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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