Most recent paper

JAACAP Open. 2025 Nov 27;4(2):254-267. doi: 10.1016/j.jaacop.2025.11.008. eCollection 2026 Apr.

ABSTRACT

OBJECTIVE: The pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is complicated by high rates of psychiatric comorbidities; thus, delineating unique vs shared functional brain perturbations is critical in elucidating illness pathophysiology. We investigated resting-state functional magnetic resonance imaging (rsfMRI)-complexity alterations among children with ADHD, oppositional defiant disorder (ODD), and obsessive-compulsive disorder (OCD), respectively, and comorbid ADHD, ODD, and OCD, within the cool and hot executive function (EF) networks.

METHOD: We leveraged baseline data from 9- to 10-year-old children in the Adolescent Brain and Cognitive Development (ABCD) Study. Data for children who singularly met all DSM-5 behavioral criteria for ADHD (n = 61), ODD (n = 38), and OCD (n = 48), respectively, were extracted, alongside data for children with comorbid ADHD, ODD, OCD, and/or other psychiatric diagnoses (n = 833). Data for a control sample of age-, sex-, and developmentally matched children were also extracted (N = 269). Voxel-wise sample entropy (SampEn) was computed using the LOFT Complexity Toolbox. Mean SampEn within all regions of interest (ROIs) of the EF networks was calculated for each participant. Hierarchical models with generalized estimating equations compared SampEn of comorbidity-free and comorbid ADHD, ODD, and OCD within the EF networks.

RESULTS: SampEn was reduced in comorbidity-free ADHD and ODD in overlapping regions of both EF networks compared with the healthy controls, including the bilateral superior frontal gyrus, anterior/posterior cingulate gyrus, and bilateral caudate (Wald statistic = 5.682-10.798, p < .05, and Benjamini-Hochberg [BH] corrected), with ADHD additionally affected in the right inferior/middle frontal gyrus and bilateral frontal orbital cortex (Wald statistic = 7.231-9.420, p < 0.05, and BH corrected). Among comorbid presentations, the presence of ADHD symptomatology was associated with significantly lower SampEn in every ROI (z = -3.973 to -2.235, p < .05, and BH corrected).

CONCLUSION: ADHD and ODD shared common impairments underlying the EF networks in the comorbidity-free presentations, with ADHD showing more widespread complexity reduction. When ADHD co-occurred with other psychiatric disorders, the reduction in SampEn extended beyond the regions affected in comorbidity-free ADHD, indicating that comorbidities amplify neural complexity deficits. In contrast, no significant SampEn alterations were observed in OCD, whether presented alone or in combination with ADHD.

PMID:41938226 | PMC:PMC13043473 | DOI:10.1016/j.jaacop.2025.11.008

J Affect Disord. 2026 Apr 3:121743. doi: 10.1016/j.jad.2026.121743. Online ahead of print.

ABSTRACT

Increasing attention has been paid to the nonlinear functional activity of human brain regions. This paper extends Chatterjee's correlation coefficient (CCC) method to model nonlinear relationships in brain functional networks explicitly. Specifically, the reliability and effectiveness comparisons between CCC and the Pearson correlation coefficient (PCC) are demonstrated using simulated data and two real resting-state functional magnetic resonance imaging (rs-fMRI) datasets: The Midnight Scan Club dataset and the UCLA dataset. The results demonstrate that CCC accounts for both linear and nonlinear dependencies and that its reliability is better than that of PCC. Additionally, from a whole-brain perspective, the number of connections in different brain regions was observed in the following order: bipolar disorder (BP) and healthy controls (HC) > adult attention-deficit/hyperactivity disorder (ADHD) and HC > schizophrenia (SZ) and HC. The commonalities among the three psychiatric disorders compared to HC were differences in occipital, default, cerebellum, and the regions connected to the occipital. Besides, using CCC: occipital performed classification best (AUC: 0.657) between ADHD and HC, and (AUC: 0.622) between BP and HC, but fronto_parietal performed classification best (AUC: 0.700) between SZ and HC. This method enhances sensitivity to group differences and may provide new insights for exploring functional networks based on fMRI in the future.

PMID:41936984 | DOI:10.1016/j.jad.2026.121743

Neurobiol Dis. 2026 Apr 3:107380. doi: 10.1016/j.nbd.2026.107380. Online ahead of print.

