Most recent paper

J Neurosci Methods. 2025 Nov 29:110646. doi: 10.1016/j.jneumeth.2025.110646. Online ahead of print.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) involves concurrent changes in brain functional activity and white matter microstructure, yet single-modality analyses often fail to capture these complex alterations.

NEW METHODS: We propose a voxel-level dual-stream Swin Transformer fusion framework (DSTFP) to investigate multimodal structure-function relationships in PD. DSTFP employs parallel transformer branches to extract temporal dynamics from resting-state functional MRI (rs-fMRI) and topological features from diffusion tensor imaging (DTI) fractional anisotropy maps. A cross-modal attention fusion module establishes voxel-wise correspondence between functional and structural features.

RESULTS: Applied to the publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, DSTFP discriminates PD, prodromal, and control groups with high robustness. Structural decoupling index (SDI) and structure-function coupling (SFC) analyses of fused features reveal distributed brain regions with characteristic alterations in functional-structural interactions.

COMPARISON WITH EXISTING METHODS: DSTFP outperforms conventional single-modality and baseline multimodal models in both classification accuracy and interpretability, providing more detailed insight into voxel-level structure-function relationships.

CONCLUSIONS: The proposed framework offers a robust, interpretable approach for multimodal neuroimaging analysis in PD. All source code is publicly available to support reproducibility (https://github.com/MAOmgg/DSTFP).

PMID:41325802 | DOI:10.1016/j.jneumeth.2025.110646

Clin Neurol Neurosurg. 2025 Nov 28;261:109277. doi: 10.1016/j.clineuro.2025.109277. Online ahead of print.

ABSTRACT

PURPOSE: The use of neuronavigation with superimposed mapping tools has enabled visualization of key fiber tracts and improved peri-operative planning. However, a limitation of these approaches is their reliance on a static underlying brain atlas, particularly in neurosurgical patients with brain tumors. A tool that enables qualification and quantification of brain region connectivity could refine approaches to surgical resection.

METHODS: We utilized a machine learning imaging platform, Quicktome™, to generate individualized functional parcels and tracts that dynamically adapt to perioperative change. The connectome was derived from a combination of diffusion tensor imaging and resting-state function magnetic resonance imaging. Matrices were generated from the functional MRI of four patients with intracranial neoplasms and the pre- and post-operative parcellation values were compared. The individual correlation and strength of regions were quantified. Hypo- and hyper-connected regions were marked as anomalous.

RESULTS: We present a case series of four patients to illustrate the correlation of the anomaly matrices with post-operative neurological changes. These include: post-operative delirium originating associated with salience network hypoconnectivity; visual hemineglect linked to hypoconnectivity in the dorsal attention network; and quantifiable improvements in the language network following the resolution of expressive aphasia. All differences between pre-and post-operative paired correlation values were statistically significant.

CONCLUSION: We demonstrate a novel approach to quantifying the extent to which anomalies in the functional connectome correlate with post-operative neurological changes. This has relevance in post-operative prognostication, provision of specialist therapy services, and could serve as a useful tool in surgical education and pre-operative planning.

PMID:41325661 | DOI:10.1016/j.clineuro.2025.109277

Pain. 2025 Nov 25. doi: 10.1097/j.pain.0000000000003843. Online ahead of print.

ABSTRACT

Neuropathic chronic low-back pain (neuCLBP) is associated with worse clinical outcomes compared with non-neuropathic or axial CLBP (non-neuCLBP) and has limited effective nonsurgical treatment options, reflecting poor understanding of its underlying pathophysiology. In this study, we compared neuCLBP and non-neuCLBP patients using standardized clinical phenotyping of the neuropathic component alongside multimodal brain functional magnetic resonance imaging (fMRI). We hypothesized that, consistent with the definition of neuropathic pain as pain arising from injury to the somatosensory nervous system, neuCLBP patients would exhibit reduced thalamic volume and/or altered thalamic shape, reduced primary somatosensory cortex (S1) thickness, and altered resting-state functional connectivity of these structures compared with non-neuCLBP patients and pain-free healthy controls. Consistent with previous literature, we observed that neuCLBP patients (n = 28) presented with more severe clinical symptoms than non-neuCLBP patients (n = 28). Structurally, neuCLBP patients exhibited extensive differences in thalamic shape but no significant differences in thalamic volume or S1 gray matter thickness. By contrast, by examining resting-state thalamic connectivity gradient maps, we found that non-neuCLBP patients exhibited the most pronounced alterations in these gradients. This study is the first to combine multimodal fMRI with rigorous, standardized phenotyping to investigate neuCLBP. While our results may be influenced by greater symptom severity in the neuCLBP patients, they indicate that these patients may display distinct central plasticity patterns. The findings also highlight the importance of distinguishing between these clinical phenotypes to reduce heterogeneity in future studies.

