Brain Imaging and Behavior最新文献

To investigate the effects of platinum-based chemotherapeutic drugs on the effective connectivity (EC) of hippocampal-prefrontal (HIP-PFC) circuit in non-small cell lung cancer (NSCLC) patients utilizing spectral dynamic causal model (sDCM), and to explore its correlation with cognitive deficits. A total of 109 patients diagnosed with NSCLC (57 without chemotherapy (CT-) and 52 with chemotherapy (CT+)) and 60 healthy controls (HCs) were enrolled. Longitudinal data pre- and post-chemotherapy were available for 30 patients. All participants underwent functional magnetic resonance imaging (fMRI) and cognitive assessments. Effective connectivity within the HIP-PFC circuit for all participants were analyzed using the sDCM approach. Relationships between abnormal connectivity strength and neuropsychological scores were evaluated. The results revealed ECs differences among three groups in the HIP-PFC circuit concentrated in the bilateral hippocampus and right frontal lobe. Analysis of paired groups identified more abnormalities across cerebral hemispheres, including excitatory connectivity from l-HIP to right lateral prefrontal cortex (LPFC). The reduced EC from the l-HIP to the medial prefrontal cortex (mPFC) was significantly negatively correlated with the MoCA scores (r = -0.315, p = 0.007), and the reduced EC from the r-HIP to the mPFC was significantly negatively correlated with the memory scores (r = -0.349, p = 0.006). These insights further bolstered the crucial role of the HIP-PFC circuit in the pathophysiology of cognitive impairment induced by platinum-based chemotherapeutic agents, highlighting its potential as a novel imaging biomarker and therapeutic target.

Due to the lack of consistent findings across different modalities, the neurobiological underpinning of bipolar disorder (BD) remains elusive. This study aims to employ a multimodal fusion algorithm, integrating multimodal imaging data, to unravel the neurobiological underpinning of BD. A data-driven multimodal fusion algorithm was utilized to analyze covariant patterns across modalities in a cohort of 125 BD patients and 113 healthy controls (HCs). The study focused on fusing regional homogeneity (ReHo), gray matter volume (GMV), and fractional anisotropy (FA) derived from MRI scans to generate group-discriminative joint independent components (jIC). That differentiated BD patients from HCs across three modalities. An inverse functional pattern was observed in the default mode network (DMN) and sensorimotor network (SMN) in BD patients, characterized by increased ReHo in the DMN and decreased ReHo in the SMN compared to healthy individuals. This inverse pattern was also mirrored in GMV, showing increase in the DMN and decreases in the SMN. Meanwhile, significant functional hyperactivation coupled with decreased structural volume in the precuneus underscores its role in cognitive function in BD. Multimodal neuroimaging fusion provides a comprehensive understanding in pathophysiology of BD, offering valuable insights that could be pivotal in advancing the diagnosis and treatment of BD.

Cognitive decline is a major non-motor symptom in Parkinson's disease (PD), often linked to brain atrophy. This study examines the relationship between cortical atrophy and age groups in predicting cognitive decline in PD over five years. 188 PD patients from the COPPADIS cohort were stratified by age: young (30-55 years, N = 47), middle-aged (56-65 years, N = 59), and older adults (66-75 years, N = 82). Baseline cortical volume was assessed using T1-weighted MRI, and cognitive decline was evaluated using the annual rate of change of the Parkinson's Disease-Cognitive Rating Scale (PD-CRS). Parametric or non-parametric tests were applied to evaluate group differences. Main analyses consist of several multiple regression analyses to examine associations between baseline brain atrophy and cognitive decline by age group. Older adults exhibited significantly greater cognitive decline in comparison to the younger age groups in the three compose scores of the PDCRS -Fronto-subcortical (H(2) = 41.08, p < 0.001), Posterior Cortical (H(2) = 22.03, p < 0.001), and Total(H(2) = 41.13, p < 0.001). Higher educational level has a significant positive effect on older adults, specifically for working memory performance, delayed verbal memory and the fronto-subcortical composed score. Multiple regression models underscored the predictive value of the bilateral hippocampus, bilateral medial orbitofrontal cortex, right precuneus, and right isthmus cingulate gyrus, together with being an older adult or having a higher education. MRI measures, age, and education predict cognitive decline in PD. Longitudinal assessments are essential for refining atrophy-cognition correlations and optimizing patient stratification.

