Most recent paper

Psychol Med. 2025 Nov 28;55:e363. doi: 10.1017/S0033291725102638.

ABSTRACT

BACKGROUND: Psychomotor disturbance has long been observed in major depressive disorder (MDD) and is thought to be a key indicator of illness course. However, dominant methods of measuring psychomotor disturbance, via self-report and clinician ratings, often lack objectivity and may be less sensitive to subtle psychomotor disturbances. Furthermore, the neural mechanisms of psychomotor disturbance in MDD remain unclear.

METHODS: To address these gaps, we measured psychomotor agitation via a force variability paradigm and collected resting fMRI in 47 individuals with current MDD (cMDD) and 93 individuals with remitted MDD (rMDD). We then characterized whether resting-state cortico-cortical and cortico-subcortical connectivity related to force variability and depressive symptoms.

RESULTS: Behaviorally, individuals with cMDD exhibited greater force variability than rMDD individuals (t(138) = 3.01, p = 0.003, Cohen's d = 0.25). Furthermore, greater force variability was associated with less visuomotor connectivity (r(130) = -0.23, p = 0.009, 95% CI [-0.38, -0.06]). Visuomotor connectivity was significantly reduced in cMDD relative to rMDD (t(130) = -2.77, p = 0.006, Cohen's d = -0.24) and mediated the group difference in force variability (ACME β = -0.06, 95% CI [-0.16, -0.01], p = 0.04).

CONCLUSIONS: Our findings represent a crucial step toward clarifying the pathophysiology of psychomotor agitation in MDD. Specifically, altered visuomotor functional connectivity emerged as a candidate neural mechanism, highlighting a promising direction for future research on dysfunctional visually guided movements in MDD.

PMID:41310957 | DOI:10.1017/S0033291725102638

Chin Med. 2025 Nov 28;20(1):207. doi: 10.1186/s13020-025-01236-5.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) is characterized by dysregulation in brain function and is closely associated with neuroinflammation. Although acupuncture has been shown to improve insomnia symptoms, its underlying mechanisms, particularly at both the macro brain connectivity and corresponding molecular levels, remain unclear METHODS: Forty-eight CID patients were randomly assigned to either an acupuncture group or a waitlist group. Clinical data and resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before and after the intervention. Changes in brain connectivity were analyzed using fMRI to assess global brain connectivity (GBC) in each group. Gene expression data from the Allen Human Brain Atlas were utilized to identify important genes contributing to these acupuncture-induced GBC changes. Gene set enrichment analysis was performed to annotate the molecular biological processes involved.

RESULTS: In the acupuncture group, fMRI analysis revealed decreased regional GBC in key regions, such as the pallidum and prefrontal cortex, correlating with symptom relief. In contrast, the waitlist group showed increased regional GBC without symptom relief. Gene set enrichment analysis revealed that specific genes associated with astrocytes and neuroinflammation-related biological processes were linked to the acupuncture-induced changes in GBC. The neuroinflammation-informed GBC-transcriptomic signatures induced by acupuncture were further validated by their significant correlation with reductions in IL-6 levels as insomnia symptoms improved.

CONCLUSION: Acupuncture may remodel brain functional connectivity by regulating neuroinflammation-related pathways, thereby improving insomnia symptoms.

PMID:41310834 | DOI:10.1186/s13020-025-01236-5

Commun Med (Lond). 2025 Nov 27;5(1):500. doi: 10.1038/s43856-025-01212-y.

ABSTRACT

BACKGROUND: This study investigated potential behavioral and neural biomarkers of adolescent impulsivity by analyzing adolescent responses in a memory test and examining task-independent brain connectivity.

METHODS: This research utilized immediate and delayed memory tasks, together with a similar distractor memory task (SMT), to examine adolescent impulsivity and its correlation with neural cognitive control strategies. Ninety-five healthy, right-handed teenagers (27 females, average age 14.9 years) participated in the functional magnetic resonance imaging (fMRI) sessions.

RESULTS: Elevated impulsivity correlates with an increased number of errors during target trials and a higher incidence of false alarms during catch trials. Neural activity and connectivity involving the insula and dorsal anterior cingulate cortex (dACC) are significantly associated with behavioral responses and individual impulsivity. Notably, both task-modulated and resting-state (intrinsic) coupling between the insula and locus coeruleus (LC), as well as between the dACC and LC, demonstrate significant positive correlation with impulsivity. These findings indicate that insula-LC and dACC-LC connectivity strength serve as reliable indicators of impulsivity.