ABSTRACT

BACKGROUND: Magnetic resonance-guided focused ultrasound (MRgFUS) thalamotomy made a breakthrough in treating essential tremor (ET), but with variable tremor responses. This study employed support vector machine regression (SVR) to predict tremor response based on preoperative functional connectivity (FC) patterns.

METHODS: Fifty-six ET patients underwent unilateral MRgFUS thalamotomy and resting-state functional MRI (rs-fMRI). The Clinical Rating Scale for Tremor evaluated 12-month post-treatment responses. Two-sample t-tests identified disease-specific FC differences, which were used in SVR to predict responses. Model performance was evaluated using Pearson's correlation coefficient (r), mean squared error (MSE), and validated via permutation and cross-validation. Longitudinal rs-fMRI in 26 patients examined dynamic changes of the connection with the highest predictive contribution.

RESULTS: Patients demonstrated significant improvement in treated hand tremor at 12-month after thalamotomy (p < 0.001), with a mean improvement of 68%. While baseline tremor severity showed a significant negative correlation with treatment improvement (r = -0.37, p = 0.005), it failed to predict individual outcomes in regression models (linear regression: p = 0.98; SVR: p = 0.46). SVR identified a preoperative FC pattern that predicted tremor response (r = 0.38, p = 0.03; MSE = 0.05, p = 0.02). Longitudinal analysis demonstrated the restoration of the connection with the highest predictive contribution, which increased post-treatment (p = 0.005) and correlated with tremor improvement ratio (r = -0.49, p < 0.001).

CONCLUSION: Preoperative FC patterns predict tremor responses to MRgFUS in ET, serving as a potential imaging biomarker for personalized treatment planning.

PMID:41936873 | DOI:10.1016/j.nbd.2026.107380

J Affect Disord. 2026 Apr 2:121733. doi: 10.1016/j.jad.2026.121733. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the interaction between HPA-axis gene polymorphisms (FKBP5, NR3C1, AVPR1B, SLC1A3, SKA2) and brain functional alterations in adolescent depression.

METHODS: Between May 2021 and June 2024, 150 medication-naïve depressed adolescents and 44 healthy controls underwent HPA-axis SNP genotyping and resting-state fMRI scanning. Logistic regression was performed to evaluate SNP-depression associations, and chi-square tests were used to assess Hardy-Weinberg equilibrium (HWE) and genotype distributions. Imaging metrics (ALFF/fALFF/ReHo/FC) were analyzed via independent t-tests (group differences) and 2 × 2 ANOVA (diagnosis/genotype main effects + interaction) with FDR correction (p < 0.05), followed by depression score correlation analysis.

RESULTS: Among the examined HPA-axis gene polymorphisms, only SKA2-rs7208505 showed a significant association with depression, with G allele carriers exhibiting increased risk (OR = 1.751-2.321, p < 0.05), whereas NR3C1-rs41423247, AVPR1-rs28373064, FKBP5-rs9470080, and SLC1A3-rs2269272 showed no associations (all p > 0.05). Depressed adolescents exhibited elevated ALFF/ReHo in the right precentral gyrus (PreCG) /supplementary motor area (SMA) and increased sensorimotor network connectivity, but decreased ALFF in the cerebellum, angular gyrus, and precuneus (all p < 0.05, FDR-corrected). A Diagnosis×SKA2-rs7208505 interaction significantly modulated functional connectivity from right PreCG to bilateral inferior temporal and postcentral gyri (all p < 0.05, Bonferroni-corrected).

CONCLUSION: SKA2-rs7208505 was associated with adolescent depression, with the G allele conferring risk. A significant Diagnosis×SNP interaction was found for functional connectivity between the right PreCG and bilateral inferior temporal/postcentral gyri, indicating this SNP and brain functional alterations are linked to adolescent depression pathogenesis. These findings provide novel insights and support for early prevention and intervention strategies.

PMID:41935751 | DOI:10.1016/j.jad.2026.121733

Psychiatry Res. 2026 Mar 27;361:117126. doi: 10.1016/j.psychres.2026.117126. Online ahead of print.

ABSTRACT

OBJECTIVES: The purpose of this study was to investigate the changes of functional connectivity (FC) and its relationship with cognitive flexibility in patients with first-episode, treatment-naïve obsessive-compulsive disorder (OCD), using bilateral insula as seed regions.

