Most recent paper

J Clin Exp Neuropsychol. 2026 Jan 21:1-23. doi: 10.1080/13803395.2026.2617353. Online ahead of print.

ABSTRACT

INTRODUCTION: Breast cancer patients undergoing adjuvant hormone therapy commonly report adverse effects that can lead to lower quality of life and treatment nonadherence. How hormone therapy, independent of other systemic therapies, may impact patient functioning is a relatively new area of research with few neuroimaging studies delineating the effects. Prior nonspecific neuropsychological findings and the multifaceted role of estrogen in the brain suggest potentially diffuse effects of hormone therapy. The current study examined intrinsic neural functional organization and cognitive correlates unique to breast cancer patients undergoing hormone therapy.

METHOD: Resting state functional magnetic resonance imaging was acquired from 24 breast cancer patients undergoing hormone therapy and 32 healthy controls. Resting-state functional connectivity (rsFC) was calculated between brain regions. Fractional amplitude of low frequency fluctuations (fALFF) was computed within a rsFC-derived mask to describe the regional properties within sites of dysconnectivity. Objective measures of cognition were obtained using neuropsychological tests and correlated with rsFC.

RESULTS: Patients demonstrated extensive dysconnectivity relative to controls, largely characterized by parietal-occipital hypoconnectivity. Reduced rsFC occurred primarily between regions with increased fALFF. A modest relationship between rsFC and visual working memory was observed in breast cancer patients but not in controls.

CONCLUSIONS: This study is the first to examine whole-brain rsFC in breast cancer patients undergoing hormone therapy. We found robust hypoconnectivity in patients, which demonstrated modest relationships with cognition. Identifying the pattern by which breast cancer and hormone therapy affect brain networks may aid in the development of therapeutic options for patients experiencing negative effects of hormone therapy, thus improving quality of life for cancer survivors. Further, the detection of abnormal brain function may help characterize treatment-associated neural changes that are not captured by standard cognitive measures.

PMID:41566912 | DOI:10.1080/13803395.2026.2617353

Sci Rep. 2026 Jan 21. doi: 10.1038/s41598-026-36953-w. Online ahead of print.

ABSTRACT

To tackle the disease-related process in early pre-dementia Lewy Body Dementia, we investigated the changes of functional brain networks and their cognitive relevance. A cohort of 38 Mild Cognitive Impairment with Lewy Bodies (MCI-LB) subjects and one of 24 healthy controls (HC) underwent neuropsychological assessment and resting state (RS) functional and structural MRI. Functional connectivity (FC) between ROIs belonging to a set of RS networks, including the Salience Network (SN), Fronto-Parietal (FPN), Default Mode (DMN), Dorsal and Ventral Attention (DAN and VAN), Somato-Motor (SMN), Visual (VN) and Language (LN) was estimated and compared between cohorts. Finally, neuropsychological scores were correlated with FC of MCI-LB and HC separately. Compared to HC, MCI-LB exhibited lower FC between DAN, FPN and LN. Higher inter-network FC was found between FPN and SN, FPN and DMN, SN and SMN and DAN and SMN. In MCI-LB the correlational analysis revealed significant positive and negative associations between cognitive performance and FC values between nodes. In conclusion, we found a possible compensation mechanism between nodes in SN and FPN, and FPN and DMN following disconnection between the control system of the FPN and the top down attention system. The complex compensatory mechanisms involving multiple networks may not be efficient to counteract the cognitive impairment in MCI-LB. Overall, in MCI-LB we found an aberrant engagement of the networks that are not primarily involved in the performance of specific tasks.

PMID:41565763 | DOI:10.1038/s41598-026-36953-w

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Jan 19:111614. doi: 10.1016/j.pnpbp.2026.111614. Online ahead of print.

ABSTRACT

BACKGROUND: This study aimed to leverage advanced radiomics analysis of resting-state functional magnetic resonance imaging (rs-fMRI) data to investigate the potential of radiomics in distinguishing patients with social anxiety disorder (SAD) from healthy controls and identifying distinct subtypes within patients.

METHODS: We analyzed the rs-fMRI data from 147 participants (78 controls, 69 patients) using three rs-fMRI metrics: regional homogeneity, fractional amplitude of low-frequency fluctuations, and degree centrality. From each of these metrics, we extracted 91 radiomics and mean signals from the amygdala, hippocampus, insula, and medial/ventromedial prefrontal cortex (mPFC/vmPFC). We employed machine learning algorithms for classification and utilized Subtype and Stage Inference (SuStaIn) model to identify disease subtypes and symptom progression.

