Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study Article Swipe
Hannes Almgren
,
Frederik Van de Steen
,
Simone Kühn
,
Adeel Razi
,
Karl Friston
,
Daniele Marinazzo
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.neuroimage.2018.08.053
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1016/j.neuroimage.2018.08.053
Dynamic causal modelling (DCM) for resting state fMRI - namely spectral DCM - is a recently developed and widely adopted method for inferring effective connectivity in intrinsic brain networks. Most applications of spectral DCM have focused on group-averaged connectivity within large-scale intrinsic brain networks; however, the consistency of subject- and session-specific estimates of effective connectivity has not been evaluated. Establishing reliability (within subjects) is crucial for its clinical use; e.g., as a neurophysiological phenotype of disease progression. Effective connectivity during rest is likely to vary due to changes in cognitive, and physiological states. Quantifying these variations may help understand functional brain architectures - and inform clinical applications. In the present study, we investigated the consistency of effective connectivity within and between subjects, as well as potential sources of variability (e.g., hemispheric asymmetry). We also addressed the effects on consistency of standard data processing procedures. DCM analyses were applied to four longitudinal resting state fMRI datasets. Our sample comprised 17 subjects with 589 resting state fMRI sessions in total. These data allowed us to quantify the robustness of connectivity estimates for each subject, and to generalise our conclusions beyond specific data features. We found that subjects showed systematic and reliable patterns of hemispheric asymmetry. When asymmetry was taken into account, subjects showed very similar connectivity patterns. We also found that various processing procedures (e.g. global signal regression and ROI size) had little effect on inference and the reliability of connectivity estimates for the majority of subjects. Finally, Bayesian model reduction significantly increased the consistency of connectivity patterns.
Related Topics
Concepts
Resting state fMRI
Default mode network
Computer science
Inference
Reliability (semiconductor)
Functional connectivity
Robustness (evolution)
Consistency (knowledge bases)
Artificial intelligence
Pattern recognition (psychology)
Psychology
Data mining
Neuroscience
Power (physics)
Physics
Chemistry
Quantum mechanics
Biochemistry
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.neuroimage.2018.08.053
- OA Status
- hybrid
- Cited By
- 66
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2790160000
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2790160000Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.neuroimage.2018.08.053Digital Object Identifier
- Title
-
Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM studyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-08-27Full publication date if available
- Authors
-
Hannes Almgren, Frederik Van de Steen, Simone Kühn, Adeel Razi, Karl Friston, Daniele MarinazzoList of authors in order
- Landing page
-
https://doi.org/10.1016/j.neuroimage.2018.08.053Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.neuroimage.2018.08.053Direct OA link when available
- Concepts
-
Resting state fMRI, Default mode network, Computer science, Inference, Reliability (semiconductor), Functional connectivity, Robustness (evolution), Consistency (knowledge bases), Artificial intelligence, Pattern recognition (psychology), Psychology, Data mining, Neuroscience, Power (physics), Physics, Chemistry, Quantum mechanics, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
66Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 4, 2024: 8, 2023: 9, 2022: 10, 2021: 15Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2790160000 |
|---|---|
| doi | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| ids.doi | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| ids.mag | 2790160000 |
| ids.pmid | https://pubmed.ncbi.nlm.nih.gov/30165254 |
| ids.openalex | https://openalex.org/W2790160000 |
| fwci | 4.92260532 |
| mesh[0].qualifier_ui | |
| mesh[0].descriptor_ui | D000328 |
| mesh[0].is_major_topic | False |
| mesh[0].qualifier_name | |
| mesh[0].descriptor_name | Adult |
| mesh[1].qualifier_ui | |
| mesh[1].descriptor_ui | D001499 |
| mesh[1].is_major_topic | False |
| mesh[1].qualifier_name | |
| mesh[1].descriptor_name | Bayes Theorem |
| mesh[2].qualifier_ui | Q000033 |
| mesh[2].descriptor_ui | D001921 |
| mesh[2].is_major_topic | False |
| mesh[2].qualifier_name | anatomy & histology |
| mesh[2].descriptor_name | Brain |
| mesh[3].qualifier_ui | Q000502 |
| mesh[3].descriptor_ui | D001921 |
| mesh[3].is_major_topic | False |
| mesh[3].qualifier_name | physiology |
| mesh[3].descriptor_name | Brain |
| mesh[4].qualifier_ui | Q000379 |
| mesh[4].descriptor_ui | D001931 |
| mesh[4].is_major_topic | False |
| mesh[4].qualifier_name | methods |
| mesh[4].descriptor_name | Brain Mapping |
| mesh[5].qualifier_ui | |
| mesh[5].descriptor_ui | D005260 |
| mesh[5].is_major_topic | False |
| mesh[5].qualifier_name | |
| mesh[5].descriptor_name | Female |
| mesh[6].qualifier_ui | |
| mesh[6].descriptor_ui | D006801 |
| mesh[6].is_major_topic | False |
| mesh[6].qualifier_name | |
| mesh[6].descriptor_name | Humans |
| mesh[7].qualifier_ui | Q000379 |
