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Imaging Neurosci (Camb). 2025 Nov 10;3:IMAG.a.995. doi: 10.1162/IMAG.a.995. eCollection 2025.

ABSTRACT

The neural organization of cognitive control has been extensively studied using neuroimaging methods, but this organization is still not well understood. We argue that two factors may have contributed to this elusiveness. First, most previous research has relied on group-averaged results, which may provide a misleading representation of individual brains. Second, most fMRI studies study the brain only under a limited number of conditions, making it challenging to provide fine-grained distinctions in the functions associated with specific regions. Recent precision neuroimaging approaches have demonstrated substantial promise in furthering understanding of the human brain through repeated sampling of individual participants. However, most precision imaging work still relies on resting-state fMRI or a small number of tasks. In the present study, we demonstrate the utility of a novel dense imaging approach, which combines precision neuroimaging with an unusually large task battery. We demonstrate that patterns of neural activity associated with cognitive control tasks are significantly more similar within-person than between people, even after controlling for anatomical similarity, suggesting that these patterns are person-specific and reliable. In addition, we demonstrate that within-person and between-person similarity changes significantly across tasks, suggesting that some tasks may be more suited for exploring individual differences in cognitive control than others. Together, our findings highlight the potential value of a precision approach and the benefit of using a large number of tasks to further understanding of cognitive control.

PMID:41230411 | PMC:PMC12603659 | DOI:10.1162/IMAG.a.995

Front Neurol. 2025 Oct 28;16:1619226. doi: 10.3389/fneur.2025.1619226. eCollection 2025.

ABSTRACT

BACKGROUND: Unruptured cerebral arteriovenous malformations (AVMs) generally do not cause focal neurological deficits, prompting limited investigation into potential neurological changes associated with them.

PURPOSE: To determine whether AVMs exhibit combined functional and structural reorganization using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging (DTI).

STUDY TYPE: Retrospective study.

POPULATION: 44 AVM patients who underwent both rs-fMRI and DTI examinations as well as an equal number of age- and sex-matched healthy controls.

SEQUENCE: Functional alterations were assessed using amplitude of low-frequency fluctuation (ALFF) analysis and functional connectivity networks, while fiber alterations were examined through fractional anisotropy (FA) analysis and tract-weighted functional connectivity (TW-FC) analysis.

ASSESSMENT: Functional alterations were evaluated by ALFF and functional connectivity networks, analyzed by neuroimaging specialists. Structural alterations were assessed through FA and TW-FC analysis, performed by experienced radiologists.

STATISTICAL TESTS: Independent two-sample t-test and the Mann- Whitney U test were used to analyze the continuous variables. Chi-squared test was used to test the categorical variables. We used permutation test with family-wise error correction while setting the statistical threshold of p < 0.05 at the cluster level. Two-tailed statistical significance was set at p < 0.05.

RESULTS: AVMs showed significant ALFF differences in 12 brain regions and altered functional connectivity networks compared to healthy controls (p < 0.05). Fiber connectivity and density were significantly reduced in AVM patients (p < 0.05). TW-FC analysis indicated significant differences across regions of interest (ROIs) between AVMs and healthy controls, suggesting integrated functional and structural reconfigurations (p < 0.05).

DATA CONCLUSION: The study reveals significant functional and structural changes in AVM patients, particularly in the visual network (VN) and sensorimotor network (SMN). These alterations suggest compensatory mechanisms that may offset functional deficits, providing insights into AVM pathophysiology and potential strategies for optimizing treatment to mitigate functional impairments and promote recovery.

PMID:41230380 | PMC:PMC12604527 | DOI:10.3389/fneur.2025.1619226

Dev Sci. 2026 Jan;29(1):e70093. doi: 10.1111/desc.70093.

ABSTRACT

Developmental dyslexia (DD) is a prevalent neurodevelopmental disorder that significantly affects academic learning and social development. Although numerous brain regions have been implicated in DD under both task-based and resting-state conditions, dysfunctions in large-scale functional coordination across brain systems in DD remains poorly understood. Using AES-SDM, we conducted a meta-analysis of seed-based whole-brain functional connectivity (FC) studies, including 12 task-based studies with 226 dyslexics and 232 age-matched controls, and 7 resting-state studies with 120 dyslexics and 145 controls. Results revealed consistently reduced FC between the left inferior frontal gyrus (IFG) and the left fusiform gyrus (FFG) in dyslexics compared with age-matched controls across both task and resting states, suggesting a core neural pathway underlying DD. In addition, task-specific abnormalities were identified, including hypoconnectivity between the left IFG and the right cerebellum, and hyperconnectivity between the left IFG and the bilateral angular gyrus (AG), anterior cingulate cortex (ACC), and left thalamus. By contrast, resting-state analyses identified additional hypoconnectivity between the left FFG and the posterior cingulate cortex (PCC). Together, these findings suggest that DD is associated with widespread disruption in functional integration across the brain, shedding new light on its neural mechanisms of DD and pointing to potential connectivity-based biomarkers for diagnosis. SUMMARY: Dyslexics exhibited consistent hypoconnectivity between the left inferior frontal gyrus and the left fusiform gyrus across both task and resting conditions. Under the task condition, dyslexics showed specific hypoconnectivity between the left inferior frontal gyrus and the right cerebellum, and hyperconnectivity with the bilateral angular gyrus, anterior cingulate cortex, and left thalamus. Under the resting condition, dyslexics showed specific hypoconnectivity between the left fusiform gyrus and the posterior cingulate cortex.

