Most recent paper

bioRxiv [Preprint]. 2026 Jun 18:2026.06.14.732139. doi: 10.64898/2026.06.14.732139.

ABSTRACT

Low back pain is a prevalent issue with few reliable treatments. Although there is great variation in clinical presentation within the low back pain population, little is known about the neurobiological mechanisms underlying these differences. In this study, we sought to stratify chronic low back pain patients (N = 275) into phenotypes characterized by correlated patterns of resting-state brain activity and sensory abnormalities (pain, numbness, and pins and needles) indicated on hand-drawn body maps. Our cross-decomposition analysis yielded phenotypes that resemble previously documented mechanistic pain types, revealing distinct brain connectivity patterns associated with different clinical presentations. Our model was then used to predict pain body maps from fMRI data in a small novel dataset of chronic pain subjects, suggesting that these relationships may generalize to other chronic pain conditions. Our results support the utility of resting-state fMRI in understanding the heterogeneity of chronic pain, which may be leveraged to develop more targeted pain treatments.

PMID:42368013 | PMC:PMC13307932 | DOI:10.64898/2026.06.14.732139

bioRxiv [Preprint]. 2026 Jun 16:2026.06.11.731643. doi: 10.64898/2026.06.11.731643.

ABSTRACT

Individual-specific cortical parcellations enable the characterization of brain network organization that is often obscured by population-level atlases, with broad implications for both basic neuroscience and translational applications. However, existing methods rely primarily on resting-state fMRI and underutilize task-evoked data, which provide complementary information about functional specialization. This limitation partly reflects the challenge of integrating heterogeneous datasets that differ in task design, sample size, and cortical coverage. Here, we present mRBM-HBP, a scalable hierarchical Bayesian framework that incorporates a multinomial restricted Boltzmann machine to model spatial dependencies, enabling efficient and flexible integration of resting-state and task fMRI across diverse datasets and inference of both group-level and individual-level cortical parcellations. We show that mRBM-HBP achieves performance comparable to state-of-the-art resting-state-based parcellation methods while substantially reducing computational cost. By integrating large-scale task-fMRI datasets, we derive a task-based parcellation and demonstrate that resting-state and task conditions reveal largely consistent macroscopic networks, while task data provide state-specific refinements of functional boundaries. Moreover, a fused rest-task group-level atlas improves the accuracy, reliability, and individual specificity of inferred parcellations, particularly when individual-level data are limited. These results indicate that integrating resting-state and task fMRI enhances precision mapping of functional brain organization.

PMID:42367978 | PMC:PMC13308068 | DOI:10.64898/2026.06.11.731643

Int J Dev Neurosci. 2026 Aug;86(5):e70145. doi: 10.1002/jdn.70145.

ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by social communication deficits and repetitive behaviours. Diagnosing ASD early is difficult for healthcare professionals due to its diverse and intricate presentation. However, early detection is vital for enhancing outcomes and enabling the children to access targeted therapies that support the development of social and communication skills. Moreover, Classical models were time-consuming and resource-intensive, and they required lengthy assessments and specialized training. To bridge these complications, this research proposes a Sales Training-Based Optimization enabled Deep High-Order Principal Component Network (STBO_DHPCNet) for ASD classification using resting-state fMRI (rs-fMRI) brain images from 1114 subjects in the ABIDE dataset. First, gamma correction is applied to enhance the quality of the autism brain image. Next, the Region of Interest (ROI) extraction is performed. Afterwards, the nub region extraction is performed based on Sales Training Based Optimization (STBO). On the other hand, feature extraction is done based on an enhanced brain image. Finally, the classification of ASD is done by using DHPCNet, and it is trained using STBO. Here, DHPCNet is developed by incorporating the Deep High-Order Attention Neural Network (DHA-Net) and Principal Component Analysis Network (PCA-Net). Moreover, the evaluation results show that the DHPCNet gained an increased range of accuracy, sensitivity and specificity as 95.62%, 94.79%, and 95.86%.

PMID:42366641 | DOI:10.1002/jdn.70145

Nat Commun. 2026 Jun 27. doi: 10.1038/s41467-026-74953-6. Online ahead of print.