METHODS: 45 OCD patients and 40 healthy controls (HC) were recruited to undergo a clinical symptom assessment, the Wisconsin Card Sorting Test (WCST), and rs-fMRI scans. Six seed regions in the bilateral insula were selected for whole-brain FC analyses. A two-sample t-test was utilized to compare the differences in FC between two groups. SPSS software was used to analyze the relationship between the strength of FC in different brain regions and cognitive flexibility in the OCD group using Pearson correlation.

RESULTS: Compared with the HC, FC between the left ventral anterior insula and left ventrolateral nucleus of thalamus was reduced in the OCD(p < 0.001), FC between the left dorsal anterior insula and right posterior central gyrus was increased (p < 0.001), and FC between the right dorsal anterior insula and right posterior central/middle temporal gyrus was increased (p < 0.001). FC between right dorsal anterior insula and right posterior central gyrus was significantly negatively correlated with cognitive flexibility(uncorrected p < 0.05). FC between left ventral anterior insula and left ventrolateral thalamus was positively correlated with cognitive flexibility(uncorrected p < 0.05). Finally, the FC between right dorsal anterior insula and right middle temporal gyrus was negatively correlated with cognitive flexibility (uncorrected p < 0.05).

CONCLUSION: Patients with OCD exhibit abnormal FC network involving insula, which are associated with cognitive flexibility, supporting a functional mechanism of cognitive inflexibility in OCD.

PMID:41935505 | DOI:10.1016/j.psychres.2026.117126

Phytomedicine. 2026 Mar 27;155:158127. doi: 10.1016/j.phymed.2026.158127. Online ahead of print.

ABSTRACT

BACKGROUND: Circadian rhythm disruption and chronic sleep deprivation are increasingly recognized as key contributors to depression, largely through gut-brain axis dysregulation and neuroinflammatory activation. IL-17A, a pro-inflammatory cytokine primarily derived from intestinal Th17 cells, has emerged as a pivotal mediator linking gut immune imbalance to central nervous system dysfunction.

PURPOSE: This study aimed to elucidate the gut-derived IL-17A-STAT3/RORγt signaling mechanism underlying sleep-deprivation-induced depression and to determine whether Schisandrin B, a lignan from Schisandra chinensis, can alleviate depressive phenotypes by restoring gut-brain axis homeostasis.

METHODS: Clinical analyses of plasma cytokines and metabolites were integrated with a mouse model of sleep-deprivation-induced depression. Behavioral tests, resting-state fMRI, gut microbiota 16S rDNA sequencing, Western blotting, ELISA, and network pharmacology with molecular docking were employed to comprehensively investigate neuroimmune, microbial, and neurofunctional alterations.

RESULTS: Patients with circadian rhythm disorder-related depression exhibited elevated IL-17A and systemic inflammatory cytokines, accompanied by metabolic dysregulation. Sleep-deprived mice showed depressive-like behaviors, intestinal barrier disruption, Th17/IL-17A pathway activation, and abnormal RS-fMRI activity in mood-regulating brain regions. Schisandrin B treatment markedly reversed these changes-restoring gut microbial balance, enhancing barrier integrity, suppressing IL-17A-driven inflammation, and normalizing neural function. Mechanistically, Schisandrin B inhibited STAT3 phosphorylation and RORγt expression, while targeting MAPK1 and GSK3β as key regulatory nodes.

CONCLUSION: This study identifies gut-derived IL-17A-STAT3/RORγt signaling as a mechanistic bridge between sleep deprivation and neuroinflammation, providing direct evidence for the immunological basis of circadian rhythm-related depression. By integrating multi-omics and neuroimaging validation, we demonstrate for the first time that Schisandrin B exerts antidepressant-like effects via coordinated modulation of the gut-brain-immune network. These findings highlight Schisandrin B as a promising natural immunomodulatory candidate for the treatment of mood disorders associated with disrupted circadian rhythms.

PMID:41935463 | DOI:10.1016/j.phymed.2026.158127

Biol Sex Differ. 2026 Apr 4. doi: 10.1186/s13293-026-00891-z. Online ahead of print.