RESULTS: Classification using radiomics from individual regions, particularly the left amygdala (accuracy: 84.3%), right hippocampus (74.2%), and mPFC (74.1%), significantly outperformed classification using mean signals from all regions (52.2%). Furthermore, the right hippocampal-based SuStaIn model revealed two distinct subtypes of SAD, social anxiety-led and general anxiety-led, with the former demonstrating more severe comorbid symptoms and poorer prognosis.

CONCLUSIONS: Radiomics features from rs-fMRI effectively classified patients with SAD and revealed clinically meaningful subtypes through SuStaIn modeling. These findings demonstrate the value of quantitative imaging approaches that capture subtle functional patterns and underscore the potential of disease-progression modeling for understanding heterogeneity in SAD.

PMID:41564919 | DOI:10.1016/j.pnpbp.2026.111614

Semin Arthritis Rheum. 2026 Jan 11;77:152916. doi: 10.1016/j.semarthrit.2026.152916. Online ahead of print.

ABSTRACT

OBJECTIVE: Cognitive dysfunction (CD) is a prevalent symptom in childhood-onset systemic lupus erythematosus (cSLE). This study aimed to investigate the neurobehavioral basis of CD in cSLE.

METHODS: Patients with cSLE (N=20) and age- and sex-matched healthy controls (HCs, N=20) completed questionnaires and multiple neurocognitive tests. The Systemic Lupus Erythematosus Disease Activity Index 2000 and laboratory markers were used to monitor patients' clinical status. Neuroimaging assessments included functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and structural MRI.

RESULTS: cSLE patients demonstrated moderate disease activity with high inflammation and immune dysregulation, alongside low medication adherence. Relative to HCs, cSLE patients showed worse cognitive functioning, higher emotional distress and more physical symptoms. fNIRS revealed higher prefrontal cortex activity in cSLE vs. HCs during the color-word Stroop task, suggesting impaired cognitive flexibility. fMRI performed during the N-back working memory task revealed altered frontal cortex and cerebellum activity, while modulations in resting-state fronto-cerebellar connectivity in the cSLE cohort were observed. Patients with cSLE were characterized by reduced gray matter morphological properties in frontal cortex and cerebellar subdivisions (e.g., crus II) alongside altered white matter structural connectivity among these cognitive processing hubs. K-means clustering analysis delineated three subgroups within the cSLE cohort based on neuroimaging profiles, where subgroups varied based on cognitive and emotional health.

CONCLUSION: This study provides evidence of fronto-cerebellar abnormalities and their associations with CD in cSLE. This investigation underscores the need for multidisciplinary research efforts to further elucidate the neurobiological underpinnings of CD in cSLE.

PMID:41564829 | DOI:10.1016/j.semarthrit.2026.152916

Neuroimage Clin. 2026 Jan 13;49:103946. doi: 10.1016/j.nicl.2026.103946. Online ahead of print.

ABSTRACT

The clinical presentation and neurobiology of mild traumatic brain injury (mTBI) - also referred to as concussion - are complex and multifaceted, and interrelationships between neurobiological measures derived from neuroimaging are poorly understood. This study applied machine learning (ML) to multimodal whole-brain functional connectomes from magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and structural connectomes from diffusion tensor imaging (DTI) in a test of discriminative accuracy in cases of mTBI. Resting state MEG (amplitude envelope correlations), fMRI (BOLD correlations), and DTI (fractional anisotropy, FA; streamline count, SC) connectome data was acquired in 26 controls without mTBI (all male; 27.6 ± 4.7 years) and 24 participants with mTBI (all male; 29.7 ± 6.7 years) in the acute-subacute phase of injury. ML with data fusion was used to optimally identify modalities and brain features for discriminating individuals with mTBI from those without. Univariate group differences were only found for MEG functional connectivity, while no differences were found for fMRI or DTI. Functional connectivity (fMRI and MEG) showed robust unimodal classification accuracy for mTBI, followed by structural connectivity (DTI), where FA showed marginally better classification performance than SC, but SC outperformed FA in data interpretation and fusion. Perfect, unsupervised separation of participants with and without mTBI was achieved through participant fusion maps featuring all three data modalities. Finally, the MEG-only full feature fusion map showed group differences, and this effect was eliminated upon integrating DTI and fMRI datasets. The markers identified here align well with prior multimodal findings in concussion and highlight modality-specific considerations for their use in understanding network abnormalities of mTBI.