ABSTRACT

Resting-state fMRI (rsfMRI) scans-acquired in the absence of experimentally controlled stimuli or task demands-are widely used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers routinely control for across-cohort disparities in age, gender, comorbidities, and head motion. Yet, studies rarely consider the possibility that systematic differences in inner experience (i.e., how subjects think and feel during the scan) directly affect FC measures. Here, using an rsfMRI dataset comprising 469 scans with retrospective experiential annotations, we show that summary descriptors of in-scanner experience are reproducible across visits and subject-specific, consistent with trait-like characteristics. We further show that widespread significant differences in FC are observed between scans that are associated with different reported experiential profiles, and that FC can predict specific experiential dimensions with performance comparable to that reported for demographic, cognitive, and clinical variables. Together, these findings highlight the key role that in-scanner experience should play when interpreting FC in the context of rsfMRI. Given that the available experiential measures are retrospective summaries, these results speak to stable experiential tendencies rather than potential moment-to-moment, state-dependent relationships between ongoing experience and concurrent brain activity.

PMID:42365014 | DOI:10.1038/s41467-026-74953-6

BMC Psychiatry. 2026 Jun 27. doi: 10.1186/s12888-026-08332-2. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a highly prevalent psychiatric condition that frequently emerges during adolescence, a critical developmental stage characterized by heightened vulnerability to emotional dysregulation. Despite increasing evidence of large-scale brain network dysfunction in adult MDD, the static and dynamic connectivity alterations underlying adolescent MDD remain poorly understood.

METHODS: We recruited 29 first-episode, treatment-naïve adolescents with MDD and 29 age- and sex-matched healthy controls (HCs). Resting-state functional magnetic resonance imaging (rs-fMRI) data were analyzed using group independent component analysis (ICA) combined with sliding-window clustering to evaluate both static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) across the default mode network (DMN), salience network (SN), central executive network (CEN), and dorsal attention network (DAN). Correlation analyses were performed between connectivity metrics and clinical severity assessed by the 17-item Hamilton Depression Rating Scale (HAMD-17).

RESULTS: Compared with HCs, adolescents with MDD exhibited significantly reduced intra- and inter-network connectivity within the DMN, SN, and CEN, alongside a trend toward increased DAN-SN connectivity. Dynamic analyses revealed reduced state transition frequency, shorter dwell time in low-connectivity states (e.g., DMN-CEN-SN interactions), and longer dwell time in high-connectivity states (e.g., DAN-SN coupling). Clinical analyses demonstrated that weaker intra-DMN and intra-DAN connectivity, as well as reduced DMN-CEN and DMN-SN connectivity, were negatively correlated with HAMD-17 scores. Conversely, prolonged dwell time in hyperconnected states positively correlated with greater symptom severity.

CONCLUSION: Our findings highlight distinct static and dynamic network abnormalities in adolescent MDD, including disrupted DMN-CEN competitive balance and maladaptive DAN-SN hyperconnectivity. These alterations may hint at developmental-stage-specific neuropathological mechanisms that could differ from adult depression, although direct comparison with adult cohorts is lacking. Integrating static and dynamic FNC analyses may provide preliminary insights into candidate neuroimaging markers for early detection and intervention strategies in adolescent MDD, though these findings require validation in independent, larger cohorts.

PMID:42365265 | DOI:10.1186/s12888-026-08332-2

J Affect Disord. 2026 Jun 27:122181. doi: 10.1016/j.jad.2026.122181. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar Disorder (BD) is characterized by severe emotional instability. While traditionally viewed through the lens of anatomical dysconnectivity, it remains unclear how the brain's anatomical scaffold abnormally constrains its functional dynamics to produce such volatile mood states.

METHODS: We investigated the multi-modal connectome in 41 BD patients and 40 healthy controls (N=81). A high-fidelity pipeline was employed to robustly reconstruct structural networks. We then applied Graph Signal Processing (GSP) and functional gradient analysis to resting-state fMRI data to quantify structure-function alignment and hierarchical network dynamics.

