Most recent paper

IEEE Trans Med Imaging. 2026 Aug 26;PP. doi: 10.1109/TMI.2026.3727625. Online ahead of print.

ABSTRACT

Neuroimaging AI remains constrained by a model-per-disease paradigm-where separate frameworks are trained for individual disorders-limiting knowledge transfer and direct cross-disorder comparisons. While resting-state fMRI provides a powerful subject-specific functional connectivity (FC) fingerprint, it suffers from low spatial resolution and limited clinical accessibility. Conversely, structural MRI (sMRI) is widely available and spatially detailed, yet existing representations typically rely on predefined features or disease labels, failing to explicitly encode the individual-level functional organization.We propose SF-DisenNet, a self-supervised framework that uses each subject's own FC matrix as a neurobiological supervision signal to learn function-aligned sMRI representations without disease labels. A ResNet-DC patch encoder and an atlas-guided Anatomical Mapping Unit (AMU) aggregate local patches into AAL-90 regional embeddings, whose pairwise relationships define a representation-based structural connectivity (SC) matrix aligned with the subject's FC. An independence regularization further promotes spatially disentangled regional representations. After one pretraining stage on UK Biobank, the encoder is transferred to downstream tasks without further updating. SF-DisenNet achieves 95.1% cross-modal fingerprint matching between sMRI-derived SC and fMRI-derived FC. The AMU exhibits emergent hemispheric lateralization across all 45 AAL-90 anatomical pairs with a median effective patch count close to one. On longitudinal ADNI data, the structural fingerprint achieves 83.5% 24-month re-identification accuracy; its drift (ΔSC) is 2.1× larger in mismatched subjects and correlates with hippocampal atrophy rate. Ultimately, this unified feature space supports fine-tuning-free transfer to AD/MCI, ASD, PD, and SWEDD tasks, enabling cross-disorder analysis within a single sMRI framework. The source code is available at https://github.com/k-Jayus/BRAIN.

PMID:42647690 | DOI:10.1109/TMI.2026.3727625

Tomography. 2026 Aug 18;12(8):115. doi: 10.3390/tomography12080115.

ABSTRACT

Background/Objectives: Auditory verbal hallucinations (AVH) are clinically heterogeneous experiences that may occur across psychiatric, neurological, sensory, and non-clinical contexts. This controlled pilot study investigated multimodal structural and functional MRI features associated with persistent AVH in a psychiatric clinical population, recruited from outpatient psychiatric clinics and diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder with psychotic features. Neurological, sensory, and non-clinical presentations of AVH were not included. Methods: This observational controlled pilot study included 14 participants with persistent auditory verbal hallucinations and 15 age- and sex-matched healthy controls. Participants with AVH had experienced the current persistent hallucinatory phase for a mean of 3.1 ± 1.6 years (range of 1-7), with a mean overall psychiatric illness duration of 12.4 ± 4.8 years. Independent component analysis assessed resting-state functional connectivity, BrainVoyager QX measured cortical thickness, and diffusion tensor imaging (DTI) evaluated white matter microstructure. Multiple comparisons were controlled using false discovery rate correction followed by 5000-iteration Monte Carlo cluster-extent correction for fMRI and Monte Carlo cluster correction for whole-brain structural metrics. Results: Participants with AVH demonstrated increased functional connectivity across default mode network (DMN) hubs (precuneus, inferior frontal, and parahippocampal gyri) and superior temporal regions. Whole-brain cortical thickness analysis revealed no significant group differences; however, secondary exploratory analyses of six regions previously implicated in AVH showed cortical thinning in participants with AVH relative to the controls after FDR correction. DTI revealed no group differences surviving whole-brain permutation correction (TFCE, FWE-corrected p < 0.05); exploratory uncorrected findings are reported as hypothesis-generating. Conclusions: This pilot study identifies structural and functional network differences between medicated individuals with persistent AVH and healthy controls, centred on frontotemporal and default mode networks. Because no psychiatric control group without AVH was included, these differences cannot be attributed specifically to AVH as opposed to the underlying psychiatric disorders or their treatment. No diffusion findings survived whole-brain permutation correction; exploratory uncorrected results are reported but are not incorporated into these conclusions. Collectively, these findings identify candidate imaging markers that require validation against psychiatric control groups.

PMID:42646917 | DOI:10.3390/tomography12080115

Alzheimers Dement. 2026 Aug;22(8):e71757. doi: 10.1002/alz.71757.

ABSTRACT

INTRODUCTION: Functional brain network alterations associated with Alzheimer's disease (AD) pathology, including amyloid beta (Aβ) and phosphorylated tau (p-tau), are difficult to interpret due to overlapping aging-associated and non-amyloid biological processes.

METHODS: We analyzed resting-state functional magnetic resonance imaging (fMRI) from 289 older adults classified as Aβ-positive (A+, n = 129) or Aβ-negative (A-, n = 160) based on cerebrospinal fluid biomarkers. A contrastive deep learning framework was used to identify A+-specific network dimensions and predict individual Aβ and p-tau levels.

