Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data Article Swipe
Christopher M. Endemann
,
Bryan M. Krause
,
Kirill V. Nourski
,
Matthew I. Banks
,
Barry Van Veen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.01.470804
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1101/2021.12.01.470804
Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region’s present activity, while simultaneously accounting for the mediating effects of other regions. Including in the model as many mediating variables as possible has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∽100 – 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (LASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that group LASSO is highly effective for recovering ground truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 s of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.
Related Topics
Concepts
Autoregressive model
Computer science
Spurious relationship
Lasso (programming language)
Ground truth
Multivariate statistics
Artificial intelligence
Dimensionality reduction
Principal component analysis
Model selection
Machine learning
Pattern recognition (psychology)
Data mining
Econometrics
Mathematics
World Wide Web
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2021.12.01.470804
- https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdf
- OA Status
- green
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200607792
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4200607792Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2021.12.01.470804Digital Object Identifier
- Title
-
Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-03Full publication date if available
- Authors
-
Christopher M. Endemann, Bryan M. Krause, Kirill V. Nourski, Matthew I. Banks, Barry Van VeenList of authors in order
- Landing page
-
https://doi.org/10.1101/2021.12.01.470804Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdfDirect OA link when available
- Concepts
-
Autoregressive model, Computer science, Spurious relationship, Lasso (programming language), Ground truth, Multivariate statistics, Artificial intelligence, Dimensionality reduction, Principal component analysis, Model selection, Machine learning, Pattern recognition (psychology), Data mining, Econometrics, Mathematics, World Wide WebTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4200607792 |
|---|---|
| doi | https://doi.org/10.1101/2021.12.01.470804 |
| ids.doi | https://doi.org/10.1101/2021.12.01.470804 |
| ids.openalex | https://openalex.org/W4200607792 |
| fwci | |
| type | preprint |
| title | Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T10241 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9998999834060669 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2805 |
| topics[0].subfield.display_name | Cognitive Neuroscience |
| topics[0].display_name | Functional Brain Connectivity Studies |
| topics[1].id | https://openalex.org/T10581 |
| topics[1].field.id | https://openalex.org/fields/28 |
| topics[1].field.display_name | Neuroscience |
| topics[1].score | 0.9998000264167786 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2805 |
| topics[1].subfield.display_name | Cognitive Neuroscience |
| topics[1].display_name | Neural dynamics and brain function |
| topics[2].id | https://openalex.org/T10429 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9973999857902527 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | EEG and Brain-Computer Interfaces |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C159877910 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7544273138046265 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2202883 |
| concepts[0].display_name | Autoregressive model |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6470034122467041 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C97256817 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6358506679534912 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1462316 |
| concepts[2].display_name | Spurious relationship |
| concepts[3].id | https://openalex.org/C37616216 |
| concepts[3].level | 2 |
| concepts[3].score | 0.612727701663971 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q3218363 |
| concepts[3].display_name | Lasso (programming language) |