RESULTS: Network-based statistics revealed no NBS-detected macroscale structural disruptions in BD. However, GSP analysis uncovered a profound pathological shift toward "functional rigidity". BD patients exhibited significantly elevated structure-function alignment (hyper-coupling), indicating that functional dynamics are excessively restricted by the underlying anatomical backbone. Furthermore, a multimodal connectomic model demonstrated good performance in distinguishing BD from healthy controls at the individual level (accuracy=74.1%, AUC=0.888, sensitivity=70.7%, specificity=77.5%, permutation p=0.001). SHAP analysis localized the dominant predictors to the prefrontal-limbic emotion-regulation circuit.

CONCLUSION: BD is characterized by profound "functional rigidity" rather than sheer anatomical degradation. This excessive structure-function tethering may constitute a candidate inter-episode connectomic signature with preliminary individual-level discrimination potential, offering new insights into the connectomic basis of inter-episode functional dysregulation in BD and motivating prospective validation in independent longitudinal cohorts.

PMID:42364661 | DOI:10.1016/j.jad.2026.122181

Mol Psychiatry. 2026 Jun 26. doi: 10.1038/s41380-026-03694-1. Online ahead of print.

ABSTRACT

Theta-band oscillation is integral to fronto-parietal connectivity in the executive control network and its top-down regulation on subcortical areas. External frontoparietal synchronization using theta-frequency transcranial alternating current (tACS) is a technology to potentially engage this network. In this pre-registered, triple-blind, sham-controlled trial (NCT03907644), we tested this intervention targeting the right frontoparietal network in people with opioid use disorder (OUD) to measure network engagement and behavioral outcomes. Sixty male participants with OUD were randomized to receive 20 min of active or sham 6 Hz tACS (HD electrodes over F4 and P4). Structural, resting-state, task-based fMRI drug cue reactivity, and repeated cue-induced craving assessments were collected immediately before and after stimulation. Pre-registered outcome measures were analyzed using time × group interaction models to examine (1) modulation of drug cue-related brain activity, (2) changes in craving, (3) alterations in functional connectivity, and (4) relationship between electric field, neural responses, and craving behavior. (1) A significant Time × Group interaction revealed decreased post-stimulation opioid cue-related activity in the active group relative to sham, involving key nodes in reward processing (ventral striatum, amygdala and ventral tegmental area) (FWE corrected α = 0.05) (2) subjective craving did not differ significantly between groups (3) Group by time generalized psychophysiological interaction analyses showed increased right frontoparietal network engagement (β = 2.63, p= 0.0308) following stimulation, and increased top-down inhibitory regulation of frontoparietal network on right ventral striatum (β = 1.99, p= 0.037) and left medial amygdala (β = 1.97, p= 0.039) (4) Electric field strength in the right frontal/parietal node predicted frontoparietal network engagement in the active group (r = 0.43, p= 0.02). Together, these findings demonstrate that theta-band frontoparietal tACS can modulate activity and task-dependent coupling within cortical-subcortical circuits in OUD, supporting network-targeted neuromodulation as a potential intervention for addiction.

PMID:42362768 | DOI:10.1038/s41380-026-03694-1

J Affect Disord. 2026 Jun 26:122179. doi: 10.1016/j.jad.2026.122179. Online ahead of print.

ABSTRACT

Late-life depression (LLD) is characterized by high treatment resistance, particularly in patients with significant cerebral amyloid-beta (Aβ) deposition. However, the underlying mechanisms through which Aβ compromises clinical recovery remain poorly understood. We investigated whether Aβ burden drives treatment resistance by inducing hierarchical rigidity in the brain's macroscale functional organization. We analyzed longitudinal resting-state fMRI and amyloid-PET data from 93 LLD patients (44 Aβ+, 49 Aβ-) over a one-year treatment interval. Functional connectivity (FC) gradients were constructed using diffusion map embedding to characterize the sensory-to-transmodal cortical hierarchy. At baseline, Aβ + patients exhibited widespread FC gradient aberrations, primarily within the frontoparietal control and default mode networks, despite equivalent depression severity to Aβ- patients. Following treatment, the Aβ + group showed significantly poorer clinical remission (p < 0.001). Longitudinal analysis revealed a lack of significant group × time interaction, indicating that gradient disorganization in Aβ + LLD remained unresolved. Cerebral Aβ acts as a physiological constraint on the hierarchical plasticity required for antidepressant response. This "hierarchical rigidity" suggests that Aβ anchors the brain in a dysfunctional, compressed state, precluding the adaptive reorganization necessary for recovery. Baseline functional gradients may serve as a potent prognostic marker for identifying amyloid-associated treatment resistance in LLD.