CONCLUSIONS: The results indicate that the connection between the salience network and the noradrenergic locus coeruleus may function as a consistent neural indicator of impulsivity. Our findings indicate that this method can discern reliable brain biomarkers for impulsivity and can guide interventions aimed at enhancing self-control during adolescence.

PMID:41310178 | DOI:10.1038/s43856-025-01212-y

Sci Rep. 2025 Nov 27;15(1):42489. doi: 10.1038/s41598-025-26490-3.

ABSTRACT

Functional anorectal pain (FAP) is a chronic condition with unclear pathophysiological mechanisms that is often linked to psychological distress. This resting-state functional magnetic resonance imaging (rs-fMRI) study investigated aberrant interhemispheric connections in 30 FAP patients versus 21 matched healthy controls (HC) via seed-based functional connectivity (FC) and voxel-mirrored homotopic connectivity (VMHC). Compared with HC, FAP patients presented enhanced FC between the left middle frontal gyrus (MFG.L) and regions such as the right MFG (MFG.R) and left superior temporal gyrus (STG.L). VMHC analysis revealed increased patterns in the MFG.L and left superior medial frontal gyrus (SFGmed.L) in FAP patients. Moreover, the strength of FC between the MFG.L and MFG.R was negatively correlated with age, indicating that this heightened connection may diminish with age. These findings indicate that FAP involves aberrant interhemispheric hyperconnectivity, which may play crucial roles in pain perception and emotional processing. The age-dependent decline in FC highlights the eroding of neuroplasticity in aging patients. These neural alterations could serve as diagnostic biomarkers and provide targets for therapeutic interventions. Our work positions FAP within a brain-gut axis dysregulation framework and suggests circuit-specific therapeutics to restore neural homeostasis.

PMID:41309853 | DOI:10.1038/s41598-025-26490-3

NPJ Parkinsons Dis. 2025 Nov 27. doi: 10.1038/s41531-025-01219-1. Online ahead of print.

ABSTRACT

Parkinson's Disease (PD) with Rapid Eye Movement Sleep Behavior Disorder (RBD) occurred before (body-first) and after (brain-first) motor symptoms may exhibit different MRI features. We aimed to investigate the structural and functional MRI pattern differences between brain-first and body-first subtypes of PD. 23 body-first and 19 brain-first PD patients, along with 20 matched healthy controls (HC) were enrolled and underwent T1-weighted and resting-state functional magnetic resonance imaging (rs-fMRI) scans in NJ-dataset. We calculated and compared amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV) across groups to identify differential brain regions, which were subsequently extracted in PPMI and OASIS3 datasets. These values were combined with clinical data for binary classification machine learning training (with PPMI including 22 body-first and 35 brain-first patients used for feature selection and OASIS3 including 5 body-first and 10 brain-first patients used for external validation) and correlated with clinical scales. The body-first group exhibited higher zALFF values in the parietal lobe and greater GMV in the frontal lobe, while showing lower zALFF and GMV values in the cerebellum and subcortical nuclei (caudate nucleus for zALFF; medulla, hippocampus, amygdala, and olfactory bulb for GMV) than brain-first group. We identified significant axial asymmetry in functional and structural MRI between brain-first and body-first Parkinson's subtypes, characterized by greater gray matter retention and higher spontaneous neural activity in neocortex in the body-first subtype. Furthermore, integrating MRI and clinical scales effectively distinguished between brain-first and body-first subtypes.

PMID:41309711 | DOI:10.1038/s41531-025-01219-1

Eur Neuropsychopharmacol. 2025 Nov 26;102:28-38. doi: 10.1016/j.euroneuro.2025.11.002. Online ahead of print.

ABSTRACT

Structural and functional default mode network (DMN) alterations are common in neuropsychiatric disorders and may contribute transdiagnostically to social dysfunction. Normative modelling enables assessment of DMN alterations at the individual level. This study investigates whether individual deviations in cortical thickness, surface area, and between-network functional connectivity of the DMN differ between schizophrenia (SZ), major depressive disorder (MDD), Alzheimer's disease (AD), and healthy controls (HC), and whether these deviations transdiagnostically relate to social dysfunction. Social dysfunction was assessed using a composite score from the Social Functioning Scale and De Jong-Gierveld Loneliness scale. Structural MRI data was collected for 329 participants (SZ=86, MDD=44, AD=82, HC=117) and resting-state fMRI data for 317 participants. Individual deviation scores of DMN integrity were computed by adapting existing normative models of cortical thickness (N = 58,836), surface area (N = 43,524), and between-network functional connectivity (N = 21,515). Extreme deviations were quantified using a z-threshold of ±1.96. DMN deviation scores were not transdiagnostically associated with social dysfunction across the sample (ps>0.05). AD patients had more extreme negative deviations in DMN cortical thickness than all other groups (ps<0.0001; z = -4.14 to -6.34) and fewer extreme positive deviations in DMN surface area relative to SZ and HC (ps<0.05; z = 2.10 to 2.71). For between-network functional connectivity of the DMN, AD and SZ patients had more extreme negative deviations than MDD and HC (ps<0.05; z = -2.09 to -3.54). To conclude, normative modelling reveals differences in individual deviations of DMN integrity between neuropsychiatric groups, but these deviations do not transdiagnostically relate to social dysfunction.

