a complex dynamical system, influenced by arousal state. Cortical synchrony supports information processing and is disrupted in Alzheimer's disease (AD). Locus coeruleus (LC) integrity and pupillometry index arousal system structure and function.

METHODS: Sixty-four AD and 26 controls underwent resting-state pupillometry-fMRI. Neuromelanin MRI and Addenbrooke's Cognitive Examination were conducted. Mean and standard deviation of blood oxygen level dependent (BOLD) phase coherence yielded synchrony and metastability, respectively. Leading Eigenvector Dynamics Analysis (LEiDA) produced coherence-based states.

RESULTS: AD had reduced global synchrony [b = -0.90, p < 0.001], metastability [b = -0.61, p < 0.01], LEiDA "global coherence state" occupancy [b = -0.06, p < 0.01], and LC integrity [b = -0.37, p = 0.01]. Synchrony [b = 0.19, p = 0.01] and LC integrity [b = 0.17, p < 0.01] related to cognition and one another [b = 0.27, p = 0.01]. Pupil-linked arousal correlated with synchrony and global coherence state maintenance.

DISCUSSION: In health, cortical activity shows widespread but dynamic synchrony across regions to meet changing demands. In AD, arousal dysfunction appears to disrupt these dynamics, impacting cognition.

PMID:42273876 | PMC:PMC13254816 | DOI:10.1002/alz.71547

Front Psychiatry. 2026 May 26;17:1835812. doi: 10.3389/fpsyt.2026.1835812. eCollection 2026.

ABSTRACT

OBJECTIVE: To investigate the anterior cingulate cortex (ACC) resting-state functional connectivity patterns in OCD patients who have not yet received therapy and analyze how they relate to the intensity of their clinical symptoms.

METHODS: Resting-state fMRI data were acquired from 46 medication-naïve participants with OCD and 33 demographically comparable neurotypical control subjects. The region of focus for the seed-based whole-brain functional connectivity investigation was bilateral ACC. Relationships between aberrant connections and clinical characteristics measured by the Y-BOCS, HAMD-17, and HAMA were investigated using Pearson's correlation coefficient and partial correlation analysis.

RESULTS: Key findings (OCD patients vs. healthy controls): Increased functional connectivity (FC) in OCD patients: Right insula (Brodmann area 48), Right hippocampus (BA 20), Right fusiform gyrus (BA 37); Decreased FC involving the anterior cingulate cortex (ACC) with: Right supplementary motor area (SMA, BA 32), Left inferior frontal gyrus (IFG, BA 47) (AlphaSim corrected, P < 0.05). Clinical association: There was a significant positive relationship between Y-BOCS total scores (a measure of OCD symptom severity) and FC strength linking the left IFG to the ACC (r = 0.351, P = 0.017). In practical terms, greater symptom severity is associated with stronger coupling between these two regions. No other clear brain-behavior relationships were found in the other regions examined.

CONCLUSION: Treatment-naïve OCD patients demonstrate distinct ACC functional connectivity alterations involving cognitive control, motor planning, and limbic processing regions. The specific association between left inferior frontal gyrus (IFG)-ACC connectivity and symptom severity suggests that this pathway may serve as a neurobiological marker for OCD pathophysiology.

PMID:42273591 | PMC:PMC13248406 | DOI:10.3389/fpsyt.2026.1835812

Quant Imaging Med Surg. 2026 Jun 1;16(6):492. doi: 10.21037/qims-2026-1-0066. Epub 2026 May 13.

ABSTRACT

BACKGROUND: Mild cognitive impairment (MCI) is a transitional stage between subjective cognitive decline and Alzheimer's dementia, representing a critical window for intervention. We characterize the small-world properties of brain networks in MCI to identify sensitive biomarkers for early detection and assessment.

METHODS: Thirty-one patients diagnosed with MCI were recruited as the experimental group, while 30 healthy elderly individuals served as the normal control (NC) group. Based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), small-world properties of the brain networks were observed using graph theory analysis. Global and nodal properties were computed to assess differences in brain network topology.

RESULTS: Both structural and functional brain networks in the MCI and NC groups exhibited small-world properties (σ>1), and significant differences were noted in nodal properties such as nodal efficiency, nodal degree centrality, and nodal shortest path length (P<0.05). Importantly, these nodal properties in brain regions were significantly correlated with Montreal Cognitive Assessment (MoCA) scores in patients with MCI (P<0.05).

