Evidence map›Paper›PMID 32109389›Full record

ArticleCurrent biology : CB2020

Transdiagnostic Brain Mapping in Developmental Disorders.

Roma Siugzdaite, Joe Bathelt, Joni Holmes, Duncan E Astle

Open access · bronzeAbstract read
In one paragraph

Article in Current biology : CB, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed, 1 pooled it
7.6field-weighted citation impact, top 2% 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

45 citing papers in PubMed, 1 synthesis or guideline pooled it, 97 citations in OpenAlex.

  1. Early-stage profiles of adolescent mental health difficulties and well-being: A systematic review of cluster analyses in large school and community samples.Journal of research on adolescence : the official journal of the Society for Research on Adolescence · 2025
    Pooled it
  2. Article
  3. The topology of adolescent mental health.medRxiv : the preprint server for health sciences · 2026
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  18. MULTIMODAL NEURAL CORRELATES OF CHILDHOOD PSYCHOPATHOLOGY.bioRxiv : the preprint server for biology · 2024
    Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

Roma SiugzdaiteMRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK.
Joe BatheltMRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK; Dutch Autism & ADHD Research Center, Department of Psychology, University of Amsterdam, Nieuwe Achtergracht 129-B, Amsterdam 1018 WS, the Netherlands.
Joni HolmesMRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK.
Duncan E AstleMRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Rd, Cambridge CB2 7EF, UK. Electronic address: duncan.astle@mrc-cbu.cam.ac.uk.
MRC Cognition and Brain Sciences Unit · GBUniversity of Cambridge · GB

Funding

Medical Research Council MC-A0606-5PQ41Medical Research Council MC_UP_A060_1103Medical Research Council MC_UU_00005/2
6 · The paper itself

Abstract

Childhood learning difficulties and developmental disorders are common, but progress toward understanding their underlying brain mechanisms has been slow. Structural neuroimaging, cognitive, and learning data were collected from 479 children (299 boys, ranging in age from 62 to 223 months), 337 of whom had been referred to the study on the basis of learning-related cognitive problems. Machine learning identified different cognitive profiles within the sample, and hold-out cross-validation showed that these profiles were significantly associated with children's learning ability. The same machine learning approach was applied to cortical morphology data to identify different brain profiles. Hold-out cross-validation demonstrated that these were significantly associated with children's cognitive profiles. Crucially, these mappings were not one-to-one. The same neural profile could be associated with different cognitive impairments across different children. One possibility is that the organization of some children's brains is less susceptible to local deficits. This was tested by using diffusion-weighted imaging (DWI) to construct whole-brain white-matter connectomes. A simulated attack on each child's connectome revealed that some brain networks were strongly organized around highly connected hubs. Children with these networks had only selective cognitive impairments or no cognitive impairments at all. By contrast, the same attacks had a significantly different impact on some children's networks, because their brain efficiency was less critically dependent on hubs. These children had the most widespread and severe cognitive impairments. On this basis, we propose a new framework in which the nature and mechanisms of brain-to-cognition relationships are moderated by the organizational context of the overall network.

Indexed as

Brain MappingAdolescentChildChild, PreschoolCognitionDevelopmental DisabilitiesEnglandFemaleHumansLearningMalecognitive skillsconnectomicscortical morphologydevelopmental disordersdiffusion weighted imaginggraph theorylearning difficultiesmachine learning

Identifiers

PMID32109389
PMCPMC7139199
OpenAlexW3008754544

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.