PMID:41564671 | DOI:10.1016/j.nicl.2026.103946

Neuroimage Clin. 2026 Jan 16;49:103949. doi: 10.1016/j.nicl.2026.103949. Online ahead of print.

ABSTRACT

Preterm birth disrupts critical phases of brain maturation, placing individuals at increased risk for long-term cognitive and functional impairments. This study investigated how very preterm birth affects the spatial and temporal organization of functional brain networks in school-aged children born very preterm using a spatiotemporal connectome framework. Multimodal MRI, including diffusion-weighted imaging and resting-state fMRI, was acquired from 25 children born before 30 gestational weeks and 25 age- and sex-matched full-term controls (8-9 years). We characterized the structure-function coupling of dynamic brain activity through Connected Components (CCs) defined as structurally constrained sets of functionally co-active regions identified on a multilayer graph. Three different metrics were computed: CC number (count of distinct co-activation patterns), CC height (peak number of regions within a CC, representing the spatial extent) and CC width (temporal span across consecutive time repetitions (TRs)). In addition, we quantified System Diversity (SD) and Spatiotemporal Diversity (STD), indices reflecting integrative richness and temporal variability of functional network dynamics. CC number decreased with age across groups, reflecting typical developmental patterns, while CC height was significantly greater in preterm children and positively associated with processing speed, suggesting altered or compensatory network co-activation. No significant group differences were observed for SD metrics. However, network-level analyses revealed significantly lower STD values in all functional networks in the preterm group, indicating possible heightened temporal stability and reduced functional flexibility. These findings suggest that very preterm birth selectively alters the dynamic engagement of functional systems, with potential implications for cognitive vulnerabilities. (243 mots; 250 max).

PMID:41564670 | DOI:10.1016/j.nicl.2026.103949

Front Psychiatry. 2026 Jan 5;16:1695904. doi: 10.3389/fpsyt.2025.1695904. eCollection 2025.

ABSTRACT

BACKGROUND: Schizophrenia is recognized as a connectivity disorder. Although functional connectivity (FC) abnormalities are frequently reported in schizophrenia patients, findings remain inconsistent. Additionally, causal connectivity in early-onset schizophrenia (EOS) is underexplored, and the association between aberrant brain measures and psychotic symptoms remains unclear.

METHODS: Resting-state fMRI data were collected from 21 first-episode, drug-naïve EOS patients and 21 matched healthy controls (HCs). A voxel-wise meta-analysis was first used to identify the consistent brain regions with altered spontaneous functional activity in EOS. These regions served as seeds for subsequent FC analysis and Granger causality analysis (GCA), and the obtained functional brain measures were examined for their associations with psychotic symptoms.

RESULTS: Relative to HCs, EOS patients exhibited reduced FC between the left middle frontal gyrus (MFG) and right Cerebellum_8 as well as left Cerebellum_7b, while the connectivity between the right caudate nucleus (CAU) and right precuneus (PCUN) was increased. The increased FC between the right CAU and right PCUN was positively correlated with PSYRATS-delusion scores. Additionally, GCA revealed increased causal flow from the right CAU to right amygdala, while effective connectivity (EC) from the triangular part of the right inferior frontal gyrus to left MFG was inhibited, but no significant association was detected between these functional changes and psychotic symptoms.

CONCLUSIONS: EOS not only showed aberrant FC in cortico-striato-cerebellar circuits, but also exhibited disrupted causal connectivity in striatal-amygdala circuits and within prefrontal cortex. Importantly, hyperconnectivity within the cortical-striatal circuits may represent a key neural mechanism underlying the psychotic symptoms of EOS.

PMID:41561987 | PMC:PMC12812569 | DOI:10.3389/fpsyt.2025.1695904

Clin Neuroradiol. 2026 Jan 20. doi: 10.1007/s00062-026-01617-9. Online ahead of print.

ABSTRACT

PURPOSE: This study examined whether task-based functional MRI (fMRI) can provide metrics of local brain activity and hemodynamics typically derived from resting-state fMRI (rsfMRI).

