Most recent paper

bioRxiv [Preprint]. 2026 Jul 1:2026.06.26.734900. doi: 10.64898/2026.06.26.734900.

ABSTRACT

The thalamus is a central hub that interfaces with widespread cortical and subcortical nodes. Thalamic deep brain stimulation (DBS) offers a principled strategy for distributed cortical modulation: since distinct thalamic nuclei project to spatially segregated cortical territories, stimulation at a single thalamic site can influence multiple cortical nodes. Realizing this potential requires accurate subject-specific estimates of directed thalamocortical effective connectivity (EC) and a computational framework for optimizing stimulation parameters that achieve desired cortical responses. Here, we address both challenges using Neural Perturbational Inference (NPI), a surrogate-brain approach that estimates EC by applying virtual perturbations to a nonlinear dynamical model fitted to resting-state fMRI data. We extend NPI to a high-resolution thalamocortical network comprising 360 cortical regions and 442 thalamic voxels spanning 12 nuclei. We introduce two innovations in training: (i) a temporal signal-to-noise ratio (tSNR)-weighted loss accounting for signal heterogeneity, and (ii) a multi-resolution, cross-scale consistency loss that regularizes model complexity. These strategies yield improved performance in synthetic benchmarks across varying tSNR regimes. Leveraging the inferred subject-specific EC, we further formulate a constrained linear control problem to identify sparse thalamic stimulation targets that achieve desired cortical activation patterns. We validate the inferred EC structure on two independent datasets: the MacStim dataset comprising two macaque monkeys with infrared neural stimulation on medial pulvinar, and the HumanTC resting-state fMRI dataset comprising twelve human subjects. Our results reveal site-specific thalamocortical EC profiles, producing interpretable predictions that align with known ground-truth structures. Together, this work establishes a computationally grounded pathway toward personalized optimization of thalamic DBS in both human and nonhuman primates.

PMID:42427509 | PMC:PMC13345149 | DOI:10.64898/2026.06.26.734900

Zhonghua Nei Ke Za Zhi. 2026 Jul 1;65(7):743-751. doi: 10.3760/cma.j.cn112138-20260305-00127.

ABSTRACT

Objective: To investigate the relationship between alterations in dynamic regional homogeneity (dReHo) and spatial navigation impairment in individuals with subjective cognitive decline (SCD) who exhibit a biased Traditional Chinese Medicine (TCM) constitution. Methods: A total of 63 participants with SCD were recruited from the Affiliated Drum Tower Hospital of Nanjing University Medical School between January 2024 and January 2026. The cohort comprised 30 individuals with a balanced constitution (mean age: 68±7 years; 17 males, 13 females) and 33 with a biased constitution (mean age: 68±6 years; 12 males, 21 females). All participants were assessed for spatial navigation ability, TCM constitution, and neuropsychological status. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired during the same period. The rs-fMRI time series was segmented using a sliding time window approach, and dReHo was subsequently calculated. Statistical analyses were performed using SPSS 23.0. Intergroup differences in dReHo variability were compared. Correlation analyses were performed between the dReHo values extracted from brain regions showing significant differences and both cognitive scale scores and spatial navigation behavioral metrics. Finally, logistic regression and receiver operating characteristic (ROC) curve analyses were conducted to evaluate the predictive value of spatial navigation behaviors, dReHo variability, and cognitive scales for distinguishing between balanced and biased TCM constitutions among participants with SCD. Results: Compared to SCD individuals with a balanced constitution, those with a biased constitution exhibited significantly lower scores on the MMSE (U=2.10, P=0.036), the Auditory Verbal Learning Test (AVLT) long-delayed recall (U=2.23, P=0.026), cued recall (U=2.08, P=0.037), and recognition (t=2.51, P=0.015). Conversely, the biased constitution group demonstrated significantly higher average error distances in egocentric- allocentric navigation (U=-2.24, P=0.025), egocentric navigation (U=-2.02, P=0.043), and delayed navigation (U=-2.16, P=0.031). Neuroimaging analysis revealed that individuals with a biased constitution displayed significantly increased dReHo variability in the right angular gyrus and bilateral supplementary motor areas (t=4.51, 3.05, respectively; all P<0.05, GRF-corrected). dReHo variability in these regions correlated positively with the average error distance in delayed spatial navigation (r=0.261, P=0.039 and r=0.286, P=0.023, respectively). Additionally, dReHo variability in the bilateral supplementary motor areas showed significant positive correlations with episodic memory and language function (r=0.271, P=0.032 and r=0.277, P=0.028, respectively), alongside a significant negative correlation with executive function (r=-0.259, P=0.040). A comprehensive prediction model integrating spatial navigation metrics, cognitive assessment scales, and dReHo variability demonstrated significant discriminative performance in distinguishing between biased and balanced TCM constitutions among individuals with SCD, yielding an area under the curve (AUC) of 0.875. Conclusion: Increased dReHo variability in the right angular gyrus and bilateral supplementary motor areas represents a potential neural mechanism underlying cognitive decline and spatial navigation deficits in individuals with SCD who display a biased constitution. Furthermore, the developed comprehensive model incorporating spatial navigation behaviors and dReHo variability exhibits high predictive efficacy in differentiating between balanced and biased TCM constitutions within this population.

