Evidence map›Paper›PMID 29159960›Full record

ArticleHuman brain mapping2018

Mapping the structural and functional network architecture of the medial temporal lobe using 7T MRI.

Preya Shah, Danielle S Bassett, Laura E M Wisse, John A Detre, Joel M Stein, Paul A Yushkevich, Russell T Shinohara, John B Pluta, Elijah Valenciano, Molly Daffner and 5 more

Abstract read
In one paragraph

Article in Human brain mapping, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Preya ShahDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.ORCID 0000-0001-8360-6400
Danielle S BassettDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.ORCID 0000-0002-6183-4493
Laura E M WissePenn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
John A DetreDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Joel M SteinDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Paul A YushkevichPenn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.ORCID 0000-0001-8543-4016
Russell T ShinoharaDepartment of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
John B PlutaPenn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Elijah ValencianoPenn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Molly DaffnerDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
David A WolkDepartment of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Mark A ElliottDepartment of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Brian LittDepartment of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Kathryn A DavisCenter for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.
Sandhitsu R DasPenn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, Pennsylvania, 19104.

Funding

Phenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4M
Training and DisseminationP41EB015893 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI REDDY, RAVINDER · 2012 to 2019
$11.3M
Inter-modal Coupling Image AnalyticsR01MH112847 · NIMH · UNIVERSITY OF PENNSYLVANIA · PI Theodore Satterthwaite, Russell Takeshi Shinohara · 2017 to 2026
$5.9M
Training Program in Biomedical Imaging and Information SciencesT32EB009384 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI GEE, JAMES C, IVES, ZACHARY · 2009 to 2023
$4.0M
Adaptive Large-Scale Framework for Automatic Biomedical Image SegmentationR01EB017255 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI YUSHKEVICH, PAUL A. · 2014 to 2018
$3.0M
Virtual Resection to Treat EpilepsyR01NS099348 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI BASSETT, DANIELLE SMITH, LITT, BRIAN · 2016 to 2019
$2.3M
Statistical methods for large and complex databases of ultra-high-dimensionalR01NS085211 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI SHINOHARA, RUSSELL TAKESHI · 2013 to 2017
$1.8M
Localizing epileptic networks using novel 7T MRI glutamate imagingK23NS092973 · NINDS · UNIVERSITY OF PENNSYLVANIA · PI DAVIS, KATHRYN ADAMIAK · 2016 to 2020
$980k
Ultra-high resolution BOLD fMRI of medial temporal lobe at 7 TeslaR03EB016923 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI DAS, SANDHITSU, LEE, JONGHO · 2014 to 2015
$160k
NCATS NIH HHS UL1 TR001878NIBIB NIH HHS P41 EB015893NIBIB NIH HHS R01 EB017255NIBIB NIH HHS R03 EB016923NIBIB NIH HHS T32 EB009384NIMH NIH HHS R01 MH112847NINDS NIH HHS K23 NS092973NINDS NIH HHS R01 NS085211NINDS NIH HHS R01 NS099348
6 · The paper itself

Abstract

Medial temporal lobe (MTL) subregions play integral roles in memory function and are differentially affected in various neurological and psychiatric disorders. The ability to structurally and functionally characterize these subregions may be important to understanding MTL physiology and diagnosing diseases involving the MTL. In this study, we characterized network architecture of the MTL in healthy subjects (n = 31) using both resting state functional MRI and MTL-focused T2-weighted structural MRI at 7 tesla. Ten MTL subregions per hemisphere, including hippocampal subfields and cortical regions of the parahippocampal gyrus, were segmented for each subject using a multi-atlas algorithm. Both structural covariance matrices from correlations of subregion volumes across subjects, and functional connectivity matrices from correlations between subregion BOLD time series were generated. We found a moderate structural and strong functional inter-hemispheric symmetry. Several bilateral hippocampal subregions (CA1, dentate gyrus, and subiculum) emerged as functional network hubs. We also observed that the structural and functional networks naturally separated into two modules closely corresponding to (a) bilateral hippocampal formations, and (b) bilateral extra-hippocampal structures. Finally, we found a significant correlation in structural and functional connectivity (r = 0.25). Our findings represent a comprehensive analysis of network topology of the MTL at the subregion level. We share our data, methods, and findings as a reference for imaging methods and disease-based research.

Indexed as

Magnetic Resonance ImagingAdultBrain MappingFemaleHumansMaleNeural PathwaysOrgan SizeRestTemporal Lobe7 teslaasymmetryconnectivityhippocampusimage segmentationnetwork neuroscienceresting-state fMRIstructural MRIsubfieldsvolume

Identifiers

PMID29159960
PMCPMC5764800

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.