PMID:41308510 | DOI:10.1016/j.euroneuro.2025.11.002

Neural Netw. 2025 Nov 21;196:108359. doi: 10.1016/j.neunet.2025.108359. Online ahead of print.

ABSTRACT

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects multiple brain systems. Altered brain function can be observed through resting-state functional magnetic resonance imaging (rs-fMRI). While machine learning offers significant advantages in capturing complex signal patterns across numerous voxels, its decision-making process often lacks transparency. This study aimed to develop an explainable machine learning pipeline to classify patients with ALS and healthy control (HC) using rs-fMRI data.

METHODS: Thirty patients with ALS and 30 HCs were enrolled. The pipeline consisted of three key components: (1) preprocessing of rs-fMRI data using independent component analysis, followed by dual regression to reduce dimensionality and generate individual network maps; (2) training of a three-dimensional convolutional neural network (3D-CNN) to classify each individual image as either ALS or HC; and (3) application of saliency map and Grad-CAM++ to visualize the reasoning behind the model's classification.

RESULTS: The 3D-CNN achieved high classification accuracy using the sensorimotor network (SMN) map (78.3%) and the visual network (VN) map (83.3%). Simultaneously, saliency map and Grad-CAM++ highlighted brain regions that contributed to the classification, and some of which were consistent with regions showing intergroup differences in the dual regression analysis.

DISCUSSION: This study developed a novel explainable machine learning model capable of extracting features and classifying rs-fMRI data. Our results showed altered functional integrity in the SMN and VN in ALS. Our pipeline holds the potential to extract features of rs-fMRI data, enabling classification of neurological diseases with explainability.

PMID:41308261 | DOI:10.1016/j.neunet.2025.108359

Neuroinformatics. 2025 Nov 27;23(4):57. doi: 10.1007/s12021-025-09756-z.

ABSTRACT

Modelling real-world networks allows investigating the structure and the dynamics of such networks, which led to significant developments in various scientific fields. One of the most used models in these investigations is the Watts-Strogatz, with a structure composed of high clustering and short path lengths known as small-world networks. This model proposes an interesting gradient between regular and random networks, but its generating process, which relies on a single rewiring probability parameter, is hard to access and to manipulate. In order to study the mechanics of the Watts-Strogatz model, the present work proposes a new method based on deep neural networks that could estimate its probability p. To illustrate its applicability, neuroimaging and phenotypic resting-state fMRI data were used from patients with ADHD and typical development children, obtained from the ADHD-200 database. The neural network efficiently estimated the probability parameter, resulting in small-world graphs for functional brain connectivity with a mean ± s.e.m. p distribution of 0.804 ± 0.003. Despite no difference was found considering the gender or diagnosis of participants, the generalized linear model revealed age as a significant predictor of p (mean ± s.e.m.: 4.410 ± 0.877; p < 0.001), indicating a great effect of neurodevelopment on the brain network's structure. The proposed approach is promising in estimating the probability of the Watts-Strogatz model, and its application has the potential to improve investigations of network connectivity with a relatively efficient and simple framework.

PMID:41307783 | DOI:10.1007/s12021-025-09756-z

J Neurol. 2025 Nov 27;272(12):787. doi: 10.1007/s00415-025-13515-0.

ABSTRACT

BACKGROUND: Resting-state functional connectivity (FC) alterations in people with multiple sclerosis (PwMS) have been hypothesized to reflect either adaptive or maladaptive plasticity. Investigating FC longitudinal evolution and its relationship with disability progression can help clarify this issue. This study examined 5-year FC changes in pwMS and their clinical relevance.