| mesh[7].descriptor_ui | D007091 |
| mesh[7].is_major_topic | False |
| mesh[7].qualifier_name | methods |
| mesh[7].descriptor_name | Image Processing, Computer-Assisted |
| mesh[8].qualifier_ui | |
| mesh[8].descriptor_ui | D008137 |
| mesh[8].is_major_topic | False |
| mesh[8].qualifier_name | |
| mesh[8].descriptor_name | Longitudinal Studies |
| mesh[9].qualifier_ui | Q000379 |
| mesh[9].descriptor_ui | D008279 |
| mesh[9].is_major_topic | False |
| mesh[9].qualifier_name | methods |
| mesh[9].descriptor_name | Magnetic Resonance Imaging |
| mesh[10].qualifier_ui | |
| mesh[10].descriptor_ui | D008297 |
| mesh[10].is_major_topic | False |
| mesh[10].qualifier_name | |
| mesh[10].descriptor_name | Male |
| mesh[11].qualifier_ui | |
| mesh[11].descriptor_ui | D008875 |
| mesh[11].is_major_topic | False |
| mesh[11].qualifier_name | |
| mesh[11].descriptor_name | Middle Aged |
| mesh[12].qualifier_ui | Q000033 |
| mesh[12].descriptor_ui | D009415 |
| mesh[12].is_major_topic | False |
| mesh[12].qualifier_name | anatomy & histology |
| mesh[12].descriptor_name | Nerve Net |
| mesh[13].qualifier_ui | Q000502 |
| mesh[13].descriptor_ui | D009415 |
| mesh[13].is_major_topic | False |
| mesh[13].qualifier_name | physiology |
| mesh[13].descriptor_name | Nerve Net |
| mesh[14].qualifier_ui | |
| mesh[14].descriptor_ui | D015203 |
| mesh[14].is_major_topic | False |
| mesh[14].qualifier_name | |
| mesh[14].descriptor_name | Reproducibility of Results |
| mesh[15].qualifier_ui | |
| mesh[15].descriptor_ui | D055815 |
| mesh[15].is_major_topic | False |
| mesh[15].qualifier_name | |
| mesh[15].descriptor_name | Young Adult |
| mesh[16].qualifier_ui | |
| mesh[16].descriptor_ui | D000328 |
| mesh[16].is_major_topic | False |
| mesh[16].qualifier_name | |
| mesh[16].descriptor_name | Adult |
| mesh[17].qualifier_ui | |
| mesh[17].descriptor_ui | D001499 |
| mesh[17].is_major_topic | False |
| mesh[17].qualifier_name | |
| mesh[17].descriptor_name | Bayes Theorem |
| mesh[18].qualifier_ui | Q000033 |
| mesh[18].descriptor_ui | D001921 |
| mesh[18].is_major_topic | False |
| mesh[18].qualifier_name | anatomy & histology |
| mesh[18].descriptor_name | Brain |
| mesh[19].qualifier_ui | Q000502 |
| mesh[19].descriptor_ui | D001921 |
| mesh[19].is_major_topic | False |
| mesh[19].qualifier_name | physiology |
| mesh[19].descriptor_name | Brain |
| mesh[20].qualifier_ui | Q000379 |
| mesh[20].descriptor_ui | D001931 |
| mesh[20].is_major_topic | False |
| mesh[20].qualifier_name | methods |
| mesh[20].descriptor_name | Brain Mapping |
| mesh[21].qualifier_ui | |
| mesh[21].descriptor_ui | D005260 |
| mesh[21].is_major_topic | False |
| mesh[21].qualifier_name | |
| mesh[21].descriptor_name | Female |
| mesh[22].qualifier_ui | |
| mesh[22].descriptor_ui | D006801 |
| mesh[22].is_major_topic | False |
| mesh[22].qualifier_name | |
| mesh[22].descriptor_name | Humans |
| mesh[23].qualifier_ui | Q000379 |
| mesh[23].descriptor_ui | D007091 |
| mesh[23].is_major_topic | False |
| mesh[23].qualifier_name | methods |
| mesh[23].descriptor_name | Image Processing, Computer-Assisted |
| mesh[24].qualifier_ui | |
| mesh[24].descriptor_ui | D008137 |
| mesh[24].is_major_topic | False |
| mesh[24].qualifier_name | |
| mesh[24].descriptor_name | Longitudinal Studies |
| mesh[25].qualifier_ui | Q000379 |
| mesh[25].descriptor_ui | D008279 |
| mesh[25].is_major_topic | False |
| mesh[25].qualifier_name | methods |
| mesh[25].descriptor_name | Magnetic Resonance Imaging |
| mesh[26].qualifier_ui | |
| mesh[26].descriptor_ui | D008297 |
| mesh[26].is_major_topic | False |
| mesh[26].qualifier_name | |
| mesh[26].descriptor_name | Male |
| mesh[27].qualifier_ui | |
| mesh[27].descriptor_ui | D008875 |
| mesh[27].is_major_topic | False |
| mesh[27].qualifier_name | |
| mesh[27].descriptor_name | Middle Aged |
| mesh[28].qualifier_ui | Q000033 |
| mesh[28].descriptor_ui | D009415 |
| mesh[28].is_major_topic | False |
| mesh[28].qualifier_name | anatomy & histology |
| mesh[28].descriptor_name | Nerve Net |
| mesh[29].qualifier_ui | Q000502 |
| mesh[29].descriptor_ui | D009415 |
| mesh[29].is_major_topic | False |
| mesh[29].qualifier_name | physiology |
| mesh[29].descriptor_name | Nerve Net |
| mesh[30].qualifier_ui | |
| mesh[30].descriptor_ui | D015203 |
| mesh[30].is_major_topic | False |
| mesh[30].qualifier_name | |
| mesh[30].descriptor_name | Reproducibility of Results |
| mesh[31].qualifier_ui | |
| mesh[31].descriptor_ui | D055815 |
| mesh[31].is_major_topic | False |
| mesh[31].qualifier_name | |
| mesh[31].descriptor_name | Young Adult |
| type | article |
| title | Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study |
| biblio.issue | |
| biblio.volume | 183 |
| biblio.last_page | 768 |
| biblio.first_page | 757 |
| 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 | 1.0 |
| 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/T10378 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9973999857902527 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2741 |
| topics[1].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[1].display_name | Advanced MRI Techniques and Applications |
| topics[2].id | https://openalex.org/T11304 |
| topics[2].field.id | https://openalex.org/fields/27 |
| topics[2].field.display_name | Medicine |
| topics[2].score | 0.9973999857902527 |
| topics[2].domain.id | https://openalex.org/domains/4 |
| topics[2].domain.display_name | Health Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2741 |