PMID:41229151 | DOI:10.1111/desc.70093

BMC Neurol. 2025 Nov 12;25(1):464. doi: 10.1186/s12883-025-04485-x.

ABSTRACT

BACKGROUND: Cognitive dysfunction has been reported in patients with glaucoma, but the underlying neural mechanisms remain unclear. This study aimed to explore functional connectivity (FC) alterations in hippocampal subregions in primary angle-closure glaucoma (PACG) patients with cognitive impairment.

METHOD: This study included forty-four PACG patients with cognitive dysfunction, and forty-six healthy controls (HCs). Participants underwent 3D high-resolution T1 structural imaging and BOLD fMRI scanning. Seven hippocampal subregions were selected as seed regions to explore changes in FC between the bilateral hippocampal subregions (Cornu Ammonis 1, Cornu Ammonis 2, Cornu Ammonis 3, Dentate gyrus, Entorhinal cortex, HATA, Subiculu) and the whole brain in PACG patients with cognitive dysfunction.

RESULTS: Compared with the HCs group, the PACG group showed decreased FC between multiple hippocampal subregions and the cerebellum, precentral gyrus, postcentral gyrus, supplementary motor area, supramarginal gyrus, inferior frontal gyrus, opercular part, lenticular nucleus, pallidum, rolandic operculum, inferior parietal, supramarginal, and angular gyri. However, increased FC was found between the bilateral hippocampal subregions and the calcarine fissure and surrounding cortex, lingual gyrus, anterior cingulate, and paracingulate gyri. We also found that FC between hippocampal subregions and some brain regions were associated with visual acuity, average cup-to-disc ratio, and retinal nerve fibre layer thickness.

CONCLUSION: These findings reveal widespread hippocampal FC alterations involving the cerebellum, sensorimotor, default mode, and visual network (VN) in PACG patients with cognitive dysfunction, contributing to a better understanding of the underlying neural mechanisms.

PMID:41225363 | DOI:10.1186/s12883-025-04485-x

Neurotherapeutics. 2025 Nov 11:e00790. doi: 10.1016/j.neurot.2025.e00790. Online ahead of print.

ABSTRACT

Racemic (R,S)-ketamine exerts rapid antidepressant effects, and growing evidence suggests its R-isomer may offer sustained efficacy with fewer side effects. However, the neurobiological mechanisms underlying (R)-ketamine's action in the human brain are largely unknown. To address this, we acquired resting-state fMRI data from 32 healthy volunteers 24 ​h before and after intranasal administration of (R)-ketamine (n ​= ​24) or placebo (n ​= ​8). We primarily assessed changes in long-range functional synchrony using degree centrality (DC) and elucidated the sources of these changes with functional connectivity (FC) analysis. (R)-ketamine significantly decreased DC in a key cognitive-motor integration hub: the supplementary motor area/middle cingulate cortex (SMA/MCC, cluster-corrected P ​< ​0.05). Critically, the reduction of DC was absent under the placebo condition, yielding a significant group-by-time interaction (P ​= ​0.01). The reduction in long-range synchrony of the SMA/MCC was primarily driven by attenuated FC with both the dorsal medial prefrontal cortex/dorsal anterior cingulate cortex (dMPFC/dACC) and the cerebellum, and was spatially correlated with serotonin, norepinephrine, and acetylcholine neurotransmitter profiles. More importantly, the clinical relevance of the neuroimaging phenotypes was established in an independent Major Depressive Disorder (MDD) cohort, where FC between the SMA/MCC and dMPFC/dACC significantly correlated with depressive symptom severity (HAMD score, P ​= ​0.019). This study provides novel, system-level evidence that intranasal (R)-ketamine modulates specific human brain networks by attenuating long-range synchrony in the SMA/MCC. The link between the neuroimaging phenotype, depression-relevant neurotransmitter profiles, and clinical symptom severity may offer a plausible therapeutic mechanism of (R)-ketamine.

PMID:41224612 | DOI:10.1016/j.neurot.2025.e00790

J Affect Disord. 2025 Nov 10:120607. doi: 10.1016/j.jad.2025.120607. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a leading cause of disability worldwide, yet antidepressant response remains highly variable, with many patients failing to achieve remission. Resting-state functional connectivity (RSFC) studies have linked MDD to large-scale brain network dysregulation, but methodological inconsistencies and limited focus on treatment response have hindered clinical translation.