PMID:42362062 | DOI:10.1016/j.jad.2026.122179

Neurosci Biobehav Rev. 2026 Jun 26:106837. doi: 10.1016/j.neubiorev.2026.106837. Online ahead of print.

ABSTRACT

The prognostic value of spontaneous brain activity in patients with Disorders of Consciousness (DoC) remains questionable due to methodological heterogeneity, small sample sizes, and the difficulty in conducting longitudinal studies on this clinical population. We performed a coordinate-based meta-analysis of the studies adopting resting state brain imaging techniques to identify whether the spontaneous activity of specific brain areas has a prognostic value for DoC patients. We included studies published until 2025 providing the peak coordinates deriving from contrasting brain activations of patients showing full consciousness recovery (Good Outcome;GO) and patients showing either no consciousness recovery or death (Bad Outcome; BO) with voxel-wise whole-brain analyses. Twelve studies were included in the meta-analysis containing data from 332 DoC patients (n= 192 BO) in the post-acute phase, assessed through resting state functional Magnetic Resonance Imaging. Compared to BO, GO patients showed increased spontaneous activity in sensory and associative areas, including visual areas, precuneus, temporo-parietal, and, marginally, premotor regions. Taken together, the results suggest preservation of posterior brain areas is pivotal in assisting prognosis, with a marginal role played by the premotor areas. However, both low-level sensory and high-level associative areas contribute to outcome prediction of DoC patients, possibly due to the need to integrate low-level sensory information to enable functional interaction with the environment.

PMID:42361934 | DOI:10.1016/j.neubiorev.2026.106837

Eur J Radiol. 2026 Jun 17;203:113018. doi: 10.1016/j.ejrad.2026.113018. Online ahead of print.

ABSTRACT

BACKGROUND: Heart failure (HF) is frequently accompanied by cognitive and affective impairments, yet the underlying neural mechanisms of altered brain information processing in HF remain poorly defined.

METHODS: We applied an integrated information decomposition framework to resting-state fMRI from 48 HF patients (including HFrEF and HFmrEF, with LVEF < 50 %) and 33 matched healthy controls, quantifying synergistic and redundant information across 246 brain regions. Group differences were assessed at global and regional levels and correlated with cardiac function, cognitive performance, and affective symptoms. Neuromaps, neurotransmitter and neuropeptide receptor maps and cognition-related meta-analytic data from NeuroSynth were used to contextualize findings.

RESULTS: HF patients exhibited significantly greater global brain synergy, with regional elevations in the thalamus, basal ganglia, and limbic-temporal areas. Altered synergistic interactions correlated with lower ejection fraction, poorer cognition, and greater depression. Spatial mapping showed negative associations between synergy and cerebral blood flow, glucose and oxygen metabolism, and microstructural integrity. Synergy patterns overlapped with the distribution of dopamine, histamine, and opioid receptors, and neuropeptides including somatostatin, vasopressin, and kinin/tensin. NeuroSynth decoding linked these patterns to memory, emotion, pain, and motor control domains.

CONCLUSIONS: HF is characterized by distinct alterations in synergistic brain information processing that relate to cognitive-emotional impairment and show exploratory spatial correspondence with normative molecular architecture. These findings provide multimodal evidence for altered heart-brain interactions in HF.

PMID:42361709 | DOI:10.1016/j.ejrad.2026.113018

J Neurol. 2026 Jun 26;273(7):425. doi: 10.1007/s00415-026-13930-x.

ABSTRACT

BACKGROUND AND PURPOSE: Altered cerebellar resting-state functional connectivity (FC) may contribute to hand motor impairment in People with Multiple Sclerosis (PwMS). We aimed to assess whether: 1. FC abnormalities of the sensorimotor and cognitive cerebellum (smCb, cCb) are associated with impaired hand dexterity; 2. these alterations contribute to impaired hand dexterity beyond structural brain and spinal cord damage.