CONCLUSIONS: Patients with MCI exhibit small-world properties in their brain networks, suggesting preserved efficiency of information transfer. Node property metrics in regions such as the posterior cingulate cortex, prefrontal cortex, and occipital lobe are promising biomarkers for early detection of MCI.

PMID:42273161 | PMC:PMC13247931 | DOI:10.21037/qims-2026-1-0066

J Appl Stat. 2025 Nov 23;53(8):1515-1537. doi: 10.1080/02664763.2025.2567979. eCollection 2026.

ABSTRACT

The classification of resting-state functional magnetic resonance imaging (rs-fMRI) data presents unique challenges in the detection and diagnosis of neuropsychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). Traditional classification approaches often prove inadequate when handling complex spatiotemporal patterns and high-dimensional fMRI data, particularly when significant variations exist both between and within diagnostic groups. To address these limitations, we introduce a novel classification framework that integrates three complementary analytical components: elastic registration for curve alignment, geometric curve length computation for capturing signal variability, and sparse principal component analysis for dimensionality reduction. Extensive simulation studies show that our proposed method significantly outperforms existing approaches, especially in scenarios where groups exhibit distinct variation patterns rather than mean differences in their functional curves. When applied to the ADHD-200 dataset, our method achieves classification accuracy rates substantially exceeding conventional approaches. The proposed framework's ability to capture subtle variability differences while maintaining computational efficiency makes it particularly valuable for biomarker discovery and clinical applications in neuropsychiatric research. Our approach's focus on signal variability rather than mean activation patterns offers new insights into the dynamic nature of brain activity differences in ADHD and provides a promising foundation for analyzing other neurological conditions.

PMID:42272799 | PMC:PMC13248495 | DOI:10.1080/02664763.2025.2567979

Imaging Neurosci (Camb). 2026 Jun 8;4:IMAG.a.1268. doi: 10.1162/IMAG.a.1268. eCollection 2026.

ABSTRACT

Much of a typical individual's mental life is characterized by spontaneous thoughts that occur independently of external stimuli. In prior studies, ongoing mental experiences and their neural correlates have been captured using thought probes presented at random intervals during functional magnetic resonance imaging (fMRI). However, this approach results in temporally imprecise estimates of brain activity relative to the arising of mental experience. In this preregistered, proof-of-concept study, we aimed to improve temporal precision using a novel method termed real-time fMRI-triggered experience sampling (rt-fMRI-ES). We analyzed blood-oxygenation level-dependent signals in real time during a wakeful resting state (n = 60) to trigger thought probes from spontaneous activations within two regions: the dorsal anterior insular cortex (daIC; a key region within salience network) and posteromedial cortex (PMC; a key region within default mode network). We tested two preregistered hypotheses: (H1) Ratings of arousal time-locked to daIC-activation trials are higher than ratings time-locked to non-daIC-activation trials; (H2) Ratings of external attention time locked to PMC-activation trials are lower than ratings time-locked to non-PMC-activation trials. After applying preregistered exclusion criteria, 42 participants (1243 trials) and 49 participants (1429 trials) were included in H1 and H2 analyses, respectively. We did not find evidence in support of H1, but we did find evidence in support of H2, as external-attention ratings were significantly lower for trials triggered by PMC activation than other trial types. Taken together, we successfully developed and validated the rt-fMRI-ES method, offering a novel technique to efficiently capture spontaneous thoughts based on ongoing neural activity. Preregistered Stage 1 Recommendation: https://osf.io/sd4hu (Date of in-principle acceptance: July 24, 2024).

PMID:42272744 | PMC:PMC13248897 | DOI:10.1162/IMAG.a.1268

Brain Connect. 2026 Jun 11:21580014261455378. doi: 10.1177/21580014261455378. Online ahead of print.

ABSTRACT

Background: Tinnitus is an auditory phantom perception in the absence of any corresponding acoustic stimulus whose pathophysiology remains poorly understood. This study aimed to investigate alterations in the functional organization of the brain in individuals with tinnitus using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory analysis.Methods: We conducted a study including 44 individuals with tinnitus and 32 healthy controls. Using rs-fMRI and graph theory measures, we characterized whole-brain topological properties, including network segregation, integration, small-worldness, and global efficiency. In addition, regional segregation and integration were assessed using clustering coefficient and participation coefficient analyses to identify alterations in brain hub regions.Results: Our findings revealed altered topological properties in the tinnitus brain, particularly in the balance between cerebral segregation and integration, leading to deviations from optimal small-world architecture. We also observed alterations in the topology of specific auditory and nonauditory brain regions associated with phantom sound perception. Notably, patients with tinnitus exhibited a decreased nodal participation coefficient in the thalamus, suggesting reduced connectivity between this region and different functional modules as well as long-range connections.Conclusions: These results suggest that tinnitus is associated with alterations in the functional organization of the brain, leading to disrupted information processing and sensory integration.