ABSTRACT

BACKGROUND: Understanding the impact of biological sex on the functional organization and dynamics of the brain is crucial for elucidating sex-specific differences in cognitive functions and neuropsychiatric disorders. Systems neuroscience often models the brain as a network of interconnected brain regions with functional connectivity (FC), i.e., the correlation between signal time courses, serving as a measure of connection strength. FC matrices, here derived from resting-state functional magnetic resonance imaging (rs-fMRI), define a network graph that can be characterized by its level of module segregation or, inversely, integration. Such parameters can be generated for the full length of the acquired data (static) or for short periods implying dynamically changing brain states. We recently made the interesting observation in a separate study (N = 63) that measures of brain integration and segregation based on dynamic functional connectivity (dFC) data differed between sexes, while graph-based measures based on static FC (sFC) did not, which we investigated in more detail in this study.

METHODS: We preregistered a replication of our analysis from the small sample in N = 501 subjects of the Human Connectome Project dataset. We performed cross-sectional comparisons between sexes of the static rs-fMRI graph parameters modularity and global efficiency, as well as the dFC parameters state prevalence, mean dwell time, mean inter-state transition time, and variability derived from a two-state model. Additionally, we explored whether sex differences in 66 cognitive and behavioral parameters are mediated by the FC integration measure with the strongest sex effect.

RESULTS: All static and dynamic measures of integration/segregation showed higher levels of functional integration in males, with effect sizes up to 0.60 for the dFC parameter prevalence. For three of the 66 explored cognitive and behavioral parameters, we observed that the prevalence of the integrated state mediated the sex difference: dexterity, agreeableness, and self-reported aggression.

CONCLUSION: We found consistent evidence across two datasets that rs-fMRI-based measures of brain integration are increased in males. An exploratory analysis, which requires replication, suggests that such differences mediate personality differences. This study highlights that biological sex differences in brain functional organization may contribute to sex-typical behaviors.

PMID:41935261 | DOI:10.1186/s13293-026-00891-z

Neuroimage. 2026 Mar 31:121904. doi: 10.1016/j.neuroimage.2026.121904. Online ahead of print.

ABSTRACT

PURPOSE: Alzheimer's disease is characterized by progressive accumulation of hyperphosphorylated tau protein, which propagates in a prion-like manner along connected neuronal pathways. However, it remains unclear whether functional connectivity between gray and white matter (FCGW) can predict tau spread. This study aimed to determine the association between FCGW and tau deposition and to evaluate its value in predicting longitudinal tau spread.

METHODS: We integrated resting-state fMRI with cross-sectional and longitudinal tau-PET data from two independent cohorts. We assessed baseline associations between FCGW and tau deposition and then constructed an individual-level spreading model to predict longitudinal tau accumulation.

RESULTS: In both cohorts, FCGW showed a positive correlation with tau deposition. Model-simulated white-matter tau deposition was associated with clinical scales and predicted cognitive decline. The spreading model, which incorporated baseline tau-PET and the top 10% of gray and white matter, yielded the highest predictive performance for future tau accumulation.

CONCLUSION: FCGW captures key network pathways underlying tau spread in AD and improves prediction of future tau accumulation. These findings highlight the importance of FCGW in understanding tau propagation and support development of network-targeted therapeutic strategies.

PMID:41933844 | DOI:10.1016/j.neuroimage.2026.121904

J Affect Disord. 2026 Mar 31:121719. doi: 10.1016/j.jad.2026.121719. Online ahead of print.

ABSTRACT

Depressive symptoms frequently co-occur in individuals with Mild Cognitive Impairment (MCI) and are thought to accelerate neurodegenerative progression. However, the underlying neural mechanisms of Depressed MCI (DMCI) remain largely unclear. This study employed a multimodal resting-state functional magnetic resonance imaging (rs-fMRI) approach combined with advanced machine learning techniques, to systematically examine spontaneous brain activity patterns and topological organization differences among DMCI, non-depressed MCI (nDMCI), and non-depressed cognitively normal controls (nDCN). The research analyzed amplitude-based rs-fMRI measures and graph-theoretical features. Voxel-wise analyses and connectivity comparisons were conducted between groups. Additionally, classification tasks were performed using classical machine learning models and a graph reinforcement learning (GRL) model. DMCI individuals exhibited increased activity in the right insula and decreased amplitude of low-frequency fluctuation (ALFF) in the left calcarine cortex, along with heightened fractional ALFF (fALFF) and percent amplitude of fluctuation (PerAF) in the precuneus and parahippocampal regions. Graph metrics revealed disrupted global and local efficiency in nDMCI compared to nDCN. Using differential matrices, machine learning achieved optimal accuracies of 0.82 ± 0.15 (DMCI vs. nDMCI) and 0.84 ± 0.15 (DMCI vs. nDCN). Conversely, the GRL model for nDMCI vs. nDCN peaked at 0.66 ± 0.02 using full matrices, dropping to 0.60 ± 0.04 with filtering, indicating deep graph models require complete topological data for subtle differences. Rs-fMRI and graph learning approaches offer promising avenues for subtype classification, highlighting the hyperactivity of the right insula and the integrity of the whole-brain functional connectivity matrix as crucial potential biomarkers of early pathological changes.

