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dc.contributor.authorKotkowski, Eithan ( Orcid Icon 0000-0002-4947-1343 )
dc.contributor.authorPrice, Larry R. ( Orcid Icon 0000-0001-6413-1286 )
dc.contributor.authorFox, P. Mickle ( Orcid Icon 0000-0002-4997-0003 )
dc.contributor.authorVanasse, Thomas J. ( Orcid Icon 0000-0003-4672-0049 )
dc.contributor.authorFox, Peter T. ( Orcid Icon 0000-0002-0465-2028 )
dc.date.accessioned2019-08-30T13:21:20Z
dc.date.available2019-08-30T13:21:20Z
dc.date.issued2018-01
dc.identifier.citationKotkowski, E., Price, L. R., Fox, P. M., Vanasse, T. J., & Fox, P. T. (2018). The hippocampal network model: A transdiagnostic metaconnectomic approach. NeuroImage: Clinical, 18, pp. 115–129.en_US
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/8572
dc.description.abstract

Purpose: The hippocampus plays a central role in cognitive and affective processes and is commonly implicated in neurodegenerative diseases. Our study aimed to identify and describe a hippocampal network model (HNM) using trans-diagnostic MRI data from the BrainMap® database. We used meta-analysis to test the network degeneration hypothesis (NDH) (Seeley et al., 2009) by identifying structural and functional covariance in this hippocampal network.

Methods: To generate our network model, we used BrainMap's VBM database to perform a region-to-whole-brain (RtWB) meta-analysis of 269 VBM experiments from 165 published studies across a range of 38 psychiatric and neurological diseases reporting hippocampal gray matter density alterations. This step identified 11 significant gray matter foci, or nodes. We subsequently used meta-analytic connectivity modeling (MACM) to define edges of structural covariance between nodes from VBM data as well as functional covariance using the functional task-activation database, also from BrainMap. Finally, we applied a correlation analysis using Pearson's r to assess the similarities and differences between the structural and functional covariance models.

Key Findings: Our hippocampal RtWB meta-analysis reported consistent and significant structural covariance in 11 key regions. The subsequent structural and functional MACMs showed a strong correlation between HNM nodes with a significant structural-functional covariance correlation of r = .377 (p = .000049).

Significance: This novel method of studying network covariance using VBM and functional meta-analytic techniques allows for the identification of generalizable patterns of functional and structural abnormalities pertaining to the hippocampus. In accordance with the NDH, this framework could have major implications in studying and predicting spatial disease patterns using network-based assays.

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dc.formatText
dc.format.extent15 pages
dc.format.medium1 file (.pdf)
dc.language.isoen_USen_US
dc.publisherElsevieren_US
dc.sourceNeuroImage: Clinical, 2018, Vol. 18, pp. 115–129
dc.subjectALEen_US
dc.subjectAnatomic likelihood estimation
dc.subjectBrainMap
dc.subjectFunctional MRI
dc.subjectFunctional covariance
dc.subjectGray matter density
dc.subjectHippocampal network model
dc.subjectHippocampus
dc.subjectMACM
dc.subjectMRI
dc.subjectMagnetic resonance imaging
dc.subjectMeta-analysis
dc.subjectMeta-analytic connectivity modeling
dc.subjectStructural MRI
dc.subjectStructural covariance
dc.subjectVBM
dc.subjectVoxel-based morphometry
dc.titleThe Hippocampal Network Model: A Transdiagnostic Metaconnectomic Approachen_US
txstate.documenttypeArticle
dc.identifier.doihttps://doi.org/10.1016/j.nicl.2018.01.002
dc.rights.licenseThis is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).
txstate.departmentCurriculum and Instruction


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