RESULTS: A+-specific signatures were localized to the right superior temporal and anterior cingulate cortices and linked to attention and memory functions, with transcriptomic enrichment implicating synaptic dysfunction and glial activity. In contrast, signature dimensions shared between A+ and A- individuals involved language-related regions and aging-associated molecular pathways.

CONCLUSION: These findings suggest that contrastive graph learning may help separate amyloid-associated functional network variation from broader background biological variability, providing insight into the heterogeneity of AD-related biomarkers and cognitive dysfunction.

PMID:42644421 | DOI:10.1002/alz.71757

Front Neurosci. 2026 Aug 7;20:1892288. doi: 10.3389/fnins.2026.1892288. eCollection 2026.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is characterized by heterogeneous developmental trajectories, yet it remains unclear whether frequency-specific resting-state functional magnetic resonance imaging (rs-fMRI) features can distinguish age-defined developmental stages within the condition.

METHODS: We analyzed rs-fMRI data from 251 participants with ASD, comprising 146 children and 105 adolescents aggregated from ten sites in the Autism Brain Imaging Data Exchange (ABIDE). ALFF and ReHo were computed across three frequency bands: Conventional (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz), and slow-5 (0.01-0.027 Hz). Region-of-interest features were extracted using the 246-region Brainnetome Atlas. To ensure rigorous generalization, participants were divided into a stratified training set (80%, n = 200) and a held-out test set (20%, n = 51), with stratification based on the child-adolescent group label and a fixed random seed of 42. CovBat harmonization parameters, feature-scaling parameters, LASSO feature selection, and classifier hyperparameters were estimated using the training data only and subsequently applied to the held-out test data. Final model performance was evaluated once on the held-out test set. Performance was evaluated using Logistic Regression (LR), Support Vector Machine, and Random Forest classifiers, with Shapley Additive Explanations (SHAP) used to characterized interpret feature contributions.

RESULTS: The slow-4 and Conventional-band features showed higher held-out ASD test-set performance than slow-5 features. The best single-metric model by area under the receiver operating characteristic curve (AUC) was slow-4 ReHo Logistic Regression, which achieved an AUC of 0.811 and accuracy of 0.745. The exploratory combined model using slow-4 ALFF and ReHo features achieved the highest overall AUC of 0.819 (accuracy = 0.725). SHAP analysis identified distributed model-contributing regions in the slow-4 ReHo model, including the inferior parietal lobule, lateral occipital cortex, middle and inferior frontal gyri, basal ganglia, and thalamus.

CONCLUSION: Frequency-specific resting-state features, particularly local synchronization in the slow-4 band, capture developmental-stage-related variation within ASD. The involvement of frontoparietal, visual, and subcortical networks suggests that developmental heterogeneity in ASD is supported by distributed reorganization of intrinsic brain activity. These findings highlight the potential of frequency-specific rs-fMRI metrics as candidate markers for characterizing neurodevelopmental stages in ASD, warranting further validation in longitudinal and independent cohorts.

PMID:42644032 | PMC:PMC13504999 | DOI:10.3389/fnins.2026.1892288

Imaging Neurosci (Camb). 2026 Aug 24;4:IMAG.a.1338. doi: 10.1162/IMAG.a.1338. eCollection 2026.

ABSTRACT

Large-scale lifespan neuroimaging studies increasingly integrate data across distinct cohorts to characterize trajectories of brain development and aging. However, systematic differences in acquisition protocols and hardware across cohorts can alter signal characteristics in ways that bias downstream analyses. Here, we examine three cohorts from the Human Connectome Project (HCP), spanning development (HCP-D), young adulthood (HCP-YA) and aging (HCP-A), to illustrate this issue and evaluate existing strategies to mitigate it. HCP has set standards for open, deeply phenotyped, high-resolution human neuroimaging, which are frequently used as high-quality reference datasets in tool validation, replication studies, and cross-cohort meta-analyses. However, neuroimaging acquisitions have differed across HCP cohorts because of changes in scanner hardware and acquisition sequences across study phases. Because of HCP's widespread usage, even modest protocol differences between cohorts-and their downstream effects-can have outsized impacts on the field of neuroscience research. Our analysis reveals that the HCP-YA cohort exhibits systematically weaker temporal signal-to-noise ratio (tSNR) relative to HCP-D/A. These signal quality discrepancies propagate to downstream analyses, leading to differences in overall resting-state functional correlations and whole-brain and node-level measures of resting-state network organization (e.g., system segregation, modularity, participation coefficient). Consistent with protocol-driven signal differences, resting-state network measures derived from HCP-YA depart from expected lifespan trajectories, as confirmed by examination of two other lifespan datasets. Harmonization approaches accounting for protocol and scanner-model differences substantially lessen these artifactual differences in brain network measures. Our findings underscore that signal differences do not merely introduce noise, but can qualitatively alter estimated lifespan trajectories of functional network organization, including partially inverting expected lifespan patterns. Without appropriate harmonization, analyses that combine HCP cohorts can therefore result in biologically misleading inferences about brain development and aging. We demonstrate how small acquisition differences bias resting-state-derived network metrics, and how these effects can be mitigated. This work advances best practices for valid inference in multi-cohort lifespan neuroscience research.

