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Title: Deep Parametric Model for Discovering Group-cohesive Functional Brain Regions
One of the primary tasks in neuroimaging is to simplify spatiotemporal scans of the brain (i.e., fMRI scans) by partitioning the voxels into a set of functional brain regions. An emerging line of research utilizes multiple fMRI scans, from a group of subjects, to calculate a single group consensus functional partition. This consensus-based approach is promising as it allows the model to improve the signalto-noise ratio in the data. However, existing approaches are primarily non-parametric which poses problems when new samples are introduced. Furthermore, most existing approaches calculate a single partition for multiple subjects which fails to account for the functional and anatomical variability between different subjects. In this work, we study the problem of group-cohesive functional brain region discovery where the goal is to use information from a group of subjects to learn “group-cohesive” but individualized brain partitions for multiple fMRI scans. This problem is challenging since neuroimaging datasets are usually quite small and noisy. We introduce a novel deep parametric model based upon graph convolution, called the Brain Region Extraction Network (BREN). By treating the fMRI data as a graph, we are able to integrate information from neighboring voxels during brain region discovery which helps reduce noise for each subject. Our model is trained with a Siamese architecture to encourage partitions that are group-cohesive. Experiments on both synthetic and real-world data show the effectiveness of our proposed approach.  more » « less
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Proceedings of the 2020 SIAM International Conference on Data Mining
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Sponsoring Org:
National Science Foundation
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    There is growing interest in detecting cerebro‐cerebellar circuits, which requires adequate blood oxygenation level dependent contrast and signal‐to‐noise ratio (SNR) throughout the brain. Although 7T scanners offer increased SNR, coverage of commercial head coils is currently limited to the cerebrum.


    To improve cerebellar functional MRI (fMRI) at 7T with high permittivity material (HPM) pads extending the sensitivity of a commercial coil.

    Study Type

    Simulations were used to determine HPM pad configuration and assess radiofrequency (RF) safety. In vivo experiments were performed to evaluate RF field distributions and SNR and assess improvements of cerebellar fMRI.


    Eight healthy volunteers enrolled in a prospective motor fMRI study with and without HPM.

    Field Strength/Sequence

    Gradient echo (GRE) echo planar imaging for fMRI, turbo FLASH for flip angle mapping, GRE sequence for SNR maps, and T1‐weighted MPRAGE were acquired with and without HPM pads at 7T.


    Field maps, SNR maps, and anatomical images were evaluated for coverage. Simulation results were used to assess SAR levels of the experiment. Activation data from fMRI experiments were compared with and without HPM pads.

    Statistical Tests

    fMRI data were analyzed using FEAT FSL for each subject followed by group level analysis using paired t‐test of acquisitions with and without HPM.


    Simulations showed 52% improvement in transmit efficiency in cerebellum with HPM and SAR levels well below recommended limits. Experiments showed 27% improvement in SNR in cerebellum and improvement in coverage on T1‐weighted images. fMRI showed greater cerebellar activation in individual subjects with the HPM pad present (Z > = 4), especially in inferior slices of cerebellum, with 59% average increase in number of activated voxels in the cerebellum. Group‐level analysis showed improved functional activation (Z > = 2.3) in cerebellar regions with HPM pads without loss of measured activation elsewhere.

    Data Conclusion

    HPM pads can improve cerebellar fMRI at 7T with a commonly‐used head coil without compromising RF safety.

    Level of Evidence: 2

    Technical Efficacy: Stage 1

    J. MAGN. RESON. IMAGING 2018;48:431–440.

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