Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state

The Human Connectome Project's neuroimaging approach

Noninvasive human neuroimaging has yielded many discoveries about the brain. Numerous methodological advances have also occurred, though inertia has slowed their adoption. This paper presents an integrated approach to data acquisition, analysis and sharing that builds upon recent advances, particularly from the Human Connectome Project (HCP). The 'HCP-style' paradigm has seven core tenets: (i) collect multimodal imaging data from many subjects; (ii) acquire data at high spatial and temporal resolution; (iii) preprocess data to minimize distortions, blurring and temporal artifacts; (iv) represent data using the natural geometry of cortical and subcortical structures; (v) accurately align corresponding brain areas across subjects and studies; (vi) analyze data using neurobiologically accurate brain parcellations; and (vii) share published data via user-friendly databases. We illustrate the HCP-style paradigm using existing HCP data sets and provide guidance for future research. Widespread adoption of this paradigm should accelerate progress in understanding the brain in health and disease

Prognostic value of lymph node ratio in node-positive breast cancer in Egyptian patients

Background: Breast cancer in Egypt is the most common cancer among women and is the leading cause of cancer mortality. Traditionally, axillary lymph node involvement is considered among the most important prognostic factors in breast cancer. Nonetheless, accumulating evidence suggests that axillary lymph node ratio should be considered as an alternative to classical pN classification. Materials and methods: We performed a retrospective analysis of patients with operable node-positive breast cancer, to investigate the prognostic significance of axillary lymph node ratio. Results: Five-hundred patients were considered eligible for the analysis. Median follow-up was 35 months (95% CI 32–37 months), the median disease-free survival (DFS) was 49 months (95% CI, 46.4–52.2 months). The classification of patients based on pN staging system failed to prognosticate DFS in the multivariate analysis. Conversely, grade 3 tumors, and the intermediate (>0.20 to ⩽0.65) and high (>0.65) LNR were the only variables that were independently associated with adverse DFS. The overall survival (OS) in this series was 69 months (95% CI 60–77). Conclusion: The analysis of outcome of patients with early breast cancer in Egypt identified the adverse prognostic effects of high tumor grade, ER negativity and intermediate and high LNR on DFS. If the utility of the LNR is validated in other studies, it may replace the use of absolute number of axillary lymph nodes