PMID:42643755 | PMC:PMC13504956 | DOI:10.1162/IMAG.a.1338

Sports Med Open. 2026 Aug 26;12(1):122. doi: 10.1186/s40798-026-01096-9.

ABSTRACT

BACKGROUND: Arthrogenic muscle inhibition (AMI) is a neurally mediated impairment of quadriceps activation following knee injury. Peripheral and spinal mechanisms are well described, but supraspinal contributions to AMI in the acute phase after injury remain underexplored.

OBJECTIVE: To investigate sensorimotor brain network connectivity changes associated with clinically classified AMI with task-based and resting-state functional magnetic resonance imaging (fMRI) in male patients with acute anterior cruciate ligament (ACL) rupture.

METHODS: In this prospective comparative cohort study, 40 male patients (mean age 24.1 ± 2.9 years) with acute ACL injuries underwent resting-state and a motor imagery task-based functional magnetic resonance imaging (fMRI). Patients were classified into AMI (n = 20) and non-AMI (n = 20) groups based on clinical assessment using the SANTI classification system.

RESULTS: The average time from injury to scanning was 22.7 ± 7 days. Clinically, the AMI group presented with qualitative vastus medialis obliquus inhibition and more frequent extension deficit. Compared with the non-AMI group, the AMI group demonstrated significantly greater functional connectivity of the right cerebellar Crus 1 with several sensory and cognitive brain regions. Exploratory (not corrected for multiple comparisons) task-based analyses revealed greater activation in the right caudate nucleus and right thalamus during motor imagery of the involved limb in the AMI group.

CONCLUSION: Male patients with acute ACL rupture and clinically classified AMI using the SANTI classification presented more frequently with knee extension deficits and consistent vastus medialis obliquus inhibition than those without AMI. In these patients, AMI was associated with altered cerebellar connectivity and, on exploratory analysis, greater subcortical activation during motor imagery. These findings support a central neural contribution to AMI and identify cerebellar-subcortical pathways as candidate targets for future mechanistic and rehabilitation studies. Trial registration NCT03950024 (ClinicalTrials.gov) registered on May 9th, 2019.

PMID:42642554 | DOI:10.1186/s40798-026-01096-9

J Affect Disord. 2026 Aug 25:122403. doi: 10.1016/j.jad.2026.122403. Online ahead of print.

ABSTRACT

Understanding functional connectivity alterations across manic, depressive, and remitted states of bipolar disorder (BD) remains an important challenge. In this cross-sectional study, we collected resting-state functional magnetic resonance imaging data from 117 BD patients, including manic BD patients (BipM: n = 38), depressive BD patients (BipD: n = 42), remitted BD patients (rBD: n = 37), and 35 healthy controls. We aimed to identify functional connectivity patterns associated with different BD episode states and shared alterations across groups, and to examine their normative reliability, heritability, and spatial correspondence with molecular and cellular brain maps. We identified shared altered connectivity patterns mainly involving ventral attention network-related regions, together with candidate state-associated patterns involving default-mode-related regions in BipM, frontoparietal/somatomotor/visual regions in BipD, and limbic-related regions in rBD. Using Human Connectome Project data, we further found that subsets of these selected connections showed normative test-retest reliability and heritability in healthy adults. Exploratory machine-learning analyses suggested that these patterns contained information related to BD episode-state classification and symptom variation, although the results require independent external validation. Spatial annotation analyses showed that the regional distribution of these connectivity patterns corresponded with normative neurotransmitter receptor, cell-type, and BD-related gene-expression maps. These findings suggest that BD episode states are associated with partially distinct and partially shared functional connectivity alterations. However, because the study was cross-sectional and single-center, and because the molecular analyses were based on normative spatial maps, the findings should be interpreted as exploratory state-associated network observations rather than causal mechanisms or validated clinical biomarkers.

PMID:42641962 | DOI:10.1016/j.jad.2026.122403

J Neurosci Methods. 2026 Aug 25:110885. doi: 10.1016/j.jneumeth.2026.110885. Online ahead of print.

ABSTRACT

BACKGROUND: Temporal domain choices and motion correction strategies are critical for rs-fMRI analysis.

NEW METHOD: This study compared the combined effect of temporal domain and motion correction choices of two widely-used and methodologically distinct preprocessing strategies implemented within FSL and CONN pipelines on test-retest reliability of the language and sensorimotor networks.

METHODOLOGY: 27 healthy volunteers underwent two rs-fMRI sessions. Data were analyzed using seed-based analysis for seven regions of interest within the language and sensorimotor networks. All preprocessing parameters were unified between FSL and CONN, except for their default temporal domain preprocessing and motion correction strategies to enable direct comparison. Test-retest reliability was primarily assessed using Intraclass Correlation Coefficient (ICC 3,1), with secondary analyses of group-level intersession FC variations.

RESULTS: The FSL pipeline yielded significantly higher voxel-level reliability (p = 0.031), particularly for the sensorimotor network. At the ROI level, FC map reliability was comparable between pipelines. Group-level analyses revealed that FSL captured changes in basal ganglia connectivity patterns not detected by CONN. Furthermore, inter-network FC stability varied markedly between both pipelines.