PMID:41325555 | DOI:10.1097/j.pain.0000000000003843

Neurosci Appl. 2025 Jun 11;4:105522. doi: 10.1016/j.nsa.2025.105522. eCollection 2025.

ABSTRACT

Electroconvulsive therapy (ECT) has been shown to induce widespread dysregulation of network connectivity in patients with mood disorders. Nevertheless, the extent to which these functional changes contribute to patients' cognitive side-effects or depressive symptoms improvement remains unclear. This study investigated cross-sectional resting-state functional connectivity (rs-FC) differences in patients with mood disorders after their 8th ECT session (ECT group, n = 33) compared to those receiving pharmacological treatment (non-ECT group, n = 36) and healthy controls (n = 34). Furthermore, we explored the association of rs-FC differences with cognitive side-effects and depressive symptom improvements assessed longitudinally in the ECT group. We focused on analyzing rs-FC within- and between the default mode network (DMN), executive control network (ECN), and frontoparietal network (FPN). Additionally, we explored the association between significant rs-FC group differences and verbal memory decline, and depressive symptoms improvement from pre-to post-ECT within the ECT group. ECT-treated patients exhibited hyper-connectivity within the left-hemisphere FPN compared to those on pharmacological treatment, along with hypo-connectivity between ECN and FPN (p-corrected<0.02). Depressive symptoms positively correlated with rs-FC within the right-hemisphere FPN (p-corrected<0.04). Notably, rs-FC differences were unrelated to verbal memory decline or symptom improvement from pre-to post-ECT (p-corrected>0.1). Our findings highlight differences in brain connectivity between remitted patients after ECT and diagnosis-matched patients following standard pharmacological treatment. Further studies are warranted to investigate longitudinal rs-FC effects of ECT to identify biomarkers predictive of treatment response and the risk of cognitive side effects after ECT.

PMID:41323433 | PMC:PMC12664636 | DOI:10.1016/j.nsa.2025.105522

Front Neurol. 2025 Nov 13;16:1666991. doi: 10.3389/fneur.2025.1666991. eCollection 2025.

ABSTRACT

INTRODUCTION: This study aimed to develop an automated approach for assessing upper limb (UL) motor impairment severity in stroke patients using a deep learning framework applied to resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Dynamic functional connectivity (dFC) was computed with the ipsilesional primary motor cortex (M1) as a seed and extracted from rs-fMRI data of 69 stroke patients. These dFC features were used to train a three-dimensional convolutional neural network (3D-CNN) for automatic classification of UL motor impairment severity. Patients were divided into two groups according to UL Fugl-Meyer Assessment (UL-FMA) scores: mild-to-moderate impairment (UL-FMA > 20; n = 29, maximum = 66) and severe impairment (0 ≤ UL-FMA ≤ 20; n = 40). UL-FMA scores served as labels for supervised learning.

RESULTS: The model achieved a balanced accuracy of 99.8% ± 0.2%, with a specificity of 99.9% ± 0.2% and a sensitivity of 99.7% ± 0.3%. Several brain regions-including the angular gyrus, medial orbitofrontal cortex, dorsolateral superior frontal gyrus, superior parietal lobule, supplementary motor area, thalamus, cerebellum, and middle temporal gyrus-were linked to UL motor impairment severity.