| concepts[4].id | https://openalex.org/C146849305 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5037989020347595 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q370766 |
| concepts[4].display_name | Ground truth |
| concepts[5].id | https://openalex.org/C161584116 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4987802505493164 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1952580 |
| concepts[5].display_name | Multivariate statistics |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.49796628952026367 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C70518039 |
| concepts[7].level | 2 |
| concepts[7].score | 0.47025954723358154 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q16000077 |
| concepts[7].display_name | Dimensionality reduction |
| concepts[8].id | https://openalex.org/C27438332 |
| concepts[8].level | 2 |
| concepts[8].score | 0.43092668056488037 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2873 |
| concepts[8].display_name | Principal component analysis |
| concepts[9].id | https://openalex.org/C93959086 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4110296070575714 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q6888345 |
| concepts[9].display_name | Model selection |
| concepts[10].id | https://openalex.org/C119857082 |
| concepts[10].level | 1 |
| concepts[10].score | 0.3898051083087921 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[10].display_name | Machine learning |
| concepts[11].id | https://openalex.org/C153180895 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3755483627319336 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[11].display_name | Pattern recognition (psychology) |
| concepts[12].id | https://openalex.org/C124101348 |
| concepts[12].level | 1 |
| concepts[12].score | 0.36687567830085754 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[12].display_name | Data mining |
| concepts[13].id | https://openalex.org/C149782125 |
| concepts[13].level | 1 |
| concepts[13].score | 0.20292559266090393 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q160039 |
| concepts[13].display_name | Econometrics |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.18031984567642212 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C136764020 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[15].display_name | World Wide Web |
| keywords[0].id | https://openalex.org/keywords/autoregressive-model |
| keywords[0].score | 0.7544273138046265 |
| keywords[0].display_name | Autoregressive model |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6470034122467041 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/spurious-relationship |
| keywords[2].score | 0.6358506679534912 |
| keywords[2].display_name | Spurious relationship |
| keywords[3].id | https://openalex.org/keywords/lasso |
| keywords[3].score | 0.612727701663971 |
| keywords[3].display_name | Lasso (programming language) |
| keywords[4].id | https://openalex.org/keywords/ground-truth |
| keywords[4].score | 0.5037989020347595 |
| keywords[4].display_name | Ground truth |
| keywords[5].id | https://openalex.org/keywords/multivariate-statistics |
| keywords[5].score | 0.4987802505493164 |
| keywords[5].display_name | Multivariate statistics |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.49796628952026367 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/dimensionality-reduction |
| keywords[7].score | 0.47025954723358154 |
| keywords[7].display_name | Dimensionality reduction |
| keywords[8].id | https://openalex.org/keywords/principal-component-analysis |
| keywords[8].score | 0.43092668056488037 |
| keywords[8].display_name | Principal component analysis |
| keywords[9].id | https://openalex.org/keywords/model-selection |
| keywords[9].score | 0.4110296070575714 |
| keywords[9].display_name | Model selection |
| keywords[10].id | https://openalex.org/keywords/machine-learning |
| keywords[10].score | 0.3898051083087921 |
| keywords[10].display_name | Machine learning |
| keywords[11].id | https://openalex.org/keywords/pattern-recognition |
| keywords[11].score | 0.3755483627319336 |
| keywords[11].display_name | Pattern recognition (psychology) |
| keywords[12].id | https://openalex.org/keywords/data-mining |
| keywords[12].score | 0.36687567830085754 |
| keywords[12].display_name | Data mining |
| keywords[13].id | https://openalex.org/keywords/econometrics |
| keywords[13].score | 0.20292559266090393 |