METHODS: We conducted a resting-state fMRI study as part of an 8-week antidepressant trial on 86 patients with MDD and 93 healthy controls (HCs). Utilizing a seed-to-voxel approach based on prior studies of neural networks implicated in antidepressant response, we examined RSFC patterns associated with treatment outcomes. Additionally, we explored their relationships with cognitive function and depressive severity to elucidate neural mechanisms underlying treatment response.

RESULTS: Compared to the non-responder group, antidepressant responders exhibited significantly higher ROI-to-ROI FC between the posterior cingulate cortex (PCC) and subgenual anterior cingulate cortex (sgACC), with follow-up seed-to-voxel maps showing higher PCC couplings with the prefrontal cortex, thalamus, and amygdala. In responders, FC between the left amygdala and supramarginal gyrus, and between the ventromedial prefrontal cortex (VMPFC) and lateral occipital cortex, was negatively correlated with cognitive scores. Conversely, in the non-responder group, FC between the left subgenual anterior cingulate cortex (sgACC) and lateral occipital cortex was negatively correlated with digit span but positively with other tasks. FCs involving the amygdala, thalamus, and VMPFC were correlated with depression scale scores.

CONCLUSIONS: Enhanced connectivity within networks related to emotion regulation, cognitive control, and attention in responders suggests neural mechanisms supporting improved emotional and cognitive flexibility.

PMID:41224014 | DOI:10.1016/j.jad.2025.120607

Cephalalgia. 2025 Nov;45(11):3331024251396031. doi: 10.1177/03331024251396031. Epub 2025 Nov 12.

ABSTRACT

BackgroundNeurovascular coupling (NVC) reflects the interaction between cerebral blood flow (CBF) and functional activity. However, the relationship between NVC and migraine chronification remains unclear. This study investigated the state of NVC in migraine patients and evaluated its potential as an imaging feature for migraine chronification using arterial spin labeling (ASL) combined with resting-state functional magnetic resonance imaging (rs-fMRI).MethodsThis was a cross-sectional study. Thirty-nine episodic migraine (EM), 61 chronic migraine (CM) patients (25 with medication overuse headache, MOH) and 42 healthy controls (HCs) were recruited in the same period. Imaging data were acquired using a 3.0 T MRI. Regional homogeneity (ReHo) represented functional activity, whereas CBF was quantified via ASL. Correlation coefficient between CBF and ReHo values of each participant in voxel level represented the whole-brain NVC status, whereas the CBF/ReHo ratio represented regional NVC status. Correlations between NVC metrics and clinical characteristics were analyzed in CM patients. Exploratory mediation analysis was conducted to identify mediators between NVC alterations and the clinical characteristics of CM patients. Finally, receiver operating characteristic (ROC) curve was generated to evaluate the diagnostic performance of NVC metrics for migraine chronification.ResultsCompared to HCs, both EM and CM patients presented significantly reduced whole-brain CBF-ReHo coupling. Compared to EM patients, CM patients presented a decreased CBF/ReHo ratio in the right precuneus. Correlation analysis revealed that z value of the CBF/ReHo ratio in the right precuneus was negatively correlated with both HIT-6 score and PHQ-9 score; HIT-6 score was positively correlated with PHQ-9 score in CM group. Exploratory mediation analysis indicated that depression mediated the relationship between abnormal NVC and clinical characteristics in CM patients. Finally, ROC curve indicated that the CBF/ReHo ratio in the right precuneus (AUC = 0.75) exhibited high sensitivity and specificity in distinguishing CM from EM patients.ConclusionAbnormal NVC in the precuneus was involved in migraine chronification, with depression potentially serving as a mediator in this process. NVC metric may serve as an imaging feature for migraine chronification in the future.

PMID:41223010 | DOI:10.1177/03331024251396031

Eur Child Adolesc Psychiatry. 2025 Nov 12. doi: 10.1007/s00787-025-02906-3. Online ahead of print.

ABSTRACT

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder that often persists from childhood into adolescence and adulthood. Resting-state functional magnetic resonance imaging (rs-fMRI) provides valuable insights into the intrinsic neural activity associated with ADHD. However, despite increasing neuroimaging research, the developmental specificity of spontaneous alterations in brain activity in children and adolescents with ADHD remains poorly understood. A comprehensive activation likelihood estimation (ALE) meta-analysis was performed on rs-fMRI data to investigate alterations in spontaneous brain activity in children and adolescents with ADHD compared with healthy controls (HCs). A contrast analysis was conducted to assess potential overlap in altered brain regions between the child and adolescent ADHD groups. The robustness of the findings was evaluated using a jackknife sensitivity analysis. A systematic review of the literature identified 28 rs-fMRI studies (1019 ADHD patients and 943 HCs). In children with ADHD, ALE revealed decreased spontaneous neural activity in the left middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, precentral gyrus, and subgyral region, with no regions showing increased activity. In adolescents with ADHD, increased activity was observed in the bilateral paracentral lobule, left postcentral gyrus, and medial frontal gyrus, whereas decreased activity was found in the cerebellar tonsil, uvula, declive, anterior lobe, and superior/medial frontal gyrus. No significant clusters were identified in the contrast analyses. The jackknife sensitivity analysis demonstrated robustness in 9 of 17 iterations for children and 4 of 5 iterations for adolescent-specific cerebellar findings. Spontaneous alterations in brain activity in children and adolescents with ADHD reflect developmentally distinct neural mechanisms and may guide future age-specific neuroimaging research.

PMID:41222592 | DOI:10.1007/s00787-025-02906-3

Mov Disord. 2025 Nov 12. doi: 10.1002/mds.70075. Online ahead of print.

ABSTRACT

BACKGROUND: Depression frequently precedes motor symptoms in Huntington's disease gene expansion carriers (HDGECs), yet the neural mechanisms remain poorly characterized.

OBJECTIVE: We investigated effective connectivity between the default mode network (DMN) and striatal regions in HDGECs.

METHODS: We analyzed 3-T resting-state functional magnetic resonance imaging data from 98 HDGECs (48.98% females; mean age, 42.82 years). Spectral dynamic causal modeling estimated subject-level connectivity, whereas parametric empirical Bayes determined group-level effective connectivity differences between participants with a diagnosed depression history and those without, across current, remitted, and never-depressed states. Brain-behavior associations with clinical depression measures were examined.

RESULTS: Model estimation was excellent (89.82% variance-explained). HDGECs with depression history showed decreased inhibitory posterior cingulate cortex-to-hippocampal connectivity, increased hippocampus-to-posterior cingulate cortex inhibition, and increased inhibitory influence of striatum on DMN. HDGECs with a depression history showed increased inhibitory striatal influence on DMN, including left putamen, a propensity for right hippocampal involvement, and disinhibitory posterior cingulate-hippocampal connectivity. Current versus never-depressed comparisons showed more pronounced dysconnectivity, with stronger striatum-to-network connections. Current versus remitted depression exhibited distinct patterns with increased medial prefrontal cortex-to-posterior cingulate cortex connectivity, increased medial prefrontal cortex self-connectivity, and decreased posterior cingulate cortex-to-medial prefrontal cortex connectivity.

CONCLUSIONS: These findings establish distinct striatal-network interaction patterns in depression for HDGECs that differ from non-neurological depression. Our findings suggested the posterior DMN-posterior cingulate and hippocampus-as drivers of depression for HDGECs and potential involvement of right DMN in keeping with compensatory patterns broadly in HD. These connectivity patterns could serve as functional biomarkers for depression in HDGECs. © 2025 International Parkinson and Movement Disorder Society. © 2025 International Parkinson and Movement Disorder Society.

PMID:41221779 | DOI:10.1002/mds.70075

Cogn Neurodyn. 2025 Dec;19(1):182. doi: 10.1007/s11571-025-10369-0. Epub 2025 Nov 8.

ABSTRACT

To automate the classification of functional brain networks in epilepsy patients using resting-state functional magnetic resonance imaging (rs-fMRI). The study introduces a deep learning framework that leverages spatial and temporal features to classify Independent Component Analysis (ICA)-derived networks into 11 functionally distinct classes, including seizure onset zone (SoZ), resting-state networks (RSNs), and artifact/noise. A hybrid deep learning architecture was developed combining a 3D Convolutional Neural Network (3D-CNN) to extract spatial features (SF) and a Long Short-Term Memory (LSTM) network to capture temporal dynamics from time-domain (TS) and frequency-domain (FS) signals. These multi-domain features were concatenated and classified into 11 distinct ICA component types. An ablation study assessed the individual and combined contributions of spatial, temporal, and spectral features. Additionally, expert neurologists independently rated four representative cases to qualitatively validate the model's interpretability and clinical relevance. The baseline 3D CNN (SF) model achieved an overall accuracy of 69% with a sensitivity of 0.52 and a ROC AUC of 0.76. Incorporating frequency-domain signals (SF + FS) enhanced sensitivity to 0.54 and improved the ROC AUC to 0.78 while maintaining a similar accuracy. Combining both time-domain and frequency-domain signals (SF + TS + FS) yielded the highest accuracy at 70%. At the class level, the Noise class consistently demonstrated robust performance (up to 0.94), whereas the temporal lobe network class Temporal class exhibited lower scores (0.14-0.24) across all configurations. Our results demonstrate that this data-driven framework can effectively automate the classification of rs-fMRI-derived functional brain networks including SoZ thereby reducing subjectivity and workload in clinical review. The inclusion of spatial, temporal, and spectral information enables a richer and more nuanced classification that supports downstream applications in epilepsy surgical planning.

PMID:41220406 | PMC:PMC12598743 | DOI:10.1007/s11571-025-10369-0

Brain Behav. 2025 Nov;15(11):e71058. doi: 10.1002/brb3.71058.

ABSTRACT

BACKGROUND: Neurovascular coupling (NVC) is the functional mechanism that links brain neural activity with the dynamic regulation of local blood flow and oxygenation. In recent years, there has been an increasing academic attention to the role of NVC in its pathophysiology and the application of new technologies.

OBJECTIVE: This study aims to map the research landscape related to NVC through scientometric analysis.

METHODS: Publications from the past 30 years were retrieved from the Web of Science Core Collection (WoSCC) database. Data were analyzed using CiteSpace, VOSviewer, and the bibliometrix R package, including co-citation and keyword co-occurrence network analyses. Key metrics such as publication counts and citation frequencies were assessed to identify trends and collaboration patterns among countries, institutions, and authors.

RESULTS: Among the 2047 articles included in the study, United States has maintained a clear leading position. Meanwhile, the number of Chinese research participants has grown rapidly over the past decade. The most prolific authors were Professors Iadecola Costantino and Tarantini Stefano. The research findings of Professor Tarantini Stefano have been widely recognized by researchers in the field. Keyword analysis identified "cerebral blood flow," "neuronal activity," and "neurovascular coupling" as dominant terms, emphasizing the central role of brain function and imaging techniques such as fMRI, TCD, and optical imaging. The emergence of "fNIRS," "resting-state fMRI," and "autoregulation" highlights the growing impact of noninvasive neuroimaging in studying brain-blood flow interactions. Cluster analysis revealed key research themes including functional connectivity, nitric oxide-mediated vascular regulation, cerebral autoregulation, Alzheimer's disease metabolism, and CO2-induced hemodynamic modulation.

CONCLUSION: Over the past three decades, NVC has emerged as a key research focus, driven by interdisciplinary collaboration and advances in brain connectivity, dysfunction, and technology. In the future, integrating artificial intelligence, multi-omics analysis, and high-resolution imaging will further elucidate NVC mechanisms in health and disease, promoting interdisciplinary translation and breakthroughs in neuroscience and brain health.

PMID:41220183 | DOI:10.1002/brb3.71058

BMC Psychiatry. 2025 Nov 11;25(1):1078. doi: 10.1186/s12888-025-07535-3.

ABSTRACT

BACKGROUND: Aerobic exercise (AE) has emerged as a promising non-pharmacological intervention for depression. However, the extent to which AE can alleviate depressive symptoms varies across individuals, and the neural or clinical factors that relate to treatment response remain incompletely understood. This study aimed to investigate whether baseline amygdala-based functional connectivity (FC) is associated with symptom improvement following AE and identify neural correlates of treatment responsiveness.

METHODS: Forty-three participants with subthreshold depression (StD) completed an AE intervention and were classified as remitters (n = 21) or non-remitters (n = 22) based on post-AE Patient Health Questionnaire-9 (PHQ-9) scores. Resting-state functional magnetic resonance imaging (rs-fMRI) data were acquired at baseline and after the intervention. Group comparisons and partial correlation analyses were conducted to assess associations between amygdala-based FC and depressive symptom outcomes.

RESULTS: Baseline left amygdala FC was positively correlated with post-AE PHQ-9 scores in the right precuneus and bilateral middle frontal gyrus (MFG), and negatively correlated with changes in PHQ-9 (ΔPHQ-9) scores in the left precuneus and left MFG. Additionally, remitters showed reduced FC between the left amygdala and left supplementary motor area (SMA) compared to non-remitters. Baseline right amygdala FC was positively correlated with post-AE PHQ-9 scores in the left inferior parietal lobe (IPL), right middle temporal gyrus (MTG), left superior medial frontal gyrus (mSFG) and left MTG, but there were no significant findings for ΔPHQ-9 or group differences. An exploratory analysis combining bilateral amygdala FC and clinical variables yielded high classification accuracy within the same sample (AUC = 0.93). A significant group effect was also observed in the right MTG for left amygdala FC, though no group × time interaction emerged.

CONCLUSIONS: Baseline amygdala FC is associated with symptom improvement during AE in StD. These exploratory findings suggest that amygdala connectivity may play a role in treatment responsiveness.

CLINICAL TRIAL NUMBER: Not applicable.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-025-07535-3.

PMID:41219928 | PMC:PMC12607056 | DOI:10.1186/s12888-025-07535-3

Zhonghua Jie He He Hu Xi Za Zhi. 2025 Nov 12;48(11):1020-1027. doi: 10.3760/cma.j.cn112147-20250303-00122.

ABSTRACT

Objective: To explore the characteristics of altered brain functional activity in patients with chronic cough using resting-state functional magnetic resonance imaging (fMRI). Methods: This was a prospective study. From January 2016 to January 2019, a total of 20 patients with refractory chronic cough [10 males and 10 females, (39.3±8.2) years], 19 patients with somatic cough syndrome [14 males and 5 females, (34.5±9.2) years], and 29 healthy controls [19 males and 10 females, (38.3±12.1) years] were recruited from the chronic cough outpatient clinic of the First Affiliated Hospital of Guangzhou Medical University for analysis. All participants underwent resting-state fMRI, as well as assessment of cough severity, and capsaicin cough challenge. The amplitude of low-frequency fluctuations (ALFF) was used to assess brain functional activity. First, differences in brain activity between patients with refractory chronic cough and healthy controls were compared. Subsequently, brain regions showing significant differences were selected as seed points, and seed-based whole-brain functional connectivity (FC) analyses were performed to examine group differences. Cough severity was evaluated using the visual analog scale (VAS), and cough sensitivity was defined as the capsaicin concentration that elicited five coughs (C5), expressed as lgC5. One-way analysis of variance (ANOVA) was used to compare the differences in lung function among groups. The Kruskal-Wallis test was applied to compare the differences in cough symptom scores (VAS) and capsaicin cough sensitivity (lgC5) among groups. The fMRI data were statistically analyzed using Rest 1.8 software, and two independent-sample t-tests were conducted for each group. Results: Patients with refractory chronic cough exhibited significantly higher ALFF values in the right cerebellar region 8 (0.96±0.14 vs. 0.72±0.15, t=5.46, P<0.001) and the right cerebellar region Crus2 (0.87±0.11 vs. 0.68±0.11, t=6.25, P<0.001) than healthy controls. Patients with somatic cough syndrome had significantly higher ALFF values in the rectus frontal muscle than healthy controls (1.19±0.26 vs. 0.90±0.16, t=4.92, P<0.001). With the right cerebellar region 8 as the seed point, the analysis of the whole brain FC showed that patients with refractory chronic cough had higher FC values in the left cerebellar region 8 (0.60±0.18 vs. 0.35±0.15, t=5.47, P<0.001), cerebellar vermis (0.85±0.17 vs. 0.69±0.16, t=5.26, P<0.001), and claustrum (0.33±0.13 vs. 0.14±0.10, t=6.02, P<0.001). With the right cerebellar region Crus2 as the seed point, the analysis of the whole brain FC showed that patients with refractory chronic had higher FC values in the right middle temporal gyrus, thalamus (0.31±0.17 vs. 0.10±0.11, t=5.57, P<0.001), right dorsolateral superior frontal gyrus (0.35±0.16 vs. 0.1±0.13, t=6.20, P<0.001) and right posterior central gyrus (0.41±0.19 vs. 0.17±0.17, t=4.52, P<0.001). In the correlation analysis, there was a moderate positive correlation (r=0.57, P=0.001) between the ALFF values of the right cerebellar region 8 and Crus2 regions in patients with refractory chronic cough. Conclusions: Enhanced FC in multiple brain regions was found in patients with refractory chronic cough and patients with somatic cough syndrome, suggesting central sensitization in these patients. The different active brain regions in patients with refractory chronic cough and patients with somatic cough syndrome indicate different central hypersensitivity mechanisms among different causes of chronic cough.

PMID:41218859 | DOI:10.3760/cma.j.cn112147-20250303-00122

J Affect Disord. 2025 Nov 9:120631. doi: 10.1016/j.jad.2025.120631. Online ahead of print.

ABSTRACT

BACKGROUND: Depression has been increasingly characterised as a disorder of functional brain connectivity. Over the last two decades aberrant functional connectivity of large-scale resting-state brain networks implicated in inhibitory cognitive control, affective regulation, and self-referential thought, has been compellingly linked to depression. Capitalising on network-based accounts of depression, subsequent research endeavours have aimed at identifying functional connectomic signatures of treatment response in depression. However, to date, there has been little research on the connectomic features of psychotherapy response.

METHODS: We investigated static and dynamic functional connectivity signatures of response to CBT in forty-six unmedicated patients with depression who underwent resting-state functional magnetic resonance imaging before and two months after completion of an Internet-delivered CBT intervention.

RESULTS: At baseline, responders dwelled in a brain state characterised by greater functional connectivity between cognitive control and affective networks. Conversely, functional connectivity between cognitive control and default mode networks was comparatively weaker in the responders group. Notably, baseline functional connectivity significantly classified CBT response at the individual level with an area under the receiver operating characteristic curve of 0.85.

CONCLUSION: These results are in accordance with current network-based accounts of CBT neural mechanisms, positing that greater cognitive control over negative emotion processing enables CBT response. This study extends previous findings on the network-based functional connectomic signatures of CBT response in depression.

PMID:41218741 | DOI:10.1016/j.jad.2025.120631

J Affect Disord. 2025 Nov 9:120652. doi: 10.1016/j.jad.2025.120652. Online ahead of print.

ABSTRACT

Metastability reflects the brain's dynamic balance between integration and segregation across networks, supporting flexible cognitive and behavioral functions. Although abnormal brain dynamics have been implicated in major depressive disorder (MDD), the alterations in metastable brain states and their clinical and transcriptomic correlates remain unclear. In this study, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from 569 patients with MDD and 563 healthy controls using leading eigenvector dynamics analysis (LEiDA), a phase-based method that captures transient brain states without predefined time windows. Between-group comparisons were performed at both global and modular levels, assessed their associations with clinical symptoms and their ability to predict depression severity. To explore underlying mechanisms, we integrated gene expression, cell-type specificity, and protein-protein interaction (PPI) networks. Patients with MDD exhibited widespread disruptions, including reduced global synchronization and metastability, but increased switching between states, particularly more frequent transitions from a globally coherent state (Global state) to a default mode-dominant state (DMN state). They also exhibited lower fractional occupancy of the Global state and higher fractional occupancy of a sensorimotor-dominant state. These disruptions were associated with symptoms such as insomnia and impaired insight, and predicted depression severity. Transcriptome-neuroimaging analysis revealed DMN state-related genes were enriched in pathways involved in presynaptic signal transduction and presynapse-to-nucleus signaling, and were preferentially expressed in excitatory and inhibitory neurons. CSMD1 emerged as a key hub gene in the PPI network. Our findings reveal widespread dynamic brain alterations in MDD and uncover their potential molecular mechanisms, providing new insights into the disorder's neurobiology.

PMID:41218738 | DOI:10.1016/j.jad.2025.120652

Sci Rep. 2025 Nov 11;15(1):39420. doi: 10.1038/s41598-025-23131-7.

ABSTRACT

Recording physiological signals during fMRI is valuable for multiple purposes but often requires additional setup, increasing complexity and participant discomfort. This is particularly challenging in simultaneous EEG-fMRI studies, which typically already include electrocardiogram (ECG) recordings. Here, we aim to leverage the known modulation of ECG by respiration to obtain an ECG-derived respiration (EDR) signal without extra equipment. We acquired EEG-fMRI data from 15 healthy subjects during resting state and two respiratory challenges (slow-paced breathing and breath-holding), with simultaneous ECG and respiratory recordings. Multiple methods were used to extract EDR signals, and the results were evaluated by comparing them with recorded respiration and assessing the quality of physiological regressors for denoising and cerebrovascular reactivity estimation. Amplitude-based EDR methods showed lower correlations with respiration, likely due to ECG distortion in the MRI. Nevertheless, coherence analysis showed that EDR preserved the relevant spectral content. EDR-based regressors were similar to those obtained from measured respiration. Notably, a method based on heart rate variability performed best overall, yielding physiological noise correction and reactivity estimates comparable to those using recorded respiration. Our results demonstrate that meaningful respiratory information can be extracted from ECG within the MRI environment, benefiting EEG-fMRI studies when respiration cannot be reliably recorded.

PMID:41219365 | DOI:10.1038/s41598-025-23131-7

Parkinsonism Relat Disord. 2025 Oct 28;142:108104. doi: 10.1016/j.parkreldis.2025.108104. Online ahead of print.

ABSTRACT

INTRODUCTION: As a deep learning algorithm, Graph convolutional network (GCN) can efficiently process graph-structured data to identify salient brain regions and brain connectivity patterns. We combine GCNs with a multi-connection pattern (MCGCN) to identify salient brain regions implicated in Essential Tremor (ET) and Dystonic Tremor (DT), aiming to explore the underlying neuropathological mechanisms of these conditions.

METHODS: Rs-fMRI data were collected from 55 ET patients, 51 DT patients, and 52 healthy controls (HCs). BOLD time series from each subject were extracted and functional connectivity (FC) matrices were constructed using three distinct connectivity modes. These matrices were then input to four GCN architectures for binary classification tasks (ET vs. HCs, DT vs. HCs, ET vs. DT). We utilized Grad-CAM to identify the more discriminative brain regions, and graph theory and correlation analyses were employed to validate the behavioral relevance of the discriminative regions identified by MCGCN, confirming the salient brain regions for ET and DT.

RESULTS: All GCN models demonstrated strong classification performance, with the highest mean accuracies of 91.36 % for DT vs. HCs, 85.91 % for ET vs. HCs, and 86.64 % for ET vs. DT. Discriminative brain regions were mainly localized in the basal ganglia, cerebello-thalamo-cortical motor circuitry, and non-motor cortical regions. Correlation analysis revealed that the nodal efficiency of the four salient brain regions was negatively correlated with clinical characteristics.

CONCLUSION: Our findings suggest the critical role of the classic tremor network in ET and DT pathogenesis, enhancing our comprehension of their FC-based pathophysiological mechanisms.

