Brain connectivity最新文献

Background: With an aging population, the prevalence of neurological disorders is increasing, leading to a rise in lower limb movement disorders and, in turn, a growing need for rehabilitation training. Previous neuroimaging studies have shown a growing scientific interest in the study of brain mechanisms in robot-assisted lower limb rehabilitation (RALLR). Objective: This review aimed to determine differences in neural activity patterns during different RALLR tasks and the impact on neurofunctional plasticity. Methods: Sixty-five articles in the field of RALLR were selected and tested using three brain function detection technologies. Results: Most studies have focused on changes in activity in various regions of the cerebral cortex during different lower limb rehabilitation tasks but have also increasingly focused on functional changes in other cortical and deep subcortical structures. Our analysis also revealed a neglect of certain task types. Conclusion: We identify and discuss future research directions that may contribute to a clear understanding of neural functional plasticity under different RALLR tasks.

Background: The basal ganglia-thalamocortical (BGTC) and cerebello-thalamocortical (CTC) networks are implicated in tremor genesis; however, exact contributions across disorders have not been studied. Objective: Evaluate the structural connectivity of BGTC and CTC in tremor-dominant Parkinson's disease (TDPD) and essential tremor plus (ETP) with the aid of probabilistic tractography and graph theory analysis. Methods: Structural connectomes of the BGTC and CTC were generated by probabilistic tractography for TDPD (n = 25), ETP (ET with rest tremor, n = 25), and healthy control (HC, n = 22). The Brain Connectivity Toolbox was used for computing standard topological graph measures of segregation, integration, and centrality. Tremor severity was ascertained using the Fahn-Tolosa-Marin tremor rating scale (FTMRS). Results: There was no difference in total FTMRS scores. Compared with HC, TDPD had a lower global efficiency and characteristic path length. Abnormality in segregation, integration, and centrality of bilateral putamen, globus pallidus externa (GPe), and GP interna (GPi), with reduction of centrality of right caudate and cerebellar lobule 8, was observed. ETP showed reduction in segregation and integration of right GPe and GPi, ventrolateral posterior nucleus, and centrality of right putamen, compared with HC. Differences between TDPD and ETP were a reduction of strength of the right putamen, and lower clustering coefficient, local efficiency, and strength of the left GPi in TDPD. Conclusions: Contrary to expectations, TDPD and ETP may not be significantly different with regard to tremor pathogenesis, with definite overlaps. There may be fundamental similarities in network disruption across different tremor disorders with the same tremor activation patterns, along with disease-specific changes.

Background: Air pollution exposure has been associated with adverse cognitive and mental health outcomes in children, adolescents, and adults, although youth may be particularly susceptible given ongoing brain development. However, the neurodevelopmental mechanisms underlying the associations among air pollution, cognition, and mental health remain unclear. We examined the impact of particulate matter (PM_2.5) on resting-state functional connectivity (rsFC) of the default mode network (DMN) and three key attention networks: dorsal attention, ventral attention, and cingulo-opercular. Methods: Longitudinal changes in rsFC within/between networks were assessed from baseline (9-10 years) to the 2-year follow-up (11-12 years) in 10,072 youth (M ± SD = 9.93 + 0.63 years; 49% female) from the Adolescent Brain Cognitive Development (ABCD^®) study. Annual ambient PM_2.5 concentrations from the 2016 calendar year were estimated using hybrid ensemble spatiotemporal models. RsFC was estimated using functional neuroimaging. Linear mixed models were used to test associations between PM_2.5 and change in rsFC over time while adjusting for relevant covariates (e.g., age, sex, race/ethnicity, parental education, and family income) and other air pollutants (O₃, NO₂). Results: A PM_2.5 × time interaction was significant for within-network rsFC of the DMN such that higher PM_2.5 concentrations were associated with a smaller increase in rsFC over time. Further, significant PM_2.5 × time interactions were observed for between-network rsFC of the DMN and all three attention networks, with varied directionality. Conclusion: PM_2.5 exposure was associated with alterations in the development and equilibrium of the DMN-a network implicated in self-referential processing-and anticorrelated attention networks, which may impact trajectories of cognitive and mental health symptoms across adolescence.

Objective: Cerebral small vessel disease (CSVD) is a primary vascular disease of cognitive impairment. Previous studies have predominantly focused on brain linear features. However, the nonlinear measure, brain entropy (BEN), has not been elaborated. Thus, this study aims to investigate if BEN abnormalities could manifest in CSVD patients with cognitive impairment. Methods: Thirty-four CSVD patients with cognitive impairment and 37 healthy controls (HCs) were recruited. Analysis of gray matter approximate entropy (ApEn) and sample entropy (SampEn) which are two indices of BEN was calculated. To explore whether BEN can provide unique information, we further performed brain linear methods, namely, amplitude of low frequency fluctuation (ALFF) and regional homogeneity (ReHo), to observe their differences. The ratios of BEN/ALFF and BEN/ReHo which represent the coupling of nonlinear and linear features were introduced. Correlation analysis was conducted between imaging indices and cognition. Subsequently, the linear support vector machine (SVM) was used to assess their discriminative ability. Results: CSVD patients exhibited lower ApEn and SamEn values in sensorimotor areas, which were correlated with worse memory and executive function. In addition, the results of BEN showed little overlap with ALFF and ReHo in brain regions. Correlation analysis also revealed a relationship between the two ratios and cognition. SVM analysis using BEN and its ratios as features achieved an accuracy of 74.64% (sensitivity: 86.49%, specificity: 61.76%, and AUC: 0.82439). Conclusion: Our study reveals that the reduction of sensorimotor system complexity is correlated with cognition. BEN exhibits distinctive characteristics in brain activity. Combining BEN and the ratios can be new biomarkers to diagnose CSVD with cognitive impairment.