COMPARISON WITH OTHER METHODS: This method evaluates the impact of complete end to end rs-fMRI preprocessing strategies as implemented in clinical practice instead of segregating their impact in previous research work.

CONCLUSION: Preprocessing strategy has a network-dependent impact on the test-retest reliability of the rs-fMRI functional connectivity of the language and sensorimotor networks. The language network benefits from CONN's aggressive artifact removal. Conversely, the sensorimotor network, specifically subcortical components, requires the preservation of higher-frequency signals, favoring FSL approach.

PMID:42641820 | DOI:10.1016/j.jneumeth.2026.110885

J Pain. 2026 Aug 25:106425. doi: 10.1016/j.jpain.2026.106425. Online ahead of print.

ABSTRACT

Chronic pain is a common non-motor symptom in Parkinson's disease (PD), but its central neural mechanisms are not fully understood. Structural and resting-state functional MRI offer non-invasive insights into brain structural and functional correlates of pain. This systematic review synthesizes neuroimaging evidence on the neural correlates of pain in PD. A search of PubMed, Scopus, Google Scholar, and Web of Science identified eight case-control studies, including 432 PD patients with pain (PDP), 225 without pain (PDNP), and 257 healthy controls (HCs). Structural MRI revealed prefrontal cortical thinning and reduced insular gyrification, while resting-state functional MRI showed altered activity and connectivity across basal ganglia, limbic, brainstem, sensorimotor, cerebellar, insula-temporo-parietal and parahippocampal networks. Key findings included reduced putamen activity negatively correlated with pain, disrupted striatal-limbic connectivity, and increased raphe nucleus connectivity positively associated with pain. Other alterations included impaired sensorimotor-cerebellar integration and amygdala hypoconnectivity linked to pain severity. Dopamine-responsive and dopamine-unresponsive pain phenotypes displayed distinct connectivity patterns. Compared to HCs, PDP exhibited reduced connectivity in pain-related networks. Dynamic connectivity of default mode and frontoparietal networks showed no consistent links with pain. Two studies employed multimodal imaging approaches. Findings converged with unimodal evidence implicating prefrontal, insular, orbitofrontal, cerebellar, and striato-limbic circuits. White matter abnormalities were observed versus HCs but not consistently between PDP and PDNP. Overall, the findings support a network-based pathophysiological framework for PD-related pain and highlight the need for multimodal, longitudinal, and mechanism- and dopaminergic response-specific investigations to clarify underlying mechanisms and inform targeted therapeutic strategies. REGISTRATION: Registered on the PROSPERO site on November 13, 2025 (ID: CRD420251230685). PERSPECTIVE: This review identifies notable structural and functional alterations in striatal, limbic, brainstem, and sensorimotor-cerebellar networks underlying pain in Parkinson's disease. These findings support a network-based framework for PD-related pain and highlight the need for multimodal, longitudinal, and pain subtype- and treatment response-specific studies to clarify mechanisms and guide targeted interventions.

PMID:42641775 | DOI:10.1016/j.jpain.2026.106425

Brain Inform. 2026 Aug 21;13(1):39. doi: 10.1186/s40708-026-00328-8.

ABSTRACT

Chronic back pain often emerges from a transitional period of subacute pain, yet no clinically applicable biomarker exists to identify which patients are at risk for chronification. Evidence suggests that this transition is driven not only by nociceptive input but by changes in brain networks involved in valuation, emotion regulation, and learning. Here, we used resting-state functional magnetic resonance imaging (rs-fMRI) and machine learning to explore whether dysconnectivity in these networks is associated with later development of chronic back pain. We analyzed functional connectivity in 46 patients with subacute back pain and 43 healthy controls from a publicly available longitudinal cohort, classifying patients one year later as either recovered or chronified based on pain outcomes. A data-driven model identified a set of six brain regions whose patterns of dysconnectivity distinguished the two patient trajectories with an area under the curve of 0.87. These regions encompass prefrontal, temporal, and somatosensory hubs implicated in reinforcement learning, avoidance behavior, and pain catastrophizing, suggesting a potential link between dysconnectivity patterns and psychological processes implicated in pain persistence. Based on these features, we introduced an exploratory rs-fMRI-based marker for pain chronification, suggesting potential prognostic relevance that requires independent validation before clinical stratification or targeted intervention can be considered.

PMID:42640485 | DOI:10.1186/s40708-026-00328-8

Front Mol Neurosci. 2026 Aug 10;19:1909269. doi: 10.3389/fnmol.2026.1909269. eCollection 2026.

ABSTRACT

BACKGROUND: Temporal lobe epilepsy (TLE) is increasingly recognized as a network disorder, yet reported regional intrinsic neural activity alterations from resting-state fMRI studies remain spatially heterogeneous. This study aimed to determine whether these heterogeneous alterations converge onto a shared functional network and to characterize its normative transcriptomic and neurochemical correlates.

