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Visualizing trigeminal neuralgia at 7 Tesla: Advancing etiological understanding and improving future clinical imaging protocols

Visualizing trigeminal neuralgia at 7 Tesla: Advancing etiological understanding and improving future clinical imaging protocols
7 特斯拉可视化三叉神经痛:促进病因学理解并改进未来的临床成像方案
批准号:
10667246
负责人:
Priti Balchandani
金额:
$53.21万
依托单位国家:
美国
项目类别:
财政年份:
2022
资助国家:
美国
项目状态:
已结题
起止时间:
2022-09-06 至 2024-05-05

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Project Summary Trigeminal Neuralgia (TN) is one of the most painful disorders ever identified and affects 4.3 out of every 100,000 people in the US. In its most typical form, it causes brief attacks of intense shock-like pain on one side of the face. Although it is known to be associated with the trigeminal or 5th cranial nerve, its overall etiology remains poorly understood. A multitude of pharmacological and surgical methods have been used to treat TN, with varying levels of long-term efficacy, but treatment remains challenging given that TN pain may be caused by any of a myriad of underlying abnormalities that may not always be identifiable using current clinical workups. Clinically, magnetic resonance imaging (MRI) is used to detect neurovascular compression (NVC), conventionally understood to be a main cause of TN, and to rule out other potential etiologies such as lesions or multiple sclerosis. However, pain eventually recurs in nearly half of patients whose NVC was treated surgically, and NVC is often identified in people who do not have TN. Although current MRI protocols are important in the pre-surgical assessment of NVC, they likely lack the resolution, quantitative accuracy, and scope required to simultaneously interrogate the entire trigeminal sensory pathway, as well as the brain networks associated with the sensation, evaluation, and modulation of pain that may also contribute to TN. There remains a critical unmet need to comprehensively study the regions and networks implicated in TN and reliably and accurately identify the true cause of pain in TN patients. MRI at ultrahigh magnetic fields such as 7 Tesla (7T) provides increased signal to noise ratio, which yields images with exquisite resolution that can elucidate subtle anatomical, vascular, microstructural, and functional alterations in unprecedented detail. Therefore, we will perform a systematic prospective study of TN patients (half with identified NVC and half idiopathic) and matched healthy controls using a state-of-the-art, TN-specific, multimodal 7T MRI protocol composed of high-resolution structural, vascular, diffusion, and functional imaging sequences. We propose three aims directed towards our central hypothesis: 1) To develop new imaging techniques to better visualize all possible brain regions implicated in TN; 2) To perform qualitative and quantitative analysis of 7T multimodal images to characterize the structural integrity of the trigeminal sensory pathway along its entire length from the trigeminal ganglion to the primary somatosensory cortex; 3) To perform whole-brain structural and functional network analyses to reveal abnormalities in networks associated with pain sensation and modulation in TN patients; and 4) To evaluate translation of our 7T findings to 3 Tesla clinical scanners. Successful completion of this study should yield imaging markers that are tightly linked to the pathophysiology of TN, and could lead to a more complete understanding of TN, ultimately resulting in more targeted and effective treatments for this painful affliction.
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Gut-brain axis in Alzheimer's disease: translational 7T MRI markers and underlying mechanisms
  • 批准号:
    10901013
  • 项目类别:
  • 资助金额:
    $81.01万
  • 财政年份:
    2023
  • 负责人:
    Priti Balchandani
  • 依托单位:
Use of 7T multimodal imaging to detect brain changes associated with light therapy in persons with mild cognitive impairment and mild Alzheimer's Disease
Use of 7T multimodal imaging to detect brain changes associated with light therapy in persons with mild cognitive impairment and mild Alzheimer's Disease
7T Neurosurgical Mapping Protocol for Endoscopic Resection of Skull Base Tumors
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