PMID:42272341 | DOI:10.1177/21580014261455378

BMC Psychiatry. 2026 Jun 10. doi: 10.1186/s12888-026-08267-8. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent psychiatric condition; however, candidate neural substrates related to resilience to MDD remain unclear.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 113 patients with MDD, 36 unaffected siblings, and 81 healthy controls (HCs). First, degree centrality (DC) analysis was performed to identify group differences. Next, regions showing significant DC differences were used as seeds for whole-brain functional connectivity (FC) analyses. Finally, associations between these significant brain regions and depressive symptom severity were examined separately within each participant group.

RESULTS: DC in the medial frontal gyrus and the right precentral gyrus, as well as FC between the medial frontal gyrus and the right postcentral gyrus, exhibited the pattern: patients with MDD < HCs < unaffected siblings. Correlation analyses revealed no significant relationships between depressive symptom severity and DC or FC values.

CONCLUSIONS: Unaffected siblings exhibited both the highest DC and the strongest FC related to the medial frontal gyrus. These neuroimaging findings provide further insight into the role of the medial frontal gyrus in potential resilience mechanisms and suggest potential directions for the prevention and treatment of MDD.

PMID:42271284 | DOI:10.1186/s12888-026-08267-8

J Headache Pain. 2026 Jun 10. doi: 10.1186/s10194-026-02409-9. Online ahead of print.

ABSTRACT

BACKGROUND: Elucidating functional brain dynamics under different blood-brain barrier (BBB) permeabilities may resolve the enigmatic pathophysiology of reversible cerebral vasoconstriction (RCVS); however, relevant investigations are lacking. We aimed determine the relationship between brain functional dynamics and BBB permeability in patients with RCVS.

METHODS: We prospectively recruited RCVS patients and healthy controls (HCs) from November 2016 to January 2023 in Headache Center in a tertiary medical center (> 3000 beds) and adjacent communities. RCVS patients who were diagnosed according to the International Headache Society criteria, and age- and sex-matched HCs were enrolled. Normalized entropy, derived from resting-state fMRI (rs-fMRI), was compared at the network and parcel levels between RCVS patients with and without BBB disruption. Dynamic contrast-enhanced MRI (DCE-MRI)-derived Ktrans BBB permeability and ultrasonographic findings were analyzed.

RESULTS: In total, 188 subjects (100 RCVS + 88 HCs) were enrolled. Compared with the HCs, the RCVS patients had greater entropy (p = 0.014), which was greater in the RCVS patients without than in those with (adjusted p = 0.040) BBB disruption. Compared with HCs, patients without BBB disruption had greater entropy between 60 and 90 days after headache onset (adjusted p = 0.006). No significant differences in entropy were noted between disease stages in BBB-disrupted patients. Parcels within subnetworks of the Default mode network (Default A and C) exhibited higher entropy in patients without BBB disruption. In specific anatomical locations, entropy values were negatively correlated with ultrasonographic Lindegaard index vasoconstriction severity (p = 4.3 × 10- 6; within the Default A network) and DCE-MRI Ktrans BBB permeability (p = 0.005; within the Default C network).

CONCLUSION: The differential changes in normalized entropy suggest that increased rs-fMRI signal complexity may reflect a compensatory functional response to abrupt vasoconstriction. In contrast, the loss of entropy fluctuations in the context of severe BBB disruption indicates a state of impaired cerebral autoregulation in patients with RCVS. The compensatory capacity decreased as vasoconstriction or BBB disruption exacerbated.

PMID:42271210 | DOI:10.1186/s10194-026-02409-9

Alcohol Alcohol. 2026 May 13;61(4):agag035. doi: 10.1093/alcalc/agag035.

ABSTRACT

BACKGROUND: Despite evidence-based pharmacological and behavioural interventions, alcohol use disorder (AUD) is associated with highly variable treatment outcomes. Functional magnetic resonance imaging (fMRI) may identify neural markers that predict treatment response, ultimately supporting a precision medicine approach to AUD.

OBJECTIVES: This systematic review synthesized evidence on fMRI predictors of treatment outcomes in individuals with AUD, evaluated methodological consistency, and identified gaps to guide biomarker development.