PMID:42427047 | DOI:10.3760/cma.j.cn112138-20260305-00127

BMC Gastroenterol. 2026 Jul 9. doi: 10.1186/s12876-026-05097-6. Online ahead of print.

ABSTRACT

BACKGROUND: Symptoms of fatigue, depression or anxiety are frequent in Crohn's Disease (CD) and may relate to disturbed brain-gut interactions. While more prevalent in active disease, these symptoms are also experienced by many individuals with CD during remission. Little is known about neural networks underlying such extraintestinal symptoms in CD and their relationship with the current disease state. Using a data fusion approach for functional MRI, this study investigated spatiotemporal markers of resting-state brain activity and associations with neurotransmitter systems and symptoms of fatigue, depression or anxiety in an active disease state or remission.

METHODS: We examined n = 71 patients with CD in an active disease state (aCD; n = 47) or in remission (rCD; n = 24) and healthy controls (HC; n = 35). All participants underwent resting-state fMRI, completed symptom assessments for fatigue, depression and anxiety, and provided stool samples for analysis of faecal calprotectin (fCal; aCD and rCD only). Joint independent component analysis (jICA) of two resting-state brain activity parameters (temporal and spatial features) identified neural networks exhibiting disease-state-dependent alterations. Network connectivity strength was correlated with symptoms of fatigue, depression, and anxiety, as well as fecal calprotectin (fCal). We further explored associations of the networks with neurotransmitter receptor maps.

RESULTS: JICA revealed three networks differentiating between disease states and/or between patients and controls. One network comprising affective orbitofrontal and temporal brain regions, exhibited reduced connectivity in active disease (HC vs. aCD: p = 0.003, pFDR = 0.01; aCD vs. rCD: p < 0.001, pFDR < 0.001) and was linked to serotonergic/dopaminergic transmission, fatigue, and fCal. Another network comprised sensorimotor brain regions and showed diminished connectivity in patients in remission (HC vs. rCD: p = 0.034, pFDR = 0.06; aCD vs. rCD: p = 0.003, pFDR = 0.01), correlating with depression, anxiety, and dopaminergic activity. The third network reflected the default-mode network topography and distinguished patients irrespective of disease status from controls (HC vs. aCD: p = 0.013, pFDR = 0.01; HC vs. rCD: p = 0.037, pFDR = 0.06), but showed no associations with symptoms.

CONCLUSIONS: Resting-state brain network connectivity in patients with CD differed between active disease and remission, and was associated with symptoms of fatigue, depression, and anxiety. Alterations in sensorimotor networks were linked to depressive and anxiety symptoms, whereas affect-related networks were associated with fatigue. These observations suggest that distinct brain networks may contribute to specific neuropsychiatric symptom clusters in Crohn's disease and underscore the role of brain-gut axis mechanisms in these manifestations.

PMID:42426651 | DOI:10.1186/s12876-026-05097-6

J Affect Disord. 2026 Jul 9:122236. doi: 10.1016/j.jad.2026.122236. Online ahead of print.

ABSTRACT

BACKGROUND: The therapeutic strategies for bipolar disorder (BD) and unipolar depression (UD) are quite different. However, the majority of patients with BD often present with a depressive episode as their initial symptom and are misdiagnosed as UD. To date, no reliable tool has been able to accurately differentiate BD patients from UD patients.

METHODS: The spontaneous brain activity derived from functional MRI of 79 BD patients and 79 matched UD patients was used to establish machine learning (ML) models for distinguishing BD patients from UD patients. Furthermore, the imaging signatures obtained from the optimal model and statistically significant clinical characteristics were incorporated into the predictive nomogram. The performance of the nomogram was evaluated by calibration curve and decision curve analysis (DCA).

RESULTS: The ML model based on spontaneous brain activity of 10 brain regions with significant differences between BD patients and UD patients achieved optimal diagnostic performance, with an AUC of 0.894 in the validation dataset. Disease duration was identified as an independent clinical predictor. A nomogram integrating disease duration with the imaging signatures derived from the optimal model demonstrated good discriminative efficacy, with a C-index of 0.926. The calibration curve and DCA indicated excellent reliability and significant net clinical benefit.