| topics[2].subfield.display_name | Radiology, Nuclear Medicine and Imaging |
| topics[2].display_name | Advanced Neuroimaging Techniques and Applications |
| funders[0].id | https://openalex.org/F4320311904 |
| funders[0].ror | https://ror.org/029chgv08 |
| funders[0].display_name | Wellcome Trust |
| funders[1].id | https://openalex.org/F4320322603 |
| funders[1].ror | https://ror.org/00cv9y106 |
| funders[1].display_name | Universiteit Gent |
| is_xpac | False |
| apc_list.value | 3450 |
| apc_list.currency | USD |
| apc_list.value_usd | 3450 |
| apc_paid.value | 2654 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 2862 |
| concepts[0].id | https://openalex.org/C66324658 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6960771679878235 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q7316120 |
| concepts[0].display_name | Resting state fMRI |
| concepts[1].id | https://openalex.org/C141516989 |
| concepts[1].level | 3 |
| concepts[1].score | 0.5723892450332642 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1182555 |
| concepts[1].display_name | Default mode network |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.530552864074707 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C2776214188 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5251845121383667 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q408386 |
| concepts[3].display_name | Inference |
| concepts[4].id | https://openalex.org/C43214815 |
| concepts[4].level | 3 |
| concepts[4].score | 0.5116408467292786 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q7310987 |
| concepts[4].display_name | Reliability (semiconductor) |
| concepts[5].id | https://openalex.org/C3018011982 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5033907294273376 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q7316120 |
| concepts[5].display_name | Functional connectivity |
| concepts[6].id | https://openalex.org/C63479239 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4953821897506714 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[6].display_name | Robustness (evolution) |
| concepts[7].id | https://openalex.org/C2776436953 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4473692774772644 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q5163215 |
| concepts[7].display_name | Consistency (knowledge bases) |
| concepts[8].id | https://openalex.org/C154945302 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3656245470046997 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[8].display_name | Artificial intelligence |
| concepts[9].id | https://openalex.org/C153180895 |
| concepts[9].level | 2 |
| concepts[9].score | 0.3626887798309326 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[9].display_name | Pattern recognition (psychology) |
| concepts[10].id | https://openalex.org/C15744967 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3475072979927063 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[10].display_name | Psychology |
| concepts[11].id | https://openalex.org/C124101348 |
| concepts[11].level | 1 |
| concepts[11].score | 0.32286763191223145 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[11].display_name | Data mining |
| concepts[12].id | https://openalex.org/C169760540 |
| concepts[12].level | 1 |
| concepts[12].score | 0.24229106307029724 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[12].display_name | Neuroscience |
| concepts[13].id | https://openalex.org/C163258240 |
| concepts[13].level | 2 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q25342 |
| concepts[13].display_name | Power (physics) |
| concepts[14].id | https://openalex.org/C121332964 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[14].display_name | Physics |
| concepts[15].id | https://openalex.org/C185592680 |
| concepts[15].level | 0 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q2329 |
| concepts[15].display_name | Chemistry |
| concepts[16].id | https://openalex.org/C62520636 |
| concepts[16].level | 1 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[16].display_name | Quantum mechanics |
| concepts[17].id | https://openalex.org/C55493867 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7094 |
| concepts[17].display_name | Biochemistry |
| concepts[18].id | https://openalex.org/C104317684 |
| concepts[18].level | 2 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q7187 |
| concepts[18].display_name | Gene |
| keywords[0].id | https://openalex.org/keywords/resting-state-fmri |
| keywords[0].score | 0.6960771679878235 |
| keywords[0].display_name | Resting state fMRI |
| keywords[1].id | https://openalex.org/keywords/default-mode-network |
| keywords[1].score | 0.5723892450332642 |
| keywords[1].display_name | Default mode network |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.530552864074707 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/inference |
| keywords[3].score | 0.5251845121383667 |
| keywords[3].display_name | Inference |
| keywords[4].id | https://openalex.org/keywords/reliability |
| keywords[4].score | 0.5116408467292786 |
| keywords[4].display_name | Reliability (semiconductor) |
| keywords[5].id | https://openalex.org/keywords/functional-connectivity |
| keywords[5].score | 0.5033907294273376 |
| keywords[5].display_name | Functional connectivity |
| keywords[6].id | https://openalex.org/keywords/robustness |
| keywords[6].score | 0.4953821897506714 |
