Most recent paper

Med Image Anal. 2026 Sep 29;115:104346. doi: 10.1016/j.media.2026.104346. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is associated with distributed disruptions in functional brain networks, yet current connectome-based biomarkers often rely on a single parcellation and show limited cross-site robustness and biological interpretability. We propose a Hierarchical Multi-Atlas Fusion Network (HMAF-Net) for robust and interpretable cross-site analysis of functional connectomes in MDD. HMAF-Net reconciles heterogeneous anatomical and functional atlases by learning a small set of shared fusion nodes that serve as latent anchors for cross-atlas semantic alignment. For each atlas, it extracts node-wise descriptors and uses Transformer-based sparse soft assignment with a structure-aware contrastive objective to align atlas nodes in a common semantic space while retaining atlas-specific information. The resulting fusion-node embeddings support subject-level classification and spatial mapping for biomarker localization and imaging-transcriptomic association. We evaluated HMAF-Net on the multi-site REST-meta-MDD cohort (N = 1,611). Under 10-fold cross-validation, HMAF-Net achieved 72.28% accuracy and 71.49% AUC, showing the highest mean accuracy among representative atlas-fusion and graph-based baselines under identical splits. Under leave-one-site-out evaluation, it reached 77.48% mean accuracy, with site-wise analyses indicating residual variability associated with cohort size and head motion. External validation on two independent MDD datasets further supported cross-dataset generalizability. Fusion-node involvement shifts revealed reproducible fronto-limbic and default-mode-salience alterations. These spatial profiles were associated with Allen Human Brain Atlas expression gradients and enriched for synaptic and neurotransmission pathways, providing a molecularly grounded axis for interpretation. By integrating cross-atlas alignment, cross-site evaluation, and transcriptomic interpretation, HMAF-Net yields compact connectomic embeddings for reproducible and mechanistically informed MDD biomarker development.

PMID:42822187 | DOI:10.1016/j.media.2026.104346

Neurophotonics. 2026 Jul;13(3):035013. doi: 10.1117/1.NPh.13.3.035013. Epub 2026 Sep 30.

ABSTRACT

SIGNIFICANCE: Resting-state functional connectivity (RSFC) is an important measure in advancing our understanding of brain function and development as well as various neurological and mental disorders. Studying RSFC with functional near-infrared spectroscopy (fNIRS) offers several advantages over functional magnetic resonance imaging (fMRI), especially for clinical and pediatric populations. However, the optimal strategy to estimate RSFC based on fNIRS, particularly in identifying reliable connectivity patterns across chromophores, remains unclear. Establishing robust analysis approaches is essential for reliable and clinically meaningful applications.

AIM: This study systematically evaluated commonly used analysis methods regarding their effectiveness to detect RSFC patterns within the motor network using both oxygenated (HbO) and deoxygenated (HbR) hemoglobin signals.

APPROACH: Near whole-head resting-state fNIRS data were analyzed from 38 participants. RSFC was estimated with five analytical approaches: three seed-based methods (SBA-GLM, SBA-GLM with respiratory regression, and SBA-Correlation) and two independent component analysis (ICA) approaches using two different contrast functions. Performance was assessed via receiver operating characteristic analyses based on both anatomical and functional definitions of motor-related connectivity. Areas under the curves (AUCs) were statistically compared with DeLong's test, and the spatial similarity between HbO and HbR RSFC was quantified by correlating RSFC patterns from the two chromophores.

RESULTS: Across reference definitions and chromophores, ICA consistently achieved higher performance ( AUC = 0.82 to 0.96) in detecting motor-related RSFC than SBA ( AUC = 0.63 to 0.86). Significant differences emerged when functionally defined connectivity references were used, with ICA outperforming SBA across chromophores. Under certain conditions, correlational SBA ( AUC = 0.66 to 0.86) significantly outperformed GLM-based methods ( AUC = 0.63 to 0.85). Finally, ICA results demonstrated greater spatial similarity between obtained HbO and HbR RSFC patterns ( r = 0.90 to 0.92) than SBA ( r = 0.84 to 0.86), indicating cross-chromophore consistency of the resulting RSFC maps.

CONCLUSIONS: ICA provides a robust and consistent framework for estimating fNIRS-based RSFC across both HbO and HbR, outperforming SBA in accuracy and cross-chromophore consistency. While correlational SBA offers a computationally efficient alternative and outperforms GLM-based methods, ICA should be preferred when reliable and chromophore-consistent RSFC estimates are required. Importantly, these findings demonstrate that HbR contains RSFC information comparable to HbO and highlights the critical role of analytical strategy and reference definition in RSFC evaluation. Collectively, these results contribute to the methodological standardization of fNIRS-based RSFC and support its use in future neuroscientific and clinical applications.

PMID:42819603 | PMC:PMC13626422 | DOI:10.1117/1.NPh.13.3.035013

Front Neurol. 2026 Sep 16;17:1915567. doi: 10.3389/fneur.2026.1915567. eCollection 2026.