DISCUSSION: These findings demonstrate that a 3D deep learning framework based on dFC features from rs-fMRI enables highly accurate and objective classification of UL motor impairment in stroke patients. This approach may provide a valuable alternative to manual UL-FMA scoring, particularly in clinical settings with limited access to experienced evaluators.

PMID:41323228 | PMC:PMC12658776 | DOI:10.3389/fneur.2025.1666991

Neuroimage Rep. 2025 Nov 12;5(4):100299. doi: 10.1016/j.ynirp.2025.100299. eCollection 2025 Dec.

ABSTRACT

Cognitive symptoms are common in Parkinson's disease (PD) and have debilitating effects on quality of life and disease trajectory; however, the underlying brain mechanisms remain poorly understood. To address this gap, we investigated the relationship between functional connectivity and cognition at multiple time points using longitudinal functional MRI (fMRI) and cognitive assessments from the Parkinson's Progression Marker Initiative (PPMI). We calculated resting-state functional connectivity across three distinct time points. We analyzed functional connectivity within and between three key cortical brain networks that have been linked with higher-order cognitive function in PD: the frontoparietal network (FPN); the salience network (SAL); and the default mode network (DMN). Global cognitive functioning was assessed with the Montreal Cognitive Assessment (MoCA) at each of the three time points, and this was our primary dependent variable. Linear mixed-effects modeling revealed that decreased FPN-DMN functional connectivity is associated with lower MoCA scores over time. A similar trend was found for SAL-DMN functional connectivity. These relationships were specific to cognition, as there were no significant associations between functional connectivity and motor symptoms, as measured with the Movement Disorders Society-Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III). These findings suggests that cortical connectivity is associated with and may contribute to the progression of cognitive symptoms in PD. Our findings advance knowledge about cognitive changes in PD and emphasize the importance of functional brain network architecture.

PMID:41322670 | PMC:PMC12657728 | DOI:10.1016/j.ynirp.2025.100299

Imaging Neurosci (Camb). 2025 Nov 26;3:IMAG.a.1027. doi: 10.1162/IMAG.a.1027. eCollection 2025.

ABSTRACT

The long-term impact of COVID-19 on the brain is multifaceted, encompassing structural and functional disruptions. A cohesive theory of the underlying mechanisms of the Post-COVID Syndrome (PCS) remains unknown, primarily due to high variability in findings across independent studies. Here, we present a multimodal, cross-sectional MRI analysis of brain morphology (T1-MRI), tissue microstructure (diffusion-MRI), functional connectivity (functional-MRI), and cerebral blood flow (arterial spin labeling MRI) in COVID-recovered patients (CRPs, N=76) and healthy controls (HCs, N = 51). Although the global brain volumes did not differ between the two groups, CRPs showed focal atrophy in the right basal ganglia and limbic structures, along with cortical thinning in paralimbic regions (prefrontal cortex, insula) (p < 0.05). Diffusion MRI analysis revealed reduced fractional anisotropy and elevated radial diffusivity in the uncinate fasciculus and cingulum. No differences were observed in resting-state functional connectivity (RSFC) and cerebral blood flow between HCs and CRPs (p > 0.05). We further investigated the effect of infection severity by stratifying the CRPs into hospitalized (HP; N = 21) and non-hospitalized (NHP; N = 46) groups. The microstructural damage was linked to infection severity, more pronounced in the HPs (p < 0.05). In HPs, RSFC was diminished between components of the default mode network and the insula and caudate as compared with HCs and NHPs (p < 0.05). Results suggest COVID-19 is associated with selective structural and functional alterations in basal ganglia-limbic-cortical circuits, with stronger effects in severe cases. Overall, our findings both validate previously reported neuroimaging biomarkers and reveal new ones associated with the post-COVID syndrome, motivating future hypothesis-driven studies on behavioral correlates and therapeutic interventions.

PMID:41322364 | PMC:PMC12658774 | DOI:10.1162/IMAG.a.1027

Biol Psychiatry Glob Open Sci. 2025 Sep 30;6(1):100623. doi: 10.1016/j.bpsgos.2025.100623. eCollection 2026 Jan.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool to reveal disrupted brain activity in major depressive disorder (MDD), but most studies have focused solely on low-frequency functional fluctuations, ignoring the fact that brain activity is composed of both low-frequency and high-frequency fluctuations. Therefore, we applied a novel approach, namely the power spectral slope (PSS), which captures the characteristics of both low- and high-frequency fluctuations to evaluate brain activity in MDD.