| keywords[6].display_name | Robustness (evolution) |
| keywords[7].id | https://openalex.org/keywords/consistency |
| keywords[7].score | 0.4473692774772644 |
| keywords[7].display_name | Consistency (knowledge bases) |
| keywords[8].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[8].score | 0.3656245470046997 |
| keywords[8].display_name | Artificial intelligence |
| keywords[9].id | https://openalex.org/keywords/pattern-recognition |
| keywords[9].score | 0.3626887798309326 |
| keywords[9].display_name | Pattern recognition (psychology) |
| keywords[10].id | https://openalex.org/keywords/psychology |
| keywords[10].score | 0.3475072979927063 |
| keywords[10].display_name | Psychology |
| keywords[11].id | https://openalex.org/keywords/data-mining |
| keywords[11].score | 0.32286763191223145 |
| keywords[11].display_name | Data mining |
| keywords[12].id | https://openalex.org/keywords/neuroscience |
| keywords[12].score | 0.24229106307029724 |
| keywords[12].display_name | Neuroscience |
| language | en |
| locations[0].id | doi:10.1016/j.neuroimage.2018.08.053 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S103225281 |
| locations[0].source.issn | 1053-8119, 1095-9572 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 1053-8119 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | NeuroImage |
| locations[0].source.host_organization | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_name | Elsevier BV |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310320990 |
| locations[0].source.host_organization_lineage_names | Elsevier BV |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | NeuroImage |
| locations[0].landing_page_url | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| locations[1].id | pmid:30165254 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306525036 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | PubMed |
| locations[1].source.host_organization | https://openalex.org/I1299303238 |
| locations[1].source.host_organization_name | National Institutes of Health |
| locations[1].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | publishedVersion |
| locations[1].raw_type | |
| locations[1].license_id | |
| locations[1].is_accepted | True |
| locations[1].is_published | True |
| locations[1].raw_source_name | NeuroImage |
| locations[1].landing_page_url | https://pubmed.ncbi.nlm.nih.gov/30165254 |
| locations[2].id | pmh:oai:archive.ugent.be:8571916 |
| locations[2].is_oa | True |
| locations[2].source.id | https://openalex.org/S4306400477 |
| locations[2].source.issn | |
| locations[2].source.type | repository |
| locations[2].source.is_oa | False |
| locations[2].source.issn_l | |
| locations[2].source.is_core | False |
| locations[2].source.is_in_doaj | False |
| locations[2].source.display_name | Ghent University Academic Bibliography (Ghent University) |
| locations[2].source.host_organization | https://openalex.org/I32597200 |
| locations[2].source.host_organization_name | Ghent University |
| locations[2].source.host_organization_lineage | https://openalex.org/I32597200 |
| locations[2].license | cc-by |
| locations[2].pdf_url | |
| locations[2].version | submittedVersion |
| locations[2].raw_type | journalArticle |
| locations[2].license_id | https://openalex.org/licenses/cc-by |
| locations[2].is_accepted | False |
| locations[2].is_published | False |
| locations[2].raw_source_name | NEUROIMAGE |
| locations[2].landing_page_url | http://hdl.handle.net/1854/LU-8571916 |
| locations[3].id | pmh:oai:europepmc.org:5236301 |
| locations[3].is_oa | True |
| locations[3].source.id | https://openalex.org/S4306400806 |
| locations[3].source.issn | |
| locations[3].source.type | repository |
| locations[3].source.is_oa | False |
| locations[3].source.issn_l | |
| locations[3].source.is_core | False |
| locations[3].source.is_in_doaj | False |
| locations[3].source.display_name | Europe PMC (PubMed Central) |
| locations[3].source.host_organization | https://openalex.org/I1303153112 |
| locations[3].source.host_organization_name | European Bioinformatics Institute |
| locations[3].source.host_organization_lineage | https://openalex.org/I1303153112 |
| locations[3].license | cc-by |
| locations[3].pdf_url | |
| locations[3].version | submittedVersion |
| locations[3].raw_type | Text |
| locations[3].license_id | https://openalex.org/licenses/cc-by |
| locations[3].is_accepted | False |
| locations[3].is_published | False |
| locations[3].raw_source_name | |
| locations[3].landing_page_url | http://europepmc.org/pmc/articles/PMC6215332 |
| locations[4].id | pmh:oai:pubmedcentral.nih.gov:6215332 |
| locations[4].is_oa | True |
| locations[4].source.id | https://openalex.org/S2764455111 |
| locations[4].source.issn | |
| locations[4].source.type | repository |
| locations[4].source.is_oa | False |
| locations[4].source.issn_l | |
| locations[4].source.is_core | False |
| locations[4].source.is_in_doaj | False |
| locations[4].source.display_name | PubMed Central |
| locations[4].source.host_organization | https://openalex.org/I1299303238 |
| locations[4].source.host_organization_name | National Institutes of Health |
| locations[4].source.host_organization_lineage | https://openalex.org/I1299303238 |
| locations[4].license | cc-by |
| locations[4].pdf_url | |
| locations[4].version | submittedVersion |
| locations[4].raw_type | Text |
| locations[4].license_id | https://openalex.org/licenses/cc-by |
| locations[4].is_accepted | False |