Purpose: Rhyming is a phonological skill that typically emerges in the preschool-age range. Prosody/rhythm processing involves right-lateralized temporal cortex, yet the neural basis of rhyming ability in young children is unclear. The study objective was to use functional magnetic resonance imaging (fMRI) to quantify neural correlates of rhyming abilities in preschool-age children. Method: Healthy pre-kindergarten child-parent dyads were recruited for a study visit including MRI and the Preschool and Primary Inventory of Phonological Awareness (PIPA) rhyme subtest. MRI included an fMRI task where the child listened to a rhymed and unrhymed story without visual stimuli. fMRI data were processed using the CONN functional connectivity (FC) toolbox, with FC computed between 132 regions of interest (ROI) across the brain. Associations between PIPA score and FC during the rhymed versus unrhymed story were compared accounting for age, sex, and maternal education. Results: In total, 45 children completed MRI (age 54 ± 8 months, 37-63; 19M 26F). Median maternal education was college graduate. FC between ROIs in posterior default mode (imagery) and right fronto-parietal (executive function) networks was more strongly positively associated with PIPA score during the rhymed compared with the unrhymed story [F(2,39) = 10.95, p-FDR = 0.043], as was FC between ROIs in right-sided language (prosody) and dorsal attention networks [F(2,39) = 9.85, p-FDR = 0.044]. Conclusions: Preschool-age children with better rhyming abilities had stronger FC between ROIs supporting attention and prosody and also between ROIs supporting executive function and imagery, suggesting rhyme as a catalyst for attention, visualization, and comprehension. These represent novel neural biomarkers of nascent phonological skills.

Introduction: This study aims to use diffusion tensor imaging (DTI) in conjunction with brain graph techniques to define brain structural connectivity and investigate its association with personal income (PI) in individuals of various ages and intelligence quotients (IQ). Methods: MRI examinations were performed on 55 male subjects (mean age: 40.1 ± 9.4 years). Graph data and metrics were generated, and DTI images were analyzed using tract-based spatial statistics (TBSS). All subjects underwent the Wechsler Adult Intelligence Scale for a reliable estimation of the full-scale IQ (FSIQ), which includes verbal comprehension index, perceptual reasoning index, working memory index, and processing speed index. The performance score was defined as the monthly PI normalized by the age of the subject. Results: The analysis of global graph metrics showed that modularity correlated positively with performance score (p = 0.003) and negatively with FSIQ (p = 0.04) and processing speed index (p = 0.005). No significant correlations were found between IQ indices and performance scores. Regional analysis of graph metrics showed modularity differences between right and left networks in sub-cortical (p = 0.001) and frontal (p = 0.044) networks. TBSS analysis showed greater axial and mean diffusivities in the high-performance group in correlation with their modular brain organization. Conclusion: This study showed that PI performance is strongly correlated with a modular organization of brain structural connectivity, which implies short and rapid networks, providing automatic and unconscious brain processing. Additionally, the lack of correlation between performance and IQ suggests a reduced role of academic reasoning skills in performance to the advantage of high uncertainty decision-making networks.

Background: The treatment of depressive episodes is well established, with clearly demonstrated effectiveness of antidepressants and psychotherapies. However, more than one-third of depressed patients do not respond to treatment. Identifying the brain structural basis of treatment-resistant depression could prevent useless pharmacological prescriptions, adverse events, and lost therapeutic opportunities. Methods: Using diffusion magnetic resonance imaging, we performed structural connectivity analyses on a cohort of 154 patients with mood disorder (MD) and 77 sex- and age-matched healthy control (HC) participants. To assess illness improvement, the patients with MD went through two clinical interviews at baseline and at 6-month follow-up and were classified based on the Clinical Global Impression-Improvement score into improved or not-improved (NI). First, the threshold-free network-based statistics (NBS) was conducted to measure the differences in regional network architecture. Second, nonparametric permutations tests were performed on topological metrics based on graph theory to examine differences in connectome organization. Results: The threshold-free NBS revealed impaired connections involving regions of the basal ganglia in patients with MD compared with HC. Significant increase of local efficiency and clustering coefficient was found in the lingual gyrus, insula, and amygdala in the MD group. Compared with the NI, the improved displayed significantly reduced network integration and segregation, predominately in the default-mode regions, including the precuneus, middle temporal lobe, and rostral anterior cingulate. Conclusions: This study highlights the involvement of regions belonging to the basal ganglia, the fronto-limbic network, and the default mode network, leading to a better understanding of MD disease and its unfavorable outcome.