Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback...
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Autor*in:	Ayelet Or-Borichev [verfasserIn] Guy Gurevitch [verfasserIn] Ilana Klovatch [verfasserIn] Ayam Greental [verfasserIn] Yulia Lerner [verfasserIn] Dino J. Levy [verfasserIn] Talma Hendler [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population

Übergeordnetes Werk:	In: NeuroImage - Elsevier, 2020, 266(2023), Seite 119822-
Übergeordnetes Werk:	volume:266 ; year:2023 ; pages:119822-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.neuroimage.2022.119822

Katalog-ID:	DOAJ082735980

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520			\|a The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.
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allfields	10.1016/j.neuroimage.2022.119822 doi (DE-627)DOAJ082735980 (DE-599)DOAJ5fafb20f457e47808df493600a12b126 DE-627 ger DE-627 rakwb eng RC321-571 Ayelet Or-Borichev verfasserin aut Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe. Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry Guy Gurevitch verfasserin aut Ilana Klovatch verfasserin aut Ayam Greental verfasserin aut Yulia Lerner verfasserin aut Dino J. Levy verfasserin aut Talma Hendler verfasserin aut In NeuroImage Elsevier, 2020 266(2023), Seite 119822- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:266 year:2023 pages:119822- https://doi.org/10.1016/j.neuroimage.2022.119822 kostenfrei https://doaj.org/article/5fafb20f457e47808df493600a12b126 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811922009430 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 AR 266 2023 119822-
spelling	10.1016/j.neuroimage.2022.119822 doi (DE-627)DOAJ082735980 (DE-599)DOAJ5fafb20f457e47808df493600a12b126 DE-627 ger DE-627 rakwb eng RC321-571 Ayelet Or-Borichev verfasserin aut Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe. Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry Guy Gurevitch verfasserin aut Ilana Klovatch verfasserin aut Ayam Greental verfasserin aut Yulia Lerner verfasserin aut Dino J. Levy verfasserin aut Talma Hendler verfasserin aut In NeuroImage Elsevier, 2020 266(2023), Seite 119822- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:266 year:2023 pages:119822- https://doi.org/10.1016/j.neuroimage.2022.119822 kostenfrei https://doaj.org/article/5fafb20f457e47808df493600a12b126 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811922009430 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 AR 266 2023 119822-
allfields_unstemmed	10.1016/j.neuroimage.2022.119822 doi (DE-627)DOAJ082735980 (DE-599)DOAJ5fafb20f457e47808df493600a12b126 DE-627 ger DE-627 rakwb eng RC321-571 Ayelet Or-Borichev verfasserin aut Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe. Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry Guy Gurevitch verfasserin aut Ilana Klovatch verfasserin aut Ayam Greental verfasserin aut Yulia Lerner verfasserin aut Dino J. Levy verfasserin aut Talma Hendler verfasserin aut In NeuroImage Elsevier, 2020 266(2023), Seite 119822- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:266 year:2023 pages:119822- https://doi.org/10.1016/j.neuroimage.2022.119822 kostenfrei https://doaj.org/article/5fafb20f457e47808df493600a12b126 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811922009430 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 AR 266 2023 119822-
allfieldsGer	10.1016/j.neuroimage.2022.119822 doi (DE-627)DOAJ082735980 (DE-599)DOAJ5fafb20f457e47808df493600a12b126 DE-627 ger DE-627 rakwb eng RC321-571 Ayelet Or-Borichev verfasserin aut Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe. Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry Guy Gurevitch verfasserin aut Ilana Klovatch verfasserin aut Ayam Greental verfasserin aut Yulia Lerner verfasserin aut Dino J. Levy verfasserin aut Talma Hendler verfasserin aut In NeuroImage Elsevier, 2020 266(2023), Seite 119822- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:266 year:2023 pages:119822- https://doi.org/10.1016/j.neuroimage.2022.119822 kostenfrei https://doaj.org/article/5fafb20f457e47808df493600a12b126 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811922009430 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 AR 266 2023 119822-
allfieldsSound	10.1016/j.neuroimage.2022.119822 doi (DE-627)DOAJ082735980 (DE-599)DOAJ5fafb20f457e47808df493600a12b126 DE-627 ger DE-627 rakwb eng RC321-571 Ayelet Or-Borichev verfasserin aut Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe. Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry Guy Gurevitch verfasserin aut Ilana Klovatch verfasserin aut Ayam Greental verfasserin aut Yulia Lerner verfasserin aut Dino J. Levy verfasserin aut Talma Hendler verfasserin aut In NeuroImage Elsevier, 2020 266(2023), Seite 119822- (DE-627)268125503 (DE-600)1471418-8 10959572 nnns volume:266 year:2023 pages:119822- https://doi.org/10.1016/j.neuroimage.2022.119822 kostenfrei https://doaj.org/article/5fafb20f457e47808df493600a12b126 kostenfrei http://www.sciencedirect.com/science/article/pii/S1053811922009430 kostenfrei https://doaj.org/toc/1095-9572 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ 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 AR 266 2023 119822-
language	English
source	In NeuroImage 266(2023), Seite 119822- volume:266 year:2023 pages:119822-
sourceStr	In NeuroImage 266(2023), Seite 119822- volume:266 year:2023 pages:119822-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population Neurosciences. Biological psychiatry. Neuropsychiatry
isfreeaccess_bool	true
container_title	NeuroImage
authorswithroles_txt_mv	Ayelet Or-Borichev @@aut@@ Guy Gurevitch @@aut@@ Ilana Klovatch @@aut@@ Ayam Greental @@aut@@ Yulia Lerner @@aut@@ Dino J. Levy @@aut@@ Talma Hendler @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	268125503
id	DOAJ082735980
language_de	englisch
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callnumber-first	R - Medicine
author	Ayelet Or-Borichev
spellingShingle	Ayelet Or-Borichev misc RC321-571 misc Neurofeedback misc Cognitive-control misc Self Neuromodulation misc Risk Taking misc Healthy Population misc Neurosciences. Biological psychiatry. Neuropsychiatry Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity
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topic_title	RC321-571 Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity Neurofeedback Cognitive-control Self Neuromodulation Risk Taking Healthy Population
topic	misc RC321-571 misc Neurofeedback misc Cognitive-control misc Self Neuromodulation misc Risk Taking misc Healthy Population misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc Neurofeedback misc Cognitive-control misc Self Neuromodulation misc Risk Taking misc Healthy Population misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_browse	misc RC321-571 misc Neurofeedback misc Cognitive-control misc Self Neuromodulation misc Risk Taking misc Healthy Population misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity
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title_full	Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity
author_sort	Ayelet Or-Borichev
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author_browse	Ayelet Or-Borichev Guy Gurevitch Ilana Klovatch Ayam Greental Yulia Lerner Dino J. Levy Talma Hendler
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title_sort	neural and functional validation of fmri-informed eeg model of right inferior frontal gyrus activity
callnumber	RC321-571
title_auth	Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity
abstract	The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.
abstractGer	The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.
abstract_unstemmed	The right inferior frontal gyrus (rIFG) is a region involved in the neural underpinning of cognitive control across several domains such as inhibitory control and attentional allocation process. Therefore, it constitutes a desirable neural target for brain-guided interventions such as neurofeedback (NF). To date, rIFG-NF has shown beneficial ability to rehabilitate or enhance cognitive functions using functional Magnetic Resonance Imaging (fMRI-NF). However, the utilization of fMRI-NF for clinical purposes is severely limited, due to its poor scalability. The present study aimed to overcome the limited applicability of fMRI-NF by developing and validating an EEG model of fMRI-defined rIFG activity (hereby termed ''Electrical FingerPrint of rIFG''; rIFG-EFP). To validate the computational model, we employed two experiments in healthy individuals. The first study (n = 14) aimed to test the target engagement of the model by employing rIFG-EFP-NF training while simultaneously acquiring fMRI. The second study (n = 41) aimed to test the functional outcome of two sessions of rIFG-EFP-NF using a risk preference task (known to depict cognitive control processes), employed before and after the training. Results from the first study demonstrated neural target engagement as expected, showing associated rIFG-BOLD signal changing during simultaneous rIFG-EFP-NF training. Target anatomical specificity was verified by showing a more precise prediction of the rIFG-BOLD by the rIFG-EFP model compared to other EFP models. Results of the second study suggested that successful learning to up-regulate the rIFG-EFP signal through NF can reduce one's tendency for risk taking, indicating improved cognitive control after two sessions of rIFG-EFP-NF. Overall, our results confirm the validity of a scalable NF method for targeting rIFG activity by using an EEG probe.
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title_short	Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity
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Neural and functional validation of fMRI-informed EEG model of right inferior frontal gyrus activity

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