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Eur J Med Res. 2025 Dec 29;30(1):1262. doi: 10.1186/s40001-025-03542-y.

ABSTRACT

BACKGROUND AND OBJECTIVE: Overactive bladder (OAB) is a complex condition involving central nervous system (CNS) processes that are not fully understood. We conducted a detailed neuroimaging study to investigate the CNS role in OAB, focusing on the bladder emptying state.

METHODS: This cross-sectional study included 168 OAB patients and 133 matched controls. Participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) during the bladder emptying state. Data were analyzed using tract-based spatial statistics (TBSS), graph theory, functional connectivity, and structure-function coupling. The Overactive Bladder Symptom Score (OABSS) and the Overactive Bladder Questionnaire Short Form (OAB-q SF) were also utilized.

KEY FINDINGS AND LIMITATIONS: TBSS revealed three white matter tracts with higher fractional anisotropy in OAB patients; the largest of these, including the body of the corpus callosum (bCC) and bilateral anterior corona radiata (ACR), correlated positively with OAB-q scores. Functional connectivity analysis indicated increased connectivity between the left dorsolateral superior frontal gyrus (SFGdor.L) and bilateral supplementary motor areas, and reduced connectivity between the left middle temporal gyrus (MTG.L) and the right inferior temporal gyrus (ITG.R). The left amygdala (AMYG.L) exhibited enhanced structure-function coupling, which was positively associated with OABSS and OAB-q scores. However, the study's cross-sectional design precludes determining causal relationships due to the lack of longitudinal data.

CONCLUSIONS AND CLINICAL IMPLICATIONS: This study identified distinct functional and structural brain alterations in OAB patients during the bladder emptying state. These findings offer new perspectives for investigating innovative treatment strategies. Trial registration This study was registered on the UK's Clinical Study Registry (ISRCTN11583354).

PMID:41462487 | PMC:PMC12752313 | DOI:10.1186/s40001-025-03542-y

J Vestib Res. 2025 Dec 29:9574271251407403. doi: 10.1177/09574271251407403. Online ahead of print.

ABSTRACT

ObjectivesBenign paroxysmal positional vertigo (BPPV) is a prevalent triggers of persistent postural-perceptual dizziness (PPPD). The maladaptation of brain function may be one of the pathophysiology in PPPD. This study aims to identify brain functional neuroimaging features and establish prediction models to predict PPPD after BPPV.MethodsThe diagnosis of BPPV and PPPD was based on the criteria established by the Bárány Society. Patients with posterior semicircular canal BPPV were treated using the Epley maneuver. Patients with geotropic lateral canal BPPV were treated with the barbecue rotation maneuver, while those with apogeotropic lateral canal BPPV were treated using the Gufoni maneuver. After successful canalith repositioning maneuver treatment, the patient underwent resting-state functional magnetic resonance imaging (fMRI) scan. Using feature selection and extraction techniques, six machine learning algorithms were implemented to predict PPPD. The models were trained with 5-fold cross-validation, and performance was evaluated using the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score (F1).ResultsA total of 101 patients were included in the final analysis, comprising 64 patients without PPPD (non-PPPD) and 37 patients with PPPD (PPPD). A total of 22 functional neuroimaging features were identified to be closely associated with PPPD after BPPV. Among the six machine learning algorithms, the Multilayer Perceptron model exhibited superior performance, with an AUC of 0.93, a recall of 0.82, a precision of 0.83, an accuracy of 0.82, and an F1 score of 0.82. SHAP analysis identified the most influential resting-state fMRI features in this model. For the top 10 important resting-state fMRI features, 3 features overlapped in all six machine learning algorithms. These features include FC between the vermis 3 and the superior frontal gyrus, orbital part, DC in the cerebellum 7b, left, and FC between the Heschl gyrus, left, and the caudate, right.ConclusionsThese findings provide brain functional neuroimaging features which may be closely associated with the transition from BPPV to PPPD, thereby offering a valuable tool for the early detection of PPPD.