| keywords[13].display_name | Econometrics |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.18031984567642212 |
| keywords[14].display_name | Mathematics |
| language | en |
| locations[0].id | doi:10.1101/2021.12.01.470804 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402567 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| locations[0].source.host_organization | https://openalex.org/I2750212522 |
| locations[0].source.host_organization_name | Cold Spring Harbor Laboratory |
| locations[0].source.host_organization_lineage | https://openalex.org/I2750212522 |
| locations[0].license | cc-by-nc-nd |
| locations[0].pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by-nc-nd |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.1101/2021.12.01.470804 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5071133985 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Christopher M. Endemann |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I135310074 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA |
| authorships[0].institutions[0].id | https://openalex.org/I135310074 |
| authorships[0].institutions[0].ror | https://ror.org/01y2jtd41 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I135310074 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Wisconsin–Madison |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Christopher Endemann |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA |
| authorships[1].author.id | https://openalex.org/A5039476381 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5203-0381 |
| authorships[1].author.display_name | Bryan M. Krause |
| authorships[1].countries | US |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I135310074 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA |
| authorships[1].institutions[0].id | https://openalex.org/I135310074 |
| authorships[1].institutions[0].ror | https://ror.org/01y2jtd41 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I135310074 |
| authorships[1].institutions[0].country_code | US |
| authorships[1].institutions[0].display_name | University of Wisconsin–Madison |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Bryan M. Krause |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA |
| authorships[2].author.id | https://openalex.org/A5065382123 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-7152-8473 |
| authorships[2].author.display_name | Kirill V. Nourski |
| authorships[2].countries | US |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I126307644 |
| authorships[2].affiliations[0].raw_affiliation_string | Department of Neurosurgery, The University of Iowa, Iowa City, IA 54, USA |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I126307644 |
| authorships[2].affiliations[1].raw_affiliation_string | Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA |
| authorships[2].institutions[0].id | https://openalex.org/I126307644 |
| authorships[2].institutions[0].ror | https://ror.org/036jqmy94 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I126307644 |
| authorships[2].institutions[0].country_code | US |
| authorships[2].institutions[0].display_name | University of Iowa |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Kirill V. Nourski |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Department of Neurosurgery, The University of Iowa, Iowa City, IA 54, USA, Iowa Neuroscience Institute, The University of Iowa, Iowa City, IA 52242, USA |
| authorships[3].author.id | https://openalex.org/A5037469144 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-1936-7529 |
| authorships[3].author.display_name | Matthew I. Banks |
| authorships[3].countries | US |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I135310074 |
| authorships[3].affiliations[0].raw_affiliation_string | Department of Neuroscience, University of Wisconsin, Madison, WI, USA |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I135310074 |
| authorships[3].affiliations[1].raw_affiliation_string | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA |
| authorships[3].institutions[0].id | https://openalex.org/I135310074 |
| authorships[3].institutions[0].ror | https://ror.org/01y2jtd41 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I135310074 |
| authorships[3].institutions[0].country_code | US |