METHODS: Two publicly open datasets from healthy individuals and brain tumor patients were retrospectively used to compare amplitude of low-frequency fluctuations (ALFF) and global signal metrics, namely, global signal correlation (GSC), global signal cross-correlation (GSXC), and global signal time delay (GSTD) derived from motor and language fMRI with those from a reference rsfMRI scan (rest-1). Factors influencing the correlations between each measurement were examined using a mixed-effects model with post-hoc pairwise comparisons.

RESULTS: Our study included 50 healthy subjects (mean age: 29 ± 3 years; 32 women) and 38 patients (45 ± 14 years; 24 men). Significant correlations (p < 0.001) were found for all metrics between those derived from motor, language, or a second rest scan (rest-2) against rest-1 scans for both healthy subjects and patients. Both fMRI type and motion magnitude affected the measurements of healthy subjects' GSC (fMRI type: F [2, 69.1] = 15.4, p < 0.001; motion: F [1, 123.1] = 8.5, p = 0.004), GSXC (fMRI type: F [2, 69.5] = 18.9, p < 0.001; motion: F [1, 123.2] = 9.89, p = 0.002), and GSTD (fMRI type: F [2, 58.7] = 9.89, p < 0.001; motion: F [1, 123.2] = 15.26, p < 0.001). FMRI type by motion interaction was significant for ALFF (F [2, 65.9] = 6.13, p = 0.004). The effects were less pronounced and observed only for GSC (F [2, 40.4] = 6.1, p = 0.005) and GSXC (F [2, 41.2] = 6.4, p = 0.004) in the clinical dataset.

CONCLUSION: Repurposing existing task-based functional MRI data for evaluating local brain activities and hemodynamics is feasible in the entire brain in healthy and brain tumor subjects.

PMID:41559198 | DOI:10.1007/s00062-026-01617-9

Dev Cogn Neurosci. 2025 Dec 19;78:101661. doi: 10.1016/j.dcn.2025.101661. Online ahead of print.

ABSTRACT

The bed nucleus of the stria terminalis (BNST) is a small subcortical region that plays a critical role in a wide array of functions, including emotion processing, reward processing, and social interactions. The BNST intrinsic functional network has been well characterized in adults. Despite evidence that BNST connectivity changes during development, maturation of the BNST network has been understudied. To address this gap, we investigated age-related changes in BNST intrinsic connectivity in youth aged 8 - 23 years using resting state functional magnetic resonance imaging scans from the Philadelphia Neurodevelopmental Cohort (PNC), a large cross-sectional dataset. We measured intrinsic connectivity within a BNST network and across the whole brain, testing for effects of age, sex, and age x sex. The BNST ROI network analysis revealed a significant decrease with age for BNST-hypothalamus connectivity and, in boys, BNST-amygdala connectivity. The whole-brain results showed that BNST connectivity was largely established by middle childhood, though there were notable increases in BNST connectivity with motor and planning regions and decreases with age in BNST-subcortical connectivity. These data suggest a shift from subcortical to control-related BNST connectivity with age during this dynamic maturational window.

PMID:41558269 | DOI:10.1016/j.dcn.2025.101661

IEEE Trans Med Imaging. 2026 Jan 20;PP. doi: 10.1109/TMI.2026.3656430. Online ahead of print.

ABSTRACT

Brain functional connectivity networks (FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) data have been widely used to identify altered brain network patterns in attention-deficit/hyperactivity disorder (ADHD). Current graph neural network (GNN) approaches using FCNs predominantly emphasize node features while underutilizing edge information. Moreover, these GNN-based methods also inadequately represent dynamic interdependencies among evolving node features across network layers, limiting their diagnostic performance. We present a graph convolutional network via joint feature learning between nodes and edges (JNEL-GCN) that integrates neuroimaging features for ADHD classification and biomarker discovery. Our framework constructs dual graph representations: (1) a node graph using amplitude of low-frequency fluctuations (ALFF) measures across multiple frequency bands as nodal features, along with functional connectivity (FC) and node feature relationship matrices as edge attributes; (2) an edge graph derived through line graph theory, enabling the interchange of node and edge roles. By leveraging the dual-graph design, our model implements an alternating feature update mechanism with optimized graph convolution operations, facilitating feature hierarchical learning of node-edge relationships across network layers. Extensive experiments demonstrate remarkable performance, achieving 97.3% accuracy on ADHD200 and 97.1% on ABIDE-I datasets, significantly outperforming current benchmarks. Meanwhile, gradient-based biomarker analysis identifies significant regions in bilateral limbic and default mode networks associated with ADHD, aligning with the findings in existing literature. Therefore, this dual-graph approach advances neuroimaging-based diagnosis by comprehensively capturing dynamic network interactions, while providing interpretable biomarkers for clinical neuroscience applications.