PMID:41933620 | DOI:10.1016/j.jad.2026.121719

Neuroimaging Clin N Am. 2026 May;36(2):367-377. doi: 10.1016/j.nic.2025.11.004. Epub 2026 Jan 23.

ABSTRACT

Functional MRI (fMRI) is a noninvasive imaging technique used to map areas of the brain important for language, motor, and visual function before surgery. Language lateralization with fMRI has proven useful as a surrogate for direct memory testing in patients with epilepsy to help predict postoperative morbidity, with Wada testing remaining useful if at risk for global amnesia. Task-based and resting-state fMRI can play a role in the evaluation of patients with generalized epilepsy before disconnective surgery or neuromodulation.

PMID:41932783 | DOI:10.1016/j.nic.2025.11.004

Biol Psychiatry Cogn Neurosci Neuroimaging. 2026 Apr 1:S2451-9022(26)00090-X. doi: 10.1016/j.bpsc.2026.03.017. Online ahead of print.

ABSTRACT

BACKGROUND: In Tourette Disorder (TD), tics are frequently associated with impulsivity, yet the mechanisms linking these dimensions and their evolution during adolescence remain unclear. We combined behavioral, clinical, and receptor-enriched by target (REACT) functional connectivity imaging to examine tic severity and impulsivity over time in TD adolescents.

METHODS: 64 TD adolescents and 56 healthy controls completed a saccadic motor waiting impulsivity (WI) task and resting-state fMRI at baseline. TD participants were reassessed 15 months later. REACT was used to examine dopamine transporter (DAT) - and serotonin 1B receptor (5-HT1B)-weighted functional connectivity within fronto-striatal motor and limbic networks. Longitudinal analyses focused on within-TD changes and their associations with clinical measures.

RESULTS: Behaviorally, no differences emerged between groups, but within TD, higher WI was associated with greater global tic severity (YGTSS/100). Longitudinally, both tics (YGTSS/50 and /100) and WI improved significantly. Compared to controls, TD showed a higher functional connectivity between caudate and anterior cingulate cortex (ACC) on the left. Longitudinally, tic reduction (YGTSS/50) was linked to increased connectivity between nucleus accumbens (NAcc) - ventral tegmental area on the left; reduced WI correlated with a higher left caudate - subgenual ACC and bilateral putamen-supplementary motor area (SMA) connectivity. In TD versus controls, REACT showed elevated both DAT-weighted connectivity between the NAcc-SMA and 5-HT1B-weighted connectivity between the raphe-SMA. Longitudinally in TD, while 5-HT1B-weighted connectivity declined DAT-weighted connectivity remained stable.

CONCLUSIONS: Changes in motor and limbic fronto-striatal functional connectivity were associated with longitudinal improvements in tics and WI in TD adolescents.

PMID:41932579 | DOI:10.1016/j.bpsc.2026.03.017

J Affect Disord. 2026 Apr 1:121718. doi: 10.1016/j.jad.2026.121718. Online ahead of print.

ABSTRACT

BACKGROUND: While brain network dysfunction characterizes attention-deficit/hyperactivity disorder (ADHD), surface-based connectivity patterns underlying its clinical heterogeneity remain underexplored. Herein, we investigated surface-based complex network architecture alterations in medication-naïve children with combined (ADHD-C) and inattentive (ADHD-I) subtypes.

METHODS: Children with ADHD-C (n = 43), ADHD-I (n = 35), and healthy controls (HCs; n = 31) were recruited for a series of clinical examinations and resting-state fMRI. We utilized surface-based graph theoretical analysis (GTA) and functional connectivity (FC) to assess network topology, correlating imaging indices with clinical variables.