METHODS: rs-fMRI data were collected from 109 patients with MDD (27.29 ± 7.11 years, 75 women) and 78 normal control participants (26.47 ± 6.19 years, 51 women). A subset of 52 patients with MDD also underwent rs-fMRI scanning after a 12-week antidepressant treatment (escitalopram/duloxetine). Both the baseline between-group comparison and follow-up within-group comparison were performed for PSS. A 2-sample t test was used for baseline comparison with a liberal Gaussian random-field correction. The follow-up comparison was tested with paired t test.

RESULTS: Patients with MDD showed significantly more negative PSS compared with normal control participants in the ventral striatum and temporal pole. After treatment, PSS in the ventral striatum increased significantly toward normalization, whereas the temporal pole's slope remained unchanged. No significant correlations were found between PSS and depression severity scores.

CONCLUSIONS: This study demonstrates that MDD is characterized by more negative PSS in key affective regions. The normalization effect of ventral striatum spectral slope following antidepressant treatment suggests a region-specific response. Taken together, the findings suggest that PSS may serve as a novel brain functional marker for MDD.

PMID:41321422 | PMC:PMC12657283 | DOI:10.1016/j.bpsgos.2025.100623

Biol Psychiatry Glob Open Sci. 2025 Sep 30;6(1):100624. doi: 10.1016/j.bpsgos.2025.100624. eCollection 2026 Jan.

ABSTRACT

Understanding the impact of prenatal cannabis exposure (PCE) on brain development is increasingly important given rising cannabis use during pregnancy. Many existing reviews on this topic are more than 5 years old and may not reflect recent social shifts that could impact cannabis use during pregnancy; they also have not utilized the recently available large longitudinal datasets for more robust and population-representative investigations. In this narrative review, we aim to provide an updated and expanded examination of the associations between PCE and magnetic resonance imaging (MRI)-based brain outcomes from in utero development to adolescence. We included studies published after 2019 that used at least one of the following measures: structural MRI, diffusion-weighted imaging, resting-state fMRI, and/or task-based fMRI. Across 9 studies that met criteria, 1 study focused on MRI outcomes in utero, 2 in infancy, and 6 in early adolescence, and only 3 studies included MRI and behavior outcomes. PCE was linked to differences in frontal, parietal, and temporal areas, spanning from in utero to adolescence across multiple MRI modalities. However, in the current state of the literature, detecting a consistent trend on PCE's impact on MRI findings was not possible. Furthermore, we found several divergences in study design: varying approaches to assessment (e.g., self-report vs. urine toxicology); difficulties in accounting for prenatal exposure to multiple substances; limited information on timing, frequency, potency, or mode of consumption; and the influence of parental or postnatal factors. Future research should implement designs that can rigorously capture the abovementioned elements to permit replication and eventual meta-analyses on this critical topic.

PMID:41321421 | PMC:PMC12663006 | DOI:10.1016/j.bpsgos.2025.100624

Biol Psychiatry. 2025 Nov 28:S0006-3223(25)01645-2. doi: 10.1016/j.biopsych.2025.11.016. Online ahead of print.

ABSTRACT

BACKGROUND: Posttraumatic stress disorder (PTSD) is a heterogeneous condition with diverse symptom presentations and emotional experiences. While fear is traditionally viewed as central, growing evidence highlights the role of non-fear-based emotions - such as sadness, guilt, and shame - collectively termed Emotional Pain. This study aimed to identify Emotional Pain and Fear-based PTSD symptom profiles and their neural correlates across two independent samples.

METHODS: In Study 1 (n=838), trauma-exposed individuals with probable PTSD completed the PTSD Checklist for DSM-5 (PCL-5) and subjective ratings of Fear and Emotional Pain. Item-level network analysis was conducted to identify central symptoms and relationships. In Study (n=162), recent trauma survivors with high PTSD symptoms underwent resting-state and task-based functional MRI (fMRI) scans 1-month post-trauma, and completed follow-up clinical assessment at 14-months post-trauma. Connectome-based predictive modeling (CPM) was used to predict chronic symptom severity for Fear and Emotional Pain-based profiles, identified in Study 1.