PMID:41557570 | DOI:10.1109/TMI.2026.3656430

Neurol Res. 2026 Jan 20:1-12. doi: 10.1080/01616412.2025.2612308. Online ahead of print.

ABSTRACT

AIMS: To investigate the neural mechanisms underlying the decline in alert attention performance following sleep deprivation (SD) using functional connectivity and graph theoretical analysis and identify potential intervention targets.

METHODS: A total of 44 participants underwent resting-state fMRI scans and psychomotor vigilance task (PVT) assessments under normal sleep (RW) and 30 h of SD conditions. Brain networks were constructed within a graph theoretical framework, and functional connectivity between networks as well as regional nodal properties, including degree and efficiency, were analyzed.

RESULTS: Compared to the RW state, SD led to a significant deterioration in PVT performance, evidenced by increased reaction time (p = 0.0012) and lapse frequency (p = 5.13e-5). Neuroimaging results revealed a complex pattern of FC alterations, including increased connectivity between the dorsal attention network (DAN) and default mode network (DMN) but decreased connectivity within the visual network post-SD. Crucially, we identified a significantly attenuated FC between the visual network and the DAN, specifically between the medio-ventral occipital cortex (MVOcC) and DAN regions (e.g., precentral gyrus, inferior parietal lobule, and fusiform gyrus). The strength of these specific inter-network connections was negatively correlated with increased PVT lapses. Furthermore, graph theory analysis demonstrated that SD significantly reduced the nodal degree and efficiency of the bilateral MVOcC, and these reductions were also negatively correlated with impaired PVT performance.

CONCLUSION: Our findings identify the MVOcC as a critical vulnerability hub where disrupted inter-network connectivity (DAN-Visual) and diminished regional topological organization contribute to SD-induced alert attention deficits, highlighting its potential as a target for interventions.

PMID:41557527 | DOI:10.1080/01616412.2025.2612308

J Behav Addict. 2026 Jan 19:2006.2025.00103. doi: 10.1556/2006.2025.00103. Online ahead of print.

ABSTRACT

BACKGROUND: Despite the rapid growth in gaming consumption and associated harms in adolescents, data-driven research to identify brain networks underlying problematic gaming remains limited. This study aimed to identify neural networks predictive of problematic-gaming severity in youth using connectome-based predictive modelling (CPM), a machine-learning approach that employs whole-brain functional connectivity data.

METHODS: From the Adolescent Brain Cognitive Development study at the two-year follow-up, 1,036 participants (Mage = 12.0, 60.7% male) were studied. CPM with 10-fold cross-validation was applied to problematic-gaming scores and functional magnetic resonance imaging (fMRI) data collected during the performance of a reward-processing task. To determine generalizability, additional CPM analyses were performed using other task-based (e.g., those relevant to response inhibition, emotion regulation, and working memory) and resting-state fMRI data.

RESULTS: CPM successfully predicted problematic-gaming scores (r = 0.12, p = 0.002). Predictive networks involved several connections within and between canonical networks implicated in visual processing (visual area 2 and visual association networks), cognitive control and executive functioning (frontoparietal and medial frontal networks), and relevance and motor response (salience and sensorimotor networks). CPM predicted problematic-gaming scores across all analyzed brain states and found shared predictive canonical networks, indicating generalizability. Applying the final reward-processing model to other task-based and resting-state fMRI data also successfully predicted problematic-gaming severity.

CONCLUSIONS: The identified large-scale networks predictive of problematic-gaming severity in adolescents may serve as promising targets for personalized and novel interventions. Before using these results to guide clinical advances, future research should use external samples to evaluate replicability of the identified network.

PMID:41556979 | DOI:10.1556/2006.2025.00103

Neurosci Biobehav Rev. 2026 Jan 17:106566. doi: 10.1016/j.neubiorev.2026.106566. Online ahead of print.

