A joint subspace mapping between structural and functional brain connectomes

Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatom...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ghosh, Sanjay [verfasserIn] Raj, Ashish [verfasserIn] Nagarajan, Srikantan S. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition

Übergeordnetes Werk:	Enthalten in: NeuroImage - Orlando, Fla. : Academic Press, 1992, 272
Übergeordnetes Werk:	volume:272

DOI / URN:	10.1016/j.neuroimage.2023.119975

Katalog-ID:	ELV009532501

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520			\|a Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
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650		4	\|a Structural connectome
650		4	\|a Functional connectome
650		4	\|a Laplacian
650		4	\|a Eigen decomposition
700	1		\|a Raj, Ashish \|e verfasserin \|4 aut
700	1		\|a Nagarajan, Srikantan S. \|e verfasserin \|0 (orcid)0000-0001-7209-3857 \|4 aut
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allfields	10.1016/j.neuroimage.2023.119975 doi (DE-627)ELV009532501 (ELSEVIER)S1053-8119(23)00121-0 DE-627 ger DE-627 rda eng 610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl Ghosh, Sanjay verfasserin (orcid)0000-0002-0474-5072 aut A joint subspace mapping between structural and functional brain connectomes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition Raj, Ashish verfasserin aut Nagarajan, Srikantan S. verfasserin (orcid)0000-0001-7209-3857 aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 272 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:272 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.64 Radiologie VZ 44.90 Neurologie VZ AR 272
spelling	10.1016/j.neuroimage.2023.119975 doi (DE-627)ELV009532501 (ELSEVIER)S1053-8119(23)00121-0 DE-627 ger DE-627 rda eng 610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl Ghosh, Sanjay verfasserin (orcid)0000-0002-0474-5072 aut A joint subspace mapping between structural and functional brain connectomes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition Raj, Ashish verfasserin aut Nagarajan, Srikantan S. verfasserin (orcid)0000-0001-7209-3857 aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 272 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:272 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.64 Radiologie VZ 44.90 Neurologie VZ AR 272
allfields_unstemmed	10.1016/j.neuroimage.2023.119975 doi (DE-627)ELV009532501 (ELSEVIER)S1053-8119(23)00121-0 DE-627 ger DE-627 rda eng 610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl Ghosh, Sanjay verfasserin (orcid)0000-0002-0474-5072 aut A joint subspace mapping between structural and functional brain connectomes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition Raj, Ashish verfasserin aut Nagarajan, Srikantan S. verfasserin (orcid)0000-0001-7209-3857 aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 272 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:272 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.64 Radiologie VZ 44.90 Neurologie VZ AR 272
allfieldsGer	10.1016/j.neuroimage.2023.119975 doi (DE-627)ELV009532501 (ELSEVIER)S1053-8119(23)00121-0 DE-627 ger DE-627 rda eng 610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl Ghosh, Sanjay verfasserin (orcid)0000-0002-0474-5072 aut A joint subspace mapping between structural and functional brain connectomes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition Raj, Ashish verfasserin aut Nagarajan, Srikantan S. verfasserin (orcid)0000-0001-7209-3857 aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 272 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:272 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.64 Radiologie VZ 44.90 Neurologie VZ AR 272
allfieldsSound	10.1016/j.neuroimage.2023.119975 doi (DE-627)ELV009532501 (ELSEVIER)S1053-8119(23)00121-0 DE-627 ger DE-627 rda eng 610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl Ghosh, Sanjay verfasserin (orcid)0000-0002-0474-5072 aut A joint subspace mapping between structural and functional brain connectomes 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability. Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition Raj, Ashish verfasserin aut Nagarajan, Srikantan S. verfasserin (orcid)0000-0001-7209-3857 aut Enthalten in NeuroImage Orlando, Fla. : Academic Press, 1992 272 Online-Ressource (DE-627)268125503 (DE-600)1471418-8 (DE-576)106869507 1095-9572 nnns volume:272 GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-LING SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_165 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2014 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 44.64 Radiologie VZ 44.90 Neurologie VZ AR 272
language	English
source	Enthalten in NeuroImage 272 volume:272
sourceStr	Enthalten in NeuroImage 272 volume:272
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topic_facet	Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition
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authorswithroles_txt_mv	Ghosh, Sanjay @@aut@@ Raj, Ashish @@aut@@ Nagarajan, Srikantan S. @@aut@@
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author	Ghosh, Sanjay
spellingShingle	Ghosh, Sanjay ddc 610 fid LING bkl 44.64 bkl 44.90 misc Brain connectivity misc Structural connectome misc Functional connectome misc Laplacian misc Eigen decomposition A joint subspace mapping between structural and functional brain connectomes
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topic_title	610 VZ LING DE-30 fid 44.64 bkl 44.90 bkl A joint subspace mapping between structural and functional brain connectomes Brain connectivity Structural connectome Functional connectome Laplacian Eigen decomposition
topic	ddc 610 fid LING bkl 44.64 bkl 44.90 misc Brain connectivity misc Structural connectome misc Functional connectome misc Laplacian misc Eigen decomposition
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title	A joint subspace mapping between structural and functional brain connectomes
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title_full	A joint subspace mapping between structural and functional brain connectomes
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title_sort	a joint subspace mapping between structural and functional brain connectomes
title_auth	A joint subspace mapping between structural and functional brain connectomes
abstract	Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
abstractGer	Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
abstract_unstemmed	Understanding the connection between the brain’s structural connectivity and its functional connectivity is of immense interest in computational neuroscience. Although some studies have suggested that whole brain functional connectivity is shaped by the underlying structure, the rule by which anatomy constraints brain dynamics remains an open question. In this work, we introduce a computational framework that identifies a joint subspace of eigenmodes for both functional and structural connectomes. We found that a small number of those eigenmodes are sufficient to reconstruct functional connectivity from the structural connectome, thus serving as low-dimensional basis function set. We then develop an algorithm that can estimate the functional eigen spectrum in this joint space from the structural eigen spectrum. By concurrently estimating the joint eigenmodes and the functional eigen spectrum, we can reconstruct a given subject’s functional connectivity from their structural connectome. We perform elaborate experiments and demonstrate that the proposed algorithm for estimating functional connectivity from the structural connectome using joint space eigenmodes gives competitive performance as compared to the existing benchmark methods with better interpretability.
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title_short	A joint subspace mapping between structural and functional brain connectomes
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author2	Raj, Ashish Nagarajan, Srikantan S.
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doi_str	10.1016/j.neuroimage.2023.119975
up_date	2024-07-06T23:29:12.676Z
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A joint subspace mapping between structural and functional brain connectomes

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