METHODS: From the Italian Neuroimaging Network Initiative database, we included 156 pwMS with two clinical visits and 3T-MRI scans acquired on the same scanner 4-6 years apart. Clinical/neuropsychological visits included the Expanded Disability Status Scale (EDSS), Nine-Hole Peg Test (9HPT), Timed 25-Foot Walk Test (T25FWT), Paced Auditory Serial Addition Test (PASAT3), and Symbol Digit Modalities Test (SDMT). One hundred fifty-six age- and sex-matched healthy subjects (HS) with baseline MRI and the same tests were also included. Based on the EDSS, pwMS were divided into three groups: low disability (0-1.5; N = 78), mild disability (2-3.5; N = 50), and high disability (≥ 4; N = 28). Resting-state networks (RSNs) were identified using independent component analysis. Baseline and longitudinal FC changes were correlated with baseline and follow-up clinical/neuropsychological measures.

RESULTS: At baseline, the low-disability group showed significantly higher FC in all RSNs (FDR-corrected p < 0.05) compared to HS, which correlated with better baseline scores (SDMT, T25FWT) and less worsening at follow-up (PASAT3, 9HPT). The mild- and high-disability groups exhibited mixed FC abnormalities, with both higher and lower FC than HS in several RSNs. In the mild-disability group, higher FC was associated with worse baseline scores (SDMT, T25FWT) and greater clinical worsening (PASAT3, 9HPT, T25FWT). In the high-disability group, higher sensorimotor baseline FC correlated only with worse baseline 9HPT. Longitudinally, all RSNs showed FC increase in the low-disability group, but a FC decrease in the other groups. FC increases in the low-disability group generally correlated with better clinical outcome (T25FWT), while FC decreases in the mild-disability group correlated with clinical worsening (9HPT, T25FWT).

CONCLUSIONS: FC increases appear to reflect compensatory mechanisms in low-disability pwMS, while in more disabled patients, FC alterations likely represent maladaptive responses. These findings support resting-state FC as a biomarker for monitoring disease progression and treatment response in MS.

PMID:41307737 | DOI:10.1007/s00415-025-13515-0

Neuroinformatics. 2025 Nov 27;23(4):55. doi: 10.1007/s12021-025-09757-y.

ABSTRACT

The fasciola cinerea (FC) is a slender archicortical band at the posterior hippocampal tail, and its human morphology and network role are poorly defined. To generate a reproducible in vivo three-dimensional (3D) model of the FC, quantify its geometry, characterize structural and functional connectivity within posterior-medial memory networks, and test a tractography-constrained computational model in which the FC acts as a multiplicative gate. Open 7 T datasets, structural, diffusion, and resting-state functional magnetic resonance imaging (fMRI) were anchored to BigBrain and Julich-Brain priors. A semi-automated, atlas-guided pipeline was used to segment the FC and derive morphometrics (volume, thickness, width, curvature, and Laplace-Beltrami spectral shape). Reliability was assessed using the Dice, 95% Hausdorff distance, and test-retest intraclass correlation coefficient (ICC). Diffusion tractography was used to estimate the FC structural pathways toward retrosplenial (RSC), parahippocampal (PHC), posterior cingulate (PCC), and thalamic targets. Resting-state coupling was summarized using Fisher-z correlations and narrowband coherence. A Wilson-Cowan neural mass model, constrained by tractography, simulated FC-dependent FC-RSC coherence with morphometric scaling of gating gain. Segmentation was reliable (Dice = 0.78 ± 0.05; 95% Hausdorff = 1.62 ± 0.41 mm; ICC_volume = 0.88; ICC_thickness = 0.82). Group morphometrics: volume 84.3 ± 17.9 mm³, mean thickness 0.92 ± 0.15 mm, width 1.86 ± 0.31 mm, centerline length 14.2 ± 2.1 mm. FC showed preferential connectivity: FC→RSC 0.21 ± 0.09; FC→PHC 0.18 ± 0.08; FC→PCC 0.11 ± 0.06; FC→Thalamus 0.06 ± 0.04. Resting-state coupling was strongest for FC-RSC (z = 0.24 ± 0.12) with a slow-band coherence enhancement. Thickness predicted the FC→RSC strength (β = 0.17 per 0.1 mm) and FC-RSC z (β = 0.08 per 0.1 mm), and higher curvature was negatively related. The gating model reproduced empirical FC-RSC coherence (r = 0.52 ± 0.11), and morphometric scaling improved the fit (Δr = + 0.06). We provide an anatomically grounded and mathematically validated 3D FC model that links microstructures to mesoscale connectivity. Preferential posterior-medial coupling and morphometry-dependent gating support the FC as a modulatory interface in human memory networks and yield testable markers for individualized mapping and clinical translation.

PMID:41307597 | DOI:10.1007/s12021-025-09757-y

Pain. 2025 Nov 13. doi: 10.1097/j.pain.0000000000003841. Online ahead of print.