| locations[4].is_published | False |
| locations[4].raw_source_name | Neuroimage |
| locations[4].landing_page_url | https://www.ncbi.nlm.nih.gov/pmc/articles/6215332 |
| indexed_in | crossref, doaj, pubmed |
| authorships[0].author.id | https://openalex.org/A5060520984 |
| authorships[0].author.orcid | https://orcid.org/0000-0001-9428-8348 |
| authorships[0].author.display_name | Hannes Almgren |
| authorships[0].countries | BE |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I32597200 |
| authorships[0].affiliations[0].raw_affiliation_string | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| authorships[0].institutions[0].id | https://openalex.org/I32597200 |
| authorships[0].institutions[0].ror | https://ror.org/00cv9y106 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I32597200 |
| authorships[0].institutions[0].country_code | BE |
| authorships[0].institutions[0].display_name | Ghent University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Hannes Almgren |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| authorships[1].author.id | https://openalex.org/A5039553008 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5715-5992 |
| authorships[1].author.display_name | Frederik Van de Steen |
| authorships[1].countries | BE |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I32597200 |
| authorships[1].affiliations[0].raw_affiliation_string | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| authorships[1].institutions[0].id | https://openalex.org/I32597200 |
| authorships[1].institutions[0].ror | https://ror.org/00cv9y106 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I32597200 |
| authorships[1].institutions[0].country_code | BE |
| authorships[1].institutions[0].display_name | Ghent University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Frederik Van de Steen |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| authorships[2].author.id | https://openalex.org/A5069589495 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-6823-7969 |
| authorships[2].author.display_name | Simone Kühn |
| authorships[2].countries | DE |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I159176309, https://openalex.org/I4210108711 |
| authorships[2].affiliations[0].raw_affiliation_string | Clinic and Polyclinic for Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I4210120221 |
| authorships[2].affiliations[1].raw_affiliation_string | Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany |
| authorships[2].institutions[0].id | https://openalex.org/I4210120221 |
| authorships[2].institutions[0].ror | https://ror.org/02pp7px91 |
| authorships[2].institutions[0].type | facility |
| authorships[2].institutions[0].lineage | https://openalex.org/I149899117, https://openalex.org/I4210120221 |
| authorships[2].institutions[0].country_code | DE |
| authorships[2].institutions[0].display_name | Max Planck Institute for Human Development |
| authorships[2].institutions[1].id | https://openalex.org/I4210108711 |
| authorships[2].institutions[1].ror | https://ror.org/01zgy1s35 |
| authorships[2].institutions[1].type | healthcare |
| authorships[2].institutions[1].lineage | https://openalex.org/I159176309, https://openalex.org/I4210108711 |
| authorships[2].institutions[1].country_code | DE |
| authorships[2].institutions[1].display_name | University Medical Center Hamburg-Eppendorf |
| authorships[2].institutions[2].id | https://openalex.org/I159176309 |
| authorships[2].institutions[2].ror | https://ror.org/00g30e956 |
| authorships[2].institutions[2].type | education |
| authorships[2].institutions[2].lineage | https://openalex.org/I159176309 |
| authorships[2].institutions[2].country_code | DE |
| authorships[2].institutions[2].display_name | Universität Hamburg |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Simone Kühn |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany, Clinic and Polyclinic for Psychiatry and Psychotherapy, University Clinic Hamburg-Eppendorf, Germany |
| authorships[3].author.id | https://openalex.org/A5045981641 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-0779-9439 |
| authorships[3].author.display_name | Adeel Razi |
| authorships[3].countries | AU, GB, PK |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I56590836 |
| authorships[3].affiliations[0].raw_affiliation_string | Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I165524941, https://openalex.org/I4210151618, https://openalex.org/I45129253 |
| authorships[3].affiliations[1].raw_affiliation_string | The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I151746803 |
| authorships[3].affiliations[2].raw_affiliation_string | Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan |
| authorships[3].institutions[0].id | https://openalex.org/I56590836 |
| authorships[3].institutions[0].ror | https://ror.org/02bfwt286 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I56590836 |
| authorships[3].institutions[0].country_code | AU |
| authorships[3].institutions[0].display_name | Monash University |
| authorships[3].institutions[1].id | https://openalex.org/I4210151618 |
| authorships[3].institutions[1].ror | https://ror.org/048b34d51 |
| authorships[3].institutions[1].type | healthcare |
| authorships[3].institutions[1].lineage | https://openalex.org/I1340918713, https://openalex.org/I4210151618 |
| authorships[3].institutions[1].country_code | GB |
| authorships[3].institutions[1].display_name | National Hospital for Neurology and Neurosurgery |