CONCLUSIONS: Our study provides a preliminary proof-of-concept that a nomogram integrating spontaneous brain activity with clinical information may serve as a potential diagnostic tool for differentiating BD patients from UD patients.

PMID:42425243 | DOI:10.1016/j.jad.2026.122236

J Alzheimers Dis. 2026 Jul 9:13872877261467275. doi: 10.1177/13872877261467275. Online ahead of print.

ABSTRACT

BackgroundLow frequency oscillations in blood-oxygen-level-dependent signal (BOLD-LFOs) are generally considered nuisance signal in connectivity analysis and discarded. However, recent evidence suggests BOLD-LFOs shed light on cerebrovascular dysfunction and preclinical Alzheimer's disease, but the mechanisms remain unclear. No investigations have assessed the relationship between BOLD-LFOs and plasma pTau217, or how it differs in apolipoprotein ε4 (APOE4) carriers who are vulnerable to cerebrovascular dysfunction and genetically predisposed to AD.ObjectiveTo study the relationship between BOLD-LFOs and plasma p-Tau217 in APOE4 carriers compared to non-carriers.MethodsIndependently living older adults (N = 118) were recruited and underwent resting-state fMRI and venipuncture. BOLD-LFOs were quantified as signal power within the 0.01-0.10 Hz frequency range. Plasma pTau217 was assessed and linear regression quantified the interactive effect of APOE4 carrier status and BOLD-LFOs on plasma pTau217. 2×2 ANCOVA was used to compare BOLD-LFOs across APOE4 carrier and amyloid positivity statuses based on previously reported pTau217 cutoffs.ResultsThe interactive effect of APOE4 carrier status and BOLD-LFO power was significantly associated with plasma pTau217 (β = -0.78, p = 0.001). This relationship was driven by an inverse relationship between BOLD-LFOs and plasma pTau217 in APOE4 carriers (β = -0.57, p = 0.0007). Amyloid-β (+) APOE4 carriers displayed lower BOLD-LFOs than amyloid-β (-) APOE4 carriers (p = 0.008) and amyloid-β (+) non-carriers (p = 0.03). Models were adjusted for age, sex, vascular risk factors, and total intracranial volume.ConclusionsFindings suggests BOLD-LFOs are implicated in preclinical AD in an APOE4 dependent manner, adding support for the continued study of BOLD-LFOs in the context of cerebrovascular contributions to AD genetic risk.

PMID:42423522 | DOI:10.1177/13872877261467275

Front Comput Neurosci. 2026 Jun 24;20:1810942. doi: 10.3389/fncom.2026.1810942. eCollection 2026.

ABSTRACT

INTRODUCTION: Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC)-the network of white matter pathways-provides the physical substrate for functional connectivity (FC), defined as the correlated activity between brain areas. While structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions.

METHODS: Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging-derived SC, validated against topological null models.

RESULTS: Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Virtual lesion experiments further demonstrate how different cortical and subcortical systems distinctively contribute to global FC.

DISCUSSION: Together, by uncovering how structural architecture governs functional interactions, this framework enables us to predict how alterations in SC, resulting from disease or surgery, propagate through functional networks and contribute to cognitive and behavioral impairments.

PMID:42422232 | PMC:PMC13341858 | DOI:10.3389/fncom.2026.1810942

Front Neurol. 2026 Jun 24;17:1858348. doi: 10.3389/fneur.2026.1858348. eCollection 2026.

ABSTRACT

BACKGROUND: Rapid eye movement sleep behavior disorder (RBD) and visual hallucinations (VH) are prognostically relevant non-motor symptoms in Parkinson's disease (PD), but their combined effects on local brain dysfunction remain unclear.

OBJECTIVE: To characterize regional brain dysfunction patterns associated with RBD and VH in PD and to explore candidate-region symptom-related effects within regions showing overall between-group differences.

METHODS: In this cross-sectional study, 96 patients with PD were divided into four groups according to the presence or absence of RBD and VH (24 per group). Resting-state functional MRI was analyzed using amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo). Whole-brain four-group analyses were first used to identify regions with overall between-group differences, followed by exploratory ROI-based 2 × 2 factorial analyses within candidate regions. Additional whole-brain voxel-wise 2 × 2 factorial analyses were performed as supplementary analyses. Correlations between imaging indices and clinical scales were also examined.

