Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder.
Sabah NisarMohammad HarisPublished in: Molecular psychiatry (2023)
Autism-spectrum disorders (ASDs) are developmental disabilities that manifest in early childhood and are characterized by qualitative abnormalities in social behaviors, communication skills, and restrictive or repetitive behaviors. To explore the neurobiological mechanisms in ASD, extensive research has been done to identify potential diagnostic biomarkers through a neuroimaging genetics approach. Neuroimaging genetics helps to identify ASD-risk genes that contribute to structural and functional variations in brain circuitry and validate biological changes by elucidating the mechanisms and pathways that confer genetic risk. Integrating artificial intelligence models with neuroimaging data lays the groundwork for accurate diagnosis and facilitates the identification of early diagnostic biomarkers for ASD. This review discusses the significance of neuroimaging genetics approaches to gaining a better understanding of the perturbed neurochemical system and molecular pathways in ASD and how these approaches can detect structural, functional, and metabolic changes and lead to the discovery of novel biomarkers for the early diagnosis of ASD.
Keyphrases
- autism spectrum disorder
- artificial intelligence
- intellectual disability
- attention deficit hyperactivity disorder
- big data
- machine learning
- genome wide
- deep learning
- mental health
- electronic health record
- healthcare
- bioinformatics analysis
- small molecule
- risk assessment
- mass spectrometry
- copy number
- gene expression
- high resolution
- single molecule
- working memory
- transcription factor
- multiple sclerosis
- high throughput
- functional connectivity