ABSTRACT

OBJECTIVE: This study investigated the macroscopic neural mechanisms of cognitive impairment after Deep Nuclei Intracerebral Hemorrhage (DNICH) from the dimensions of brain activity and brain networks, and further identified characteristic fingerprints of cognitive representation.

METHOD: Patients with cognitive impairment after DNICH and healthy controls (HC) were prospectively and consecutively enrolled. Resting-state fMRI (rs-fMRI) data and the cognitive scale Mini-Mental State Examination (MMSE) were collected. Spontaneous brain activity measured by fractional Amplitude of Low-Frequency Fluctuations (fALFF) was used to identify hub brain regions of cognitive representation, which were then used as Regions of Interest (ROI) for Functional Connectivity (FC) analysis, in order to investigate the potential mechanisms after DNICH cognitive impairment and identify characteristic fingerprints for distinguishing patients with dementia (dementia was defined as MMSE ≤ 17).

RESULT: This study enrolled 139 patients with DNICH and 100 HC. There were no significant differences in baseline characteristics including age, gender, years of education, height, and weight between the two groups. Hematomas were predominantly located in the bilateral basal ganglia, more commonly on the left side, with varying degrees of thalamic and white matter involvement. Whole-brain zfALFF analysis showed that MMSE scores were significantly correlated with zfALFF values in brain regions including right dorsolateral prefrontal cortex (R-DLPFC) and posterior cingulate cortex (PCC), with R-DLPFC positively correlated and PCC negatively correlated with cognition (AlphaSim correction, p < 0.01; cluster size >19). In addition, FC within the executive control network (ECN) and default mode network (DMN) was abnormally altered in patients with cognitive impairment and was closely associated with cognitive function (r = -0.29-0.51). FC fingerprints demonstrated stable predictive ability for dementia status (AUC = 0.70-0.73).

CONCLUSION: Abnormal coordination of intra-network and inter-network FC of ECN and DMN may represent a shared neural mechanism of cognitive impairment after DNICH. Modulating FC between these hub brain regions (R-DLPFC and PCC) may provide new therapeutic strategies for cognitive recovery after DNICH.

PMID:42818636 | PMC:PMC13623672 | DOI:10.3389/fneur.2026.1915567

bioRxiv [Preprint]. 2026 Sep 11:2026.09.04.749545. doi: 10.64898/2026.09.04.749545.

ABSTRACT

Psychotic disorders are severe mental conditions whose biological underpinnings remain elusive. This is in part due to marked heterogeneity, which may have precluded advances in precise understanding and treatment of the disorders. Previous work has tried to elucidate this heterogeneity; nevertheless, sex, as an important biological variable, has not been well accounted for despite established sex differences. Using resting-state fMRI data from the BSNIP consortium (N=1753; 64.3% probands with psychosis include 571 females and 556 males; 35.7% of controls includes 369 females and 257 males), we extracted multiscale functional network connectivity and employed unsupervised learning methods to identify potential subgroups with distinct neurobiological profiles, characterized by an overrepresentation of males or females (termed here as sex-dominant Psychosis Imaging Neurosubtypes, PINs). Four sex-dominant neurosubtypes emerged, two female-dominant (fPIN-1, fPIN-2) and two male-dominant (mPIN-1, mPIN-2). Each PIN exhibited distinct, replicable dysconnectivity patterns that uniquely contributed to cognitive deficits. Brain-Predicted Cognitive performance derived from these PIN-specific dysconnectivity, prominent among the triple network (default mode and salience), subcortical-basal ganglia, higher cognition-insular temporal, and visual-occipitotemporal subdomains, correlated with measured cognitive performance and showed PIN-specific reductions relative to controls, confirming neurosubtype-congruent associations. Moreover, fPIN-2's distinct dysconnectivity also predicted positive and negative symptom scores. Our findings lend support for sex-dominant neurobiological heterogeneity, with sex-dominant PINs presenting unique dysconnectivity patterns that contribute to cognitive and symptom outcomes, even though neurosubtypes appear clinically similar. These results establish sex as a critical biological variable in deconstructing psychosis heterogeneity, which holds promise in revealing more precise biomarkers for disease characterization and guiding personalized treatment strategies.

PMID:42818538 | PMC:PMC13622486 | DOI:10.64898/2026.09.04.749545

ArXiv [Preprint]. 2026 Aug 7:arXiv:2608.07287v1.