RESULTS: Significant intra- and inter-network FC disruptions emerged in children with ADHD, particularly in default mode (DMN), ventral attentional (VAN), and somatosensory-motor (SMN) networks. In particular, the ADHD-C group (vs HC) exhibited more FC abnormalities involving SMN and DMN, whereas the ADHD-I group (vs HC) showed slightly more abnormal FC between VAN and dorsal attentional network (DAN). Crucially, ADHD-C patients demonstrated significantly weaker intra-SMN and SMN-DMN connectivity than the ADHD-I group. Generally, children with ADHD showed diminished global modularity, assortativity, and disrupted left lateral prefrontal cortex (PFCl1_L) nodal centrality. Additionally, higher FRCQ scores were significantly associated with increased assortativity in ADHD. The hypo-connectivity linking the DMN (Default-PFC7_L) and SMN (SomMot27_L) was correlated with both higher SNAP-IV Hyperactivity/Impulsivity and Total scores.

CONCLUSION: These findings elucidate neural substrates associated with sensorimotor and attentional deficits across ADHD subtypes. Surface-based network profiling underscores the disorder's biological heterogeneity and advances the mechanistic understanding of its complex neurodevelopment.

PMID:41932505 | DOI:10.1016/j.jad.2026.121718

Addict Behav. 2026 Apr 1;179:108700. doi: 10.1016/j.addbeh.2026.108700. Online ahead of print.

ABSTRACT

BACKGROUND: Alcohol Use Disorder (AUD) is increasingly understood as a disorder of connectomic dysregulation. However, node-centric models fail to capture the brain's overlapping functional architecture. We employed an edge-centric framework to quantify functional diversity from overlapping communities and investigated its neurobiological basis in AUD.

METHODS: We analyzed resting-state fMRI data from 93 individuals with AUD and 91 matched healthy controls. We quantified nodal functional diversity using normalized entropy derived from overlapping edge communities. In this context, high diversity (entropy approaching 1) reflects flexible, multi-network engagement, while low diversity (entropy approaching 0) reflects functional specialization. A Partial Least Squares Discriminant Analysis (PLS-DA) identified the whole-brain functional diversity pattern maximizing group separation. This pattern was then correlated with normative neurotransmitter receptor and gene expression data.

RESULTS: A PLS component significantly separated the groups (p < 0.001). This pattern was defined by decreased functional diversity in the nucleus accumbens and globus pallidus, and increased functional generalization in the insula and inferior frontal gyrus. This AUD-related pattern was negatively predicted by D1 and NMDA receptor distributions and positively by the 5-HTT transporter. Spatially, this pattern correlated with genes enriched for "synapse structure" and "cellular responses to stress".

CONCLUSION: Our edge-centric approach identified a bidirectional reorganization of functional diversity in AUD. This pattern, reflecting a specialized striatum and generalized insula, is spatially anchored to core dopaminergic/glutamatergic receptor maps and genetic pathways for synaptic plasticity and cellular stress, highlighting functional diversity as a novel, multilevel biomarker for AUD.

PMID:41932004 | DOI:10.1016/j.addbeh.2026.108700

Sci Rep. 2026 Apr 3. doi: 10.1038/s41598-026-43617-2. Online ahead of print.

ABSTRACT

Breathing rhythmically coordinates neural oscillations across the brain, yet how the breathing mode (nasal vs. oral) modulates large-scale functional networks over time remains unclear. Building on prior static connectivity findings, this study applied dynamic functional connectivity (dFC) analysis using a hidden Markov model (HMM) to resting-state fMRI data from 20 healthy adults during nasal and oral breathing, focusing on the 0.1-0.2 Hz frequency band. Three recurrent brain states were identified: (1) a weakly connected, segregated state; (2) a globally integrated state dominated by default mode, frontoparietal, salience, and limbic networks; and (3) a partially segregated intermediate state. Compared with oral breathing, nasal breathing stabilized the integrated state, increasing its lifetime (p-FDR = 0.03) and reducing switching rates (p-FDR = 0.002). Oral breathing showed greater fractional occupancy of the intermediate state (p-FDR = 0.03) and a higher probability of transitions from integration to fragmentation (p-FDR = 0.02). Graph-theoretic analysis also revealed that nasal breathing supported a configuration with higher efficiency and lower modularity. Taken together, this study provides the first respiration-entrained, HMM-based dFC analysis of resting-state fMRI, demonstrating that nasal breathing entrains a stable, globally coherent state, whereas oral breathing disrupts this stability and promotes fragmented network organization.