ABSTRACT

INTRODUCTION: Research on the effects of intranasal oxytocin (IN-OXT) on social behavior has often yielded contradictory results, likely due to variability in sample characteristics and research methodologies. To understand oxytocin's influence on neurophysiology and the associated social behaviors, this review synthesizes findings from the past decade on oxytocin's impact on brain physiology, mainly measured by functional magnetic resonance imaging (fMRI) Blood Oxygenation Level Dependent (BOLD) signals. This includes studies on resting-state connectivity, task-based functional connectivity during social cognition tasks.

METHODS: Following the PRISMA guidelines, we reviewed 27 studies sourced from Embase, Medline, and APA PsycINFO. Key characteristics of each study were summarized, including sample size, subject demographics, oxytocin dosage and administration duration, BOLD signal processing and analytical methods, social behavior assessments, and the primary fMRI and behavioral outcomes related to oxytocin's effects.

RESULTS: During resting-state, IN-OXT predominantly modulates the amygdala, precuneus and insula, influencing the functional organization of large- scale networks such as the default mode network and the salience network. Task-based fMRI studies revealed that oxytocin exerted stress-regulatory and context- dependent neurobehavioral effects through amygdala-centered activity, with direction and magnitude varying according to individual traits, sex, and dosage.

DISCUSSION: We further discussed factors that influence IN-OXT's effects on social behaviours. Future research should integrate molecular and neuromodulatory techniques, recruit more diverse samples, and employ ecologically valid paradigms such as hyperscanning to capture real-world social interaction dynamics.

PMID:41554388 | DOI:10.1016/j.neubiorev.2026.106566

Biol Psychiatry Glob Open Sci. 2025 Nov 13;6(2):100655. doi: 10.1016/j.bpsgos.2025.100655. eCollection 2026 Mar.

ABSTRACT

BACKGROUND: Dissociation, an altered state of consciousness in which individuals feel detached from their body, environment, and sense of self, is a common feature of posttraumatic stress disorder (PTSD). Despite its significance, the neurocognitive processes underlying dissociation remain poorly understood, potentially limiting diagnostic precision and treatment efficacy in PTSD.

METHODS: To address this gap, we applied network control theory to resting-state functional magnetic resonance imaging to examine neural dynamics during dissociative states in 2 contexts: healthy volunteers (n = 30) undergoing intravenous administration of ketamine, an anesthetic known to induce dissociative states, and patients with PTSD receiving an intervention aimed at alleviating dissociative symptoms (a secondary analysis of data from 78 patients who participated in previously conducted clinical trials).

RESULTS: Ketamine administration led to resting-state brain dynamics resembling those observed in patients with PTSD before treatment, characterized by an increased dominance of a default mode network (DMN) meta-state and a decreased dominance of a somatomotor network (SOM) meta-state. Posttreatment reduction in the dominance of the DMN meta-state correlated with a decrease in dissociative symptoms in patients with PTSD. Computational modeling analysis revealed that after treatment, patients with PTSD exhibited a more organized and less entropic brain state. However, contrary to our hypothesis, ketamine administration did not lead to significant changes in these entropy-related indices.

CONCLUSIONS: Dissociative states, whether induced by pharmacological manipulation or clinical condition, are accompanied by increased dominance of the DMN meta-state and reduced dominance of the SOM meta-state.

PMID:41552776 | PMC:PMC12803903 | DOI:10.1016/j.bpsgos.2025.100655

Neurobiol Pain. 2025 Dec 25;19:100206. doi: 10.1016/j.ynpai.2025.100206. eCollection 2026 Jan-Jun.

ABSTRACT

Both opioid use and peripheral nerve injury can lead to hyperalgesia. Whereas in peripheral nerve injury, the central neuroplastic is secondary to sustained peripheral signaling, opioid-induced hyperalgesia (OIH) involves maladaptive alterations in both the peripheral and central nervous systems. However, the precise neurobiological mechanisms underlying these two distinct forms of hyperalgesia remain incompletely understood. In this study, OIH and spared nerve injury (SNI), a model of peripheral nerve injury, were established in male rats to investigate the similarities and differences in brain activity. Resting-state fMRI and mechanical stimulus task-state fMRI were employed to identify the differential brain regions between those two groups. Both resting-state fMRI and task-state fMRI revealed substantial differences in pain-related functional networks between these two models. Notably, OIH was characterized by a widespread reduction in whole-brain activity, whereas SNI primarily exhibited abnormal activation in specific pain-processing regions. Specifically, enhanced synchrony between the medial parietal association cortex (MPtA) and the ventral posterior thalamic nucleus (VP) was observed in the OIH model, but not in the SNI model. These abnormal changes were further confirmed through in vivo electrophysiological recordings. This study reveals a whole-brain activity responses to resting state and mechanical stimuli in both OIH and SNI models, while also identifying a special thalamo-parietal circuit involved in opioid-induced hyperalgesia. It provides new insights into the neural mechanisms between OIH and SNI, potentially guiding the new strategies for hyperalgesia therapy.

