Biswal KF-3802 Driver
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Biswal KF-3802 Driver
For the control contrast, it was deactivated for the scrambled picture matching whereas activated for the tone judgement and number judgement task. Having formally established the contrastive cognitive fMRI signatures across the task-independent networks, Biswal KF-3802 then employed representational similarity analysis RSA in a Biswal KF-3802 way to quantify the cognitive signature of the networks and compare the similarity of activation patterns found in task-active fMRIs to the similarity pattern predicted by task-independent networks. The three basic steps were as follows Fig.
The task-independent networks were used to construct the hypothesized model RDMs rsfMRI RDM and tractography RDM by assuming that each network had unique pattern of activity so there would be no similarity between the networks, whereas within the networks, all nodes Biswal KF-3802 have the same Biswal KF-3802 of activity Fig. Representational similarity analysis.
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Three task conditions including semantic, control, and baseline induced activity in Biswal KF-3802 ROIs Biswal KF-3802 representations. We computed the dissimilarity for each pair of ROIs using 1-correlation across conditions. The RDM is typically symmetric about a diagonal of zeros. The model RDM can similarly be computed from the hypothesis for the task-independent networks.
By correlating RDMs black double arrowwe can assess to what extent the brain representation reflects experimental conditions and can be accounted for by the hypothesized model. The SPC and Heschl-lingual network were completely distinctive from the other networks but themselves. In the tractography networks, each network presented their own characteristics in the pattern of dissimilarity. The FTP network was different from the other networks but showed the task-dependent pattern of dissimilarity. The basal—temporal network showed the greatest dissimilarity to the pSTG network. The SPC network was entirely different Biswal KF-3802 the other networks.
The pattern of dissimilarities of networks. Each color line represents 3 fMRI studies; red—Visser et al.
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Many studies have identified various brain networks using task-free datasets such as rsfMRI and diffusion imaging Biswal et al. Biswal KF-3802 these studies assert functional characteristics for the identified networks, the true cognitive functions of them are rarely probed directly. This is a crucial step if we are to elucidate the relationship between distributed brain networks and higher cognitive functions. Here, we applied a new method to derive the cognitive profiles of brain networks estimated from task-free datasets. Utilizing graph-theory network analysis in two task-independent datasets rsfMRI and DWIwe revealed the distributed connectivity networks present across frontal, parietal, and temporal associative cortices. The different functional signature of each network was then derived using three task-active fMRI datasets.
Our results demonstrated that there was a strong association between the connectivity-based networks Biswal KF-3802 in the task-independent datasets and the pattern of network activity in the task-active fMRI datasets. Thus, our findings suggest that the topology of structural and functional connectivity in the associative cortices reflects higher cognitive functions including cognitive control, semantic representation, memory, visuospatial function, numerical processing, and perception. We employed RSA as a new method to directly compare the task-free and task-related networks.
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RSA characterizes the representation in brain regions to compare the brain activity patterns representing experimental Biswal KF-3802 to each other in fMRI Kriegeskorte et al. It has become a popular method in brain information processing, for example, by revealing voxels corresponding to experimental conditions such as low-visual features lines, colors and higher visual features faces, objects and comparing the representations from different sources neural activities, behaviors, and theoretical models Mur et al.
We applied this method to the network-level of brain activity and successfully measured network representations RDMs. The quantified signature of network Biswal KF-3802 for each dataset was statistically compared and demonstrated a direct relationship between task-free and task-active networks as well as between two task-free networks acquired by different Biswal KF-3802 techniques DWI and rsfMRI. It revealed that not only various associative cortical regions but also multiple networks are involved in higher cognitive functions e.
Thus, our results indicate that the cognitive signature of networks can be directly evaluated by utilizing a new method—RSA.
The topology of structural and functional network was not significantly Biswal KF-3802 but, of course, there were some variations. There are reasons for expecting Biswal KF-3802 results not to be perfectly identical—specifically the quality and nature of the two datasets are different. Sources of potential variation include: