Researchers used MRI brain scans and machine learning techniques at birth to predict cognitive development at age 2 years with 95 percent accuracy.
"This prediction could help identify children at risk for poor cognitive development shortly after birth with high accuracy," said senior author. "For these children, an early intervention in the first year or so of life - when cognitive development is happening - could help improve outcomes. For example, in premature infants who are at risk, one could use imaging to see who could have problems."
The study, which was published by the journal NeuroImage, used an application of artificial intelligence called machine learning to look at white matter connections in the brain at birth and the ability of these connections to predict cognitive outcomes.
The senior author said researchers are working to find imaging biomarkers of risk for poor cognitive outcomes and for risk of neuropsychiatric conditions such as autism and schizophrenia. In this study, the researchers replicated the initial finding in a second sample of children who were born prematurely.
"Our study finds that the white matter network at birth is highly predictive and may be a useful imaging biomarker. The fact that we could replicate the findings in a second set of children provides strong evidence that this may be a real and generalizable finding," the senior author said.
http://news.unchealthcare.org/news/2019/march/ai-and-mris-at-birth-can-predict-cognitive-development-at-age-2-unc-study-finds
https://www.sciencedirect.com/science/article/pii/S1053811919301569?via%3Dihub
Predicting cognitive development at age 2 using AI and MRI
- 1,776 views
- Added
Edited
Latest News
Detecting gut microbes that activate immune cells
Shell microelectrode arrays (MEAs) for brain organoids
Why heat makes us sleepy
Nasal spray peptide can reduce seizure activity, protect neurons in Alzheimer's
How faulty metabolism triggers adrenal cancer
Other Top Stories
New screening method to identify genetic defects in human embryos
Maternal age effect on germline de novo mutations
Genetic underpinnings of congenital heart disease
Turning the volume of gene expression up and down
Memory suppressing micro RNA implicated in autism
Protocols
Simultaneous recording of neuronal and vascular activity in the rodent brain using fiber- photom…
VDJdb in the pandemic era: a compendium of T cell receptors specifc for SARS-CoV-2
A scalable organoid model of human autosomal dominant polycystic kidney disease for disease mecha…
An improved organotypic cell culture system to study tissue-resident macrophages ex vivo
Protocol for spike-triggered closed-loop auditory stimulation during sleep in patients with epilepsy
Publications
Calcium homeostasis and cancer: insights from endoplasmic reticulum-centered organelle communicat…
Systemic inflammation after stroke: implications for post-stroke comorbidities
Systemic IgG repertoire as a biomarker for translocating gut microbiota members
Mitochondrial microproteins link metabolic cues to respiratory chain biogenesis
Shell microelectrode arrays (MEAs) for brain organoids
Presentations
Hydrogels in Drug Delivery
Lipids
Cell biology of carbohydrate metabolism
RNA interference (RNAi)
RNA structure and functions
Posters
ASCO-2020-HEALTH SERVICES RESEARCH AND QUALITY IMPROVEMENT
ASCO-2020-HEAD AND NECK CANCER
ASCO-2020-GENITOURINARY CANCER–KIDNEY AND BLADDER
ASCO-2020-GENITOURINARY CANCER–PROSTATE, TESTICULAR, AND PENILE
ASCO-2020-GYNECOLOGIC CANCER