RESULTS: Patients with both RBD and VH showed the greatest clinical burden and worse cognitive performance. Whole-brain analyses revealed abnormalities in frontal, temporal, cerebellar, supplementary motor, and precuneus regions. Exploratory candidate-region analyses with Benjamini-Hochberg FDR correction showed RBD-related patterns in precuneus ReHo, cerebellar lobule VIII ReHo, and SMA ALFF; VH-related patterns in OFC ReHo, precuneus ReHo, cerebellar Crus I ReHo, SMA ALFF, and temporal pole ALFF; and interaction-like patterns in OFC ReHo and temporal pole ALFF. These ROI-based findings were interpreted as post hoc exploratory results rather than independent confirmatory evidence. Imaging abnormalities were correlated with RBD severity, freezing of gait, hallucination burden, and cognition.

CONCLUSION: Coexisting RBD and VH may identify a clinically more severe PD subtype associated with regional abnormalities involving cerebellar, motor, default mode, and association-related regions. Symptom-related main and interaction patterns should be interpreted as candidate-region exploratory findings requiring further confirmation in larger studies.

PMID:42422208 | PMC:PMC13341548 | DOI:10.3389/fneur.2026.1858348

Behav Brain Res. 2026 Jul 8:116361. doi: 10.1016/j.bbr.2026.116361. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescent major depressive disorder (MDD) emerges during a sensitive neurodevelopmental period and is frequently accompanied by non-suicidal self-injury (NSSI), a clinically important behavioral phenotype associated with affective dysregulation. Although prior resting-state fMRI studies have identified distributed functional abnormalities in adolescent MDD, it remains unclear whether MDD with NSSI is associated with alterations in macroscale functional organization, stepwise cross-network propagation, or both. We therefore combined functional gradient (FG) analysis and stepwise functional connectivity (SFC) to characterize group-related differences from the complementary perspectives of gradient-based functional organization and multi-step network propagation.

METHODS: Using resting-state fMRI data from 135 adolescents classified as healthy controls (HC), adolescents with major depressive disorder and non-suicidal self-injury (MDD-NSSI), and adolescents with major depressive disorder without NSSI (MDD-noNSSI), we estimated functional gradients based on the Schaefer-400 parcellation. Between-group differences in Gradient 1 (G1) and Gradient 2 (G2) were assessed using pairwise Welch's t-tests, followed by global BH-FDR correction across all 2400 ROI-wise tests. Bonferroni correction was additionally used as a conservative sensitivity analysis. To avoid circular seed selection, weighted SFC was performed using an a priori sgACC/BA25 seed set rather than ROIs selected from the gradient analysis. Stepwise propagation maps were computed from step 1 to step 7. For ROI-wise SFC comparisons, pairwise Welch's t-tests were performed within each contrast and step, followed by exploratory within-network BH-FDR correction separately within each canonical functional network.

RESULTS: Functional gradient analysis revealed distinct gradient profiles across the two patient groups. After global BH-FDR correction across 2400 ROI-wise tests, G1 showed extensive differences in MDD-noNSSI versus HC and in MDD-NSSI versus MDD-noNSSI, whereas the G1 difference between MDD-NSSI and HC was relatively limited. By contrast, G2 showed extensive and robust differences in MDD-NSSI versus HC and in MDD-NSSI versus MDD-noNSSI, while MDD-noNSSI showed a weaker G2 difference relative to HC. The exploratory sgACC/BA25-seeded SFC analysis showed HC-relative propagation abnormalities mainly involving limbic temporal pole and orbitofrontal parcels. Compared with HC, MDD-NSSI showed broader exploratory within-network-corrected limbic involvement and additional somatomotor reductions, whereas MDD-noNSSI showed a more focal left temporal pole propagation pattern. No direct SFC difference between MDD-NSSI and MDD-noNSSI survived exploratory within-network BH-FDR correction.

CONCLUSION: FG findings provided direct evidence for different gradient-related profiles between adolescent MDD with and without NSSI, whereas sgACC/BA25-seeded SFC provided exploratory HC-relative propagation evidence. MDD-noNSSI was mainly associated with broad G1-related displacement, whereas MDD-NSSI was associated with G2-related redistribution and broader HC-relative limbic-cortical propagation abnormalities from the sgACC/BA25 seed.

PMID:42419463 | DOI:10.1016/j.bbr.2026.116361

J Affect Disord. 2026 Jul 8:122226. doi: 10.1016/j.jad.2026.122226. Online ahead of print.

ABSTRACT

INTRODUCTION: Bipolar depression (BD) is associated with altered intrinsic brain activity, and possibly impaired neurovascular coupling (NVC). Although rapid-acting chronotherapies are effective in BD, their neurophysiological mechanisms remain unclear.