PMID:41552279 | PMC:PMC12808912 | DOI:10.1016/j.ynpai.2025.100206

Front Neurol. 2026 Jan 2;16:1641297. doi: 10.3389/fneur.2025.1641297. eCollection 2025.

ABSTRACT

BACKGROUND: Amnestic mild cognitive impairment (aMCI), characterized by progressive memory decline, represents a prevalent transitional state in global aging populations and exhibits high conversion rates to Alzheimer's disease (AD), constituting a critical window for preventive interventions. While accumulating evidence supports acupuncture's efficacy in enhancing cognitive performance, the precise neural mechanisms underlying its therapeutic effects remain poorly characterized. This neuroimaging investigation aims to elucidate the cerebral reorganization patterns mediating acupuncture-induced cognitive improvement in aMCI pathophysiology.

METHODS AND ANALYSIS: A systematic search strategy was implemented across eight electronic databases supplemented by manual searches, covering publications from each database's inception to May 1, 2025. Eligible study designs included randomized controlled trials (RCTs), prospective case-control studies, and observational investigations. Two independent investigators performed literature screening and data extraction, with discrepancies resolved through consensus or third-party adjudication. Methodological quality appraisal was conducted using the validated Agency for Healthcare Research and Quality (AHRQ) checklist. Primary outcomes focused on resting-state functional MRI (rs-fMRI) whole-brain functional imaging parameters. Meta-analytic synthesis of neuroimaging data will utilize seed-based d mapping with permutation of subject images (SDM-PSI, version 6.21), while clinical outcome analyses will be performed using RevMan 5.3 software (Cochrane Collaboration). Reporting will strictly adhere to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

CONCLUSION: This study synthesizes findings from independent neuroimaging investigations to establish comprehensive evidence supporting the neurotherapeutic effects of acupuncture in aMCI.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/, Identifier CRD420251033511.

PMID:41551313 | PMC:PMC12807973 | DOI:10.3389/fneur.2025.1641297

iScience. 2025 Dec 9;29(1):114381. doi: 10.1016/j.isci.2025.114381. eCollection 2026 Jan 16.

ABSTRACT

Alzheimer's disease is characterized by progressive cognitive decline, and its effects are mitigated by cognitive reserve. We investigated whether long-term cognitive stimulation, initiated before amyloid deposition, preserves brain function in male and female TgF344-AD rats. Transgenic and wild-type (WT) rats underwent cognitive training or remained untrained. Resting-state fMRI assessed functional connectivity, the novel object recognition test evaluated memory, and molecular analyses examined synaptic plasticity, inhibitory signaling, and microglial reactivity. At baseline, females showed greater task engagement and higher synaptic protein levels (PSD95, TrkB, and VGLUT) than males. Cognitive training improved connectivity and memory in males, with limited benefits in females. At 19 months, trained transgenic rats maintained entorhinal-hippocampal connectivity resembling WT rats, with males showing sustained plasticity markers and reduced parvalbumin-positive interneurons. Trained 11-month-old rats showed enhanced microglial recruitment to plaques and a less reactive phenotype. Overall, early and sustained cognitive stimulation enhances brain resilience, with sex-specific mechanisms shaping outcomes.

PMID:41550754 | PMC:PMC12804168 | DOI:10.1016/j.isci.2025.114381

CNS Neurosci Ther. 2026 Jan;32(1):e70755. doi: 10.1002/cns.70755.

ABSTRACT

CONTEXT: Neurodegenerative diseases are particularly prevalent among patients with acromegaly, but their functional alterations remain poorly understood.

OBJECTIVE: To explore the neurobiological mechanisms of excess growth hormone (GH) on brain functional activity and connectivity in acromegaly.