| authorships[3].institutions[0].display_name | University of Wisconsin–Madison |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Matthew I. Banks |
| authorships[3].is_corresponding | True |
| authorships[3].raw_affiliation_strings | Department of Anesthesiology, University of Wisconsin, Madison, WI, USA, Department of Neuroscience, University of Wisconsin, Madison, WI, USA |
| authorships[4].author.id | https://openalex.org/A5112947264 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Barry Van Veen |
| authorships[4].countries | US |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I135310074 |
| authorships[4].affiliations[0].raw_affiliation_string | Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA |
| authorships[4].institutions[0].id | https://openalex.org/I135310074 |
| authorships[4].institutions[0].ror | https://ror.org/01y2jtd41 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I135310074 |
| authorships[4].institutions[0].country_code | US |
| authorships[4].institutions[0].display_name | University of Wisconsin–Madison |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Barry Van Veen |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Department of Electrical and Computer Engineering, University of Wisconsin, Madison, WI, USA |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Multivariate autoregressive model estimation for high dimensional intracranial electrophysiological data |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T10241 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9998999834060669 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2805 |
| primary_topic.subfield.display_name | Cognitive Neuroscience |
| primary_topic.display_name | Functional Brain Connectivity Studies |
| related_works | https://openalex.org/W3113091479, https://openalex.org/W2162899405, https://openalex.org/W941090075, https://openalex.org/W2044987316, https://openalex.org/W3134374554, https://openalex.org/W2237480245, https://openalex.org/W2075065631, https://openalex.org/W2519167559, https://openalex.org/W4311248832, https://openalex.org/W3015383640 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1101/2021.12.01.470804 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402567 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| best_oa_location.source.host_organization | https://openalex.org/I2750212522 |
| best_oa_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| best_oa_location.license | cc-by-nc-nd |
| best_oa_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.1101/2021.12.01.470804 |
| primary_location.id | doi:10.1101/2021.12.01.470804 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402567 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | bioRxiv (Cold Spring Harbor Laboratory) |
| primary_location.source.host_organization | https://openalex.org/I2750212522 |
| primary_location.source.host_organization_name | Cold Spring Harbor Laboratory |
| primary_location.source.host_organization_lineage | https://openalex.org/I2750212522 |
| primary_location.license | cc-by-nc-nd |
| primary_location.pdf_url | https://www.biorxiv.org/content/biorxiv/early/2021/12/03/2021.12.01.470804.full.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc-nd |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.1101/2021.12.01.470804 |
| publication_date | 2021-12-03 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W1987924998, https://openalex.org/W2979615686, https://openalex.org/W2112064183, https://openalex.org/W1974235453, https://openalex.org/W1860935432, https://openalex.org/W2056895937, https://openalex.org/W2068000292, https://openalex.org/W2133280087, https://openalex.org/W2045880882, https://openalex.org/W1999653836, https://openalex.org/W2028365182, https://openalex.org/W2922422128, https://openalex.org/W2108055197, https://openalex.org/W2007581301, https://openalex.org/W2528135897, https://openalex.org/W2094195939, https://openalex.org/W2033546704, https://openalex.org/W1972823717, https://openalex.org/W2178225550, https://openalex.org/W1607114662, https://openalex.org/W2047028564, https://openalex.org/W3159510851, https://openalex.org/W2052644075, https://openalex.org/W2003094610, https://openalex.org/W2082603656, https://openalex.org/W2220576269, https://openalex.org/W2132569683, https://openalex.org/W2947626232, https://openalex.org/W4293005804, https://openalex.org/W2136401474, https://openalex.org/W2410654512, https://openalex.org/W2345483071, https://openalex.org/W2604810444, https://openalex.org/W2060581589, https://openalex.org/W2124088880, https://openalex.org/W2563279629, https://openalex.org/W2125113735, https://openalex.org/W1517682131, https://openalex.org/W2332715186, https://openalex.org/W2020245429, https://openalex.org/W2002429803, https://openalex.org/W2135046866, https://openalex.org/W2156803951, https://openalex.org/W2509890485, https://openalex.org/W2783014967, https://openalex.org/W4362230038 |