METHODS: Six hundred and sixty-seven subjects from the Italian Neuroimaging Network Initiative database were included: two hundred forty-eight PwMS with impaired (iPwMS) and two hundred forty-eight with preserved hand dexterity (pPwMS), and one hundred seventy-one healthy controls (HC). We obtained measures of white matter lesion load, cortical, deep gray matter and cerebellar volumes, and C2-C3 spinal cord area. Cerebellar FC of the smCb and cCb was evaluated using voxel-wise seed-based analyses. Group comparisons were performed using ANCOVA models, with and without adjusting for global and regional structural measures.

RESULTS: Compared to HC and pPwMS, iPwMS showed widespread FC reductions of both smCb and cCb in several cortical, subcortical, and cerebellar regions (p < 0.05, FDR-corrected), whereas pPwMS did not differ significantly from HC. After adjusting for structural measures, FC reductions in iPwMS persisted, although to a lesser extent, particularly in cortical and cerebellar regions.

CONCLUSION: Cerebellar FC reductions involving both sensorimotor and cognitive domains are robustly associated with impaired hand dexterity in PwMS and remain significant even after controlling for brain and spinal cord structural damage. This suggests that cerebellar FC alterations are a key, only partly structure-dependent contributor to dexterity impairment, highlighting cerebellar connectivity as a promising target for motor rehabilitation in MS.

PMID:42360506 | DOI:10.1007/s00415-026-13930-x

Ann Neurosci. 2026 Jun 23:09727531261452860. doi: 10.1177/09727531261452860. Online ahead of print.

ABSTRACT

BACKGROUND: Traumatic brain injury (TBI) is a significant global health concern resulting in persistent physical, cognitive, and social impairments that affect daily functioning and quality of life. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable neuroimaging modality offering novel insights into brain function at rest.

SUMMARY: This integrative review synthesised evidence from studies indexed in PubMed, ScienceDirect, and Scopus on resting-state network (RSN) alterations and their association with cognitive dysfunction in TBI. Consistent alterations in functional connectivity were identified across multiple RSNs in TBI population, particularly within the default mode network (DMN), frontoparietal network (FPN), and salience network. DMN connectivity showed strong associations with attention, processing speed, and memory. FPN connectivity changes corresponded with executive function deficits, while sensorimotor network disruptions were linked to attentional impairments. Dynamic longitudinal changes in network connectivity further suggested underlying neuroplasticity mechanisms.

KEY MESSAGE: Resting-state functional connectivity demonstrates as a potential sensitive biomarker for TBI diagnosis, prognosis, and rehabilitation monitoring. With implications for individualised rehabilitation, these findings highlight the need for a network oriented approach for understanding, diagnosing, and treating TBI. Future research should explore longitudinal changes in RSN connectivity and their implications for neuro-rehabilitation strategies.

PMID:42359244 | PMC:PMC13290743 | DOI:10.1177/09727531261452860

Front Neurol. 2026 Jun 10;17:1839830. doi: 10.3389/fneur.2026.1839830. eCollection 2026.

ABSTRACT

OBJECTIVE: Focusing on patients with epilepsy and left-sided epileptogenic foci, this study aimed to clarify language lateralization differences from healthy individuals, explore Broca's/Wernicke's area lateralization and dominant hemisphere shift, and construct a laterality index-activation map combined scheme. It provides a theoretical basis for optimizing epilepsy surgery and reducing postoperative language impairment risk.

METHODS: We retrospectively studied 36 patients with left-sided epileptogenic foci and 45 healthy controls (2018-2023, Hebei Medical University Second Hospital). After preprocessing rs-fMRI data, 12 bilateral language-related seed points were selected to calculate functional connectivity and generate activation maps. We further computed two types of laterality indexes: the global laterality index (LI) reflecting overall whole-brain language lateralization, and the regional LI assessing independent lateralization of Broca's area (6 frontal seed points) and Wernicke's area (6 temporal seed points). The Kappa coefficient was used to analyze the consistency between different methods, and SEEG cortical stimulation (only performed in surgical candidates) combined with postoperative follow-up was applied to verify clinical reliability.

RESULTS: Non-classical language dominance was higher in epilepsy (69.4% vs. 45.5%, p = 0.024), especially in Wernicke's area (81.8% vs. Broca's 50.0%). Laterality index-activation map consistency was 83.3% (Kappa = 0.586); regional laterality index-activation map consistency was 75.0% (Kappa = 0.40). SEEG stimulation and surgical verification confirmed that the left Broca's area retained core language function, whereas the left Wernicke's area showed significant functional impairment (only 10.0% positive stimulation rate, and no language deficits occurred after partial resection in 6 patients).

CONCLUSION: Left-sided epilepsy patients show language lateralization remodeling, with Wernicke's area more prone to shift. Bilateral language dominance does not equate to equal functional contribution; the left Broca's area retains core motor language function and requires priority protection in all epilepsy surgeries involving the left frontotemporal lobe, especially left frontal lobe epileptogenic focus resection. The combined laterality index-activation map scheme reliably supports preoperative language lateralization, optimizes surgery, and reduces language impairment risk, with clinical value.

PMID:42358935 | PMC:PMC13290606 | DOI:10.3389/fneur.2026.1839830

Brain Sci. 2026 Jun 4;16(6):610. doi: 10.3390/brainsci16060610.

ABSTRACT

Neuroticism, a Big Five trait characterized by emotional instability and susceptibility to negative affect, is a robust transdiagnostic predictor for the onset, severity, and persistence of anxiety disorders, major depressive disorder (MDD), and other affective conditions. Recent advances in functional magnetic resonance imaging (fMRI) techniques-including resting-state fMRI, multimodal neuroimaging, and their integration with machine learning-have enabled multi-perspective investigations into the neural substrates of neuroticism. Current research in this field primarily follows three complementary approaches: cross-sectional studies identifying key brain regions for emotional processing and cognitive control (e.g., amygdala (AMG), prefrontal cortex); longitudinal studies capturing neural mechanisms evolution across adolescence, middle age, and old age to elucidate relationships between neuroticism and brain plasticity; and intervention studies exploring plastic pathways for reshaping the neural representations of neuroticism, challenging the classic "trait stability" paradigm. This review synthesizes recent progress in the cognitive neuroscience of neuroticism across these three approaches, proposes a unified emotion-cognition neural model centered on the AMG-prefrontal-default mode network circuit, and outlines a hypothesized lifespan trajectory of Limbic Sensitivity → Regulatory Strain → Prefrontal Decline. While accumulated evidence broadly supports the cross-sectional and interventional pillars of this framework, the lifespan trajectory remains a theoretically informed working model requiring further longitudinal validation. The field still faces critical limitations, including small effect sizes, methodological heterogeneity, and unresolved questions regarding causality and circuit specificity. This review aims to provide a conceptual integration of existing findings, identify key uncertainties, and propose evidence-based future directions. We further link the proposed neural model to clinical phenotypic characteristics of high neuroticism and discuss its implications for targeted neural interventions, thereby advancing our understanding of the biological basis of neuroticism and providing a theoretical framework for prevention and intervention in neuroticism-related affective disorders.

PMID:42352621 | DOI:10.3390/brainsci16060610

Brain Sci. 2026 May 23;16(6):557. doi: 10.3390/brainsci16060557.

ABSTRACT

BACKGROUND: Mild traumatic brain injury (mTBI) is frequently followed by persistent cognitive, affective, and sensory complaints despite unremarkable conventional structural imaging. Resting-state functional MRI (rs-fMRI) has been increasingly employed to detect subtle alterations in large-scale brain networks. However, variability in analytical approaches and the potential influence of neurovascular factors complicate interpretation of BOLD-derived connectivity findings.

OBJECTIVE: This study provides a focused, evidence-informed synthesis integrating umbrella review principles with a targeted narrative analysis of recent high-quality rs-fMRI studies in mild traumatic brain injury (mTBI). Rather than a comprehensive systematic review, the aim was to identify convergent patterns of network dysfunction while critically examining methodological constraints, including neurovascular confounds and variability in analytical approaches.

CONCLUSIONS: This synthesis supports a network-level model of mTBI characterized by distributed connectivity disturbances. However, given the limited number of eligible studies and substantial methodological heterogeneity, findings should be interpreted as qualitative convergence rather than quantitative generalization. Future longitudinal, multimodal, and standardized imaging approaches are required to clarify the translational relevance of rs-fMRI findings.

PMID:42352566 | DOI:10.3390/brainsci16060557

Biomedicines. 2026 Jun 9;14(6):1307. doi: 10.3390/biomedicines14061307.