ABSTRACT

Impairments in lumbar sensory perception, including reduced tactile acuity, occur in patients with nonspecific low back pain (LBP). Tactile acuity is linked to primary somatosensory cortex (S1) activity and structure, but neural markers of lumbar-specific tactile acuity tests remain unvalidated. This cross-sectional study investigated associations between lumbar two-point discrimination (TPD) and estimation (TPE) with functional and structural properties of S1, as well as S1-thalamic connectivity. Resting-state functional MRI and diffusion-weighted MRI assessed S1-thalamic functional connectivity (FC) and structural connectivity, as well as regional homogeneity (ReHo) and mean diffusivity (MD) of S1 grey matter in 78 LBP patients and 39 pain-free controls. Participants with LBP were subdivided into 2 groups: 1 with pain (LBP+, n = 39) and 1 without pain (LBP-, n = 39) on the day of assessment. Higher TPD (ie, worse tactile acuity) was associated with higher contralateral S1-thalamic FC (β = 19.97 mm, 95% CI = 8.47-31.46 mm) and lower contralateral S1-MD (β = -76.98 mm, 95% CI = -142.83 to -11.13 mm). Higher TPE was associated with higher S1-ReHo (β = 19.67 mm, 95% CI = 0.35-39 mm). Two-point discrimination and two-point estimation were positively correlated (r = 0.25, P < 0.001). No between-group differences were found for the MRI variables or TPE, but the LBP+ group showed higher TPD thresholds than pain-free controls (MDiff. = 6.05 mm, Padj. = 0.023). Our findings question the validity of TPE as a measure of tactile acuity. Both neural markers of TPD may not explain tactile acuity impairments in LBP but instead reflect a baseline indicator of tactile performance capability, suggesting poor validity as an LBP-specific marker of neuroplasticity.

PMID:41307249 | DOI:10.1097/j.pain.0000000000003841

Eur J Neurosci. 2025 Nov;62(10):e70338. doi: 10.1111/ejn.70338.

ABSTRACT

Despite a renewed scientific interest in lysergic acid diethylamide (LSD), its local neural effects remain underexplored. This functional magnetic resonance imaging (fMRI) study explored and compared LSD-induced changes in local activity (amplitude of low-frequency fluctuations: ALFF) and local connectivity (regional homogeneity: ReHo), assessing their relationship to regional receptor density. Imaging data of 15 healthy adults from an open dataset were analyzed. For each participant, two pairs of resting-state runs were available (rest1 and rest2), one performed under placebo and one following the intravenous administration of 75-μg LSD. Voxel-wise paired t-tests compared ALFF and ReHo in the LSD versus placebo conditions. Rest1*rest2 test-retest reliability and ALFF*ReHo cross-modal associations were assessed with conjunction maps and vertex-wise correlations. Finally, neurochemical enrichment analyses related LSD-induced ALFF and ReHo changes to cortical density maps of LSD-related neurotransmitter receptors and transporters. Both ALFF and ReHo decreased in somatosensory/visual cortices under LSD compared to placebo. Specific decreases were observed for ALFF in associative regions belonging to the default mode and frontoparietal networks, and for ReHo in subcortical regions (cluster-based corrected p < 0.05). Test-retest reliability was high for ALFF (rho = 0.80, p = 0.001) and moderate for ReHo (rho = 0.46, p = 0.001). ALFF*ReHo LSD-induced changes were moderately associated (rest1: rho = 0.36, p = 0.001; rest2: rho = 0.56, p = 0.001). Neurochemical enrichment analysis showed that LSD-induced ALFF/ReHo alterations were reliably and negatively correlated with the density of D2 and 5-HT1A receptors (FDR-corrected p < 0.05). These preliminary findings suggest that LSD may engage complex and dynamic neurochemical processes beyond its known 5-HT2A receptor target, warranting further investigation.

PMID:41305961 | DOI:10.1111/ejn.70338

Brain Sci. 2025 Nov 20;15(11):1245. doi: 10.3390/brainsci15111245.