METHODS: Using a coordinate-based network mapping approach (functional connectivity network mapping, FCNM), we delineated a common brain network functionally connected to regional intrinsic neural activity alterations reported across 20 published neuroimaging studies. The robustness of the resulting network was assessed in an independent cross-scanner validation connectome and across different seed sizes. We further characterized this TLE-related network by correlating its spatial topography with microscale gene expression data from the Allen Human Brain Atlas (AHBA) and with normative neurotransmitter receptor and transporter distributions derived from the JuSpace toolbox.

RESULTS: Twenty studies comprising 345 foci of regional intrinsic neural activity alteration in TLE were included. The FCNM analysis revealed that heterogeneous regional alterations in TLE converged onto a common functional brain network. This network exhibited the greatest spatial overlap with the default mode network (DMN), while also showing substantial overlap with the limbic network (LN). Transcriptomic analysis revealed that the network's topography was spatially correlated with gene expression profiles significantly enriched in adaptive immune response pathways, particularly antigen processing and presentation. Neurochemically, the TLE-related network exhibited a significant positive spatial correlation with the distribution of the 5-hydroxytryptamine receptor 1A (5-HT1A).

CONCLUSION: Our findings reconcile previously inconsistent reports of regional intrinsic neural activity alterations in TLE by demonstrating their convergence onto a shared brain network, primarily the DMN and LN. By linking this TLE-related network to specific normative transcriptomic and neurochemical signatures, we propose a multi-scale neurobiological framework for the disorder. These findings should be interpreted as spatial associations based on normative datasets rather than direct evidence of disease-specific molecular alterations. This framework reframes TLE from a collection of disparate regional changes toward a core network dysfunction with distinct molecular correlates, thereby opening new avenues for targeted, network-based interventions.

PMID:42639001 | PMC:PMC13500546 | DOI:10.3389/fnmol.2026.1909269

J Psychiatry Neurosci. 2026 Jan 1;51:1-11. doi: 10.1139/jpn-2026-0057.

ABSTRACT

BACKGROUND: While analyzing distance-dependent functional connectivity strength (FCS) yields valuable information about brain activity, whether the FCS alterations found in obsessive-compulsive disorder (OCD) are linked to inter-regional anatomical distance is still unknown. This study investigated distance-associated functional reorganization in OCD and its associations with clinical assessment data and neurotransmitter receptor/transporter densities.

METHODS: To quantify FCS across global, long-range, and short-range scales, we analyzed resting-state functional MRI data from 99 OCD patients and 104 matched healthy controls (HCs), followed by a post hoc exploratory seed-based functional connectivity (FC) analysis to further characterize the key brain regions that may contribute to the observed FCS abnormalities. Group differences and correlations with clinical measures (symptom severity, illness duration, and age of onset) and positron emission tomography-derived neurotransmitter receptor/transporter densities were examined.

RESULTS: Compared to HCs, OCD patients exhibited reduced long-range FCS in the bilateral fusiform and parahippocampal gyri, primarily driven by weakened connections with frontal, superior/middle temporal, precuneus, and cingulate regions. Short-range FCS was increased in the right dorsolateral prefrontal cortex but decreased in the precentral gyrus and middle cingulate cortex, mainly due to altered local connections involving the middle frontal gyrus, precentral gyrus, precuneus, and cerebellum. These alterations were robust in cross-validation. No significant correlations were found with clinical measures. However, both long- and short-range FCS changes correlated significantly with neurotransmitter receptors/transporter densities, particularly the excitatory-inhibitory ratio profile.

LIMITATIONS: Neurotransmitter receptor/transporter maps were derived from publicly available datasets, which may not capture individual variation in receptor density.

CONCLUSION: This study highlights distance-dependent functional connectivity reorganization in OCD and its neurochemical underpinnings, providing new insights into the disorder's pathophysiology.

PMID:42635498 | DOI:10.1139/jpn-2026-0057

Hum Brain Mapp. 2026 Aug 15;47(12):e70629. doi: 10.1002/hbm.70629.

ABSTRACT

The coupling between brain excitatory activity and positive blood oxygen level-dependent (BOLD) responses is well-established. Although often associated with inhibition, negative BOLD remains partially understood. Moving away from neurovascular coupling, apparent diffusion coefficient (ADC)-fMRI provides a more direct measure of excitatory activity, possibly mediated by transient cellular deformations. Diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, combines vascular and microstructural contributions. While decreases in ADC align with positive BOLD, the possible translation of negative BOLD into positive ADC and the ability of ADC-fMRI to capture inhibitory activity remain unexplored in humans. In this study, we used transcranial magnetic stimulation (TMS)-fMRI on the right primary motor cortex (M1) to selectively induce contralateral negative BOLD responses, whilst acquiring interleaved fMRI. TMS was applied at 5 Hz, 90% resting motor threshold, for both BOLD-fMRI and ADC-fMRI contrasts, in n = 12 and n = 11 healthy participants, respectively. We replicated previously reported negative BOLD clusters in the contralateral M1 and primary somatosensory cortex (S1). This was accompanied by a negative dfMRI response, but no ADC-fMRI response, indicating minimal microstructural fluctuations. In ipsilateral M1/S1, no BOLD response was detected while dfMRI revealed a positive cluster, suggesting different sensitivity to the excitatory/inhibitory balance. Overall, combining the findings from BOLD-fMRI and ADC-fMRI provides new insights into the vascular and neuronal responses underlying subthreshold TMS and negative BOLD.

