Evidence map›Paper›PMID 40399385›Full record

ArticleScientific reports2025

Predictive machine learning and multimodal data to develop highly sensitive, composite biomarkers of disease progression in Friedreich ataxia.

Susmita Saha, Louise A Corben, Louisa P Selvadurai, Ian H Harding, Nellie Georgiou-Karistianis

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

5 authors.

Susmita SahaSchool of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, 18 innovation walk, Clayton campus, Clayton, Victoria, Australia.
Louise A CorbenSchool of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, 18 innovation walk, Clayton campus, Clayton, Victoria, Australia.
Louisa P SelvaduraiSchool of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, 18 innovation walk, Clayton campus, Clayton, Victoria, Australia.
Ian H HardingDepartment of Neuroscience, School of Translational Medicine, Monash University, Melbourne, Australia.
Nellie Georgiou-KaristianisSchool of Psychological Sciences, The Turner Institute for Brain and Mental Health, Monash University, 18 innovation walk, Clayton campus, Clayton, Victoria, Australia. nellie.georgiou-karistianis@monash.edu.

Funding

Monash University Platform Access Grant 2022 PAG22-6161757974
6 · The paper itself

Abstract

Friedreich ataxia (FRDA) is a rare, inherited progressive movement disorder for which there is currently no cure. The field urgently requires more sensitive, objective, and clinically relevant biomarkers to enhance the evaluation of treatment efficacy in clinical trials and to speed up the process of drug discovery. This study pioneers the development of clinically relevant, multidomain, fully objective composite biomarkers of disease severity and progression, using multimodal neuroimaging and background data (i.e., demographic, disease history, genetics). Data from 31 individuals with FRDA and 31 controls from a longitudinal multimodal natural history study IMAGE-FRDA, were included. Using an elasticnet predictive machine learning (ML) regression model, we derived a weighted combination of background, structural MRI, diffusion MRI, and quantitative susceptibility imaging (QSM) measures that predicted Friedreich ataxia rating scale (FARS) with high accuracy (R

Indexed as

BiomarkersFriedreich AtaxiaMachine LearningAdolescentAdultDisease ProgressionFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedNeuroimagingSeverity of Illness IndexYoung AdultBiomarkersComposite biomarkersDisease progressionFriedreich ataxiaMachine learningMultimodal dataNeuroimaging

Identifiers

PMID40399385
PMCPMC12095658

What Socratic 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 Socratic graph.