NON-INVASIVE SINGLE NEURON ELECTRICAL MONITORING (NISNEM Technology)
NON-INVASIVE SINGLE NEURON ELECTRICAL MONITORING (NISNEM Technology)
批准号:
EP/T020970/1
负责人:
Dario Farina
金额:
$712.66万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2021
资助国家:
英国
项目状态:
未结题
起止时间:
2021 至 --
中文摘要
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英文摘要
We propose the development of a new technology for Non-Invasive Single Neuron Electrical Monitoring (NISNEM). Current non-invasive neuroimaging techniques including electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI) provide indirect measures of the activity of large populations of neurons in the brain. However, it is becoming apparent that information at the single neuron level may be critical for understanding, diagnosing, and treating increasingly prevalent neurological conditions, such as stroke and dementia. Current methods to record single neuron activity are invasive - they require surgical implants. Implanted electrodes risk damage to the neural tissue and/or foreign body reaction that limit long-term stability. Understandably, this approach is not chosen by many patients; in fact, implanted electrode technologies are limited to animal preparations or tests on a handful of patients worldwide. Measuring single neuron activity non-invasively will transform how neurological conditions are diagnosed, monitored, and treated as well as pave the way for the broad adoption of neurotechnologies in healthcare. We propose the development of NISNEM by pushing frontier engineering research in electrode technology, ultra-low-noise electronics, and advanced signal processing, iteratively validated during extensive tests in pre-clinical trials. We will design and manufacture arrays of dry electrodes to be mounted on the skin with an ultra-high density of recording points. By aggressive miniaturization, we will develop microelectronics chips to record from thousands of channels with beyond state-of-art noise performance. We will devise breakthrough developments in unsupervised blind source identification of the activity of tens to hundreds of neurons from tens of thousands of recordings. This research will be supported by iterative pre-clinical studies in humans and animals, which will be essential for defining requirements and refining designs. We intend to demonstrate the feasibility of the NISNEM technology and its potential to become a routine clinical tool that transforms all aspects of healthcare. In particular, we expect it to drastically improve how neurological diseases are managed. Given that they are a massive burden and limit the quality of life of millions of patients and their families, the impact of NISNEM could be almost unprecedented. We envision the NISNEM technology to be adopted on a routine clinical basis for: 1) diagnostics (epilepsy, tremor, dementia); 2) monitoring (stroke, spinal cord injury, ageing); 3) intervention (closed-loop modulation of brain activity); 4) advancing our understanding of the nervous system (identifying pathological changes); and 5) development of neural interfaces for communication (Brain-Computer Interfaces for locked-in patients), control of (neuro)prosthetics, or replacement of a "missing sense" (e.g., auditory prosthetics). Moreover, by accurately detecting the patient's intent, this technology could be used to drive neural plasticity -the brain's ability to reorganize itself-, potentially enabling cures for currently incurable disorders such as stroke, spinal cord injury, or Parkinson's disease. NISNEM also provides the opportunity to extend treatment from the hospital to the home. For example, rehabilitation after a stroke occurs mainly in hospitals and for a limited period of time; home rehabilitation is absent. NISNEM could provide continuous rehabilitation at home through the use of therapeutic technologies.The neural engineering, neuroscience and clinical neurology communities will all greatly benefit from this radically new perspective and complementary knowledge base. NISNEM will foster a revolution in neurosciences and neurotechnology, strongly impacting these large academic communities and the clinical sector. Even more importantly, if successful, it will improve the life of millions of patients and their relatives
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Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals
具有局部敏感角损失的深度度量学习,用于神经尖峰信号的自校正源分离
DOI:
10.48550/arxiv.2110.07046
发表时间:
2021
期刊:
影响因子:
--
作者:
[Clarke A]
通讯作者:
Clarke A
Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units
更大、更密集:肌电图电极表面网格的优化设计,以识别更多、更具代表性的运动单位样本
DOI:
10.1101/2023.02.18.529050
发表时间:
2023
期刊:
影响因子:
--
作者:
[Caillet A]
通讯作者:
Caillet A
DOI:
10.1113/jp284170
发表时间:
2023
期刊:
The Journal of physiology
影响因子:
--
作者:
[Casolo A]
通讯作者:
Casolo A
DOI:
10.1109/tcyb.2023.3290825
发表时间:
2023-07
期刊:
IEEE Transactions on Cybernetics
影响因子:
11.8
作者:
[A. Clarke;D. Farina]
通讯作者:
A. Clarke;D. Farina
DOI:
10.1523/jneurosci.1265-22.2023
发表时间:
2023-04-19
期刊:
JOURNAL OF NEUROSCIENCE
影响因子:
5.3
作者:
[Del Vecchio, Alessandro, Germer, Carina Marconi, Enoka, Roger M.]
通讯作者:
Enoka, Roger M.
共 6 条
A portable skin deformation measurement platform for user-specific wearable interface design (U-WEAR)
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批准号:EP/X037916/1
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项目类别:Research Grant
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资助金额:$16.47万
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财政年份:2023
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负责人:Dario Farina
-
依托单位:
海外基金