| authorships[3].institutions[2].id | https://openalex.org/I45129253 |
| authorships[3].institutions[2].ror | https://ror.org/02jx3x895 |
| authorships[3].institutions[2].type | education |
| authorships[3].institutions[2].lineage | https://openalex.org/I124357947, https://openalex.org/I45129253 |
| authorships[3].institutions[2].country_code | GB |
| authorships[3].institutions[2].display_name | University College London |
| authorships[3].institutions[3].id | https://openalex.org/I165524941 |
| authorships[3].institutions[3].ror | https://ror.org/02704qw51 |
| authorships[3].institutions[3].type | facility |
| authorships[3].institutions[3].lineage | https://openalex.org/I124357947, https://openalex.org/I165524941, https://openalex.org/I45129253, https://openalex.org/I87048295 |
| authorships[3].institutions[3].country_code | GB |
| authorships[3].institutions[3].display_name | Wellcome Centre for Human Neuroimaging |
| authorships[3].institutions[4].id | https://openalex.org/I151746803 |
| authorships[3].institutions[4].ror | https://ror.org/05db8zr24 |
| authorships[3].institutions[4].type | education |
| authorships[3].institutions[4].lineage | https://openalex.org/I151746803 |
| authorships[3].institutions[4].country_code | PK |
| authorships[3].institutions[4].display_name | NED University of Engineering and Technology |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Adeel Razi |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Department of Electronic Engineering, NED University of Engineering and Technology, Karachi, Pakistan, Monash Institute of Cognitive and Clinical Neurosciences and Monash Biomedical Imaging, Monash University, Clayton, Australia, The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK |
| authorships[4].author.id | https://openalex.org/A5086852785 |
| authorships[4].author.orcid | https://orcid.org/0000-0001-7984-8909 |
| authorships[4].author.display_name | Karl Friston |
| authorships[4].countries | GB |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I165524941, https://openalex.org/I4210151618, https://openalex.org/I45129253 |
| authorships[4].affiliations[0].raw_affiliation_string | The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK |
| authorships[4].institutions[0].id | https://openalex.org/I4210151618 |
| authorships[4].institutions[0].ror | https://ror.org/048b34d51 |
| authorships[4].institutions[0].type | healthcare |
| authorships[4].institutions[0].lineage | https://openalex.org/I1340918713, https://openalex.org/I4210151618 |
| authorships[4].institutions[0].country_code | GB |
| authorships[4].institutions[0].display_name | National Hospital for Neurology and Neurosurgery |
| authorships[4].institutions[1].id | https://openalex.org/I45129253 |
| authorships[4].institutions[1].ror | https://ror.org/02jx3x895 |
| authorships[4].institutions[1].type | education |
| authorships[4].institutions[1].lineage | https://openalex.org/I124357947, https://openalex.org/I45129253 |
| authorships[4].institutions[1].country_code | GB |
| authorships[4].institutions[1].display_name | University College London |
| authorships[4].institutions[2].id | https://openalex.org/I165524941 |
| authorships[4].institutions[2].ror | https://ror.org/02704qw51 |
| authorships[4].institutions[2].type | facility |
| authorships[4].institutions[2].lineage | https://openalex.org/I124357947, https://openalex.org/I165524941, https://openalex.org/I45129253, https://openalex.org/I87048295 |
| authorships[4].institutions[2].country_code | GB |
| authorships[4].institutions[2].display_name | Wellcome Centre for Human Neuroimaging |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Karl Friston |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London, WC1N 3BG, UK |
| authorships[5].author.id | https://openalex.org/A5047882948 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-9803-0122 |
| authorships[5].author.display_name | Daniele Marinazzo |
| authorships[5].countries | BE |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I32597200 |
| authorships[5].affiliations[0].raw_affiliation_string | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| authorships[5].institutions[0].id | https://openalex.org/I32597200 |
| authorships[5].institutions[0].ror | https://ror.org/00cv9y106 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I32597200 |
| authorships[5].institutions[0].country_code | BE |
| authorships[5].institutions[0].display_name | Ghent University |
| authorships[5].author_position | last |
| authorships[5].raw_author_name | Daniele Marinazzo |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Belgium |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Variability and reliability of effective connectivity within the core default mode network: A multi-site longitudinal spectral DCM study |
| has_fulltext | False |
| 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 | 1.0 |
| 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/W2143570064, https://openalex.org/W2948361065, https://openalex.org/W2793605638, https://openalex.org/W4394367129, https://openalex.org/W3205149984, https://openalex.org/W1520477417, https://openalex.org/W1585336261, https://openalex.org/W3150454329, https://openalex.org/W4250679599, https://openalex.org/W4402630632 |
| cited_by_count | 66 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 4 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 8 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 9 |