METHODS: Neuropsychological assessments and resting-state functional magnetic resonance imaging (fMRI) were conducted on 27 patients with acromegaly and 25 healthy controls. The amplitude of low-frequency fluctuations (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) were compared between groups via voxel-based analyses, while graph theory was used to assess brain network topology. T-tests and multikernel support vector machine (MK-SVM) were used to identify discriminative connectome features for classification.

RESULTS: Patients with acromegaly exhibited lower Montreal Cognitive Assessment scores, increased ALFF in the default mode network regions, and decreased fALFF in the frontal-parietal control network areas. ReHo was elevated in the visual network but reduced in the frontal-parietal network. Disruptions were observed in key hub nodes within the default mode and visual networks. The MK-SVM achieved 85.11% accuracy and 80.00% sensitivity in classifying patients.

CONCLUSIONS: Patients with acromegaly exhibited altered brain function and network disruptions. These results offer novel insights into the mechanisms of excess GH in the brain.

PMID:41550023 | DOI:10.1002/cns.70755

Neurobiol Dis. 2026 Jan 15:107274. doi: 10.1016/j.nbd.2026.107274. Online ahead of print.

ABSTRACT

BACKGROUND: Essential tremor (ET) is one of the most prevalent neurological diseases and is recognized as a disorder involving multiple neural network dysfunctions. Previous resting-state fMRI studies in ET ignored brain network important dynamic nature. This study aimed to investigate the alterations of dynamic functional connectivity (DFC) and its functional topology in ET.

METHODS: Resting-state fMRI data were collected from 144 ET and 131 normal controls (NC). Sliding-window approach with K-means clustering algorithm was used to identify dynamic functional states and graph theory analysis was performed to explore related topological organization of each state in ET.

RESULTS: Two distinct and switchable DFC states (State 1: "cerebrum-dominant" state, with hyperconnected functional architecture in cerebrum; State 2: "cerebellum-dominant" state, with higher functional independence in cerebellum) were identified. Compared to NC, higher fractional windows and longer mean dwell time of cerebellum-dominant state, and fewer state transitions were observed in ET. Higher fractional windows and longer dwell time of cerebellum-dominant state were correlated with more severe tremor. In the topological analysis, compared to NC, ET demonstrated decreased nodal degree centrality and nodal efficiency in cerebrum regions (e.g., orbital inferior frontal gyrus and temporal pole) within two states, but increased nodal betweenness centrality in cerebellum regions (e.g., Cerebellum Crus 2 and Vermis) within cerebellum-dominant state.

CONCLUSIONS: These findings revealed that ET was characterized by prolonged cerebellum-dominant state and disrupted functional topology within both states, providing novel insights for better understanding the fundamental neurobiological mechanisms in ET.

PMID:41547468 | DOI:10.1016/j.nbd.2026.107274

BMC Gastroenterol. 2026 Jan 16. doi: 10.1186/s12876-026-04615-w. Online ahead of print.

ABSTRACT

BACKGROUND: Crohn's disease (CD) and ulcerative colitis (UC), the two major forms of inflammatory bowel disease (IBD), are associated with emotional disturbances, but their shared and distinct neurobiological substrates remain unclear. This neuroimaging study aimed to characterize shared and distinct patterns of spontaneous neural activity in CD and UC patients using resting-state functional MRI (rs-fMRI).

METHODS: Using cortical surface-based analysis of rs-fMRI data, we compared intrinsic neural activity, measured by amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), in 248 patients with IBD (180 CD, 68 UC) and 190 healthy controls (HC), controlling for gray matter volume. Demographic, clinical, and neuropsychological data were collected. Group comparisons and correlation analyses were performed.

RESULTS: Compared to HC, both CD and UC patients exhibited reduced ALFF and ReHo in bilateral somatosensory and motor cortices. Disease-specific patterns emerged: CD showed lower ReHo in lateral temporal cortices, while UC demonstrated higher ReHo in medial temporal and superior parietal regions. Correlation analyses revealed that in CD, motor cortex activity was linked to systemic symptoms and emotional function, whereas in UC, it correlated primarily with somatization.

CONCLUSION: This study identifies a common neural signature of sensory-motor dysfunction in IBD, alongside subtype-specific cortical patterns. By directly comparing CD and UC with a multimodal, surface-based approach and controlling for structural differences, our findings provide novel evidence for distinct brain-gut pathophysiology, highlighting neuroimaging as a potential tool for mechanistic insight and patient stratification.

PMID:41545829 | DOI:10.1186/s12876-026-04615-w