PMID:42634012 | DOI:10.1002/hbm.70629

Cortex. 2026 Aug 14;204:115-132. doi: 10.1016/j.cortex.2026.08.001. Online ahead of print.

ABSTRACT

Language is one of the most extensively studied lateralised cognitive functions in the human brain, predominantly relying on the left hemisphere in most individuals. However, the mechanisms by which a stable white matter architecture underpins individual language functions remain unclear. Previous studies have employed structural connectivity (SC) and functional connectivity (FC) coupling for individual fingerprinting and task decoding, suggesting that variability in brain entropy may serve as a distinguishing characteristic for language lateralisation. We examined a large cohort of healthy adults (n = 285) to investigate SC-FC coupling and identify markers distinguishing different language laterality groups. Functional connectivity was measured using resting-state fMRI (rs-fMRI) time-series data, whereas structural connectivity was determined using probabilistic fiber tractography. SC-FC coupling was investigated using the SENSAAS language atlas and defined as the Pearson correlation between the non-zero elements of the regional structural and functional connectivity profiles. Group differences were assessed using the PALM toolbox in the FSL. Our findings revealed that increased SC-FC coupling in the left precentral sulcus was associated with typical language lateralisation, while increased coupling in the right middle temporal gyrus and left anterior insula was observed in individuals with atypical language lateralisation (pFDR <.05). Non-lateralised individuals exhibited increased coupling in the left anterior insula compared to lateralised (pFDR<.05). SC-FC coupling offers a promising framework to uncover functional and anatomical differences among individuals with varying language lateralisation. This regional specificity indicates that typical, atypical, and non-lateralised profiles rely on different structural-functional alignments, likely reflecting the recruitment of alternative pathways for language processing.

PMID:42632393 | DOI:10.1016/j.cortex.2026.08.001

Clin Neurophysiol. 2026 Aug 19;191:2112375. doi: 10.1016/j.clinph.2026.2112375. Online ahead of print.

ABSTRACT

OBJECTIVE: Transcranial magnetic stimulation (TMS) of the dorsolateral prefrontal cortex (DLPFC) may achieve its antidepressant effect via transsynaptic activation of the subgenual cingulate (SGC). SGC activation by DLPFC TMS can be quantified by concurrent EEG (TMS-EEG) via source localization, while the connection between the DLPFC and SGC can be quantified by resting state functional connectivity (RSFC). Here, the relationship between SGC source activation and DLPFC-SGC anticorrelation is characterized in depression patients.

METHODS: TMS-EEG and fMRI were collected from 42 depression patients before a course of accelerated individualized intermittent theta-burst stimulation treatment. SGC activation by single pulse TMS of the individual target was quantified by the cortical evoked activity (CEA, 20-500 ms) and the negative peak near 100 ms (N100) of the region's evoked potential. Correlations between the TMS-EEG measures and DLPFC-SGC RSFC were calculated.

RESULTS: Stronger DLPFC-SGC anticorrelation was associated with smaller SGC N100 (Rho = -0.36, Pval = 0.03). There was no correlation between the baseline depression symptoms assessed with MADRS and neither the DLPFC-SGC anticorrelation nor the TEP-based SGC measures.

CONCLUSIONS: Higher anticorrelation between the DLPFC and SGC enables TMS pulses delivered to the DLPFC to better modulate activity at the SGC.

SIGNIFICANCE: This study adds to current understanding of the DLPFC to SGC pathway. Multimodal studies with TMS-EEG and fMRI measures of network pathways may provide missing links in understanding of depression pathophysiology.

PMID:42632228 | DOI:10.1016/j.clinph.2026.2112375

J Sex Med. 2026 Aug 5;23(9):qdag259. doi: 10.1093/jsxmed/qdag259.

ABSTRACT

INTRODUCTION: Sexual-interest symptoms are common in major depressive disorder (MDD), yet their neurodynamic correlates remain unclear. This study examined whether 17-item Hamilton Depression Rating Scale (HAMD-17) Item-14 sexual-interest symptom status in MDD is associated with variation in resting-state brain co-fluctuation dynamics.

METHODS: Resting-state functional magnetic resonance imaging (fMRI) data were obtained from MDD patients with HAMD-17 Item-14 sexual-interest symptoms (SI+, n = 111), MDD patients without such symptoms (SI-, n = 110), and healthy controls (HC, n = 203). Edge-time series (ETS) were computed as the element-wise products of z-scored blood-oxygen-level-dependent signals between pairs of brain regions. The co-fluctuation strength of each ETS was quantified at each frame using the root sum square (RSS), and frames were categorized into high (top 5%), intermediate (normal 90%), and low (bottom 5%) amplitude states to characterize global co-fluctuation amplitude, temporal persistence, and transition dynamics. Principal component analysis (PCA) was further applied to high-amplitude frames to examine default mode network (DMN)-related co-fluctuation patterns.