METHODS: In this longitudinal resting-state fMRI study, we examined fractional amplitude of low-frequency fluctuations (fALFF), indexing spontaneous neural activity, and hemodynamic response function (HRF) parameters, used as proxies of NVC, in 50 BD inpatients undergoing three cycles of combined total sleep deprivation and light therapy (TSD + LT), and in 30 healthy controls (HCs). Patients were scanned before (day 0) and after treatment (day 7), and depressive symptoms were evaluated with the Beck Depression Inventory Short Form.

RESULTS: At baseline, BD patients showed reduced fALFF in frontal, temporal, insular-opercular, and cerebellar regions relative to HCs. After TSD + LT, fALFF increased in widespread occipito-temporal, frontal, and cerebellar clusters in BD patients. HRF analyses showed no baseline between-group differences, but revealed a significant post-treatment increase in HRF response height across occipital, temporal, frontal, and cerebellar regions. Remission after TSD + LT was associated with fALFF changes, whereas larger HRF response height increases were observed in patients who did not require antidepressant treatment switch or augmentation during hospitalization.

CONCLUSION: TSD + LT was associated with modulation of both intrinsic neural activity and resting-state hemodynamic responses, with fALFF and HRF reflecting partly distinct aspects of short-term clinical outcome.

PMID:42419456 | DOI:10.1016/j.jad.2026.122226

Neural Netw. 2026 Jun 29;205(Pt A):109305. doi: 10.1016/j.neunet.2026.109305. Online ahead of print.

ABSTRACT

Brain Network Analysis (BNA) from resting-state functional MRIs (rs-fMRIs) has been widely applied to the prediction and understanding of brain disorders, by modeling connectivities among brain regions of interest (ROIs) to identify potential biomarkers. However, the majority of existing studies construct static brain networks utilizing a single predefined spatial scale (i.e., the number of brain ROIs), neglecting the inherently hierarchical nature of brain networks across various temporal and spatial scales. To address these limitations, we propose AdapHBNA, an Adaptive Hierarchical spatio-temporal Brain Network Analysis framework for brain disorder diagnosis. Specifically, we incorporate feature-channel-guided temporal hierarchical learning and Modular Brain Clustering (MBC)-driven spatial hierarchical learning strategies into the spatio-temporal encoding process of brain network, utilizing Mamba and Graph Neural Networks. It seamlessly merges the multi-scale learning, hierarchical brain representation learning and automatic spatio-temporal fusion into a unified end-to-end framework, which adaptively adjusts the temporal and spatial scales to capture hierarchical complementary brain representations across a spectrum of fine-to-coarse granularities. Extensive validation on the ABIDE, ADNI and REST_MDD datasets for Autism Spectrum Disorder, Early Mild Cognitive Impairment and Major Depressive Disorder demonstrates that AdapHBNA outperforms state-of-the-art methods by leveraging complementary diagnostic insights across multiple scales.

PMID:42419253 | DOI:10.1016/j.neunet.2026.109305

Cereb Cortex. 2026 Jun 2;36(6):bhag076. doi: 10.1093/cercor/bhag076.

ABSTRACT

The lateral frontoparietal (FP) network, also referred to as the central executive network, is critical for goal-directed behavior in primates. Resting-state functional MRI (rs-fMRI) studies have revealed functional homologies between humans and macaques; however, methodological disparities, particularly the use of anesthesia in non-human primates, raise questions about the validity of interspecies comparisons. Anesthetic agents such as isoflurane have been shown to alter functional connectivity (FC), but whether they differentially affect lateral FP subnetworks remains unclear. Here, we investigated the impact of isoflurane on FC patterns in dorsal and ventral FP subnetworks by acquiring rs-fMRI data in awake and anesthetized states from the same macaques. Using anatomically precise seeds and regions of interest defined by sulcal landmarks and known short- and long-range FP connections, we demonstrate that anesthesia reduces FC within short-range lateral FP subnetworks, with preserved connectivity in long-range projections. Notably, we also observed increased FC between lateral frontal and posterior medial cortical regions under isoflurane, suggesting a shift in network dynamics. These findings underscore the non-uniform effects of anesthesia on FP circuitry and offer insights into network reconfigurations associated with unconscious states.

PMID:42418823 | DOI:10.1093/cercor/bhag076

Alpha Psychiatry. 2026 Jun 25;27(3):49872. doi: 10.31083/AP49872. eCollection 2026 Jun.

ABSTRACT

BACKGROUND: Etomidate misuse (EM) has recently become an increasing public health concern in East and Southeast Asia, but its neurobiological mechanisms are still not well understood. Although substance use disorders (SUDs) are commonly associated with disruptions in large-scale brain network organization, the effects of EM on brain network topology remain largely unexplored.

