Multivariate stochastic volatility modeling of neural data
Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
eLife Sciences Publications Ltd
2019-08-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/42950 |
| _version_ | 1811253026461057024 |
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| author | Tung D Phan Jessica A Wachter Ethan A Solomon Michael J Kahana |
| author_facet | Tung D Phan Jessica A Wachter Ethan A Solomon Michael J Kahana |
| author_sort | Tung D Phan |
| collection | DOAJ |
| description | Because multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding. |
| first_indexed | 2024-04-12T16:43:34Z |
| format | Article |
| id | doaj.art-b3daa38222d847589b624247eadde3f5 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-12T16:43:34Z |
| publishDate | 2019-08-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-b3daa38222d847589b624247eadde3f52022-12-22T03:24:40ZengeLife Sciences Publications LtdeLife2050-084X2019-08-01810.7554/eLife.42950Multivariate stochastic volatility modeling of neural dataTung D Phan0https://orcid.org/0000-0001-5957-7566Jessica A Wachter1Ethan A Solomon2https://orcid.org/0000-0003-0541-7588Michael J Kahana3University of Pennsylvania, Philadelphia, United StatesUniversity of Pennsylvania, Philadelphia, United StatesUniversity of Pennsylvania, Philadelphia, United StatesUniversity of Pennsylvania, Philadelphia, United StatesBecause multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding.https://elifesciences.org/articles/42950iEEGmodel-based connectivityfree-recallstochastic volatilitymachine learning |
| spellingShingle | Tung D Phan Jessica A Wachter Ethan A Solomon Michael J Kahana Multivariate stochastic volatility modeling of neural data eLife iEEG model-based connectivity free-recall stochastic volatility machine learning |
| title | Multivariate stochastic volatility modeling of neural data |
| title_full | Multivariate stochastic volatility modeling of neural data |
| title_fullStr | Multivariate stochastic volatility modeling of neural data |
| title_full_unstemmed | Multivariate stochastic volatility modeling of neural data |
| title_short | Multivariate stochastic volatility modeling of neural data |
| title_sort | multivariate stochastic volatility modeling of neural data |
| topic | iEEG model-based connectivity free-recall stochastic volatility machine learning |
| url | https://elifesciences.org/articles/42950 |
| work_keys_str_mv | AT tungdphan multivariatestochasticvolatilitymodelingofneuraldata AT jessicaawachter multivariatestochasticvolatilitymodelingofneuraldata AT ethanasolomon multivariatestochasticvolatilitymodelingofneuraldata AT michaeljkahana multivariatestochasticvolatilitymodelingofneuraldata |