ABSTRACT

Headache is the most common neurological disorder in children and substantially affects quality of life. We investigated whether resting-state functional MRI (rs-fMRI) can support pediatric headache classification using machine learning. We encoded rs-fMRI data using NeuroSTORM, a recent foundation model, and fine-tuned it to distinguish healthy controls from children with headache and subsequently classify headache subtypes. We compared NeuroSTORM with a standard neuroscience approach using functional-connectivity (FC) matrices derived from brain activity as predictors. Using 189 rs-fMRI scans from 110 individuals collected across two visits (prevalence of any headache: 74%), NeuroSTORM achieved an area under the receiver operating characteristic curve (AUROC) of 0.82 (95% CI, 0.82-0.82) and an area under the precision-recall curve (AUPRC) of 0.93 (95% CI, 0.93-0.94) for discriminating headache from non-headache. In contrast, models trained on FC matrices showed lower performance (AUROC, 0.67 [95% CI, 0.67-0.67]; AUPRC, 0.85 [95% CI, 0.85-0.85]). In multiclass classification of healthy controls, chronic migraine, and non-chronic headaches (e.g., post-viral headache, new daily persistent headache, post-traumatic headache), NeuroSTORM achieved a macro-AUROC of 0.69 (95% CI, 0.68-0.69). Results suggest that the approach can distinguish chronic migraine but has difficulty differentiating other headache subtypes from chronic migraine. Overall, under limited-data conditions, NeuroSTORM appears to capture latent rs-fMRI representations that transfer to headache-related tasks without relying on FC features. These findings provide proof of concept for fMRI-based prediction of pediatric headache and highlight potential future utility for subtype identification and individualized treatment strategies.

PMID:42818527 | PMC:PMC13622986

medRxiv [Preprint]. 2026 Sep 21:2026.02.04.26345538. doi: 10.64898/2026.02.04.26345538.

ABSTRACT

Bullying is an adverse childhood experience affecting up to one-third of the global population and linked to psychosis-like experiences (PLEs), which increase the risk of psychotic disorders. This study aimed to investigate the association between the severity and persistence of bullying and PLEs, including non-paranoid, hallucination-like experiences and their distress, and whether multiscale brain functional network connectivity (msFNC) mediates the pathway from bullying to PLEs.

METHODS: We used data from the ABCD Study, a large, multisite, population-based prospective cohort study following U.S. adolescents. We included adolescents with complete bullying and PLEs assessments at the 2- and 3-year follow-ups (T1: n=10,939, 47% females; T2: n=10,102). We examined the associations between bullying severity and temporal exposure and PLEs using linear mixed-effects models. In a 2-year resting-state fMRI subsample (n=5,280), we used a Neuromark framework to analyze whether msFNC statistically mediated the pathway from bullying to PLEs.

RESULTS: Higher PLEs were associated with the presence and severity of bullying (non-bullied vs. mild bullying: d=-0.24, CI:-0.30 to -0.19, p<0.0001; moderate vs. severe bullying: d=-0.62, CI:-0.69 to -0.56, p<0.0001). When bullying ceased, PLEs returned to non-bullied levels, whereas persistence over two years led to greater elevations (d=-0.36, CI:-0.43 to -0.29, p<0.0001), with similar patterns for non-paranoid and hallucination-like experiences and their distress. msFNC in paralimbic, default-mode, central executive, somatomotor, temporoparietal, insulotemporal, and frontal networks statistically mediated the association.

CONCLUSIONS: Bullying is time- and dose-dependently associated with PLEs. msFNC may provide convergent evidence for distributed neurobiological correlates of the association between bullying exposure and PLEs.

PMID:42818432 | PMC:PMC13622310 | DOI:10.64898/2026.02.04.26345538

medRxiv [Preprint]. 2026 Sep 23:2026.09.18.26363166. doi: 10.64898/2026.09.18.26363166.

ABSTRACT

Trauma-related intrusive memories (TRIMs) are a core symptom of posttraumatic stress disorder and cause significant distress and impairment for trauma-exposed individuals due to their vivid sensory features that intrude on ongoing thought. These phenomenological properties point to a neuropathology that spans autobiographical memory, spontaneous thought, and sensory perception. The hierarchical organization of the cortex maximally segregates the default mode network (DMN), which supports autobiographical memory and ongoing thought, from the sensory cortices (SC). Accruing evidence implicates altered interactions between the DMN and SC in PTSD and TRIMs, suggesting a breakdown in this segregation. We sought to test whether diminished DMN-SC segregation, indexed by a shorter functional distance between the DMN and SC within cortical connectivity gradients, was associated with the vividness and intrusiveness of TRIMs. One-hundred eight trauma-exposed adults completed two weeks of ecological momentary assessments (EMAs) of TRIM properties followed by resting-state fMRI. Gradient analysis of cortical functional connectivity was performed to compute the Euclidean distance between resting-state networks. Pearson correlations revealed an association between a shorter DMN-SC distance and greater TRIM vividness and intrusiveness, with multivariate models indicating a specificity for intrusiveness. Importantly, the DMN-SC distance was not associated with clinician-rated symptom severity and explained unique variance above and beyond conventional functional connectivity measures. These findings suggest that a loss of functional segregation between the DMN and SC - a core organizational feature of the cortical hierarchy - is associated with the intrusive sensory vividness of TRIMs, offering insights into candidate mechanisms of and neurotherapeutic targets for intrusive trauma memories. .

PMID:42818426 | PMC:PMC13622322 | DOI:10.64898/2026.09.18.26363166

bioRxiv [Preprint]. 2026 Sep 24:2026.09.17.752468. doi: 10.64898/2026.09.17.752468.

ABSTRACT

INTRODUCTION: White matter (WM) functional activity, quantified as slow-4 fractional amplitude of low-frequency fluctuations (fALFF; 0.027-0.073 Hz), may reflect Alzheimer's disease (AD) pathology, but its utility relative to established structural imaging markers remains poorly characterized.

METHODS: We analyzed 369 ADNI-3 participants with baseline resting-state fMRI. Global WM slow-4 fALFF (JHU-20 atlas) was compared against hippocampal volume, entorhinal cortical thickness, FA, and MD across 11 outcomes: plasma biomarkers (pTau217, Aβ42/40, NfL, GFAP; n = 108), PET (amyloid Centiloid, tau SUVR, FDG; n = 66-207), and cognition (MEM, EF, mPACC, MMSE; n ≈ 222-225) using head-to-head benchmarking and variance decomposition.

RESULTS: WM fALFF was significantly associated with GFAP (partial RZ = 5.35%), amyloid PET (partial RZ = 2.17%), tau PET (partial RZ = 3.52%), executive function (partial RZ = 2.61%), and mPACC (partial RZ = 3.53%). In trimmed models, fALFF explained unique variance in NfL and GFAP beyond FA and MD (unique ΔRZ: 3.65% and 4.45%) - nearly 8-fold greater than DTI's unique contribution for NfL. Gray matter markers showed larger associations with cognition and PET pathology. The incremental value of fALFF was modality-specific: it persisted beyond DTI markers (NfL, GFAP, amyloid PET, executive function, mPACC) and beyond gray matter markers (NfL, GFAP), but in every domain - plasma included - fALFF was no longer significant when all five imaging markers were entered simultaneously.

DISCUSSION: WM slow-4 fALFF captures neurodegeneration-related variance - particularly astroglial reactivity and axonal injury - incremental to DTI microstructure and partially independent of gray matter atrophy. These findings position WM functional activity as a complementary, non-invasive imaging marker in the multimodal AD biomarker landscape.

RESEARCH IN CONTEXT: Evidence before this study: White matter (WM) resting-state BOLD signals carry physiologically valid functional information. WM slow-4 fALFF is reduced in preclinical Alzheimer's disease (AD) and correlates with amyloid burden and cognition. Direct benchmarking against established structural imaging markers within matched samples has remained scarce.Added value of this study: In 369 ADNI-3 participants, we conducted a direct same-sample head-to-head comparison of global WM slow-4 fALFF versus hippocampal volume, entorhinal thickness, global FA, and MD across 11 pre-specified AD biomarker and cognitive outcomes. WM fALFF showed the largest covariate-adjusted association with GFAP (partial RZ = 5.35%) and uniquely explained ∼8× more NfL variance than DTI in trimmed models, contributing non-redundant functional information.Implications of all the available evidence: Global WM slow-4 fALFF, derived from standard resting-state fMRI, is a non-invasive complement to structural imaging that showed associations with markers of astroglial reactivity and neuroaxonal injury, and could augment multimodal AD biomarker panels without additional scanning cost.

PMID:42818102 | PMC:PMC13622072 | DOI:10.64898/2026.09.17.752468

CNS Neurosci Ther. 2026 Oct;32(10):e71159. doi: 10.1002/cns.71159.

ABSTRACT

BACKGROUND: Adolescents with non-suicidal self-injury (NSSI) frequently exhibit severe sleep disturbances and heterogeneous cognitive impairments, yet the underlying neurophysiological basis remains unclear. The glymphatic system, a sleep-dependent macroscopic clearance network, is critical for metabolic waste removal and neurocognitive homeostasis. Therefore, this study aimed to investigate the physiological role of glymphatic function in linking sleep burden to distinct cognitive performances in adolescents with NSSI.

METHODS: We assessed 44 NSSI adolescents and 33 healthy controls (HCs). Using resting-state functional magnetic resonance imaging (rs-fMRI), glymphatic function was quantified by calculating the coupling strength between global blood-oxygen-level-dependent (gBOLD) signals and cerebrospinal fluid (CSF) flow. To capture the heterogeneity in their cognitive profiles, we evaluated "hot" affective decision-making via the Iowa Gambling Task (IGT) and "cold" executive function via the Wisconsin Card Sorting Test (WCST). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI).

RESULTS: In the NSSI group, poorer sleep quality was positively correlated with stronger gBOLD-CSF coupling (r = 0.317, p = 0.036), and stronger gBOLD-CSF coupling predicted better IGT performance, especially during the high-load strategy phase (Block 3) (r = 0.447, p = 0.002). Furthermore, glymphatic function fully mediated the relationship between sleep quality and affective decision-making performance (indirect effect = 0.682, 95% CI [0.062, 1.515]). Interaction analyses confirmed this physiological-behavioral coupling was disease-specific (present only in NSSI; interaction p = 0.011) and domain-specific (supporting "hot" affective cognition rather than "cold" executive function).

CONCLUSIONS: In NSSI adolescents, sleep-associated glymphatic upregulation reflects a stress-induced compensatory response. This mechanism domain-specifically preserves high-load affective decision-making against severe psychopathological stress. These findings highlight brain metabolic clearance as a novel physiological target for bolstering cognitive resilience in high-risk youth.

PMID:42817812 | DOI:10.1002/cns.71159

CNS Neurosci Ther. 2026 Oct;32(10):e71178. doi: 10.1002/cns.71178.

ABSTRACT

AIMS: Compound Ciwujia Granules (CWJ) have shown antidepressant-like effects, but the neural and molecular mechanisms underlying these effects remain unclear. This study aimed to investigate the mechanisms by which CWJ ameliorates anxiety- and depression-like behaviors.

METHODS: A 6-week chronic unpredictable mild stress (CUMS) model was established in rats, and behavioral tests were performed to assess the antidepressant-like effects of CWJ. Resting-state functional magnetic resonance imaging (rs-fMRI), in vivo fiber photometry, and chemogenetic manipulation were used to identify the brain region and neuronal population involved in the effects of CWJ. Transcriptomic analysis combined with network pharmacology was used to predict candidate molecular pathways, which were further validated by quantitative real-time polymerase chain reaction, immunofluorescence, western blotting, and pharmacological interventions.

RESULTS: CWJ increased the open-arm ratio in the EPM, reduced FST immobility, and lowered serum corticosterone (CORT) levels in CUMS rats. Convergent rs-fMRI, c-Fos immunostaining, and fiber photometry identified that the dorsomedial prefrontal cortex (dmPFC) was a key brain region involved in the antidepressant-like effects of CWJ. CWJ increased glutamatergic neuronal activity and reduced neuroinflammatory responses associated with TNF-related inflammatory signaling in the dmPFC. Moreover, pharmacological activation of TNF-related inflammatory signaling partially attenuated the behavioral benefits of CWJ, supporting the involvement of TNF-related inflammatory signaling in mediating its antidepressant effects.

CONCLUSIONS: These findings suggest that CWJ attenuated CUMS-induced anxiety- and depression-like behaviors by restoring dmPFC glutamatergic neuronal activity and suppressing TNF-related neuroinflammation in the dmPFC.

PMID:42817052 | DOI:10.1002/cns.71178

Hum Brain Mapp. 2026 Oct 1;47(14):e70651. doi: 10.1002/hbm.70651.

ABSTRACT

Despite accounting for only 2% of body weight, the human brain requires significant amounts of glucose, even at rest, underscoring the importance of functional-metabolic relationships. Previous studies revealed moderate associations between resting-state fMRI functional connectivity (FC) and local metabolism via [18F]FDG-PET, yet much remains to be understood, particularly regarding their coupling between functional and metabolic networks. To this end, we employed multivariate Partial Least Squares Correlation (PLSC) to investigate the functional-metabolic relationship at both nodal and network levels. From dynamic [18F]FDG-PET data we estimated parameters describing glucose metabolism-delivery rate (K1), phosphorylation rate (k3), and fractional uptake (Ki)-and generated within-individual metabolic connectivity (MC) networks. FC was derived from fMRI data filtered into two frequency bands and summarized as region-wise strength to capture nodal characteristics. Our findings revealed that glucose delivery is linked with FC strength, particularly when fMRI signal frequencies include greater hemodynamic contributions. Even stronger functional-metabolic coupling occurs at the network level in the low-frequency fMRI band, with higher MC between sensory/attention and transmodal networks supporting stronger FC within sensory/attention areas. By leveraging PLSC, this work deepens our understanding of the functional-metabolic synergy in the healthy brain, providing new insights into its organization.

PMID:42816435 | DOI:10.1002/hbm.70651

Korean J Pain. 2026 Oct 1;39(4):593-606. doi: 10.3344/kjp.26221.

ABSTRACT

BACKGROUND: Chronic pain is understood as a disturbance of large-scale brain-network organization involving systems that coordinate internally and externally oriented processing. In knee osteoarthritis (KOA), altered connectivity involving attention networks and the default mode network (DMN) has been reported, but directed organization and reconfiguration during non-noxious somatosensory input remain unclear.

METHODS: In this cross-sectional neuroimaging study, 148 patients with KOA and 36 healthy controls (HCs) underwent resting-state fMRI followed by non-noxious knee thermal-stimulation fMRI. Directed effective connectivity (EC) was estimated separately for the resting-state and thermal-stimulation runs using regression dynamic causal modeling and summarized within the Yeo 7-network framework. Between-group analyses assessed directed network organization at rest and stimulation-related reconfiguration, defined as thermal-stimulation minus resting-state connectivity (ΔEC = ECthermal - ECrest). Network in-degree, out-degree, and exploratory clinical associations were examined.

RESULTS: Compared with HC, KOA patients exhibited reduced directed inputs to the dorsal attention network (DAN) from multiple networks and increased directed drive toward the DMN at rest. During thermal stimulation, KOA patients showed greater increases in directed connectivity toward the DAN but attenuated DAN→DMN connectivity changes. Core edge-level effects were robust in sensitivity analyses. Degree analyses supported altered input-output profiles of the DAN and DMN. Exploratory associations with pain severity and functional impairment did not survive false-discovery-rate correction.

CONCLUSIONS: KOA was characterized by altered directed interactions between attention networks and the DMN at rest, together with atypical reconfiguration from rest to non-noxious somatosensory stimulation. These findings are consistent with altered coordination between externally oriented attention and internally oriented processing.

PMID:42816135 | DOI:10.3344/kjp.26221

Neurosci Biobehav Rev. 2026 Sep 30:107003. doi: 10.1016/j.neubiorev.2026.107003. Online ahead of print.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to investigate intrinsic brain organisation and spontaneous neural dynamics. Entropy-based methods have emerged as important tools for quantifying signal complexity, uncertainty, and information processing in rs-fMRI data. This systematic review examines the application of five entropy measures, including approximate entropy, sample entropy, multiscale entropy, Shannon entropy, and transfer entropy, in resting-state fMRI research. A systematic search of the Web of Science database identified studies published between 2000 and 2025, of which 111 met inclusion criteria for methodological analysis. Studies were categorised by entropy measure, analytical scope, and subject population. Applications clustered by analytical scope. Shannon entropy dominated connectivity-level analyses, while sample and multiscale entropy were favoured for regional complexity. Transfer entropy was applied primarily in studies of directed connectivity. Across studies, entropy-based metrics demonstrated sensitivity to age and pharmacological modulation, with entropy alterations frequently mapping onto established functional brain networks. However, substantial heterogeneity was observed in preprocessing strategies, parameter selection, and statistical inference, limiting cross-study comparability. The evidence supports entropy as a flexible framework for characterising resting-state brain dynamics while highlighting significant methodological challenges related to standardisation and reproducibility. Future research should prioritise harmonisation of entropy parameterisation, validation against null models, and broader application in clinical populations.

PMID:42815816 | DOI:10.1016/j.neubiorev.2026.107003

Phys Med Biol. 2026 Sep 30. doi: 10.1088/1361-6560/aeae9e. Online ahead of print.

ABSTRACT

The human brain is a complex network, whose functional properties are constrained by its anatomy and energetic constraints. Metabolic connectivity refers to coherent metabolic interactions between spatially distinct regions, and is most commonly measured using [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). While metabolic connectivity as a field predates fMRI-derived functional connectivity measures, it was not until recently that it was possible to estimate a metabolic network at the individual person level. Here, we review the theoretical and historical contexts of metabolic connectivity, its physiological bases, major methodological approaches, and key findings in health and disease. The review highlights that metabolic connectivity yields unique insight into the functional interactions of the brain relative to fMRI, is predictive of cognition and individual phenotype, and displays unique patterns of time-variant state switching and gradient architecture. We conclude the review with a forward-looking perspective of the future of metabolic connectivity, and recommendations for extending the conceptual framework of the measure.

PMID:42815527 | DOI:10.1088/1361-6560/aeae9e

Psychiatry Res Neuroimaging. 2026 Sep 23;364:112343. doi: 10.1016/j.pscychresns.2026.112343. Online ahead of print.

ABSTRACT

BACKGROUND: Accelerated resting-state fMRI (rs-fMRI) may reduce scan time and participant burden, but whether shortened acquisitions preserve functional connectivity profiles and clinical associations remains unclear. This study compared hyperband and conventional acquisitions in characterizing default mode network (DMN) connectivity and associations with substance use.

METHODS: A community sample of 108 young adults with recent heavy substance use (59.93% female; mean age = 20.58 ± 1.13) completed a 9-minute conventional scan and a 4.5-minute hyperband scan. DMN connectivity was assessed using ROI-to-ROI analyses and graph-theory metrics. Substance use measures included cannabis use severity (CUDIT), urine tetrahydrocannabinol positivity (THC+), and hazardous drinking (AUDIT).

RESULTS: Quality-control metrics did not differ between acquisitions. Conventional rs-fMRI showed higher connectivity between bilateral lateral parietal regions and between the posterior cingulate cortex and left lateral parietal cortex, although overall patterns were comparable. Agreement between acquisitions was low (mean ICC = 0.358 ± 0.123). Graph-theory metrics showed few differences, but most ICCs were non-significant. Nominal associations with substance use were inconsistent across acquisitions.

CONCLUSIONS: Hyperband and conventional rs-fMRI produced similar group-level DMN patterns, but hyperband acquisitions did not reliably preserve individual-level estimates or substance use associations, suggesting hyperband rs-fMRI data should not be assumed as equivalent or readily harmonizable.