METHODS: Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired from individuals with EM and healthy controls (HC). Graph theoretical analyses were employed to and characterize global and nodal topological properties of functional brain networks. Clinical assessments captured substance use characteristics, craving, impulsivity, and addiction severity. Partial correlation analyses were conducted to examine associations between network metrics and substance use characteristics. Additonally, a support vector machine (SVM) classifier was implemented to discriminate individuals with EM from HC based on network features.

RESULTS: A total of 103 individuals with EM and 57 HC were included in the final analysis. Global topological organization that appeared was largely preserved in the EM group, with the exception of a significantly reduced clustering coefficient. At the nodal level, individuals with EM exhibited significant alterations in degree centrality, betweenness centrality, and nodal efficiency across regions predominantly distributed within the default mode, attention, and sensorimotor networks. Correlation analyses revealed no significant associations between network metrics and substance use characteristics following correction for multiple comparisons. Furthermore, the SVM model achieved moderate classification performance (accuracy = 66.7%) with an area under the curve (AUC) of 0.711.

CONCLUSIONS: This study provides the first systematic investigation of the brain network topology in EM. The findings indicate widespread alterations in nodal network properties alongside relatively preserved global topological organization. While these results may offer preliminary indicators related to EM, their clinical relevance requires further in future research.

PMID:42416184 | PMC:PMC13339792 | DOI:10.31083/AP49872

Imaging Neurosci (Camb). 2026 Jul 6;4:IMAG.a.1295. doi: 10.1162/IMAG.a.1295. eCollection 2026.

ABSTRACT

Sex differences in child neurobehavioral health suggest that male and female brains differ early in development. We took advantage of recent advances for in-utero magnetic resonance imaging (MRI) to conduct a pre-registered systematic review and meta-analysis of sex differences in brain structure and network connectivity of human fetuses. PubMed literature searching yielded 4,738 studies published between 2002 and 2025. All studies were screened by two independent reviewers and included if either structural or functional MRI was used to image brains of healthy human fetuses in utero and any results were reported stratified by sex. After title and abstract screening, 545 studies remained for full-text screening, resulting in 34 total studies meeting inclusion criteria. Analysis focused on 28 of these that reported sex-disaggregated data on the same measure across three or more independent samples. Pooled effect sizes revealed significantly larger male brains based on both linear measures (cerebral fronto-occipital and biparietal diameters and corpus callosum length) and global volumes (intracranial, total brain, lateral ventricles) by the start of the third trimester. Among 11 studies reporting brain growth trajectories, a majority reported faster growth in males. Among nine studies measuring functional connectivity using resting state functional MRI (rs-fMRI), six reported no significant sex differences and the others reported sporadic differences that were not replications. Together with large ultrasound studies, this review demonstrates larger brain size and faster brain growth in human males compared to females beginning in the second trimester, comparable to overall body size and other internal organ volumes. However, existing MRI and ultrasound research has not identified specific brain regions that differ disproportionately between male and female fetuses or any reliable sex differences in functional connectivity. Faster fetal growth in males, including the brain, does not readily explain neonatal male vulnerability and appears to be a product of genetic, rather than hormonal influences. These findings provide a reference for the emergence of brain sex differences later in development.

PMID:42416082 | PMC:PMC13338799 | DOI:10.1162/IMAG.a.1295

Neurosciences (Riyadh). 2026 Mar;31(3):262-271. doi: 10.17712/1658-3183.2804. Epub 2026 Jun 26.

ABSTRACT

OBJECTIVES: To determine how cytoarchitectonically defined subdivisions of the primary motor cortex, somatosensory cortex, and supplementary motor area (SMA) reorganize their integration with large-scale brain networks during healthy aging. This study investigated the lifespan trajectories of functional connectivity within cytoarchitectonically defined subregions of the primary motor cortex, somatosensory cortex, and supplementary motor area (SMA).

METHODS: We conducted a cross-sectional analysis of resting-state fMRI data from 150 healthy individuals (aged 23-80 years), stratified into young, middle-aged, and older groups. Data were sourced from the Southwest University Adult Lifespan Dataset (SALD) and analyzed between June 2024 and June 2025. Using the Jülich Brain Atlas, we defined seed regions for the M1 (BA4a, BA4p), premotor cortex (6d1-3), PSC (BA1-3), and SMA (pre-SMA, SMA proper). Functional coupling was calculated between these seeds and canonical large-scale networks, including the default mode (DMN), salience, dorsal attention, and frontoparietal systems.