PMID:41932966 | DOI:10.1038/s41598-026-43617-2

Front Neurosci. 2026 Mar 18;20:1756997. doi: 10.3389/fnins.2026.1756997. eCollection 2026.

ABSTRACT

BACKGROUND: Migraine is a common neurological disorder involving network-level dysfunction. Increasing evidence suggests that migraine involves network-level dysfunction and is associated with altered resting-state functional connectivity. Traditional static functional connectivity analyses are limited in capturing the temporal dynamics of large-scale brain networks. The Hidden Markov Model (HMM) provides an advanced analytical framework to identify discrete, recurrent brain states and characterize their temporal properties without the constraints of arbitrary windowing assumptions.

OBJECTIVE: To characterize dynamic functional connectivity alterations in young patients with migraine without aura (MWoA) using HMM and examine associations between dynamic state metrics and clinical disability.

METHODS: Resting-state fMRI data were obtained from 200 participants (100 young MWoA patients and 100 matched healthy controls). Using the Dosenbach 160 ROI template (cerebellum excluded; N = 142), HMM identified recurring brain states. Group differences in fractional occupancy (FO), mean dwell time (MDT), and state transition probabilities were assessed. State-specific functional connectivity patterns were compared, and correlations with clinical indices (MIDAS, VAS, HIT-6) were evaluated.

RESULTS: Eleven robust dynamic brain states were identified. Compared with controls, migraine patients demonstrated increased FO and MDT in States 7 (dorsal attention network-dominant) and 9 (multisensory integration), alongside reduced values in sensorimotor states (States 3, 4, 8, 11). State 9 exhibited significant abnormalities in DMN-SC and DMN-VAN connectivity (FDR-corrected q < 0.05). Transition analyses revealed enhanced self-transitions and increased incoming transitions to States 7 and 9, whereas transitions among sensorimotor states were diminished. MDT in State 9 was positively correlated with MIDAS scores (r = 0.38, p < 0.05), indicating its association with functional disability.

CONCLUSIONS: Young MWoA patients exhibit a dual-mode dysfunction in brain dynamics: heightened external vigilance (State 7) and impaired segregation of internal-external processing (State 9), accompanied by instability in baseline sensorimotor configurations. Prolonged dwelling in State 9 and its correlation with disability highlight this multisensory integration state as a potential biomarker of migraine-related functional impairment. These findings provide new insights into neurobiological mechanisms and support dynamic network-based therapeutic strategies.

PMID:41929702 | PMC:PMC13038879 | DOI:10.3389/fnins.2026.1756997

bioRxiv [Preprint]. 2026 Mar 28:2026.03.25.714361. doi: 10.64898/2026.03.25.714361.

ABSTRACT

Compared with model-based and phenomenological descriptions of the spatiotemporal dynamics of resting-brain activity, statistical characterizations of resting-state fMRI (rs-fMRI) data remain relatively underexplored. Some sophisticated analysis techniques, such as Mapper-based topological data analysis (TDA) and innovation-driven coactivation pattern analysis (iCAP), can distinguish real data from phase-randomized (PR) surrogates, suggesting that rs-fMRI data are not as simple as stationary Gaussian processes. However, the exact statistical properties that distinguish real rs-fMRI data from PR surrogates have not yet been determined. In this study, we conducted system identification analysis and surrogate data analysis to specify key statistical properties that allow TDA and iCAP to discriminate real rs-fMRI data from PR surrogates. We first analyzed rs-fMRI data concatenated across scans using autoregressive (AR) modeling and found that the scan-concatenated rs-fMRI data were weakly non-Gaussian. However, non-Gaussianity alone was insufficient to reproduce realistic TDA and iCAP results because of non-stationarity across scans. AR modeling of single-scan data revealed that rs-fMRI data were statistically indistinguishable from a Gaussian distribution within a single scan, although TDA and iCAP results still differed between the real data and PR surrogates. A new surrogate dataset designed to preserve non-stationarity successfully reproduced realistic TDA and iCAP results, suggesting that TDA and iCAP likely capture the non-stationarity of rs-fMRI data to distinguish it from PR surrogates. Together, these results indicate approximate Gaussianity and non-stationarity in rs-fMRI data, providing a data-driven and statistical characterization of resting-state brain activity that can serve as a quantitative reference for whole brain simulations and generative models.

