Neuronal responses exhibit two stimulus or task-related components: evoked and induced. The functional role of induced responses has been ascribed to ‘top-down’ modulation through backward connections and lateral interactions; as opposed to the bottom-up driving processes that may predominate in evoked components. The implication is that evoked and induced components may reflect different neuronal processes. The conventional way of separating evoked and induced responses assumes that they can be decomposed linearly; in that induced responses are the average of the power minus the power of the average (the evoked component). However, this decomposition may not hold if both components are generated by nonlinear processes. In this work, we propose a Dynamic Causal Model that models evoked and induced responses at the same time. This allows us to explain both components in terms of shared mechanisms (coupling) and changes in coupling that are necessary to explain any induced components. To establish the face validity of our approach, we used Bayesian Model Selection to show that the scheme can disambiguate between models of synthetic data that did and did not contain induced components. We then repeated the analysis using MEG data during a hand grip task to ask whether induced responses in motor control circuits are mediated by ‘top-down’ or backward connections. Our result provides empirical evidence that induced responses are more likely to reflect backward message passing in the brain, while evoked and induced components share certain characteristics and mechanisms.

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Purpose: Neuroblastoma (NB) is a neural crest-derived tumor that commonly occurs in childhood. b-1,4-Galactosyltransferase III (B4GALT3) is highly expressed in human fetal brain and is responsible for the generation of poly-N-acetyllactosamine, which plays a critical role in tumor progression. We therefore investigated the expression and role of B4GALT3 in NB.Experimental Design: We examined B4GALT3 expression in tumor specimens from 101 NB patients by immunohistochemistry and analyzed the correlation between B4GALT3 expression and clinicopathologic factors or survival. The functional role of B4GALT3 expression was investigated by overexpression or knockdown of B4GALT3 in NB cells for in vitro and in vivo studies.Results: We found that B4GALT3 expression correlated with advanced clinical stages (P ¼ 0.040), unfavorable Shimada histology (P < 0.001), and lower survival rate (P < 0.001). Multivariate analysis showed that B4GALT3 expression is an independent prognostic factor for poor survival of NB patients. B4GALT3 overexpression increased migration, invasion, and tumor growth of NB cells, whereas B4GALT3 knockdown suppressed the malignant phenotypes of NB cells. Mechanistic investigation showed that B4GALT3-enhanced migration and invasion were significantly suppressed by b1-integrin blocking antibody. Furthermore, B4GALT3 overexpression increased lactosamine glycans on b1-integrin, increased expression of mature b1-integrin via delayed degradation, and enhanced phosphorylation of focal adhesion kinase. Conversely, these properties were decreased by knockdown of B4GALT3 in NB cells.Conclusions: Our findings suggest that B4GALT3 predicts an unfavorable prognosis for NB and may regulate invasive phenotypes through modulating glycosylation, degradation, and signaling of b1-integrin in NB cells.

Metastasis often occurs in colorectal cancer (CRC) patients and is the main difficulty in cancer treatment. The upregulation of poly-N-acetyllactosamine-related glycosylation is found in CRC patients and is associated with progression and metastasis in cancer. β-1,4-Galactosyltransferase III (B4GALT3) is an enzyme responsible for poly-N-acetyllactosamine synthesis, and therefore, we investigated its expression in CRC patients. We found that B4GALT3 negatively correlated with poorly differentiated histology (P < 0.001), advanced stages (P = 0.0052), regional lymph node metastasis (P = 0.0018) and distant metastasis (P = 0.0463) in CRC patients. B4GALT3 overexpression in CRC cells suppressed cell migration, invasion and adhesion, whereas B4GALT3 knockdown enhanced malignant cell phenotypes. The β1 integrin-blocking antibody reversed the B4GALT3-mediated increase in cell invasion. B4GALT3 expression altered glycosylation on the N-glycan of β1 integrin probably through changes in poly-N-acetyllactosamine expression. Furthermore, more activated β1 integrin along with the activation of its downstream signaling transduction were found in B4GALT3 knockdown cells, whereas overexpression of B4GALT3 suppressed the expression of active β1 integrin and inhibited its downstream signaling. Our results suggest that B4GALT3 is negatively associated with CRC metastasis and suppresses cell invasiveness through inhibiting activation of β1 integrin.

Rehabilitation is the main therapeutic approach for reducing poststroke functional deficits in the affected upper limb; however, significant between-patient variability in rehabilitation efficacy indicates the need to target patients who are likely to have clinically significant improvement after treatment. Many studies have determined robust predictors of recovery and treatment gains and yielded many great results using linear approachs. Evidence has emerged that the nonlinearity is a crucial aspect to study the inter-areal communication in human brains and abnormality of oscillatory activities in the motor system is linked to the pathological states. In this study, we hypothesized that combinations of linear and nonlinear (cross-frequency) network connectivity parameters are favourable biomarkers for stratifying patients for upper limb rehabilitation with increased accuracy. We identified the biomarkers by using 37 prerehabilitation electroencephalogram (EEG) datasets during a movement task through effective connectivity and logistic regression analyses. The predictive power of these biomarkers was then tested by using 16 independent datasets (i.e. construct validation). In addition, 14 right handed healthy subjects were also enrolled for comparisons. The result shows that the beta plus gamma or theta network features provided the best classification accuracy of 92%. The predictive value and the sensitivity of these biomarkers were 81.3% and 90.9%, respectively. Subcortical lesion, the time poststroke and initial Wolf Motor Function Test (WMFT) score were identified as the most significant clinical variables affecting the classification accuracy of this predictive model. Moreover, 12 of 14 normal controls were classified as having favourable recovery. In conclusion, EEG-based linear and nonlinear motor network biomarkers are robust and can help clinical decision making.

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