Evidence map›Paper›PMID 33488507›Full record

ReviewFrontiers in neurology2020

Multi-Modal Imaging in Down's Syndrome: Maximizing Utility Through Innovative Neuroimaging Approaches.

Stephanie S G Brown, Elijah Mak, Shahid Zaman

Abstract readReview
In one paragraph

Review in Frontiers in neurology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Longitudinal diffusion tensor imaging correlates with amyloid burden in Down syndrome.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  2. Article
  3. Article
  4. Review
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

3 authors.

Stephanie S G BrownCambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Elijah MakCambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.
Shahid ZamanCambridge Intellectual and Developmental Disabilities Research Group, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom.

Funding

Medical Research Council G1002252
6 · The paper itself

Abstract

In recent decades, the field of neuroimaging has experienced a surge of popularity and innovation which has led to significant advancements in the understanding of neurological disease, if not immediate clinical translation. In the case of Down's syndrome, a complex interplay of neurodevelopmental and neurodegenerative processes occur as a result of the trisomy of chromosome 21. The substantial potential impact of improved clinical intervention and the limited research under-taken to date make it a prime candidate for longitudinal neuroimaging-based study. However, as with a multitude of other multifaceted brain-based disorders, singular utilization of lone modality imaging has limited interpretability and applicability. Indeed, a present challenge facing the neuroimaging community as a whole is the methodological integration of multi-modal imaging to enhance clinical understanding. This review therefore aims to assess the current literature in Down's syndrome utilizing a multi-modal approach with regards to improvement upon consideration of a single modality. Additionally, we discuss potential avenues of future research that may effectively combine structural, functional and molecular-based imaging techniques for the significant benefit of the understanding of Down's syndrome pathology.

Indexed as

Alzheimer's diseaseDown's syndrome (DS)MRImulti-modalneuroimagingPET

Identifiers

PMID33488507
PMCPMC7817620

What OpenQuestion holds

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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.