| referenced_works_count | 46 |
| abstract_inverted_index.a | 31 |
| abstract_inverted_index.s | 246 |
| abstract_inverted_index.10 | 245 |
| abstract_inverted_index.We | 215 |
| abstract_inverted_index.as | 74, 78, 242, 244 |
| abstract_inverted_index.be | 105, 157 |
| abstract_inverted_index.in | 47, 71, 228, 264 |
| abstract_inverted_index.is | 10, 53, 220 |
| abstract_inverted_index.of | 7, 13, 55, 67, 83, 88, 99, 115, 141, 163, 187, 205, 236, 247, 259, 269, 273 |
| abstract_inverted_index.on | 96 |
| abstract_inverted_index.or | 49 |
| abstract_inverted_index.to | 2, 26, 43, 125, 129, 175, 183, 201, 211, 256 |
| abstract_inverted_index.we | 120, 149, 171 |
| abstract_inverted_index.200 | 136 |
| abstract_inverted_index.The | 249 |
| abstract_inverted_index.and | 143, 165, 192, 197, 276 |
| abstract_inverted_index.are | 30 |
| abstract_inverted_index.can | 104, 156 |
| abstract_inverted_index.fit | 126 |
| abstract_inverted_index.for | 34, 63, 223, 238 |
| abstract_inverted_index.has | 80 |
| abstract_inverted_index.one | 48 |
| abstract_inverted_index.the | 4, 8, 11, 41, 64, 72, 81, 86, 97, 113, 185, 203, 229, 257, 267, 270 |
| abstract_inverted_index.use | 172 |
| abstract_inverted_index.– | 135 |
| abstract_inverted_index.MVAR | 100, 116, 127, 154 |
| abstract_inverted_index.They | 39 |
| abstract_inverted_index.data | 110, 132, 164, 178, 213, 231, 263 |
| abstract_inverted_index.from | 108, 133, 160 |
| abstract_inverted_index.have | 253 |
| abstract_inverted_index.here | 252 |
| abstract_inverted_index.high | 152 |
| abstract_inverted_index.long | 161 |
| abstract_inverted_index.make | 111 |
| abstract_inverted_index.many | 75 |
| abstract_inverted_index.more | 50 |
| abstract_inverted_index.odds | 87 |
| abstract_inverted_index.past | 45 |
| abstract_inverted_index.show | 150, 216 |
| abstract_inverted_index.such | 36 |
| abstract_inverted_index.that | 103, 151, 217 |
| abstract_inverted_index.time | 261 |
| abstract_inverted_index.with | 179, 241 |
| abstract_inverted_index.Here, | 119 |
| abstract_inverted_index.LASSO | 219 |
| abstract_inverted_index.Next, | 170 |
| abstract_inverted_index.basis | 272 |
| abstract_inverted_index.brain | 9, 18, 51 |
| abstract_inverted_index.broad | 254 |
| abstract_inverted_index.data. | 248 |
| abstract_inverted_index.group | 193, 218 |
| abstract_inverted_index.human | 130 |
| abstract_inverted_index.known | 180 |
| abstract_inverted_index.least | 194 |
| abstract_inverted_index.model | 73, 101, 209 |
| abstract_inverted_index.other | 68 |
| abstract_inverted_index.sites | 138 |
| abstract_inverted_index.these | 173 |
| abstract_inverted_index.truth | 226 |
| abstract_inverted_index.which | 44 |
| abstract_inverted_index.while | 60 |
| abstract_inverted_index.yield | 166 |
| abstract_inverted_index.(MVAR) | 22 |
| abstract_inverted_index.First, | 148 |
| abstract_inverted_index.avenue | 33 |
| abstract_inverted_index.bounds | 95 |
| abstract_inverted_index.causal | 14, 37, 91 |
| abstract_inverted_index.degree | 42 |
| abstract_inverted_index.dozens | 140 |
| abstract_inverted_index.during | 208 |
| abstract_inverted_index.ground | 225 |
| abstract_inverted_index.highly | 221 |
| abstract_inverted_index.links. | 38 |
| abstract_inverted_index.little | 243 |
| abstract_inverted_index.models | 117, 128, 155, 174, 240 |
| abstract_inverted_index.neural | 271 |
| abstract_inverted_index.number | 98, 204 |
| abstract_inverted_index.reduce | 202 |
| abstract_inverted_index.series | 262 |
| abstract_inverted_index.∽100 | 134 |
| abstract_inverted_index.(LASSO) | 200 |
| abstract_inverted_index.another | 56 |
| abstract_inverted_index.applied | 25 |
| abstract_inverted_index.benefit | 82 |
| abstract_inverted_index.between | 16 |
| abstract_inverted_index.effects | 66 |
| abstract_inverted_index.explore | 184 |
| abstract_inverted_index.fitting | 210 |
| abstract_inverted_index.limited | 109, 230 |
| abstract_inverted_index.methods | 250 |
| abstract_inverted_index.present | 58 |
| abstract_inverted_index.regime, | 232 |
| abstract_inverted_index.regions | 52 |
| abstract_inverted_index.shorter | 212 |
| abstract_inverted_index.utility | 186 |
| abstract_inverted_index.utilize | 121 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.However, | 93 |
| abstract_inverted_index.absolute | 195 |
| abstract_inverted_index.activity | 46 |
| abstract_inverted_index.analysis | 191, 258 |
| abstract_inverted_index.applying | 188 |
| abstract_inverted_index.arousal. | 277 |
| abstract_inverted_index.cortical | 146 |
| abstract_inverted_index.distinct | 145 |
| abstract_inverted_index.estimate | 40 |
| abstract_inverted_index.features | 235 |
| abstract_inverted_index.generate | 176 |
| abstract_inverted_index.modeling | 23 |
| abstract_inverted_index.operator | 199 |
| abstract_inverted_index.possible | 79 |
| abstract_inverted_index.reducing | 85 |
| abstract_inverted_index.regions. | 19, 69, 147 |
| abstract_inverted_index.reliably | 107 |
| abstract_inverted_index.segments | 162 |
| abstract_inverted_index.spanning | 139 |
| abstract_inverted_index.spurious | 90 |
| abstract_inverted_index.Including | 70 |
| abstract_inverted_index.activity, | 59 |
| abstract_inverted_index.capturing | 233 |
| abstract_inverted_index.component | 190 |
| abstract_inverted_index.detecting | 89 |
| abstract_inverted_index.different | 17 |
| abstract_inverted_index.effective | 94, 222 |
| abstract_inverted_index.estimated | 106, 159 |
| abstract_inverted_index.important | 234 |
| abstract_inverted_index.mediating | 65, 76 |
| abstract_inverted_index.multisite | 27 |
| abstract_inverted_index.plausible | 167 |
| abstract_inverted_index.potential | 114 |
| abstract_inverted_index.presented | 251 |
| abstract_inverted_index.principal | 189 |
| abstract_inverted_index.profiles. | 169 |
| abstract_inverted_index.promising | 32 |
| abstract_inverted_index.recording | 137 |
| abstract_inverted_index.segments. | 214 |
| abstract_inverted_index.selection | 198 |
| abstract_inverted_index.shrinkage | 196 |
| abstract_inverted_index.synthetic | 177 |
| abstract_inverted_index.variables | 77 |
| abstract_inverted_index.accounting | 62 |
| abstract_inverted_index.assessment | 12 |
| abstract_inverted_index.cognition, | 275 |
| abstract_inverted_index.exploiting | 112 |
| abstract_inverted_index.functional | 5 |
| abstract_inverted_index.parameters | 206 |
| abstract_inverted_index.predictive | 54 |
| abstract_inverted_index.recordings | 29 |
| abstract_inverted_index.recovering | 224 |
| abstract_inverted_index.region’s | 57 |
| abstract_inverted_index.sensation, | 274 |
| abstract_inverted_index.techniques | 24, 124 |
| abstract_inverted_index.Fundamental | 1 |
| abstract_inverted_index.dimensional | 153 |
| abstract_inverted_index.drastically | 84 |
| abstract_inverted_index.elucidating | 3 |
| abstract_inverted_index.elucidation | 268 |
| abstract_inverted_index.identifying | 35 |
| abstract_inverted_index.Multivariate | 20 |
| abstract_inverted_index.anatomically | 142 |
| abstract_inverted_index.challenging. | 118 |
| abstract_inverted_index.coefficients | 102 |
| abstract_inverted_index.connectivity | 168, 182, 227, 237 |
| abstract_inverted_index.facilitating | 266 |
| abstract_inverted_index.functionally | 144 |
| abstract_inverted_index.ground-truth | 181 |
| abstract_inverted_index.interactions | 15 |
| abstract_inverted_index.intracranial | 131 |
| abstract_inverted_index.organization | 6 |
| abstract_inverted_index.successfully | 158 |
| abstract_inverted_index.(connections) | 207 |
| abstract_inverted_index.applicability | 255 |
| abstract_inverted_index.connectivity. | 92 |
| abstract_inverted_index.neuroscience, | 265 |
| abstract_inverted_index.autoregressive | 21 |
| abstract_inverted_index.simultaneously | 61 |
| abstract_inverted_index.high-dimensional | 239, 260 |
| abstract_inverted_index.well-established | 122 |
| abstract_inverted_index.electrophysiological | 28 |
| abstract_inverted_index.dimensionality-reduction | 123 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5037469144 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I135310074 |
| citation_normalized_percentile.value | 0.18330341 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |