A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings
Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine con...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381192300085X |
| _version_ | 1797869832939503616 |
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| author | Roser Sanchez-Todo André M. Bastos Edmundo Lopez-Sola Borja Mercadal Emiliano Santarnecchi Earl K. Miller Gustavo Deco Giulio Ruffini |
| author_facet | Roser Sanchez-Todo André M. Bastos Edmundo Lopez-Sola Borja Mercadal Emiliano Santarnecchi Earl K. Miller Gustavo Deco Giulio Ruffini |
| author_sort | Roser Sanchez-Todo |
| collection | DOAJ |
| description | Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry. |
| first_indexed | 2024-04-10T00:17:49Z |
| format | Article |
| id | doaj.art-4fcf149e02154309982605f16247192f |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-10T00:17:49Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-4fcf149e02154309982605f16247192f2023-03-16T05:03:01ZengElsevierNeuroImage1095-95722023-04-01270119938A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordingsRoser Sanchez-Todo0André M. Bastos1Edmundo Lopez-Sola2Borja Mercadal3Emiliano Santarnecchi4Earl K. Miller5Gustavo Deco6Giulio Ruffini7Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Center of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, SpainDepartment of Psychology and Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, United StatesDepartment of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, SpainDepartment of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, SpainPrecision Neuroscience & Neuromodulation Program, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USAThe Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USACenter of Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Instituci’o Catalana de la Recerca i Estudis Avan,ats (ICREA), Passeig Llu’s Companys 23, Barcelona, 08010, Spain; Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; School of Psychological Sciences, Monash University, Melbourne, Clayton, VIC 3800, AustraliaCorresponding author.; Department of Brain Modeling, Neuroelectrics SL, Av. Tibidabo 47b, 08035 Barcelona, Spain; Starlab Barcelona, Av. Tibidabo 47b, 08035 Barcelona, Spain; Haskins Laboratories, 300 George Street, New Haven, CT, 06511, USACortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM) that represent the mean activity of large numbers of neurons. Here, we provide first a new framework called laminar NMM, or LaNMM for short, where we combine conduction physics with NMMs to simulate electrophysiological measurements. Then, we employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. We define a minimal model capable of generating coupled slow and fast oscillations, and we optimize LaNMM-specific parameters to fit multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology by selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. In closing, we discuss how this hybrid modeling framework can be more generally used to infer cortical circuitry.http://www.sciencedirect.com/science/article/pii/S105381192300085XLaminar NMMLocal field potentialsLFPBipolar LFPCSDRelative power |
| spellingShingle | Roser Sanchez-Todo André M. Bastos Edmundo Lopez-Sola Borja Mercadal Emiliano Santarnecchi Earl K. Miller Gustavo Deco Giulio Ruffini A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings NeuroImage Laminar NMM Local field potentials LFP Bipolar LFP CSD Relative power |
| title | A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| title_full | A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| title_fullStr | A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| title_full_unstemmed | A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| title_short | A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| title_sort | physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings |
| topic | Laminar NMM Local field potentials LFP Bipolar LFP CSD Relative power |
| url | http://www.sciencedirect.com/science/article/pii/S105381192300085X |
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