PMID:41929222 | PMC:PMC13041960 | DOI:10.64898/2026.03.25.714361

bioRxiv [Preprint]. 2026 Mar 26:2026.03.26.713939. doi: 10.64898/2026.03.26.713939.

ABSTRACT

Functional magnetic resonance imaging (fMRI) captures whole-brain activity fluctuations non-invasively in humans and animals. Beyond task/stimuli-locked responses, fMRI measures large-scale patterned activity during rest. An established method for identifying patterned activity in fMRI data, termed quasi-periodic pattern (QPP) analysis, identifies waves of activity which unfold over seconds and have consistent spatiotemporal characteristics. Notably, certain fMRI-QPPs are well-preserved across species and altered in various neuropsychiatric and neurodegenerative diseases. Yet, our collective understanding of their neural underpinnings is limited given the indirect nature of blood-oxygen-level dependent (BOLD) fMRI signals. Simultaneous measures of local field potentials have provided some affirmation that fMRI-QPPs have neural origins, but these point-measurements are limited to a handful of sites. Here, we use a unique multimodal implementation of simultaneous wide-field calcium (WF-Ca 2+ ) imaging and fMRI to investigate the neural origins of fMRI-QPPs.. We uncover a robust time-locked correlation between QPPs detected by cortex-wide fluorescent WF-Ca 2+ imaging of neural activity and QPPs of BOLD-fMRI. These data validate the hypothesis that BOLD QPPs derive from preceding slow waves of neural activity with regional and temporal precision.

PMID:41929218 | PMC:PMC13042064 | DOI:10.64898/2026.03.26.713939

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10980924. Epub 2025 May 12.

ABSTRACT

This study examines how multi-scale intrinsic connectivity networks (ICNs) relate to cognitive and behavioral functions in adolescents, focusing on attention/vigilance, working memory, and behavioral regulation. Leveraging the NeuroMark 2.2 multi-scale ICN template obtained from over 100,000 subjects, we obtained multi-scale ICNs from baseline resting-state fMRI data from the ABCD Study. For this study, we are interested in "the fronto- thalamo-cerebellar (FTC) circuitry" and choose the subdomains of Neuromark 2.2 that cover it: Cerebellar (CB), Subcortical - Extended Thalamic (SC-ET), Higher Cognition - Insular Temporal (HC-IT), and Higher Cognition - Frontal (HC-FR), previously identified as relevant to cognitive and behavioral functions. Employing a multivariate approach combining principal component analysis (PCA) and canonical correlation analysis (CCA), we examined associations between these multi-scale ICNs and cognitive-behavioral outcomes. Our findings revealed significant associations, particularly for one of the estimated canonical components, linking multi-scale ICNs to cognitive and behavioral measures across both discovery and replication sets. This connectivity pattern may serve as a potential marker for attention, working memory, and behavioral regulation, offering new insights into a wide spectrum of neurodevelopmental disorders including Attention-Deficit/Hyperactivity Disorder (ADHD).

PMID:41928912 | PMC:PMC13042259 | DOI:10.1109/isbi60581.2025.10980924

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10980889. Epub 2025 May 12.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable insights into the human brain's functional organization and is a powerful tool for investigating the relationship between brain function and cognitive processes as it allows for the functional organization of the brain to be captured without relying on a specific task or stimuli. In this study, we introduce a novel attention mechanism for graphs with subnetworks, named Self Clustering Graph Transformer (SCGT), designed to handle the issue of uniform node updates in graph transformers. By using static functional connectivity (FC) correlation features as input to the transformer model, SCGT effectively captures the sub-network structure of the brain by performing cluster-specific updates to the nodes unlike uniform node updates like vanilla graph transformers, further allowing us to learn and interpret the subclusters. We validate our approach on the Adolescent Brain Cognitive Development (ABCD) dataset, comprising 7,957 participants, for the prediction of total cognitive score and gender classification. Our results demonstrate that SCGT outperforms the vanilla graph transformer method, and other recent models, offering a promising tool for modeling brain functional connectivity and interpreting the underlying subnetwork structures.

PMID:41928911 | PMC:PMC13042258 | DOI:10.1109/isbi60581.2025.10980889

J Headache Pain. 2026 Apr 2. doi: 10.1186/s10194-026-02351-w. Online ahead of print.

NO ABSTRACT

PMID:41928092 | DOI:10.1186/s10194-026-02351-w