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Dynamic Brain Imaging of Speech in Primary Progressive Aphasia

Dynamic Brain Imaging of Speech in Primary Progressive Aphasia
原发性进行性失语症言语的动态脑成像
批准号:
10740640
负责人:
MARIA LUISA GORNO TEMPINI
金额:
$237.31万
依托单位国家:
美国
项目类别:
财政年份:
2017
资助国家:
美国
项目状态:
未结题
起止时间:
2017-09-15 至 2026-06-30

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中文摘要
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英文摘要
PROJECT SUMMARY Primary progressive aphasia (PPA) is a clinical syndrome characterized by isolated, progressive loss of speech and language abilities due to frontotemporal lobar degeneration (FTLD) or Alzheimer’s disease (AD). Three clinical variants of PPA have been identified: i) logopenic variant (lvPPA) associated with loss of phonological abilities, left temporal-parietal atrophy and atypical AD pathology; ii) nonfluent variant (nfvPPA) with motor speech/grammar deficits, left inferior frontal damage and FTLD pathology; and iii) semantic variant (svPPA), with conceptual knowledge loss, anterior temporal damage and mostly FTLD-type pathology. In the first funding period, we demonstrated distinct functional neuropathophysiology across PPA variants (>40 pubs). Building upon these findings, in this renewal, we focus on revealing important mechanistic neural circuit abnormalities in PPA variants. Such mechanistic understanding is the foundation for development of better neuromodulatory or behavioral interventions for PPA. We leverage the unmatched temporal resolution of magnetoencephalography imaging (MEGI) with structural and diffusion MRI (to account for neurodegeneration of grey and white matter), spectral graph modeling, speech motor control modeling, machine learning, as well as detailed cognitive and language phenotyping. The specific aims are: 1) To determine distinct resting-state structure-function imaging of neural oscillations in PPA; 2) To examine mechanisms of control and learning in the speech production in PPA; 3) To examine neural interactions between speech production and semantic representations in PPA. This unique combination of multimodal brain imaging (MEGI, structural MRI & diffusion MRI), modeling (spectral graph, speech motor control), machine learning, and speech neuroscience will: a) delineate the functional manifestations of brain network dysfunction in PPA variants, and b) identify putative brain network targets and strategies for behavioral, or neuromodulation therapies for PPA.
期刊论文(18)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1038/s41467-022-32115-4
发表时间: 2022-07-29
期刊: Nature communications
影响因子: 16.6
作者: []
通讯作者:
Subspace-based interference removal methods for a multichannel biomagnetic sensor array.
多通道生物磁传感器阵列的基于子空间的干扰消除方法。
DOI: 10.1088/1741-2552/aa7693
发表时间: 2017
期刊: Journal of neural engineering
影响因子: 4
作者: [Sekihara,Kensuke, Nagarajan,SrikantanS]
通讯作者: Nagarajan,SrikantanS
A Novel Scanning Algorithm for MEG/EEG imaging using Covariance Partitioning and Noise Learning.
使用协方差分区和噪声学习的 MEG/EEG 成像的新型扫描算法。
DOI: 10.1109/embc.2019.8856953
发表时间: 2019
期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子: --
作者: [Cai,Chang, Sekihara,Kensuke, Nagarajan,SrikantanS]
通讯作者: Nagarajan,SrikantanS
Beta-band activity in medial prefrontal cortex predicts source memory encoding and retrieval accuracy.
内侧前额叶皮层的β带活动可预测源记忆编码和检索的准确性。
DOI: 10.1038/s41598-019-43291-7
发表时间: 2019
期刊: Scientific reports
影响因子: 4.6
作者: [Subramaniam,Karuna, Hinkley,LeightonBN, Mizuiri,Danielle, Kothare,Hardik, Cai,Chang, Garrett,Coleman, Findlay,Anne, Houde,JohnF, Nagarajan,SrikantanS]
通讯作者: Nagarajan,SrikantanS
8
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