PMID:42815437 | DOI:10.1016/j.pscychresns.2026.112343

Dev Cogn Neurosci. 2026 Sep 29;82:101829. doi: 10.1016/j.dcn.2026.101829. Online ahead of print.

ABSTRACT

Preterm birth affects up to 10.6% of neonates and increases neurodevelopmental risks, yet functional brain alterations remain poorly understood. Using resting-state fMRI data from 437 neonates and a one-dimensional convolutional neural network, this study investigated frequency-specific functional differences between gyri and sulci across three groups: preterm-born neonates scanned at preterm age, preterm-born neonates scanned at term-equivalent age, and term-born neonates scanned at term age. Associations with gene expression were examined. Group differences in the model-derived frequency characteristics were observed predominantly in the frontal lobe across all frequency bands, and in the temporal and parietal lobes at high frequencies. These differences were more pronounced for sulcal than gyral characteristics, and sulcal dominance was more spatially extensive in neonates scanned at term-equivalent age than in preterm neonates scanned before term-equivalent age. Furthermore, model-derived gyro-sulcal frequency characteristics were significantly associated with regional cortical gene-expression patterns. This study bridges frequency-specific brain imaging with cortical gene expression, highlighting the prominent contribution of sulcal-related frequency characteristics to the observed group differences. These findings reveal regional associations between gyro-sulcal frequency characteristics and gene expression and suggest their potential relevance for early risk assessment, which requires further validation.

PMID:42815198 | DOI:10.1016/j.dcn.2026.101829

Hum Brain Mapp. 2026 Oct 1;47(14):e70646. doi: 10.1002/hbm.70646.

ABSTRACT

Resting-state functional connectivity (FC) derived from functional MRI (fMRI) is widely used to characterize brain functional organization, yet its electrophysiological basis remains incompletely understood. Here, we investigated how electrophysiological connectivity relates to both static and dynamic fMRI connectivity using simultaneous intracranial EEG (icEEG)-fMRI recordings from 48 adults with focal epilepsy. Linear mixed-effects models were applied to sampled electrode-pair connections to examine frequency-resolved associations between band-limited icEEG connectivity and corresponding fMRI FC within non-epileptogenic brain regions. Time-averaged icEEG connectivity was robustly associated with spatial variation in static fMRI connectivity across sampled connections and frequency bands, with strongest effects in the beta and low-gamma ranges. At the window level, dynamic fMRI connectivity showed significant positive associations with window-specific icEEG FC deviations in the alpha, beta, and low-gamma bands. Exploratory spatial stratification suggested that cross-modal coupling varied across clinically sampled frontal, temporal, and insular region pairs, and the main findings remained robust after motion-control analyses. Together, these findings indicate that resting-state fMRI connectivity is associated with both average electrophysiological coupling across sampled connections and window-level fluctuations in electrophysiological connectivity, with the strongest primary band-wise evidence observed in the alpha, beta, and low-gamma ranges.

PMID:42813515 | DOI:10.1002/hbm.70646

J Integr Neurosci. 2026 Sep 21;25(9):52929. doi: 10.31083/JIN52929.

ABSTRACT

BACKGROUND: Type 2 diabetes mellitus-associated cognitive dysfunction (T2DACD) is strongly linked to disruption of cognition-supporting brain networks. Electroacupuncture (EA) has increasingly been examined as a nonpharmacological intervention targeting cognitive impairment; however, the longitudinal neurobiological patterns accompanying EA in T2DACD have not been fully characterized. In this exploratory pilot study, we evaluated alterations in resting-state functional connectivity (RSFC) across hippocampal subregions and default mode network (DMN) nodes in individuals with T2DACD.

METHODS: For this exploratory, open-label, non-randomized, uncontrolled pilot investigation, we enrolled 20 individuals with T2DACD and 26 healthy controls (HCs). Longitudinal analyses included 15 participants who had usable post-treatment data. T2DACD was defined according to clinical type 2 diabetes mellitus criteria, and a Montreal Cognitive Assessment (MoCA) score <26. Patients underwent an 8-week Adjust Zang-fu and Arouse Spirit electroacupuncture (AZASE) intervention. Cognitive, emotional, metabolic, and seed-based resting-state functional magnetic resonance imaging (rs-fMRI) outcomes were evaluated. Clinical longitudinal analyses were carried out according to protocol, whereas imaging analyses were limited to participants with usable scans after treatment.

RESULTS: At baseline, individuals with T2DACD showed lower MoCA scores than HCs and had poorer executive and working-memory outcomes. Among the 15 participants with complete longitudinal neuroimaging data, MoCA scores increased from 23.27 ± 1.71 to 26.07 ± 1.53 over the intervention period. Within-group alterations were observed for Flanker-conflict reaction time and accuracy, Stroop-conflict reaction time, 1-back reaction time, Self-Rating Anxiety Scale score, and Hamilton Depression Rating Scale score. Omnibus group analyses detected significant effects for the left anterior hippocampus-left angular gyrus connection and the left posterior hippocampus-right middle occipital gyrus connection. Post-treatment scans demonstrated reduced RSFC in several hippocampal-DMN and intra-DMN connections. The association between change in left anterior hippocampus-left angular gyrus RSFC and change in Stroop-conflict performance was no longer statistically significant after Bonferroni correction. For neuroimaging outcomes, cluster-level family-wise error (FWE) correction (p < 0.05) was applied to control for multiple comparisons; clinical outcomes are reported descriptively as hypothesis-generating findings.

CONCLUSIONS: In this exploratory pilot study, we identified preliminary alterations in cognitive outcomes and hippocampal-DMN connections over an 8-week EA intervention in individuals with T2DACD. In view of the small sample size and the open-label, non-randomized, uncontrolled design, the findings should be interpreted as hypothesis-generating observations rather than definitive evidence of a specific causal treatment effect. Larger controlled studies will be needed to determine whether the observed neural and behavioral alterations are reproducible and clinically meaningful in practice.

CLINICAL TRIAL REGISTRATION: No: ChiCTR2000040268, https://www.chictr.org.cn/hvshowproject.html?id=76145&v=1.3.

PMID:42811891 | DOI:10.31083/JIN52929

Neuroimage Clin. 2026 Sep 27;52:104070. doi: 10.1016/j.nicl.2026.104070. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is characterized by progressive cognitive decline associated with synaptic and large-scale network dysfunction. Resting-state alterations may provide functional correlates of AD pathology, but associations among intrinsic brain activity, biomarkers, and domain-specific cognition have not been systematically integrated across spectral and connectivity frameworks. We systematically reviewed PubMed, Scopus, and Web of Science through May 2025 (PROSPERO CRD42024575913). Thirty studies (N = 5898) examining resting-state EEG, MEG, or fMRI, AD biomarkers, and cognition were synthesized following SWiM guidance. Across studies, resting-state alterations showed converging associations with amyloid and tau measures from CSF, PET, and plasma. Five of six electrophysiological studies reported increased delta/theta activity and three of six reduced alpha/beta activity, supporting neural slowing. Greater low-frequency activity was generally associated with poorer global cognition and episodic memory, whereas reduced higher-frequency activity was linked to poorer attention and processing speed. Among 21 connectivity studies, 10 reported hyperconnectivity and 13 hypoconnectivity, with non-mutually exclusive categories. Hyperconnectivity, particularly within medial and anterior temporal circuits, was more frequent in earlier-stage groups and showed heterogeneous cognitive associations, including poorer and better-preserved performance. Broader hypoconnectivity across posterior default mode, medial temporal-posterior medial, and frontoparietal networks was more prominent in later-stage groups and more consistently associated with poorer global cognition, episodic memory, and executive function. These findings support spectral slowing and stage-related network reorganization as functional correlates of cognitive impairment in biomarker-characterized AD. However, 25 of 30 studies were cross-sectional; therefore, these connectivity patterns should not be interpreted as a demonstrated within-person trajectory.

PMID:42810101 | DOI:10.1016/j.nicl.2026.104070

Cereb Cortex. 2026 Sep 1;36(9):bhag159. doi: 10.1093/cercor/bhag159.

ABSTRACT

This study examined dynamic resting-state functional connectivity and graph-theoretic measures in 71 patients with left intra-axial tumors (40 left frontal lobe tumors and 31 left temporal lobe tumors) and 53 age- and sex-matched healthy controls. Dynamic functional connectivity was estimated across 8 80-s sliding windows, region of interest-to-region of interest Fisher-z connectivity values from 132 atlas regions were harmonized using ComBat, and graph metrics were calculated at a 20% proportional density. The primary within-study comparison was restricted to left frontal lobe tumors and left temporal lobe tumors to avoid the confounding between diagnosis and the public healthy control datasets. In restricted maximum likelihood mixed-effects models with AR(1) within-subject covariance, adjusted for z-scored age, categorical TR, and window, none of the 11 graph metrics showed an overall tumor-location effect (P = 0.219 to 0.928; partial eta-squared < 0.001 to 0.019). However, tumor location-by-window interactions survived correction across 11 metrics for clustering coefficient, transitivity, modularity, and assortativity, at T8, adjusted values were higher in left temporal lobe tumors than left frontal lobe tumors for all 4 metrics. Grade-stratified analyses provided no corrected evidence that grade moderated these temporal location effects, while adjustment for peritumoral edema or standardized log lesion volume did not materially alter the primary tumor-location findings. Secondary comparisons indicated lower integration and altered path-based and centrality measures in tumor groups relative to healthy controls, but diagnosis was partially confounded with acquisition protocol; healthy control-only models identified residual acquisition-batch effects for betweenness centrality and eccentricity after correction. Global connectivity laterality index differences did not survive multiple-comparison correction (minimum q = 0.334) and are interpreted descriptively. These findings distinguish temporally specific topology differences from generalized tumor-location effects and require confirmation in uniformly acquired, clinically characterized cohorts.

PMID:42809634 | DOI:10.1093/cercor/bhag159