Evidence map›Paper›PMID 27453952›Full record

ArticleBiological psychiatry. Cognitive neuroscience and neuroimaging2016

Diffusion Imaging of White Matter In Schizophrenia: Progress and Future Directions.

Katherine H Karlsgodt

Abstract read
In one paragraph

Article in Biological psychiatry. Cognitive neuroscience and neuroimaging, 2016. 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
–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

33 citing papers in PubMed.

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  17. White matter microstructure in schizophrenia patients with a history of violence.European archives of psychiatry and clinical neuroscience · 2021
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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

1 author.

Katherine H KarlsgodtPsychiatry Research Division, Zucker Hillside Hospital and Feinstein Institute for Medical Research; Department of Psychiatry, Hofstra NorthShore LIJ School of Medicine.

Funding

Multimodal Imaging of Executive and Reward Networks Across the Psychosis SpectrumR01MH101506 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI KARLSGODT, KATHERINE HELEN · 2013 to 2017
$2.5M
NIMH NIH HHS R01 MH101506
6 · The paper itself

Abstract

Diffusion tensor imaging (DTI) is a powerful tool for the in-vivo assessment of white matter microstructure. The application of DTI methodologies to the study of schizophrenia has supported and advanced the hypothesis of schizophrenia as a disorder of disrupted connectivity. In the context of impaired structural connectivity, the extended time frame of white matter development may offer unique opportunities for treatment that can capitalize on the neural flexibility that is still present in the period leading up to and after disease onset. Therefore, it is important to gain a clear understanding of white matter deficits and how they may emerge and change across the illness. However, while there is broad consistency in the findings of white matter deficits in patients with schizophrenia, there is also a great deal of variability in specific findings across studies. In this review, the aim is to move beyond summarizing case-control analyses, to consider the many factors that may impact DTI measures, to explain variability of findings, and to explore future directions for the field. The topics explored include ways to parse DTI patterns associated with different disease subtypes, ways in which novel and established treatments might interact with or enhance white matter, ways of dissociating developmental change from the disease process itself, and understanding the role of emerging analytic methodologies.

Indexed as

developmentdiffusion imagingdiffusion tensor imagingDTIschizophreniawhite matter

Identifiers

PMID27453952
PMCPMC4955654

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Registered trials

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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.