ABSTRACT

Background/Objectives: Deep brain stimulation (DBS) of the globus pallidus interna (GPi) is a safe and established therapy for management of refractory motor fluctuations and dyskinesia in Parkinson's disease (PD). However, the relationship between stimulation site connectivity and improvement of axial gait symptoms remains poorly understood, particularly when stimulating in the GPi. This study investigated functional and structural connectivity patterns specifically associated with axial symptom outcomes following bilateral GPi-DBS, and, as a secondary exploratory analysis, examined whether Volumes of tissue activated (VTAs)-based connectivity related to overall UPDRS-III change. Methods: We retrospectively analyzed 19 PD patients who underwent bilateral GPi-DBS at the University of Florida (2002-2017). Unified Parkinson's Disease Rating Scale (UPDRS-III) axial gait subscores were assessed at baseline and 36-month follow-up. VTAs were reconstructed using Lead-DBS and coregistered to Montreal Neurological Institute (MNI) space. Structural connectivity was evaluated with diffusion tractography, and functional connectivity was estimated using normative resting-state fMRI datasets. Correlations between VTA connectivity and clinical improvement were examined using Spearman correlation and voxelwise analyses. Results: Patients with axial improvement in motor scales demonstrated specific VTA connectivity to sensorimotor and supplementary motor networks, particularly lobule V and lobules I-IV of the cerebellum. These associations were specific to axial gait subscores. In contrast, worsening axial gait symptoms correlated with connectivity to cerebellar Crus II, cerebellum VIII, calcarine cortex, and thalamus (p < 0.05). Total UPDRS-III scores did not show a significant positive correlation with supplementary motor area or primary motor cortex connectivity; a non-significant trend was observed for VTA-M1 connectivity (R = 0.41, p = 0.078). Worsening total motor scores were associated with cerebellar Crus II and frontal-parietal networks. These findings suggest that distinct connectivity patterns underlie differential trajectories in axial and global motor outcomes following GPi-DBS. Conclusions: Distinct connectivity profiles might underlie axial gait symptom outcomes following GPi-DBS. Connectivity to motor and sensorimotor pathways supports improvement, whereas involvement of Crus II and occipital networks predicts worsening. Additional studies to confirm and expand on these findings are needed, but our results highlight the value of connectomic mapping for refining patient-specific targeting and developing future programming strategies.

PMID:41300251 | DOI:10.3390/brainsci15111245

Brain Sci. 2025 Oct 24;15(11):1139. doi: 10.3390/brainsci15111139.

ABSTRACT

Background/Objectives: Accumulating evidence suggests that cognitive training can induce functional reorganization of intrinsic connectivity networks involved in higher-order cognitive processes. However, few interventions have specifically targeted language, an essential domain tightly interwoven with memory, attention, and executive functions. Given their foundational role in communication, reasoning, and knowledge acquisition, enhancing language-related abilities may yield widespread cognitive benefits. This study investigated the neural impact of a new structured, language-based cognitive training program on neurotypical older adults. Methods: Twenty Brazilian Portuguese-speaking women (aged 63-77 years; schooling 9-20 years; low-to-medium socioeconomic status) participated in linguistic activities designed to engage language and general cognitive processing. Behavioral testing and resting-state functional Magnetic Resonance Imaging (fMRI) were conducted before and after the intervention. Results: Functional connectivity analyses revealed significant post-intervention increases in connectivity within the frontoparietal network, critical for language processing, and the ventral attentional network, associated with attentional control. Conclusions: The observed neural enhancements indicate substantial plasticity in cognitive networks among older adults, highlighting the effectiveness of linguistic interventions in modulating critical cognitive functions. These findings provide a foundation for future research on targeted cognitive interventions to promote healthy aging and sustain cognitive vitality.

PMID:41300147 | DOI:10.3390/brainsci15111139

Brain Sci. 2025 Oct 23;15(11):1136. doi: 10.3390/brainsci15111136.

ABSTRACT

BACKGROUND: Autism spectrum disorder (ASD) is a highly heterogeneous neurodevelopmental condition for which accurate and automated diagnosis is crucial to enable timely intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) serves as one of the key modalities for diagnosing ASD and elucidating its underlying mechanisms. Numerous existing studies using rs-fMRI data have achieved accurate diagnostic performance. However, these methods often rely on a single brain atlas for constructing brain networks and overlook the data heterogeneity caused by variations in imaging devices, acquisition parameters, and processing pipelines across multiple centers.

METHODS: To address these limitations, this paper proposes a multi-view, low-rank subspace graph structure learning method to integrate multi-atlas and multi-center data for automated ASD diagnosis, termed M3ASD. The proposed framework first constructs functional connectivity matrices from multi-center neuroimaging data using multiple brain atlases. Edge weight filtering is then applied to build multiple brain networks with diverse topological properties, forming several complementary views. Samples from different classes are separately projected into low-rank subspaces within each view to mitigate data heterogeneity. Multi-view consistency regularization is further incorporated to extract more consistent and discriminative features from the low-rank subspaces across views.

RESULTS: Experimental results on the ABIDE-I dataset demonstrate that our model achieves an accuracy of 83.21%, outperforming most existing methods and confirming its effectiveness.

