Therefore, multimodal approaches may improve the estimation of brain connectivity by combining the strengths of each individual modality (dMRI and fMRI) 10. This can happen when synchronous activity arises between anatomically distinct regions, possibly driven by common sources 28. Conversely, functional connectivity may exist between nodes that are directly or indirectly connected. It is particularly susceptible to missing “weak” long-range connections 26, 27. As a result, tractography is prone to false-negative and false-positive results, either under-estimating or generating spurious connections. However, structural connectivity estimation can be especially challenging in white matter areas with complex fiber orientations (e.g., fiber crossings) 25. In most brain networks studies, the edges’ weights are defined based on either structural or functional connectivity information. Significant advances in multimodal imaging techniques, analysis, and modeling have been made to investigate brain changes or group differences 23, 24. Recently, an increasing number of multimodal explorations, especially combining fMRI and dMRI data, has been conducted to improve our understanding of brain mechanisms, and interactions between functional and structural networks 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21. The mapping of brain functions using either functional or structural networks has been widely investigated 1, 2, 4, 5, 6, 7, 8. By analogy with an electrical circuit that incorporates wiring, components and functionalities, a brain circuit is defined by the anatomical (sub-)network that is associated with a specific function. On the other hand, if the edges are characterized by statistical relationships from functional signals such as fMRI, electro-encephalogram (EEG) or magneto-encephalogram (MEG), the network is generally referred to as a functional network. A network with edges characterized by properties related to white matter pathways is defined as a structural network. Gray matter areas are responsible for information (e.g., motor, visual, auditory, language) processing and are physically interconnected via axonal pathways in the white matter, thereby producing functional networks of cortical/sub-cortical areas associated to specific tasks. The brain can be modeled as a network where nodes represent cortical or sub-cortical gray matter areas, and edges model relationships (connectivity) between nodes 1, 2, 3, as shown in Fig. Results on a phantom (Fibercup) also indicate improvements in structural connectivity mapping by rejecting false-positive connections with insufficient support from fMRI, and enhancing under-estimated connectivity with strong functional correlation. In contrast to correlation-based or independent component analysis (ICA) functional connectivity mapping, detailed anatomical connectivity patterns are revealed for each functional module. Results on Human Connectome Project (HCP) data demonstrate the benefits of the approach by successfully identifying function-specific anatomical circuits, such as the language and resting-state networks. The proposed framework explicitly models the interactions between structural and functional connectivity measures thereby improving anatomical circuit estimation. Additionally, tractography techniques are prone to limitations, which can result in erroneous pathways. The proposed method jointly analyzes whole brain dMRI and fMRI data, allowing the estimation of complete function-specific structural networks instead of interactively investigating the connectivity of individual cortical/sub-cortical areas. Usually, seed regions for tractography are selected from fMRI activation maps to extract the white matter pathways of interest. Incorporating information from functional MRI (fMRI) into diffusion MRI to estimate brain circuits is a challenging task. A joint structural-functional brain network model is presented, which enables the discovery of function-specific brain circuits, and recovers structural connections that are under-estimated by diffusion MRI (dMRI).
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