RESULTS: Our analysis identified distinct age-dependent connectivity patterns. While all groups maintained robust motor-somatosensory coupling, older adults exhibited a significant loss of network segregation. Specifically, younger adults displayed strong sensorimotor integration with negative DMN coupling, whereas older adults showed widespread, diffuse positive connectivity across the DMN and frontoparietal networks. Middle-aged participants presented a transitional profile with expanded salience network interactions.

CONCLUSION: Aging is associated with a gradual shift from segregated sensorimotor processing to a more dedifferentiated, globally connected architecture. These findings highlight the importance of analyzing specific cytoarchitectonic subdivisions to detect subtle compensatory neuroplasticity mechanisms.

PMID:42415974 | PMC:PMC13340598 | DOI:10.17712/1658-3183.2804

Hum Brain Mapp. 2026 Jul;47(10):e70590. doi: 10.1002/hbm.70590.

ABSTRACT

Resting-state fMRI (rs-fMRI) is widely used to assess intrinsic brain activity, yet concerns about its test-retest reliability and reproducibility persist. Circadian rhythms strongly influence brain physiology, but their impact on rs-fMRI reliability remains poorly understood. In this study, we scanned 39 healthy young adults six times within a single day (08:00-20:00) under standardized conditions. For each session, we computed four common rs-fMRI metrics, including amplitude of low-frequency fluctuations (ALFF), wavelet-transformed ALFF (wALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo), and assessed reliability using intraclass correlation coefficients (ICCs). ReHo showed relatively higher and more stable reliability across sessions, whereas amplitude-based metrics, particularly fALFF, exhibited greater diurnal variation. Both network-level and region-specific analyses revealed low reliability in the limbic and subcortical structures, with a mid-morning dip at 10:00. Moreover, ICCs for ALFF, wALFF, and fALFF declined with increasing inter-scan intervals, whereas ReHo remained robust. These findings demonstrate diurnal fluctuations in rs-fMRI reliability, with different metrics exhibiting distinct temporal stability profiles. We recommend that scan timing and circadian influences should be explicitly considered in the design, analysis, and interpretation of future rs-fMRI studies.

PMID:42415272 | DOI:10.1002/hbm.70590

Commun Biol. 2026 Jul 7. doi: 10.1038/s42003-026-10599-w. Online ahead of print.

ABSTRACT

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each child's networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization-including finer-scale subdivisions and emerging language lateralization well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

PMID:42414561 | DOI:10.1038/s42003-026-10599-w

Neuroimage. 2026 Jul 7:122106. doi: 10.1016/j.neuroimage.2026.122106. Online ahead of print.

ABSTRACT

Resting-state functional connectivity (RSFC) and networks (RSNs) provide insight into large-scale brain organization and its disruption in neurological disease. RSNs are most commonly assessed using fMRI, yet its translational use is constrained by high cost, motion sensitivity, and limited feasibility for repeated measurements. Functional near-infrared spectroscopy (fNIRS) offers a portable alternative, but its reliability for RSFC and RSN mapping remains insufficiently established. Near whole-head fNIRS data and fMRI-BOLD signals of corresponding cortical regions were extracted, based on which RSN organization was compared across two independent cohorts of 31 participants each. Cross-modal convergence and divergence were assessed using bivariate and partial correlations across multiple network levels. Edgewise analyses revealed substantial modality differences with bivariate correlations (50-61% of edges), which were markedly reduced using partial correlations (<3%). Group-averaged connectivity patterns showed moderate cross-modal similarity (r ≈ 0.37). At nodal level, net strength, local efficiency, and path-length differed substantially between modalities, while normalized strength and assortativity were largely comparable. Across nodes, graph-metric distributions derived from group-averaged matrices were broadly similar for normalized strength, assortativity, local efficiency, and path length (rho ≈ 0.27-0.5). At network-level, fNIRS-derived modules significantly overlapped with fMRI modules, particularly based on bivariate correlations, identifying default mode, attentional, executive, salience, sensorimotor, and visual networks (Jaccard ≈ 0.27-0.5). Overall, fNIRS captured key features of large-scale RSFC and RSN organization observed with fMRI, supporting meaningful cross-modal correspondence and translational utility. While partial correlations enhanced edge-level agreement, they attenuated nodal and modular recovery, suggesting greater suitability for targeted connectivity analyses rather than whole-network characterization.

PMID:42413889 | DOI:10.1016/j.neuroimage.2026.122106

Neuroimage Clin. 2026 Jul 3;51:104030. doi: 10.1016/j.nicl.2026.104030. Online ahead of print.