CONCLUSIONS: The proposed method was validated using the publicly available Autism Brain Imaging Data Exchange (ABIDE) dataset. Experimental results demonstrate that the M3ASD method not only improves ASD diagnostic accuracy but also identifies common functional brain connections across atlases, thereby enhancing the interpretability of the method.

PMID:41300144 | DOI:10.3390/brainsci15111136

Brain Sci. 2025 Oct 22;15(11):1133. doi: 10.3390/brainsci15111133.

ABSTRACT

Background: Resting-state functional connectivity (RSFC) is widely used to identify abnormal brain function associated with depression. Resting-state functional magnetic resonance imaging (fMRI) scans have many potential confounds, and task-based FC might provide complementary information leading to better insight on brain function. Methods: We used MATLAB's (version 2024b) CONN toolbox (version 22a) to evaluate FC in 40 adults with and without major depressive disorder (MDD) (nMDD = 23, nHC = 17). fMRI acquisition was performed while participants were at rest and while performing the Selves Task, an individualized goal priming task. Seed-based analyses were performed using two seeds: medial prefrontal cortex (mPFC) and left hippocampus. Results: Both groups showed strong positive RSFC between the mPFC and other DMN regions, including the anterior cingulate cortex and precuneus, which had more focal positive FC to the mPFC during the task in both groups. Additionally, the MDD group had significantly lower RSFC between the mPFC and several regions, including the right inferior temporal gyrus. The left hippocampus seed-based analysis revealed a pattern of hypoconnectivity to multiple brain regions in MDD, including the cerebellum, which was present at rest and during the task. Conclusions: Our results indicated multiple FC differences between adults with and without MDD, as well as distinct FC patterns and contrast results in resting state and task-based analyses, including differential FC between mPFC-cerebellum and hippocampus-cerebellum. These results emphasize that resting-state and task-based fMRI capture distinct patterns of brain connectivity. Further investigation into combining resting-state and task-based FC could inform future neuroimaging research.

PMID:41300141 | DOI:10.3390/brainsci15111133

Sci Rep. 2025 Nov 27. doi: 10.1038/s41598-025-28507-3. Online ahead of print.

ABSTRACT

This study aims to explore the neural correlates of postoperative pain and its relationship with preoperative psychological issues in patients with rotator cuff tear (RCT). Functional MRI data were collected from 78 RCT patients and 48 healthy controls (HC). Voxel-wise comparisons assessed regional homogeneity (ReHo) differences between groups. Pearson correlation and mediation analyses investigated the links between clinical data and brain changes. Additionally, machine learning using support vector machines (SVM) classified RCT patients based on postoperative pain intensity. RCT patients showed functional alterations in brain areas such as the dorsal anterior cingulate cortex (dACC), primary somatosensory cortex (SI), precuneus, and cerebellum. Increased depression levels correlated positively (r² = 0.249, P < 0.001) with ReHo in the dACC. The relationship between depression and postoperative pain intensity was mediated by dACC ReHo (indirect effect: 0.22, CI: 0.01-0.26). The combined analysis of ReHo patterns and clinical data achieved a classification accuracy of 90.4% for distinguishing RCT patients with postoperative pain. Our findings indicate a notable link between depression and postoperative pain in RCT patients, potentially linked to functional abnormalities in the dACC. Neuroimaging markers may help identify individuals at higher risk for postoperative pain.

PMID:41298760 | DOI:10.1038/s41598-025-28507-3

J Affect Disord. 2025 Nov 24:120725. doi: 10.1016/j.jad.2025.120725. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic stress impacts brain function and emotion regulation, increasing depression risk. How chronic stress shapes neural dynamics in response to acute stress remains unclear. This study investigates how chronic stress influences neural responses after acute stress, focusing on ventromedial prefrontal cortex (vmPFC)-amygdala and vmPFC-hippocampus functional connectivity (FC) and their relationship to depression symptoms.

METHODS: Eighty-seven adults underwent resting-state fMRI at baseline, during acute stress, and during recovery. Participants were divided into High and Low chronic stress groups based on perceived stress over the past 4 weeks. Depression symptoms were measured with the Symptom Checklist-90. Linear mixed-effect model and repeated-measures ANOVA were used to analyse neural dynamics and interaction effects. Recovery-related changes in FC were calculated as differences between acute stress and recovery.

RESULTS: Distinct neural dynamics patterns across stress phases emerged between groups. The Low group showed significant decreases in vmPFC-amygdala and vmPFC-hippocampus connectivity from acute stress to recovery, while the High group exhibited no changes. Chronic stress moderated the association between the recovery-related changes in vmPFC-amygdala connectivity and depression symptoms. In the High chronic stress group, greater decreases in FC from stress to recovery were associated with higher depression symptoms.