RESULTS: Emotional Pain was rated as more impairing than Fear by most participants (69%). Symptom networks showed distinct patterns: Fear was associated with flashbacks, nightmares, distressing memories, exaggerated startle, and external avoidance; Emotional Pain was linked to anhedonia, negative beliefs, negative emotions, sleep disturbance and emotional reactivity. CPM predicted chronic Fear-based symptom severity (rho=0.228, p<0.001), but not Emotional Pain (rho=0.167, p=0.055). Predictive features included connections across anterior default-mode, central executive, salience, motor-sensory and subcortical networks.

CONCLUSIONS: Emotional Pain and Fear may represent distinct PTSD dimensions. Disentangling their neural signatures may improve diagnostic precision and guide personalized, mechanism-based interventions for trauma-related psychopathology.

PMID:41319905 | DOI:10.1016/j.biopsych.2025.11.016

Neuroimage. 2025 Nov 27:121579. doi: 10.1016/j.neuroimage.2025.121579. Online ahead of print.

ABSTRACT

Data collection technology in functional magnetic resonance imaging (fMRI) is rapidly developing, leading to continuous growth of spatio-temporal resolution. The need to understand brain dynamics, as it plays a crucial role in understanding brain function, continues to push innovation in this direction as limits on the frequency of data measurement limit the kinds of questions that may be asked. In parallel, researchers continue to amass large volumes of fMRI data using the highest sampling frequencies available with current technology. A common and plausible assumption is that higher measurement frequencies may lead to more informative data about the brain dynamics and help mitigate physiological noise from neurovascularly coupled signal. This assumption leads to the tendency to discard the older datasets collected with lower temporal resolution in favor of more recent collections. Moreover, as we will show, it leads to under-utilizing the current MRI technology by only collecting at the fastest available rate. A recent theoretical study demonstrated that combining high frequency data with data collected at a deliberately slower sampling rate can, in some conditions, lead to gains in information about the dynamics. We hypothesize that similar effects can be observed in fMRI datasets where data is collected at multiple timescales, as opposed to datasets created by subsampling from a single acquisition rate. A resting state fMRI dataset collected from 10 subjects at a slow (2150 ms) and fast (100 ms) repetition time (TR) is analyzed, demonstrating informative gains in predictive power by combining the two. This gain is in contrast to diminishing returns in the single TR dataset performance, where the data has been manually-undersampled to a slower sampling rate and combined with the original. Performance outcomes were also compared in gender prediction across a multi-rate dataset and single rate dataset, with multi-rate results showing gains in composite features. Our experiments demonstrate agreement with the theoretical results in showing that features formed as a combination of slow and fast sampling rates yield greater predictive power than features from either slow or fast rates alone in some settings.

PMID:41318042 | DOI:10.1016/j.neuroimage.2025.121579

Drug Alcohol Depend. 2025 Nov 22;278:112972. doi: 10.1016/j.drugalcdep.2025.112972. Online ahead of print.

ABSTRACT

Alcohol intake disrupts cognitive and sensory processing. However, its effects on the role of individual structures within cortical networks, or on the larger network structure, remain unclear. This acute alcohol administration study addressed this gap using graph theory analysis. Healthy individuals (n = 107, 21-45yrs, 61 women) consumed alcohol (0.08g/dL target BrAC) or a placebo drink in 2 double-blinded sessions and self-reported their perceived intoxication using a visual analog scale. Resting state fMRI was acquired with a Siemens Prisma 3T scanner 30min after consumption. The effect of alcohol on graph theory outcomes in a network of 106 cerebral ROIs was identified using the CONN toolbox. We also determined the association between graph theory metrics and subjective intoxication. Results revealed alcohol 1) significantly decreased global efficiency in several occipital nodes and increased global efficiency for nodes within the frontal and temporal cortex; 2) increased local efficiency at a network level as well as in specific nodes in the temporal and frontal cortices; 3) increased degree in frontal and temporal regions; 4) decreased closeness centrality and increased mean path length in parietal and occipital regions as well at the network level compared with placebo conditions. Additionally, decreases in global efficiency and increases in local efficiency and clustering coefficient in the alcohol vs. placebo condition significantly predicted subjective intoxication. Taken together, results provide new evidence that alcohol intake produces changes in the overall topography of the cerebral network that at least partially underlie individual differences in subjective alcohol response.