RESULTS: The clearest subgroup differences were observed in intermediate-amplitude co-fluctuation and trough-to-trough duration. Relative to SI-, SI+ showed higher intermediate-amplitude RSS, shorter duration, and less persistent transition dynamics, suggesting heightened but more temporally unstable resting-state coordination. During high-amplitude events, PCA identified an antagonistic mode between the somatomotor/ventral attention networks and the DMN, with DMN-related differences most evident between SI+ and HC.

CONCLUSIONS: 17-item Hamilton Depression Rating Scale Item-14 sexual-interest symptom status in MDD was associated with altered resting-state co-fluctuation dynamics, with DMN-related differences most evident relative to healthy controls. These findings suggest that this routinely assessed symptom marker may help identify clinically relevant heterogeneity within MDD and may support greater clinical attention to symptom dimensions potentially relevant to treatment adherence and patient-reported outcomes. However, the findings should be interpreted as non-specific resting-state correlates of an Item-14-defined clinical profile, rather than as direct neural indices of sexual interest or as a basis for mechanistic or treatment guidance. Further studies using detailed sexual-function assessments and longitudinal or multimodal designs are needed to clarify the specificity and clinical relevance of these associations.

PMID:42632088 | DOI:10.1093/jsxmed/qdag259

Front Neurol. 2026 Aug 7;17:1796906. doi: 10.3389/fneur.2026.1796906. eCollection 2026.

ABSTRACT

BACKGROUND AND PURPOSE: Clinical evaluation of subjective tinnitus mainly depends on patients' self-reported auditory complaints, and standardized neuroimaging biomarkers for characterizing its central brain functional abnormalities remain lacking. The aim of this study is to utilize the resting-state functional magnetic resonance imaging (rs-fMRI) machine learning technique based on the region of interest (ROI), and to construct an exploratory classification framework for subjective tinnitus by analyzing functional activities and connectivity.

METHODS: The rs-fMRI data of 63 patients with subjective tinnitus (38.79 ± 15.79) and 84 healthy controls (HCs) (42.01 ± 9.45) were collected from the Department of Otorhinolaryngology, the first affiliated Hospital of Nanchang University. Five analysis methods were used: regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), fraction amplitude of low frequency fluctuation (fALFF), resting state functional connectivity (RSFC) and degree centrality (DC). A total of 7,134 features are extracted after z conversion. Then, the predicted features were selected through Mann-Whitney U test, the variables with high pairwise correlation (the correlation coefficient is greater than 0.75) were removed, the least absolute shrinkage and selection operator method was used to screen the features. Finally, a machine learning model was constructed by combining logistic regression (LR), support vector machine (SVM) and random forest (RF), and the performance differences of the three models were compared.

RESULTS: 21 features are retained, including 3 zRSFCs, 1 zALFFs, 6 zfALFFs, 3 zDCs, and 8 zReHos. Based on these 21 features, the model accuracy and area under the curve constructed by LR, SVM and RF were 75.51% and 0.80, 80.27% and 0.82, 73.47% and 0.79, respectively.

CONCLUSION: Our findings indicate that the ROI-based rs-fMRI machine-learning provides preliminary proof-of-concept evidence for the objective confirmation of subjective tinnitus. The imaging information based on rs-fMRI has the potential to become a neuroimaging biomarker for tinnitus.

PMID:42630179 | PMC:PMC13493256 | DOI:10.3389/fneur.2026.1796906

Neuropsychopharmacology. 2026 Aug 21. doi: 10.1038/s41386-026-02515-x. Online ahead of print.

ABSTRACT

Evidence of heterogeneity in the trajectory of psychosis is abundant. Here, we applied group-based trajectory modeling on positive symptoms and collected resting state fMRI data of 81 medication-naïve first episode psychosis (FEP) patients during a 16-week antipsychotic drug (APD) trial. Data from 131 healthy controls (HCs) were also acquired during the trial for comparison. Three major trajectory subgroups were identified and labeled as Fast (50.6%), Delay (35.8%), and Partial responders (13.6%). Logistic regressions revealed that Fast responders (vs. Partial) showed significantly lower psychopathology symptoms prior to treatment and lower APD dosage after the trial. Moreover, Delay responders showed significant increases in executive control network resting state functional connectivity (ECN FC) during the trial towards a normalization (using HCs as reference), while Fast and Partial responders to a lesser extent. These changes were associated with treatment response (reduction in positive symptoms after the trial). Future work should harness the potential of ECN FC to inform the mechanism of delayed responses with the potential to start unraveling psychotic heterogeneity which has hampered our ability to identify new treatment strategies and lead to better clinical outcomes. (Clinical trial registration: Trajectories of Treatment Response as Window into the Heterogeneity of Psychosis: A Longitudinal Multimodal Imaging Study, NCT03442101 https://clinicaltrials.gov/ct2/show/NCT03442101 . Glutamate, Brain Connectivity and Duration of Untreated Psychosis (DUP), NCT02034253 https://clinicaltrials.gov/ct2/show/NCT02034253 ).