| counts_by_year[3].year | 2022 |
| counts_by_year[3].cited_by_count | 10 |
| counts_by_year[4].year | 2021 |
| counts_by_year[4].cited_by_count | 15 |
| counts_by_year[5].year | 2020 |
| counts_by_year[5].cited_by_count | 7 |
| counts_by_year[6].year | 2019 |
| counts_by_year[6].cited_by_count | 11 |
| counts_by_year[7].year | 2018 |
| counts_by_year[7].cited_by_count | 1 |
| counts_by_year[8].year | 2017 |
| counts_by_year[8].cited_by_count | 1 |
| locations_count | 5 |
| best_oa_location.id | doi:10.1016/j.neuroimage.2018.08.053 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S103225281 |
| best_oa_location.source.issn | 1053-8119, 1095-9572 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 1053-8119 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | NeuroImage |
| best_oa_location.source.host_organization | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_name | Elsevier BV |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| best_oa_location.source.host_organization_lineage_names | Elsevier BV |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | NeuroImage |
| best_oa_location.landing_page_url | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| primary_location.id | doi:10.1016/j.neuroimage.2018.08.053 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S103225281 |
| primary_location.source.issn | 1053-8119, 1095-9572 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 1053-8119 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | NeuroImage |
| primary_location.source.host_organization | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_name | Elsevier BV |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310320990 |
| primary_location.source.host_organization_lineage_names | Elsevier BV |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | NeuroImage |
| primary_location.landing_page_url | https://doi.org/10.1016/j.neuroimage.2018.08.053 |
| publication_date | 2018-08-27 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W2085491842, https://openalex.org/W2110065044, https://openalex.org/W2123586737, https://openalex.org/W1899689456, https://openalex.org/W2002923643, https://openalex.org/W2102436032, https://openalex.org/W2748280743, https://openalex.org/W2111902267, https://openalex.org/W2061564920, https://openalex.org/W2117663940, https://openalex.org/W2015822010, https://openalex.org/W2140952948, https://openalex.org/W2179079598, https://openalex.org/W2741941708, https://openalex.org/W2039046404, https://openalex.org/W2298846790, https://openalex.org/W1687468892, https://openalex.org/W2016625584, https://openalex.org/W2557111525, https://openalex.org/W2068499862, https://openalex.org/W2950512494, https://openalex.org/W2768663757, https://openalex.org/W2414157550, https://openalex.org/W2255828118, https://openalex.org/W2087658007, https://openalex.org/W2031921396, https://openalex.org/W2079450984, https://openalex.org/W2460529817, https://openalex.org/W2078291120, https://openalex.org/W2533819121, https://openalex.org/W2022671987, https://openalex.org/W2080660199, https://openalex.org/W6684886136, https://openalex.org/W2770137529, https://openalex.org/W2024450732, https://openalex.org/W2171982986, https://openalex.org/W3103014656, https://openalex.org/W2265460619 |
| referenced_works_count | 38 |
| abstract_inverted_index.- | 8, 12, 102 |
| abstract_inverted_index.a | 14, 71 |
| abstract_inverted_index.17 | 158 |
| abstract_inverted_index.In | 107 |
| abstract_inverted_index.We | 132, 191, 215 |
| abstract_inverted_index.as | 70, 122, 124 |
| abstract_inverted_index.in | 25, 88, 166 |
| abstract_inverted_index.is | 13, 63, 81 |
| abstract_inverted_index.of | 31, 47, 52, 74, 115, 127, 139, 176, 200, 237, 243, 253 |
| abstract_inverted_index.on | 36, 137, 232 |
| abstract_inverted_index.to | 83, 86, 148, 172, 183 |
| abstract_inverted_index.us | 171 |
| abstract_inverted_index.we | 111 |
| abstract_inverted_index.589 | 161 |
| abstract_inverted_index.DCM | 11, 33, 144 |
| abstract_inverted_index.Our | 155 |
| abstract_inverted_index.ROI | 227 |
| abstract_inverted_index.and | 17, 49, 90, 103, 119, 182, 197, 226, 234 |
| abstract_inverted_index.due | 85 |
| abstract_inverted_index.for | 4, 21, 65, 179, 240 |
| abstract_inverted_index.had | 229 |
| abstract_inverted_index.has | 55 |
| abstract_inverted_index.its | 66 |
| abstract_inverted_index.may | 96 |
| abstract_inverted_index.not | 56 |
| abstract_inverted_index.our | 185 |
| abstract_inverted_index.the | 45, 108, 113, 135, 174, 235, 241, 251 |
| abstract_inverted_index.was | 205 |
| abstract_inverted_index.Most | 29 |
| abstract_inverted_index.When | 203 |
| abstract_inverted_index.also | 133, 216 |
| abstract_inverted_index.been | 57 |
| abstract_inverted_index.data | 141, 169, 189 |
| abstract_inverted_index.each | 180 |
| abstract_inverted_index.fMRI | 7, 153, 164 |
| abstract_inverted_index.four | 149 |
| abstract_inverted_index.have | 34 |
| abstract_inverted_index.help | 97 |
| abstract_inverted_index.into | 207 |
| abstract_inverted_index.rest | 80 |
| abstract_inverted_index.that | 193, 218 |
| abstract_inverted_index.use; | 68 |
| abstract_inverted_index.vary | 84 |
| abstract_inverted_index.very | 211 |
| abstract_inverted_index.well | 123 |
| abstract_inverted_index.were | 146 |
| abstract_inverted_index.with | 160 |
| abstract_inverted_index.(DCM) | 3 |
| abstract_inverted_index.(e.g. | 222 |
| abstract_inverted_index.These | 168 |
| abstract_inverted_index.brain | 27, 42, 100 |