PMID:41218287 | DOI:10.1016/j.parkreldis.2025.108104

Psychoradiology. 2025 Oct 3;5:kkaf028. doi: 10.1093/psyrad/kkaf028. eCollection 2025.

ABSTRACT

In contemporary neuroscience, mapping the human brain's functional connectomes is essential to understanding its functional organization. Functional organizations in the brain gray matter have been the subject of previous research, but the functional information in white matter (WM), the other half of the brain, has been relatively underexplored. However, the dynamics of functional magnetic resonance imaging (fMRI) have been reliably identified in the brain WM. This review summarizes current knowledge about task-free (resting-state) fMRI neuroimaging analyses for the WM functional connectome. We present comparative findings of the WM functional connectome, including its mapping, physiological underpinnings, cognitive neuroscience relationships, and clinical applications. Furthermore, we explore the emerging consensus that WM functional networks have valid topological characteristics that can distinguish between individuals with brain diseases and healthy controls, predict general intelligence, and identify inter-subject variabilities. Lastly, we emphasize the need for further studies and the limitations, challenges, and future directions for the WM functional connectome. An overview of these developments could lead to new directions for cognitive neuroscience and clinical neuropsychiatry.

PMID:41216611 | PMC:PMC12596274 | DOI:10.1093/psyrad/kkaf028

Aging Dis. 2025 Oct 27. doi: 10.14336/AD.2025.0887. Online ahead of print.

ABSTRACT

Obesity is a recognized risk factor for age-related cognitive decline, with central (abdominal) obesity posing a particular strong threat to brain health. In a cross-sectional study of 89 cognitively healthy adults (52-79 years, mean 65.7 ± 6.4; 58 women), we compared the effects of central versus overall obesity on brain connectivity measured with resting-state fMRI. We focused on network segregation, an index of functional specialization that captures the balance between connections within and across large-scale brain networks. Central obesity, but not overall obesity, was associated with reduced segregation in associative and sensorimotor networks, even after adjusting for overall obesity, highlighting the role of abdominal fat accumulation. To explore underlying mechanisms, we combined a widely used clinical index of peripheral insulin resistance (HOMA-IR) with multimodal neuroimaging, including structural MRI for cortical thickness, T1w/T2w MRI for intracortical myelin, FDG-PET for glucose metabolism, and FBB-PET for Aβ load. Mediation analyses showed that central obesity was associated with insulin resistance, which was related to alterations in intracortical myelin, cortical glucose metabolism, and cortical Aβ accumulation. These changes were collectively linked to reduced network segregation. Modeling cortical Aβ load as preceding cortical glucose metabolism further revealed stronger and more widespread network disruption, which may reflect bidirectional interactions between amyloid pathology and metabolic dysfunction. These findings describe a pattern of metabolic and structural brain changes linked to central obesity that may compromise brain functional integrity. Although causality cannot be inferred from this cross-sectional design, targeting abdominal fat and related metabolic factors could help preserve brain health and reduce cognitive vulnerability with aging.

PMID:41213081 | DOI:10.14336/AD.2025.0887

IEEE Trans Med Imaging. 2025 Nov 10;PP. doi: 10.1109/TMI.2025.3631105. Online ahead of print.

ABSTRACT

Accurate identification of brain disorders enables timely intervention and improved patient outcomes. While numerous studies have developed AI models for resting-state functional magnetic resonance imaging (rs-fMRI) analysis, most rely on supervised learning, which can overlook hidden patterns that are less discriminatively associated with labels and require large annotated datasets. To address these limitations, we propose leveraging normative modeling, an unsupervised approach that constructs a model of normality based on healthy controls' data. Deviations from normality indicate potential disorders. However, applying normative modeling to rs-fMRI faces two significant challenges: constructing normality and ensuring explainability. To tackle these challenges, we propose BRAINEXA, a novel framework enhancing normative modeling for rs-fMRI-based brain disorder identification. Specifically, to construct accurate and stable normality, BRAINEXA introduces a training strategy that predicts more informative regions from less informative regions, discouraging trivial self-supervised learning solutions and improving representation learning without additional overhead. Furthermore, we incorporate spatiotemporal mutual information regularization to preserve distinctiveness between more informative regions and less informative regions during latent encoding, preventing potential representational distortions. For interpretability, BRAINEXA extracts normality-defining (ND) subregions, the core regions that characterize normal brain function. By combining ND subregions with anomaly scores, BRAINEXA can offer region- and connection-wise explanations that help identify clinically meaningful disruptions of normality in an unsupervised setting. We demonstrate the effectiveness of BRAINEXA on four public rs-fMRI datasets: REST-meta-MDD, ABIDE I, ADHD-200, and OASIS-3. Our code is available at https://github.com/ku-milab/BRAINEXA.

PMID:41212695 | DOI:10.1109/TMI.2025.3631105