PMID:42629434 | DOI:10.1038/s41386-026-02515-x

J Autism Dev Disord. 2026 Aug 21. doi: 10.1007/s10803-026-07358-5. Online ahead of print.

ABSTRACT

PURPOSE: Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by atypical interactions between brain networks. These interactions and the resulting dynamic shifts are effectively captured by Hidden Markov Models (HMMs) in comparison to static functional connectivity (FC)-based approaches. This review aims to meta-analyze the application of HMM to resting-state fMRI (rs-fMRI) in individuals with ASD.

METHODS: A systematic search of PubMed, Scopus, and Web of Science was conducted in May 2025. Screening followed PRISMA 2020 guidelines, with independent review and consensus resolution. Eligible studies were peer-reviewed, English-language publications that have applied HMMs to rs-fMRI in ASD, with either classification performance or state-metric outcomes reported.

RESULTS: Seven studies met eligibility criteria. HMM-derived metrics demonstrated diagnostic utility, with a pooled log odds ratio (log OR) of 2.86 (95% CI: 1.74-3.98; z = 5.01, p < 0.001; I² = 92%) and a pooled AUC of 0.85 (95% CI: 0.72-0.98; I² = 96.9%). Substantial heterogeneity exists across both accuracy outcomes. Relative to typically developing controls, individuals with ASD showed markedly reduced mean lifetime (MLT) in default mode network (DMN)-associated states (pooled Hedges' g = -4.19; 95% CI: -5.52 to -2.85; I² = 98%) and prolonged MLT in sensory/attention hyperactivation states (g = 3.80; 95% CI: 3.43-4.16; I² = 65%), the latter rated as high certainty evidence. Fractional occupancy (FO) in DMN states was also substantially reduced (g = -6.22; 95% CI: -9.87 to -2.58; I² = 99.6%), though this outcome was rated low certainty. Narrative synthesis across consistently identified reduced FO and MLT in DMN-hypersynchrony states alongside increased occupancy in sensory-motor and attention states, replicated across two studies despite variation in atlas choice, number of HMM states, and participant samples. HMM-derived metrics were significantly negatively correlated with ADOS scores (pooled r = -0.20; 95% CI: -0.27 to -0.13; I² = 0%; p < 0.001), rated as high certainty evidence. Transition probability analyses, reported narratively due to incompatible state taxonomies, indicated reduced transitions from sensory-related to DMN-related states and increased self-transitions in sensory-motor states in ASD. Overall risk of bias was low to moderate, with incomplete confounder adjustment being the most common limitation.

CONCLUSIONS: ASD involves rigid, imbalanced temporal dynamics, with reduced engagement of integrative networks and dominance of sensory states. Leveraging its high diagnostic accuracy, HMMs capture these alterations and hold promise for mechanistic insight and personalized diagnostics, though heterogeneity remains a challenge.

REVIEW REGISTRATION: PROSPERO CRD420251057196.

PMID:42627436 | DOI:10.1007/s10803-026-07358-5

Front Psychiatry. 2026 Aug 6;17:1886224. doi: 10.3389/fpsyt.2026.1886224. eCollection 2026.

ABSTRACT

BACKGROUND: The default mode network (DMN) plays an important role in depression and sleep regulation. However, whether sleep symptoms are associated with alterations in DMN homogeneity in adolescent major depressive disorder (MDD) remains unclear. This study aimed to investigate whether sleep symptom burden is associated with alterations in DMN homogeneity in adolescents with MDD.

METHODS: Following quality control procedures, the final sample comprised 162 first-episode, drug-naïve adolescents with MDD, including 86 participants with higher sleep symptom burden (MDD-S) and 76 participants with lower sleep symptom burden (MDD-NS), together with 53 healthy controls (HCs). Sleep grouping was based on Hamilton Depression Rating Scale (HAMD) sleep items. The DMN was identified using group independent component analysis (ICA). Voxel-wise network homogeneity (NH) values within the DMN were calculated and compared among groups. Correlation analyses and exploratory support vector machine (SVM) analyses were performed.

RESULTS: Opposite-direction NH alterations in the precuneus were observed between groups with different sleep symptom burdens. Compared with HCs, the MDD-NS group showed increased NH in the right precuneus, whereas the MDD-S group exhibited decreased NH in the bilateral precuneus. Relative to the MDD-NS group, the MDD-S group showed increased NH in the left anterior cingulate cortex, posterior cingulate cortex, and angular gyrus, together with decreased NH in the right middle temporal gyrus. Exploratory SVM analyses showed modest discriminative performance, indicating limited utility of NH alterations alone for individual-level classification.

CONCLUSION: Adolescents with MDD and different levels of sleep symptoms exhibit distinct DMN homogeneity patterns. Sleep symptoms may be associated with altered DMN synchronization patterns, particularly within the precuneus. The opposite-direction precuneus alterations observed across MDD subgroups may provide a potential perspective for understanding some of the heterogeneous DMN findings reported in previous depression studies.

PMID:42626602 | PMC:PMC13492238 | DOI:10.3389/fpsyt.2026.1886224