| abstract_inverted_index.e.g., | 69 |
| abstract_inverted_index.found | 192, 217 |
| abstract_inverted_index.model | 247 |
| abstract_inverted_index.size) | 228 |
| abstract_inverted_index.state | 6, 152, 163 |
| abstract_inverted_index.taken | 206 |
| abstract_inverted_index.these | 94 |
| abstract_inverted_index.(e.g., | 129 |
| abstract_inverted_index.beyond | 187 |
| abstract_inverted_index.causal | 1 |
| abstract_inverted_index.during | 79 |
| abstract_inverted_index.effect | 231 |
| abstract_inverted_index.global | 223 |
| abstract_inverted_index.inform | 104 |
| abstract_inverted_index.likely | 82 |
| abstract_inverted_index.little | 230 |
| abstract_inverted_index.method | 20 |
| abstract_inverted_index.namely | 9 |
| abstract_inverted_index.sample | 156 |
| abstract_inverted_index.showed | 195, 210 |
| abstract_inverted_index.signal | 224 |
| abstract_inverted_index.study, | 110 |
| abstract_inverted_index.total. | 167 |
| abstract_inverted_index.widely | 18 |
| abstract_inverted_index.within | 39, 118 |
| abstract_inverted_index.(within | 61 |
| abstract_inverted_index.Dynamic | 0 |
| abstract_inverted_index.adopted | 19 |
| abstract_inverted_index.allowed | 170 |
| abstract_inverted_index.applied | 147 |
| abstract_inverted_index.between | 120 |
| abstract_inverted_index.changes | 87 |
| abstract_inverted_index.crucial | 64 |
| abstract_inverted_index.disease | 75 |
| abstract_inverted_index.effects | 136 |
| abstract_inverted_index.focused | 35 |
| abstract_inverted_index.present | 109 |
| abstract_inverted_index.resting | 5, 151, 162 |
| abstract_inverted_index.similar | 212 |
| abstract_inverted_index.sources | 126 |
| abstract_inverted_index.states. | 92 |
| abstract_inverted_index.various | 219 |
| abstract_inverted_index.Bayesian | 246 |
| abstract_inverted_index.Finally, | 245 |
| abstract_inverted_index.account, | 208 |
| abstract_inverted_index.analyses | 145 |
| abstract_inverted_index.clinical | 67, 105 |
| abstract_inverted_index.however, | 44 |
| abstract_inverted_index.majority | 242 |
| abstract_inverted_index.patterns | 199 |
| abstract_inverted_index.quantify | 173 |
| abstract_inverted_index.recently | 15 |
| abstract_inverted_index.reliable | 198 |
| abstract_inverted_index.sessions | 165 |
| abstract_inverted_index.specific | 188 |
| abstract_inverted_index.spectral | 10, 32 |
| abstract_inverted_index.standard | 140 |
| abstract_inverted_index.subject, | 181 |
| abstract_inverted_index.subject- | 48 |
| abstract_inverted_index.subjects | 159, 194, 209 |
| abstract_inverted_index.Effective | 77 |
| abstract_inverted_index.addressed | 134 |
| abstract_inverted_index.asymmetry | 204 |
| abstract_inverted_index.comprised | 157 |
| abstract_inverted_index.datasets. | 154 |
| abstract_inverted_index.developed | 16 |
| abstract_inverted_index.effective | 23, 53, 116 |
| abstract_inverted_index.estimates | 51, 178, 239 |
| abstract_inverted_index.features. | 190 |
| abstract_inverted_index.increased | 250 |
| abstract_inverted_index.inference | 233 |
| abstract_inverted_index.inferring | 22 |
| abstract_inverted_index.intrinsic | 26, 41 |
| abstract_inverted_index.modelling | 2 |
| abstract_inverted_index.networks. | 28 |
| abstract_inverted_index.networks; | 43 |
| abstract_inverted_index.patterns. | 214, 255 |
| abstract_inverted_index.phenotype | 73 |
| abstract_inverted_index.potential | 125 |
| abstract_inverted_index.reduction | 248 |
| abstract_inverted_index.subjects) | 62 |
| abstract_inverted_index.subjects, | 121 |
| abstract_inverted_index.subjects. | 244 |
| abstract_inverted_index.asymmetry. | 202 |
| abstract_inverted_index.cognitive, | 89 |
| abstract_inverted_index.evaluated. | 58 |
| abstract_inverted_index.functional | 99 |
| abstract_inverted_index.generalise | 184 |
| abstract_inverted_index.procedures | 221 |
| abstract_inverted_index.processing | 142, 220 |
| abstract_inverted_index.regression | 225 |
| abstract_inverted_index.robustness | 175 |
| abstract_inverted_index.systematic | 196 |
| abstract_inverted_index.understand | 98 |
| abstract_inverted_index.variations | 95 |
| abstract_inverted_index.Quantifying | 93 |
| abstract_inverted_index.asymmetry). | 131 |
| abstract_inverted_index.conclusions | 186 |
| abstract_inverted_index.consistency | 46, 114, 138, 252 |
| abstract_inverted_index.hemispheric | 130, 201 |
| abstract_inverted_index.large-scale | 40 |
| abstract_inverted_index.procedures. | 143 |
| abstract_inverted_index.reliability | 60, 236 |
| abstract_inverted_index.variability | 128 |
| abstract_inverted_index.Establishing | 59 |
| abstract_inverted_index.applications | 30 |
| abstract_inverted_index.connectivity | 24, 38, 54, 78, 117, 177, 213, 238, 254 |
| abstract_inverted_index.investigated | 112 |
| abstract_inverted_index.longitudinal | 150 |
| abstract_inverted_index.progression. | 76 |
| abstract_inverted_index.applications. | 106 |
| abstract_inverted_index.architectures | 101 |
| abstract_inverted_index.physiological | 91 |
| abstract_inverted_index.significantly | 249 |
| abstract_inverted_index.group-averaged | 37 |
| abstract_inverted_index.session-specific | 50 |
| abstract_inverted_index.neurophysiological | 72 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| corresponding_author_ids | https://openalex.org/A5060520984 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I32597200 |
| citation_normalized_percentile.value | 0.94850582 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |