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To contribute to the brain health of humans
with unwavering adventure and passion.



–To Benefit Humans by Innovating Therapeutics for Diseases 


–Global Leading Player in Neuro Digital Therapeutics


-We take pride in our in-house convergence science, technology and clinical professionals comprised of neuroscientists, neurologists, psychologists,

AI scientists, software engineers and clinical trial managers. It enables us bring neuroscience,

medicine and IT together to change how medicine is designed and delivered. 
We are building a platform to identify target biomarkers and develop novel digital therapeutics to treat or manage various neuropsychological disorders.
We have initiated a pivotal clinical trial of our first product, Nunap Vision,

which provides visual perceptual training to treat visual field

defects caused by brain damage.

It is the first digital therapeutics clinical trial approved by MFDS in South Korea.



Nunaps stands for Neuron and Synapse, which are key elements that form the basis of our brain. We, Nunapsians, are people who explore the roots of the brain. With constant challenges and passion, we will give new hopes and values to those suffering from diseases.


Perceptual Learning

Perceptual learning is defined as experience- or training-dependent performance

improvements on a sensory task and is regarded as a manifestation of adult plasticity.

In particular, visual perceptual learning (VPL) has attracted attention because of its benefits for visual perceptual ability,

and there have been continuous endeavors to use VPL in clinical settings,

such as to treat amblyopia, presbyopia, and stroke, and sports settings to enhance sports performance. 

Perceptual Learning

–Jeong S, Lee EJ, Kim YH, Woo JC, Ryu OW, Kwon M, Kwon SU, Kim JS, Kang DW. Deep Learning Approach Using Diffusion-weighted Imaging to Estimate the Severity of Aphasia in Stroke Patients. J Stroke (in press)

–Kim BJ, Jang SK, Kim YH, Lee EJ, Chang JY, Kwon SU, Kim JS, Kang DW. Diagnosis of Acute Central Dizziness with Simple Clinical Information Using Machine Learning. Front Neurol 2021 Jul 12;12:691057. 

–Kim H, Lee Y, Kim YH, Lim YM, Lee JS, Woo J, Jang SK, Oh YJ, Kim HW, Lee EJ, Kang DW, Kim KK. Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis. Front Neurol 2020 Nov 30;11:599042. 

–Jang SK, Chang JY, Lee JS, Lee EJ, Kim YH, Han JH, Chang DI, Cho HJ, Cha JK, Yu KH, Jung JM, Ahn SH, Kim DE, Sohn SI, Lee JH, Park KP, Kwon SU, Kim JS, Kang DW; KOSNI Investigators. Reliability and Clinical Utility of Machine Learning to Predict Stroke Prognosis: Comparison with Logistic Regression. J Stroke 2020 Sep;22(3):403-406. 

–Lee H, Lee EJ, Ham S, Lee HB, Lee JS, Kwon SU, Kim JS, Kim N, Kang DW. Machine Learning Approach to Identify Stroke Within 4.5 Hours. Stroke 2020 Mar;51(3):860-866.

–Choi MJ, Kim H, Nah HW, Kang DW. Digital Therapeutics: Emerging New Therapy for Neurologic Deficits after Stroke. J Stroke 2019 Sep;21(3):242-258.

–Kim YH, Cho AH, Kim D, Kim SM, Lim HT, Kwon SU, Kim JS, Kang DW. Early Functional Connectivity Predicts Recovery from Visual Field Defects after Stroke. J Stroke 2019 May;21(2):207-216.

–Kang DW, Kim D, Chang LH, Kim YH, Takahashi E, Cain MS, Watanabe T, Sasaki Y. Structural and Functional Connectivity Changes Beyond Visual Cortex in a Later Phase of Visual Perceptual Learning. Sci Rep 2018 Mar 26;8(1):5186. 

–Lee EJ, Kim YH, Kim N, Kang DW. Deep into the Brain: Artificial Intelligence in Stroke Imaging. J Stroke 2017 Sep;19(3):277-285. 

–Kim BJ, Kim YH, Kim N, Kwon SU, Kim SJ, Kim JS, Kang DW. Lesion location-based prediction of visual field improvement after cerebral infarction. PLoS One 2015 Nov 25;10(11):e0143882.

–Lim JS, Kang DW. Stroke connectome and its implications for cognitive and behavioral sequela of stroke. J Stroke 2015 Sep;17(3):256-67.

–Kim YH, Kang DW, Kim D, Kim HJ, Sasaki Y, Watanabe T. Real-time strategy video game experience and visual perceptual learning. J Neuroscience 2015 Jul 22;35(29):10485-92.



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