PMID:41317510 | DOI:10.1016/j.drugalcdep.2025.112972

J Headache Pain. 2025 Nov 27;26(1):272. doi: 10.1186/s10194-025-02210-0.

ABSTRACT

BACKGROUND: New daily persistent headache (NDPH) is a rare, refractory primary headache with an unclear pathophysiological mechanism. Previous neuroimaging studies on NDPH have been largely limited to 3T MRI, which fails to thoroughly reveal microstructural changes, particularly subregional abnormalities and iron metabolism alterations. Based on this, the present study employs 7T multimodal imaging techniques to investigate structural, functional, and iron metabolism abnormalities in the whole brain and, in particular, the changes in subregions of the limbic system.

METHODS: A total of 23 individuals with NDPH and 23 healthy controls (HCs) underwent 7T MRI, including T1-weighted three-dimensional magnetization-prepared 2 rapid acquisition gradient echo (3D-T1WI-MP2RAGE) and resting-state fMRI (rs-fMRI); among these patients, 19 also underwent quantitative susceptibility mapping (QSM). Structural volumes, functional metrics (fractional amplitude of low-frequency fluctuations [fALFF], regional homogeneity [ReHo]), and iron deposition (assessed via QSM) were analyzed, and their correlations with clinical parameters (e.g., headache history, anxiety/depression scores) were examined.

RESULTS: Compared to HCs, individuals with NDPH exhibited significantly less volume in the right accumbens area and left caudal anterior cingulate cortex after false discovery rate (FDR) correction. Widespread changed fALFF and ReHo values were observed, with correlations to clinical features. QSM values were decreased in right paracentral, right cuneus and left precentral with increasing in left rostral middle frontal.

CONCLUSION: 7T multimodal MRI identifies widespread structural, functional, and iron metabolism abnormalities in NDPH, particularly in limbic subregions, highlighting a “pain-emotion” interaction mechanism. These findings provide preliminary insights into NDPH pathogenesis.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s10194-025-02210-0.

PMID:41310432 | PMC:PMC12659571 | DOI:10.1186/s10194-025-02210-0

Hum Brain Mapp. 2025 Dec 1;46(17):e70424. doi: 10.1002/hbm.70424.

ABSTRACT

Transcranial magnetic stimulation (TMS) guided by magnetic resonance imaging (MRI) has significantly advanced the treatment of mood disorders by enabling precise targeting of brain circuits implicated in their pathophysiology. The integration of neuronavigation systems, which utilize real-time MRI-based coil positioning, has improved spatial targeting accuracy, individualization, and therapeutic outcomes. Despite these advancements, achieving optimal stimulation efficacy requires careful consideration of MRI techniques, including anatomical imaging, functional MRI (fMRI), and connectivity-based methods. Anatomical MRI provides a reliable structural foundation for neuronavigation but lacks specificity regarding functional neural networks implicated in mood disorders. In contrast, fMRI, through task-based and resting-state paradigms, enhances target selection precision by identifying patient-specific neural activity and functional connectivity patterns, although this approach is vulnerable to variability and imaging artifacts. Connectivity-based MRI neuronavigation represents a promising advancement by explicitly targeting disrupted neural networks. This review critically examines recent technological and methodological progress in MRI-guided neuronavigation for TMS, addressing current challenges such as image acquisition quality, co-registration accuracy, artifact mitigation, and practical constraints in clinical settings. Finally, it discusses emerging opportunities and innovations poised to enhance neuronavigation precision, foster wider clinical adoption, and ultimately improve therapeutic outcomes in interventional psychiatry for mood disorders.

PMID:41310980 | DOI:10.1002/hbm.70424

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