PMID:41460105 | DOI:10.1177/09574271251407403

Cogn Neurodyn. 2026 Dec;20(1):24. doi: 10.1007/s11571-025-10397-w. Epub 2025 Dec 26.

ABSTRACT

Numerous studies in the bilingual literature have shown that cognitive control adapts to several factors related to second language (L2) learning. However, whether third language (L3) learning influences cognitive control remains underexplored. In this longitudinal study, we analyzed behavioral performance and functional magnetic resonance imaging (fMRI) data among Chinese-English bilinguals at resting-state and during a flanker task both prior to English (L2) or Japanese (L3) learning and one year later. During brain resting-states for these same learners, we conducted a correlation analysis between language exam scores and functional connectivity strength of resting-state data after one year of study. The connectivity between the left anterior cingulate cortex (ACC) and the left precuneus was positively correlated with English listening performance, while the connectivity between the right supramarginal gyrus (SMG) and the right inferior parietal lobe (IPL) was negatively correlated with English oral performance. The behavioral results from the flanker task showed that after one year of L2 learning in a classroom setting, a significantly smaller flanker effect emerged among Chinese-English bilinguals. Moreover, brain imaging revealed that incongruent flanker trials elicited greater activation of the left superior frontal gyrus (SFG) than congruent trials. These behavioral and neural patterns were not found among Chinese-English bilinguals who had studied Japanese for one year. Taken together, these findings suggest that cognitive control adapts to L2 learning, but appears to be unaffected by L3 learning.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-025-10397-w.

PMID:41458475 | PMC:PMC12743047 | DOI:10.1007/s11571-025-10397-w

Front Neuroimaging. 2025 Dec 12;4:1653206. doi: 10.3389/fnimg.2025.1653206. eCollection 2025.

ABSTRACT

INTRODUCTION: Functional brain connectivity measures extracted from resting-state functional magnetic resonance imaging (fMRI) scans have generated wide interest as potential noninvasive biomarkers. In this context, performing global signal regression (GSR) as a preprocessing step remains controversial. Specifically, while it has been shown that a considerable fraction of global signal variations is associated with physiological and motion sources, GSR may also result in removing neural activity.

METHODS: Here, we address this question by examining the fundamental sources of resting global signal fluctuations using simultaneous electroencephalography (EEG)-fMRI data combined with cardiac and breathing recordings.

RESULTS: Our results suggest that systemic physiological fluctuations account for a significantly larger fraction of global signal variability compared to electrophysiological fluctuations. Furthermore, we show that GSR reduces artifactual connectivity due to heart rate and breathing fluctuations, but preserves connectivity patterns associated with electrophysiological activity within the alpha and beta frequency ranges.

DISCUSSION: Overall, these results provide evidence that the neural component of resting-state fMRI-based connectivity is preserved after the global signal is regressed out.

PMID:41458206 | PMC:PMC12740878 | DOI:10.3389/fnimg.2025.1653206

J Pain Res. 2025 Dec 22;18:6993-7003. doi: 10.2147/JPR.S553431. eCollection 2025.

ABSTRACT

BACKGROUND: This exploratory study investigated the neurobiological mechanisms of cancer pain by examining functional brain alterations using resting-state functional magnetic resonance imaging (fMRI), aiming to characterize neural network changes and identify potential neuroimaging biomarkers.

METHODS: A cross-sectional study was conducted from October 2021 to October 2022, involving 20 cancer pain patients and 20 age-, sex-, and education-matched healthy controls. Participants underwent comprehensive clinical assessments and 3.0T resting-state fMRI scanning. Inclusion criteria were patients aged ≥18 years with pathologically confirmed malignant neoplasms experiencing moderate to severe pain (NRS ≥ 4). Functional connectivity and low-frequency amplitude analyses were performed using the right nucleus accumbens as a seed region.

