ReviewFunctional neurology
Neuroimaging-based methods for autism identification: a possible translational application?
Review in Functional neurology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Towards a brain-based predictome of mental illness.Human brain mapping · 2020Pooled it
- Identifying Diagnostic Biomarkers for Autism Spectrum Disorder From Higher-order Interactions Using the PED Algorithm.Neuroinformatics · 2024Article
- Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting.NPJ digital medicine · 2023Review
- Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.IEEE transactions on bio-medical engineering · 2021Article
- Autism Spectrum Disorder and Childhood Apraxia of Speech: Early Language-Related Hallmarks across Structural MRI Study.Journal of personalized medicine · 2020Article
- Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.NeuroImage. Clinical · 2018Article
- Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.NeuroImage · 2017Review
- The Broad Autism (Endo)Phenotype: Neurostructural and Neurofunctional Correlates in Parents of Individuals with Autism Spectrum Disorders.Frontiers in neuroscience · 2016Review
- One-Class Support Vector Machines Identify the Language and Default Mode Regions As Common Patterns of Structural Alterations in Young Children with Autism Spectrum Disorders.Frontiers in neuroscience · 2016Article
- Rehabilitative Interventions and Brain Plasticity in Autism Spectrum Disorders: Focus on MRI-Based Studies.Frontiers in neuroscience · 2016Article
- The effect of gender on the neuroanatomy of children with autism spectrum disorders: a support vector machine case-control study.Molecular autism · 2016Article
- Examining the Dominant Presence of Brain Grey Matter in Autism During Functional Magnetic Resonance Imaging.Basic and clinical neuroscienceReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification methods based on machine learning (ML) techniques are becoming widespread analysis tools in neuroimaging studies. They have the potential to enhance the diagnostic power of brain data, by assigning a predictive index, either of pathology or of treatment response, to the single subject's acquisition. ML techniques are currently finding numerous applications in psychiatric illness, in addition to the widely studied neurodegenerative diseases. In this review we give a comprehensive account of the use of classification techniques applied to structural magnetic resonance images in autism spectrum disorders (ASDs). Understanding of these highly heterogeneous neurodevelopmental diseases could greatly benefit from additional descriptors of pathology and predictive indices extracted directly from brain data. A perspective is also provided on the future developments necessary to translate ML methods from the field of ASD research into the clinic.
Indexed as
Identifiers
25764253PMC4370436W1974003734What OpenQuestion holds
Registered trials
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.