ABSTRACT

Aphasia is increasingly understood as a disorder of disrupted language networks rather than damage to isolated language regions. However, most resting-state functional connectivity studies treat people with aphasia as a single group or classify individuals by overall severity, potentially obscuring qualitative differences in network organization across aphasia types. The present study examined how resting-state functional connectivity varies across aphasia types and how aphasia severity relates to network organization following left-hemisphere stroke. Resting-state fMRI data were analyzed from 89 individuals in the chronic stage of recovery drawn from the open-source Aphasia Recovery Cohort dataset. Network-level and ROI-to-ROI functional connectivity analyses were conducted across 32 predefined dorsal and ventral stream language regions. Lesion-overlap analyses were additionally performed to characterize group-level lesion distributions. Network-level analyses revealed relatively limited effects, whereas ROI-to-ROI analyses demonstrated substantial heterogeneity across aphasia subgroups. Broca's aphasia and severe aphasia demonstrated clearer and more spatially convergent connectivity patterns, whereas anomic, mild, and moderate aphasia groups showed greater heterogeneity and limited group-level effects. Groups showing clearer functional connectivity patterns were also characterized by greater convergence in lesion distributions. Within Broca's aphasia, milder impairment was associated with stronger left-hemisphere and interhemispheric connectivity. Overall, aphasia type-based analyses revealed more differentiated connectivity patterns than severity-based groupings alone. These findings suggest that post-stroke language network organization varies across aphasia types characterized by partially shared clinical and lesion features and may not be fully captured by severity measures alone.

PMID:42413174 | DOI:10.1016/j.nicl.2026.104030

Eur Child Adolesc Psychiatry. 2026 Jul 7. doi: 10.1007/s00787-026-03122-3. Online ahead of print.

ABSTRACT

Abundant resting-state fMRI studies have shown altered brain activation and functional connectivity (FC) in children and adolescents with Attention Deficit Hyperactivity Disorder (ADHD) compared with typically developing (TD) subjects, with specific disruptions in brain networks associated with attention and cognitive control. Most prior meta-analyses have focused on populations across all ages, leaving a significant gap in understanding how these alterations unfold during childhood and adolescence. The current meta-analysis reviewed 36 studies (1867 subjects, 989 with ADHD, 878 TD), including 20 seed-based connectivity (SBC) studies and 16 non-seed-based connectivity (Non-SBC) studies, with a particular focus on FC within and between seven major brain networks. Activation Likelihood Estimation (ALE) analyses were conducted separately for the SBC and Non-SBC datasets. In the SBC analysis, we found significant reductions in FC between the Default Mode Network (DMN) and the Dorsal Attention Network (DAN), as well as between the Somatomotor Network (SMN) and the Ventral Attention Network (VAN) in the ADHD group, whereas no significant alterations were identified in the Non-SBC analysis. Our findings are consistent with aspects of two theoretical frameworks, the DMN interference hypothesis and the multi-network model of ADHD, suggesting that weakened inter-network connectivity may contribute to the core cognitive and attentional difficulties observed in ADHD. These findings contribute to a better understanding of the network-level neural features of ADHD and may provide a basis for future studies on early intervention.

PMID:42412230 | DOI:10.1007/s00787-026-03122-3

Psychol Med. 2026 Jul 7;56:e217. doi: 10.1017/S0033291726104899.

ABSTRACT

BACKGROUND: Cognitive difficulties, including problems with attention and executive processing, are common in major depressive disorder (MDD), and strongly predict psychosocial and occupational functioning. Impairment in sustained attention contributes to increased intra-individual variability (IIV) in reaction times observed during cognitive tasks. Understanding brain network changes associated with IIV could guide novel neuromodulation strategies targeting cognitive difficulties.

METHODS: We analyzed baseline resting-state fMRI data from 209 patients with moderate-to-severe treatment-resistant MDD who participated in the BRIGhTMIND neuromodulation trial. Following a preregistered analytic protocol, we examined associations between: functional connectivity across three core brain networks (executive control, ECN; default mode, DMN; and salience network, SN); components of IIV derived from a choice reaction time task (using a three-parameter ex-Gaussian model); and functioning.

RESULTS: Greater IIV was linked to increased ECN-DMN functional connectivity. The ECN supports top-down control and externally directed cognition, while the DMN supports internal mentation and rumination. ECN-DMN connectivity was modulated by the SN, which prioritizes salient internal and external stimuli. Higher SN-ECN connectivity was associated with lower ECN-DMN connectivity and with faster mean reaction times. Both IIV and mean reaction time predicted functioning, with poorer functioning related to a slowed and inflexible response pattern.

CONCLUSIONS: Distinct components of reaction time variability are associated with specific patterns of brain network connectivity, largely independent of mood severity. Connectivity between the salience and executive control networks may represent a promising target for neuromodulation interventions focused on cognitive deficits in MDD.

PMID:42410874 | DOI:10.1017/S0033291726104899