ABSTRACT

Spinal cord stimulation (SCS) is an established therapy for chronic pain, yet treatment response remains highly variable and patient selection largely empirical. The identification of biomarkers with the potential to predict and monitor therapeutic response is therefore critical for advancing toward precision neuromodulation. This study provides a structured narrative synthesis of current evidence on biomarkers in SCS, focusing on their predictive and monitoring roles and their translational potential. Available studies were analysed across electrophysiological, neuroimaging, autonomic, and molecular domains and conceptually organized into predictive biomarkers-reflecting baseline biological states associated with treatment susceptibility-and monitoring biomarkers, capturing physiological and molecular adaptations following stimulation. Among predictive approaches, intraoperative electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown promising but exploratory discriminative performance. However, EEG findings are derived from intraoperative settings, limiting their applicability to pre-implantation patient selection. In contrast, monitoring biomarkers-including heart rate variability, metabolic imaging, and immunological parameters-provide objective measures of treatment-induced changes but do not currently support predictive use. Molecular and genomic biomarkers, while mechanistically informative, remain exploratory and lack validated clinical utility. A central limitation of the field is the fragmentation of biomarker research, with most studies evaluating single modalities in isolation. To address this gap, we propose a translational framework integrating predictive and monitoring biomarkers through a two-stage model combining baseline stratification with longitudinal response assessment. Although biomarker research in SCS is rapidly evolving, its clinical application remains limited. The development of multimodal, validated biomarker strategies may support improved patient selection and more objective evaluation of treatment response, enabling a transition toward mechanism-based neuromodulation.

PMID:42351735 | DOI:10.3390/biomedicines14061307

Sci Rep. 2026 Jun 26. doi: 10.1038/s41598-026-59851-7. Online ahead of print.

ABSTRACT

Persistent olfactory dysfunction after SARS-CoV-2 infection is common, yet its central neural profile remains poorly defined. We combined ultra-high-field 7 Tesla resting-state functional MRI with surface-based cortical morphometry to characterise olfactory-network organisation and cortical structure in long-term post-COVID dysosmia. Thirty adults (14 with persistent dysosmia; 16 normosmic controls) completed psychophysical olfactory testing and 7 Tesla imaging. Connectivity analyses across 56 olfaction-related regions revealed a coherent pattern centred on insular, orbitofrontal and thalamic nodes: connectivity was reduced between the insula and the orbitofrontal and entorhinal cortices, and between the ventral posterior thalamus and the ventral insula, and increased between interhemispheric orbitofrontal regions and among anterior thalamic nuclei. Significant connections were predominantly right-lateralised or interhemispheric. Morphometry showed no volumetric differences but selective left orbitofrontal thinning. Across the whole sample, greater orbitofrontal and insular thickness was associated with better olfactory performance; however, this reflected the difference between patients and controls rather than a graded relationship within the patient group, and did not persist after accounting for group and age. Together, these findings provide a preliminary, proof-of-concept characterisation of an orbitofronto-insular signature of long-term post-COVID dysosmia and nominate candidate imaging markers for testing in larger, multi-centre cohorts.

PMID:42350777 | DOI:10.1038/s41598-026-59851-7

BMC Psychiatry. 2026 Jun 25. doi: 10.1186/s12888-026-08326-0. Online ahead of print.

ABSTRACT

OBJECTIVE: To address the clinical difficulty of differentiating Generalized Anxiety Disorder (GAD) from Major Depressive Disorder (MDD), this study aimed to create a machine learning framework with strong interpretability, leveraging a comprehensive suite of resting-state functional MRI features to enhance potential relevance.

METHODS: A comprehensive set of functional measures, encompassing both local neural activity and global functional connectivity, was extracted as neuroimaging features from resting-state fMRI data. These data were obtained from 91 patients with Generalized Anxiety Disorder (GAD), 94 with Major Depressive Disorder (MDD), and 71 healthy controls (HCs). Five machine learning algorithms were used for classification, with performance estimated within a rigorous nested cross-validation framework. Then, partial correlations were conducted to assess the associations between top-ranked contributive neuroimaging features and symptom severity.