CONCLUSIONS: Chronic stress modulates neural dynamics during acute stress response and recovery, and their association with depression symptoms. Individuals with higher chronic stress exhibit blunted cortical-limbic circuit dynamics, potentially increasing depression vulnerability. Rapid disengagement of emotion regulation circuits may represent a maladaptive response supporting the allostatic load model. These findings clarify stress, brain, and depression relationships.

PMID:41297681 | DOI:10.1016/j.jad.2025.120725

Behav Brain Res. 2025 Nov 24:115962. doi: 10.1016/j.bbr.2025.115962. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to investigate the neural correlates of cognitive fusion (CF) in drug-naïve patients with obsessive-compulsive disorder (OCD) and to explore the potential involvement of neurotransmitter systems in these abnormalities.

METHODS: Following quality control, 54 first-episode, drug-naïve OCD patients and 56 matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI) scanning. The amplitude of low-frequency fluctuations (ALFF) and functional connectivity analyses were performed to examine differences in brain activity between the groups. Clinical assessments, including the Yale-Brown Obsessive Compulsive Scale, Beck Anxiety Inventory, Beck Depression Inventory, and CF questionnaire, were administered to measure the severity of obsessive-compulsive, anxiety, and depressive symptoms, as well as CF levels. Mediation and correlation analyses were conducted to explore the relationships between brain activity, CF, and OCD symptoms. Additionally, spatial correlation analyses were conducted to investigate the relationship between neural abnormalities and neurotransmitter systems.

RESULTS: OCD patients exhibited elevated ALFF in prefrontal regions. Crucially, the activity of the left dorsolateral superior frontal gyrus (SFGdl) mediated 45.11% of CF's effect on obsessive-compulsive symptoms (indirect effect = 0.060, 95%CI = [0.005,0.133]). Moreover, neurochemical analysis revealed significant negative correlations between regional ALFF in the left SFGdl and neurotransmitter systems, including dopamine, acetylcholine, and glutamate.

CONCLUSION: Our findings suggest that CF is associated with altered brain activity in prefrontal regions, which may contribute to the cognitive and emotional dysfunction observed in OCD. The negative correlations between these neural abnormalities and neurotransmitter systems provide further insight into the neurochemical mechanisms underlying OCD. These results offer novel perspectives on the pathophysiology of OCD and highlight potential targets for future therapeutic interventions.

PMID:41297562 | DOI:10.1016/j.bbr.2025.115962

Neuroimage Clin. 2025 Nov 22;48:103913. doi: 10.1016/j.nicl.2025.103913. Online ahead of print.

ABSTRACT

OBJECTIVE: Disruptions in resting-state functional brain networks are consistently observed in dementia, yet their underlying relationships remain incompletely understood. This study aimed to investigate potential associations between resting-state functional MRI (rs-fMRI) phenotypes and various dementia subtypes.

METHODS: We performed bidirectional two-sample Mendelian randomization (MR) analyses using summary statistics from 191 rs-fMRI phenotypes (n = 34,691) and five types of dementia (n = 6,618 to 373,159). Forward MR assessed the effects of rs-fMRI phenotypes on dementia risk, while reverse MR evaluated the impact of dementia on rs-fMRI phenotypes.

RESULTS: Forward MR analysis identified seven rs-fMRI phenotypes significantly associated with dementia risk. Enhanced dorsolateral superior frontal gyrus connectivity, part of the default mode network, was linked to reduced Alzheimer's disease risk (odds ratio (OR) = 0.52, 95 % confidence interval (CI): 0.41-0.66, P = 1.10 × 10-7). Increased connectivity within the default mode and central executive networks correlated with lower vascular dementia risk (OR = 0.60, 95 % CI: 0.48-0.75, P = 9.44 × 10-6). Reverse MR revealed significant associations between dementia subtypes and rs-fMRI phenotypes, including Alzheimer's disease-related increases in limbic connectivity and decreases in default mode and central executive networks. For Lewy body dementia, heightened connectivity in salience and sensorimotor networks and reduced default mode connectivity were observed.

INTERPRETATION: Our findings identify functional networks whose connectivity patterns may be associated with dementia risk and could provide potential insights for biomarker discovery or preventive research. However, these results are based on statistical inference and require further validation in longitudinal and experimental studies to confirm their clinical relevance and potential translational implications.

PMID:41297292 | DOI:10.1016/j.nicl.2025.103913