Artificial Intelligence and the detection of pediatric concussion using epigenomic analysis

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Bahado-Singh R. O., Vishweswaraiah S., Er A., Aydas B., Turkoglu O., Taskin B. D., ...Daha Fazla

BRAIN RESEARCH, cilt.1726, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 1726
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1016/j.brainres.2019.146510
  • Dergi Adı: BRAIN RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Chemical Abstracts Core, EMBASE, Linguistics & Language Behavior Abstracts, MEDLINE, Psycinfo, Veterinary Science Database
  • Anahtar Kelimeler: Pediatric concussion, Methylation, Illumina Infinium MethylationEPIC BeadChip assay, Epigenetics, Artificial Intelligence, Biomarkers, TRAUMATIC BRAIN-INJURY, NITRIC-OXIDE SYNTHASE, LONG NONCODING RNA, NEUROTROPHIC FACTOR, DNA METHYLATION, CEREBRAL-CORTEX, CELL-VOLUME, INOSITOL, RECEPTOR, PROTEIN
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area of intense scientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine ('CpG') methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium Methylation EPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FOR p-values were calculated for mTBI detection based on CpG methylation levels. Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent individual accuracy (AUC >= 0.90-1.00) while 119 displayed good accuracy (AUC >= 0.80-0.89) for the prediction of mTBI. The CpG methylation changes a 10% were observed in many CpG loci after concussion suggesting biological significance. Pathway analysis identified several biologically important neurological pathways that were perturbed including those associated with: impaired brain function, cognition, memory, neurotransmission, intellectual disability and behavioral change and associated disorders. The combination of epigenomic and clinical predictors were highly accurate for the detection of concusion using Al techniques. Using DL/AI, a combination of epigenomic and clinical markers had sensitivity and specificity >= 95% for prediction of mTBI. In this novel study, we identified significant methylation changes in multiple genes in response to mTBI. Gene pathways that were epigenetically dysregulated included several known to be involved in neurological function, thus giving biological plausibility to our findings.