Architecture for complex network measures of brain connectivity
Architecture for complex network measures of brain connectivity
Cognitive and motor disorders are growing socio-economic concerns where drug treatments although being the first line of action, are not always effective in restoring cognitive and motor functionality. Research has shown that functional brain connectivity, signifying information exchange among different brain regions, is correlated with efficient execution of cognitive and motor tasks. Hence, to analyze the connectivity parameters in real-time for automated disease prognosis and control, an optimized accelerator/hardware design is required which can be integrated within the sensing device. Here we have designed and implemented an optimized hardware architecture of the graph theoretic parameters (computed concurrently) for the clinically significant functional connectivity measure (Phase Lag Index) of human brain network. To the best of our knowledge, this is a first study on the implementation of the complex network topology parameters of brain connectivity measure which has been synthesized at 25 Mhz, using STMicroelectronics 130-nm technology library and having a dynamic power consumption of 10 nW, making it amenable for real-time high speed operations.
Pal, Chandrajit
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Biswas, Dwaipayan
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Maharatna, Koushik
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Chakrabati, Amlan
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Pal, Chandrajit
7acdbe27-ca3a-43f9-b16d-384bad820e3e
Biswas, Dwaipayan
bc8a9147-64df-451f-b00b-e1265087b6f3
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Chakrabati, Amlan
4f8c4421-b996-4691-ae0c-e3d6f319cc02
Pal, Chandrajit, Biswas, Dwaipayan, Maharatna, Koushik and Chakrabati, Amlan
(2017)
Architecture for complex network measures of brain connectivity.
In Proceedings - IEEE International Symposium on Circuits and Systems.
IEEE..
(doi:10.1109/ISCAS.2017.8050239).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Cognitive and motor disorders are growing socio-economic concerns where drug treatments although being the first line of action, are not always effective in restoring cognitive and motor functionality. Research has shown that functional brain connectivity, signifying information exchange among different brain regions, is correlated with efficient execution of cognitive and motor tasks. Hence, to analyze the connectivity parameters in real-time for automated disease prognosis and control, an optimized accelerator/hardware design is required which can be integrated within the sensing device. Here we have designed and implemented an optimized hardware architecture of the graph theoretic parameters (computed concurrently) for the clinically significant functional connectivity measure (Phase Lag Index) of human brain network. To the best of our knowledge, this is a first study on the implementation of the complex network topology parameters of brain connectivity measure which has been synthesized at 25 Mhz, using STMicroelectronics 130-nm technology library and having a dynamic power consumption of 10 nW, making it amenable for real-time high speed operations.
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Architecture for complex network measures of brain
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Accepted/In Press date: 15 August 2017
e-pub ahead of print date: 28 September 2017
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Local EPrints ID: 416784
URI: http://eprints.soton.ac.uk/id/eprint/416784
PURE UUID: ce398e17-b0a5-4ea5-ae2b-ed9ec67a9055
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Date deposited: 10 Jan 2018 17:30
Last modified: 15 Mar 2024 17:46
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Contributors
Author:
Chandrajit Pal
Author:
Dwaipayan Biswas
Author:
Koushik Maharatna
Author:
Amlan Chakrabati
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