RESULTS: Significant neuroplastic changes were observed in cancer pain patients, primarily in the right hemisphere. Low-frequency amplitude analysis revealed reduced spontaneous neural activity in critical brain regions, including the right medial prefrontal cortex (T = -4.36), right superior/middle frontal gyrus (T = -5.21), and right precuneus (T = -4.15). Functional connectivity analysis showed substantially decreased connectivity between the right nucleus accumbens and bilateral medial prefrontal cortex (T = -4.86), left temporal pole (T = -5.62), and right superior temporal gyrus (T = -5.05).

CONCLUSION: The study provides preliminary evidence of right hemispheric neuronal dysfunction in cancer pain, highlighting altered functional connectivity in emotion regulation and pain processing neural circuits. These findings offer insights into the neurobiological mechanisms of cancer pain and potential objective assessment approaches.

PMID:41458190 | PMC:PMC12742303 | DOI:10.2147/JPR.S553431

Front Neurol. 2025 Dec 12;16:1634902. doi: 10.3389/fneur.2025.1634902. eCollection 2025.

ABSTRACT

Speech production and comprehension are coordinated by a large-scale language network. The dynamic balance of intrahemispheric and interhemispheric connectivity within this network is essential for normal language processing. Stroke often significantly disrupts both the functional integrity and dynamic balance of the language network, leading to language deficits (aphasia). However, the brain's adaptive potential to compensate for lesions in post-stroke aphasia (PSA) remains incompletely understood. A key unresolved question is whether recovery of language function in PSA is primarily facilitated by compensatory mechanisms within the left hemisphere, increased recruitment ("upregulation") in the right hemisphere, or both. Building on prior research, we defined a language network encompassing canonical language areas. We employed resting-state functional magnetic resonance imaging (rs-fMRI) to quantify functional connectivity (FC) and investigated differences in intrahemispheric and interhemispheric connectivity within this network between 32 patients with PSA and 70 healthy controls (HCs). Furthermore, we examined the association between altered connectivity patterns at baseline and subsequent improvement in language function in the PSA group. Compared to the HCs, the patients with PSA exhibited increased intrahemispheric FC at baseline. Crucially, this increased intrahemispheric FC was positively correlated with the magnitude of language function improvement from baseline to follow-up. In addition, intrahemispheric FC was significantly higher than interhemispheric FC in the PSA group at baseline. These findings suggest that aberrant connectivity within the language network represents a neural substrate of language impairment in PSA and that heightened intrahemispheric connectivity within the residual left hemisphere language network may predict better recovery of language function in patients with subacute PSA. Collectively, network-based pathology analysis enhances our understanding of the neural mechanisms underlying both lesion effects and functional recovery in PSA.

PMID:41458121 | PMC:PMC12740746 | DOI:10.3389/fneur.2025.1634902

Brain Res Bull. 2025 Dec 26;234:111709. doi: 10.1016/j.brainresbull.2025.111709. Online ahead of print.

ABSTRACT

The abnormal functional integration of DMN was widely observed in the psychosis. However, few studies focused on DMN in individuals at Clinical High Risk for Psychosis (CHR), especially under different cognitive loads. The present research predominantly focused on DMN and its antagonism with other networks using the functional MRI. To characterize the specificity of cognitive load-dependent antagonism between DMN and its anti-correlated networks in CHR, this study simulated a graded cognitive load continuum by implementing resting-state fMRI (Minimal cognitive load), passive SSVEP task (low cognitive load), and Emotional Face-Matching Task (high cognitive load). There were 36 CHR individuals and 39 healthy controls (HC) enrolled. Static and dynamic functional connectivity (sFC and dFC) were analyzed. The CHR subjects exhibited significantly reduced antagonism between higher-order cortices and DMN under low cognitive condition. Conversely, they demonstrated enhanced antagonism with greater fluctuation under high cognitive condition, likely a compensatory mechanism to maintain cognitive performance. Concurrently, the primary cortex demonstrated compensatory fluctuations during low cognitive load task. The neural signature reflects inefficient neural resource allocation and cognitive flexibility deficits, suggesting that dynamic brain network indicators based on cognitive load may become sensitive biomarkers for the early identification and intervention of CHR.

PMID:41456742 | DOI:10.1016/j.brainresbull.2025.111709