Evidence map›Paper›PMID 29058357›Full record

ArticleHuman brain mapping2018

Identifying disease foci from static and dynamic effective connectivity networks: Illustration in soldiers with trauma.

D Rangaprakash, Michael N Dretsch, Archana Venkataraman, Jeffrey S Katz, Thomas S Denney, Gopikrishna Deshpande

Open access · bronzeAbstract 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 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

33 citing papers in PubMed, 58 citations in OpenAlex.

  1. Neuroimaging correlates and predictors of response to repeated-dose intravenous ketamine in PTSD: preliminary evidence.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2021
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  17. Saliva microRNA Biomarkers of Cumulative Concussion.International journal of molecular sciences · 2020
    Article
  18. Toward Robust Anxiety Biomarkers: A Machine Learning Approach in a Large-Scale Sample.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2020
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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

6 authors at 4 institutions in 1 country.

D RangaprakashAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.ORCID 0000-0001-9553-1354
Michael N DretschU.S. Army Aeromedical Research Laboratory, Fort Rucker, Alabama.
Archana VenkataramanDepartment of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland.
Jeffrey S KatzAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
Thomas S DenneyAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.
Gopikrishna DeshpandeAU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, USA.ORCID 0000-0001-7471-5357
Advanced Imaging Research (United States) · USJohns Hopkins University · USUnited States Army Aeromedical Research Lab · USUniversity of California, Los Angeles · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Brain connectivity studies report group differences in pairwise connection strengths. While informative, such results are difficult to interpret since our understanding of the brain relies on region-based properties, rather than on connection information. Given that large disruptions in the brain are often caused by a few pivotal sources, we propose a novel framework to identify the sources of functional disruption from effective connectivity networks. Our approach integrates static and time-varying effective connectivity modeling in a probabilistic framework, to identify aberrant foci and the corresponding aberrant connectomics network. Using resting-state fMRI, we illustrate the utility of this novel approach in U.S. Army soldiers (N = 87) with posttraumatic stress disorder (PTSD), mild traumatic brain injury (mTBI) and combat controls. Additionally, we employed machine-learning classification to identify those significant connectivity features that possessed high predictive ability. We identified three disrupted foci (middle frontal gyrus [MFG], insula, hippocampus), and an aberrant prefrontal-subcortical-parietal network of information flow. We found the MFG to be the pivotal focus of network disruption, with aberrant strength and temporal-variability of effective connectivity to the insula, amygdala and hippocampus. These connectivities also possessed high predictive ability (giving a classification accuracy of 81%); and they exhibited significant associations with symptom severity and neurocognitive functioning. In summary, dysregulation originating in the MFG caused elevated and temporally less-variable connectivity in subcortical regions, followed by a similar effect on parietal memory-related regions. This mechanism likely contributes to the reduced control over traumatic memories leading to re-experiencing, hyperarousal and flashbacks observed in soldiers with PTSD and mTBI. Hum Brain Mapp 39:264-287, 2018. © 2017 Wiley Periodicals, Inc.

Indexed as

AdolescentAdultBrainConnectomeHumansMagnetic Resonance ImagingMaleMiddle AgedMilitary PersonnelNeural PathwaysPsychiatric Status Rating ScalesRestSensitivity and SpecificitySeverity of Illness IndexStress Disorders, Post-TraumaticSupport Vector Machinedisease focidynamic connectivityeffective connectivityFunctional MRImachine learningmild traumatic brain injuryposttraumatic stress disorder

Identifiers

PMID29058357
PMCPMC6866312
OpenAlexW2766833230

What OpenQuestion holds

Textmetadata
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.