METHODS: A comprehensive search of PubMed/MEDLINE, Embase, and PsycINFO combined terms related to fMRI, AUD, and treatment outcomes. Eligible studies included participants with AUD receiving pharmacological, behavioural, or neuromodulatory interventions with fMRI measures collected before or early in treatment to predict clinical outcomes. Screening and extraction were conducted in duplicate using Covidence, and study quality was assessed with the Grading of Recommendations Assessment, Development, and Evaluation framework.

RESULTS: Of 342 records, 15 studies met the inclusion criteria. Most used alcohol cue reactivity tasks (k = 11), with others using resting-state fMRI (k = 2), a monetary reward task (k = 1), or an alcohol-specific Go/No-Go task (k = 1). Pharmacological treatments were most common (k = 8), followed by behavioural therapies (k = 6) and one neuromodulation trial. Across paradigms, neural activity in the ventral striatum, orbitofrontal cortex, and anterior cingulate cortex commonly predicted outcomes. Greater prefrontal engagement predicted improvement, while heightened striatal cue reactivity predicted relapse. Resting-state findings suggested reduced reward- and stress-network connectivity corresponded with better outcomes. Across studies, however, considerable heterogeneity and inconsistency were present and sample sizes tended to be small.

CONCLUSIONS: Evidence implicates frontostriatal and salience circuitry in predicting AUD treatment outcomes, but inconsistency and underpowered studies limit firm conclusions. Larger longitudinal studies are needed to robustly validate clinically useful biomarkers.

PMID:42268834 | PMC:PMC13253039 | DOI:10.1093/alcalc/agag035

Neuroinformatics. 2026 Jun 10;24(2):35. doi: 10.1007/s12021-026-09792-3.

ABSTRACT

Dynamic functional connectivity (dFC) analysis in functional magnetic resonance imaging (fMRI) faces a fundamental challenge: conventional sliding-window methods must trade temporal resolution against statistical reliability, while rare transient neural events risk becoming undetectable when included in training data. We introduce HESREN (Hermite-Enhanced Software Reservoir Network), a novel framework integrating echo state networks with derivative-informed Hermite-type neural operators to enable windowless dFC estimation and leakage-free transient detection. HESREN employs a leaky-integrator reservoir that projects multivariate fMRI time series into high-dimensional state spaces, augmented with Gaussian-smoothed temporal derivatives to form enhanced feature vectors encoding value, velocity, and acceleration. Strict temporal partitioning trains all components exclusively on baseline segments while evaluating on complete time series, preserving transient events as out-of-distribution signals. Teacher-student distillation transfers the temporal precision of micro-window connectivity estimates into stable windowless operators via ridge-regularised linear readout; all hyperparameters and initialisation procedures are fully specified to ensure reproducibility. Validation on the NEBULA101 resting-state fMRI dataset across [Formula: see text] participants demonstrates consistent and substantial improvements over conventional methods. Transient event detection achieves AUC[Formula: see text] and average precision AP[Formula: see text], compared to AUC[Formula: see text] for raw-derivative baselines (Wilcoxon [Formula: see text], [Formula: see text], Cohen's [Formula: see text]), with phase-randomised surrogate testing confirming statistical robustness in all participants ([Formula: see text], [Formula: see text] surrogates). Comparison against mainstream dFC alternatives shows that HESREN statistically significant performance gains Gaussian Hidden Markov Models (AUC[Formula: see text]), temporal convolutional networks (AUC[Formula: see text]), LSTM autoregressive predictors (AUC[Formula: see text]), and conventional sliding-window correlation (AUC[Formula: see text]), with all advantages statistically significant ([Formula: see text]). Windowless dFC trajectories attain lag-corrected correlation [Formula: see text] with micro-window teachers while providing 3-[Formula: see text] finer temporal resolution than 25-TR sliding windows. Network-level analysis reveals that HESREN detects transient events an average of 4.5 TR (9 s) earlier than sliding-window methods, selectively amplifies within-language-network coupling by [Formula: see text] and default-mode-network coupling by [Formula: see text] during detected events, and is the only evaluated method to yield a positive network segregation index ([Formula: see text]), consistent with the known modular organisation of resting-state brain networks. HESREN overcomes fundamental limitations of sliding-window dFC through derivative-aware reservoir dynamics, offering a computationally efficient, mathematically principled framework for capturing transient neural reconfigurations with temporal precision previously improved in fMRI connectivity analysis. The modular architecture facilitates adaptation to diverse neuroimaging applications, from basic neuroscience to real-time clinical monitoring systems.

PMID:42268529 | PMC:PMC13253793 | DOI:10.1007/s12021-026-09792-3