Presbycusis is a common disease in the elderly linked to cognitive decline. Studies have shown structural alterations in brain regions associated with hearing and cognitive function in presbycusis patients. However, the neural mechanisms linking brain structure with hearing loss and cognitive impairment remain unclear. This study investigated neuroanatomical alterations associated with auditory and cognitive functions. A total of 67 presbycusis patients and 68 normal-hearing controls underwent magnetic resonance imaging, auditory assessment, and cognitive assessment. Presbycusis patients were divided into presbycusis cognitive preservation (presbycusis-CP) and presbycusis cognitive impairment (presbycusis-CI) subgroups according to Montreal Cognitive Assessment scores. Atrophy of gray matter volume (GMV) was observed in presbycusis patients. The results indicated that GMV of the hippocampus, superior frontal cortex (SFC), and posterior cingulate cortex (PCC) mediated the connection between hearing loss and cognitive impairment. Interestingly, the hippocampus mediated the relationship between the GMV of the SFC and PCC in presbycusis and with the strongest mediating effect (56.14%) in presbycusis-CI patients. Notably, this was not valid in normal-hearing controls and presbycusis-CP patients. Overall, patients with presbycusis, especially presbycusis-CI patients, exhibit a distinct reorganization pattern in auditory and cognitive-related brain areas, with the hippocampus potentially being a key target for presbycusis-related cognitive impairment.

Emerging evidence highlights the cerebellum's involvement in Parkinson's disease (PD), yet its intrinsic functional connectivity with cortical-subcortical networks remains largely unexplored. This study aims to investigate cerebellar functional network alterations in PD and their associations with motor, cognitive, and emotional symptoms, as well as age-related effects. 82 participants with PD (PwPD) and 38 healthy controls (HCs) underwent structural and resting-state functional MRI scans. We conducted volumetric analysis in the cerebellum and examined the involvement of cerebellar functional networks in PD, both within-network and between-network connections regarding cortical-subcortical large-scale networks. Additionally, we explored correlations with motor, cognitive, and emotional symptoms, as well as age-related associations in PwPD. No significant differences in cerebellar volume were observed between PwPD and HCs. PwPD exhibited reduced functional connectivity within the cerebellar network and between the cerebellum (CER) and the salience network (SN) (P < 0.001). Decreased CER-SN connectivity correlated with lower MoCA scores and higher HAMD scores, indicating associations with cognitive impairment and depressive symptoms. Additionally, CER-MTL (medial temporal lobe) connectivity positively correlated with both HAMD and HAMA scores in PwPD. Age-related differences in CER-SN connectivity were significant between PwPD and HCs (P = 0.008). Our study suggests the involvement of cerebellar large-scale functional network connectivity in PD, emphasizing the role of cerebellar functional networks in the emotional and cognitive aspects of PD.

Accumulating neuroimaging evidence suggests that abnormal functional and structural brain connectivity plays a cardinal role in the pathophysiology of autism spectrum disorder (ASD). Here, we constructed brain networks of functional, structural, and morphological connectivity using data from functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI), respectively. The neuroimaging data from a cohort of 50 individuals with ASD and 47 age-, gender- and handedness-matched TDC (age range: 5-18 years) were selected from the Autism Brain Image Data Exchange database. The combination of the fMRI, sMRI and DTI modalities connectivity features resulted in a classification accuracy of 82.69% for differentiating individuals with ASD from TDC. This accuracy surpassed that of any single modality or combination of fMRI and DTI modalities previously examined. Among the fMRI, sMRI and DTI modalities, the most distinguishing connectivity features were observed in the temporal, parietal, and occipital lobes from the DTI modality, the prefrontal and parietal lobes from the fMRI modality, and the temporal lobe from the sMRI modality. In addition, we also found that these distinguishing connectivity features can predict abnormal social interaction behaviours in ASD. These results highlight the complementary information provided by multimodal approaches, further emphasizing the pivotal role of multimodal connectivity patterns in unravelling the intricate mechanisms involved in the pathophysiology of ASD.

Post-stroke cognitive impairment is a common and highly disabling multi-domain cognitive decline with unclear pathophysiological mechanisms. Its extent may be predicted by functional neuroimaging techniques, which could enhance our understanding of its underlying pathophysiology. Synthesize the state-of-the-art knowledge on resting-state functional magnetic resonance imaging findings in post-stroke cognitive impairment, their correlation with cognitive performance and the secondary compensatory connectivity changes. We performed a qualitative systematic literature review of reported data of resting-state functional magnetic resonance imaging abnormalities in post-stroke cognitive impairment, consulting the electronic databases Medline, Scopus, Web of Science, Cochrane, and BASE until June 2024. We selected 23 studies. They reported several key brain areas and network abnormalities associated with post-stroke cognitive impairment compared to controls and non-demented patients. The main areas with decreased functional connectivity were the medial prefrontal cortex, the inferior frontal gyrus, the posterior and anterior cingulate cortex, the precuneus and the hippocampus. The principal network altered was the default mode network, which includes the majority of the brain structures previously mentioned. Additionally, increased functional connectivity in other contralesionally areas may represent maladaptive or compensatory changes post-stroke. Resting-state functional magnetic resonance imaging is a promising tool for studying functional changes associated with post-stroke cognitive impairment. It may enhance our understanding of its pathophysiology and improve selection of suitable period or candidates for cognitive rehabilitation.