RESULTS: For the discrimination tasks, the machine learning models achieved area under the curve (AUC) values of 0.783 (GAD vs. MDD), 0.824 (GAD vs. HCs), and 0.867 (MDD vs. HCs), demonstrating moderate classification performance. Feature importance analysis revealed that precuneus function served as a prominently differential neurobiological signature and it showed opposing relationships with the severity of anxiety (positive correlation) and depression (negative correlation) across different diagnostic categories.

CONCLUSION: These findings could guide the creation of a computational framework based on neuroimaging to effectively differentiate GAD from MDD. This is underscored by the pivotal role that the precuneus appears to play in the neurobiological processes associated with symptoms of both disorders.

TRIAL REGISTRATION: This is a non-interventional study with a control group. Clinical trial registration is not applicable.

PMID:42351064 | DOI:10.1186/s12888-026-08326-0

J Am Acad Child Adolesc Psychiatry. 2026 Jun 25:S0890-8567(26)00281-9. doi: 10.1016/j.jaac.2026.06.016. Online ahead of print.

ABSTRACT

OBJECTIVE: Early-onset schizophrenia (EOS), defined as onset before age 18, is associated with more severe symptoms, higher genetic load, and poorer functional outcomes compared to adult-onset schizophrenia (AOS). Alterations in brain network organization have been widely reported in EOS and may contribute to its distinct clinical profile, although interactive effects of age and disease on connectome topology remain poorly understood.

METHOD: This study included 45 first-episode drug-naïve patients with EOS, 47 first-episode drug-naïve patients with AOS, and 79 age-matched healthy controls (29 younger, 50 older), with additional exploration in medicated groups (91 EOS and 79 AOS). Using resting-state fMRI and graph theory analysis, we compared topological properties of whole-brain and subnetworks across groups.

RESULTS: We identified significant diagnosis × age-stratum interactions specifically on clustering coefficients of the somatosensory-motor network (SMN) and auditory network (AN). EOS showed increased normalized clustering coefficients (gamma), whereas AOS exhibited reduced gamma. Crucially, these aberrant patterns in SMN persisted after antipsychotic treatment.

CONCLUSION: These findings suggest that EOS and AOS may differ in the topological organization of brain networks, particularly within SMN and AN. The persistence of SMN abnormalities after antipsychotic treatment further supports the possibility that EOS has a distinct network-level pathophysiology.

DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. We worked to ensure sex balance in the selection of non-human subjects. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group.

PMID:42349780 | DOI:10.1016/j.jaac.2026.06.016

Neuroscience. 2026 Jun 25:S0306-4522(26)00423-9. doi: 10.1016/j.neuroscience.2026.06.034. Online ahead of print.

ABSTRACT

PURPOSE: Parkinson's disease with rapid eye movement sleep behavior disorder(RBD) often represents a more aggressive subtype, presenting more serious clinical manifestations even in early-stage disease. However, this subtype's specific whole-brain functional network alterations in the early stages of the disease remain unclear. Research was designed to explore the unique functional network alterations patterns in early PD with RBD and underlying abnormal neural network development mechanisms.

METHOD: We gathered resting-state fMRI data of35 healthy controls (HC), 33 early PD with probable RBD (PD + pRBD), and 32 early PD without RBD (PD-RBD). We utilized graph theory along with network-based methods to analyze the data. Research was conducted on correlations between network indicators and clinical scores.

RESULT: PD + pRBD exhibited significantly decreased local efficiency than PD-RBD (p = 0.031). Compared to HC, PD + pRBD demonstrated more severe network disruptions than PD-RBD, after FDR correction, including significantly reduced small-world properties (p = 0.003), more widespread nodal alterations (p < 0.05), and extensive functional connectivity disruptions (p < 0.05). Modular analysis revealed functional network connectivity abnormalities within and between multiple networks, characterized by concurrent reductions and abnormal enhancements in functional connectivity.

CONCLUSION: Early PD + pRBD exhibit specific and more severe neurofunctional network impairment pattern. Characterized by more significantly disrupted neurofunctional network topology and widespread functional connectivity abnormalities across multiple brain networks, with potential functional network reorganization. These functioning abnormalities may serve as imaging biomarkers for this clinically malignant subtype and provide potential neurobiological mechanism for understanding its poorer clinical phenotype.

PMID:42349540 | DOI:10.1016/j.neuroscience.2026.06.034