Gun Ahn

Massachusetts Institute of Technology (MIT), Brain and Cognitive Sciences, Graduate Student

Seoul National University, Material science and Engineering, Undergraduate

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I worked with Institute Professor Ann Graybiel at MIT BCS during my Master’s program and am now pursuing a Ph.D. in Brain and Cognitive Sciences at MIT with my amazing mentor John Gabrieli. My focus on Precision Psychiatry—inspired by my own journey through depression—fuels my belief that we can revolutionize mental health.
Supervisors: John Gabrieli

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Seoul National University

Wajahat Ali Khan

Bishwajit Bhowmik

Hajera Mahtab

University of Dhaka, Bangladesh

Laura Aponte

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Papers by Gun Ahn

Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions

We demonstrate here that astrocytes in the striatum interact with striatal dopamine in bidirectio... more We demonstrate here that astrocytes in the striatum interact with striatal dopamine in bidirectional signaling with dopamine release actively driving surges in astrocytic Ca ++ , which in turn modulate and reduce subsequent dopamine release. These Ca ++ surges accurately predict behavioral state changes from task-engaged to task-disengaged states, but fail to predict detailed action parameters. We propose that interactions between striatal astrocytes and dopamine are strong candidates to modulate nigro-striato-nigral loop function underlying ongoing behavioral state dynamics.

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Striosomes control dopamine via dual pathways paralleling canonical basal ganglia circuits

by Gun Ahn and Ara Mahar

Current Biology, 2024

HIGHLIGHTS ■ Direct S-D1 and Indirect S-D2 striosomal pathways target SNpc dopamine cells ■ The S... more HIGHLIGHTS ■ Direct S-D1 and Indirect S-D2 striosomal pathways target SNpc dopamine cells ■ The S-D2 indirect pathway targets a distinct central external pallidal zone (cGPe) ■ Stimulation of S-D2 increases, of S-D1 decreases, striatal dopamine and movement ■ S-D1 SPNs activity brackets task, inverse to a mid-task peak of dopamine release

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Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study

eClinicalMedicine, 2024

Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident ty... more Diabetes is a major public health concern. We aimed to evaluate the long-term risk of incident type 2 diabetes in a non-diabetic population using a deep learning model (DLM) detecting prevalent type 2 diabetes using electrocardiogram (ECG). Methods In this retrospective study, participants who underwent health checkups at two tertiary hospitals in Seoul, South Korea, between Jan 1, 2001 and Dec 31, 2022 were included. Type 2 diabetes was defined as glucose ≥126 mg/ dL or glycated haemoglobin (HbA1c) ≥ 6.5%. For survival analysis on incident type 2 diabetes, we introduced an additional variable, diabetic ECG, which is determined by the DLM trained on ECG and corresponding prevalent diabetes. It was assumed that non-diabetic individuals with diabetic ECG had a higher risk of incident type 2 diabetes than those with non-diabetic ECG. The one-dimensional ResNet-based model was adopted for the DLM, and the Guided Grad-CAM was used to localise important regions of ECG. We divided the non-diabetic group into the diabetic ECG group (false positive) and the non-diabetic ECG (true negative) group according to the DLM decision, and performed a Cox proportional hazard model, considering the occurrence of type 2 diabetes more than six months after the visit. Findings 190,581 individuals were included in the study with a median follow-up period of 11.84 years. The areas under the receiver operating characteristic curve for prevalent type 2 diabetes detection were 0.816 (0.807-0.825) and 0.762 (0.754-0.770) for the internal and external validations, respectively. The model primarily focused on the QRS duration and, occasionally, P or T waves. The diabetic ECG group exhibited an increased risk of incident type 2 diabetes compared with the non-diabetic ECG group, with hazard ratios of 2.15 (1.82-2.53) and 1.92 (1.74-2.11) for internal and external validation, respectively. Interpretation In the non-diabetic group, those whose ECG was classified as diabetes by the DLM were at a higher risk of incident type 2 diabetes than those whose ECG was not. Additional clinical research on the relationship between the phenotype of ECG and diabetes to support the results and further investigation with tracked data and various ECG recording systems are suggested for future works.

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The sexual brain, genes, and cognition: A machine‐predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Human Brain Mapping

Sex impacts the development of the brain and cognition differently across individuals. However, t... more Sex impacts the development of the brain and cognition differently across individuals. However, the literature on brain sex dimorphism in humans is mixed. We aim to investigate the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive performance. To this end, we tested whether the individual difference in brain sex would be linked to that in cognitive performance that is influenced by genetic factors in prepubertal children (N = 9,658, ages 9-10 years old; the Adolescent Brain Cognitive Development study). To capture the interindividual variability of the brain, we estimated the probability of being male or female based on the brain morphometry and connectivity features using machine learning (herein called a brain sex score). The models accurately classified the biological sex with a test ROC-AUC of 93.32%. As a result, a greater brain sex score correlated significantly with greater intelligence (pfdr < .001, η p 2 $$ {\eta}_p^2 $$ = .011-.034; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (pfdr < .001, η p 2 $$ {\eta}_p^2 $$ < .005). Structural equation models revealed that the GPS-intelligence association was significantly modulated by the brain sex score, such that a brain with a higher maleness score (or a lower femaleness score) mediated a positive GPS effect on intelligence (indirect effects = .006-.009; p = .002-.022; sex-stratified analysis). The finding of the sex modulatory effect on the gene-brain-cognition relationship presents a likely biological pathway to the individual and sex differences in the brain and cognitive performance in preadolescence.

High‐Resolution Knee Plain Radiography Image Synthesis Using Style Generative Adversarial Network Adaptive Discriminator Augmentation

by Changwung Jo and Gun Ahn

Journal of Orthopaedic Research, 2022

High‐Resolution Knee Plain Radiography Image Synthesis Using Style Generative Adversarial Network Adaptive Discriminator Augmentation

Journal of Orthopaedic Research

Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging

AI for Disease Surveillance and Pandemic Intelligence

Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging-Derived Phenotypes

Suicide is among the leading causes of death in youth worldwide. Early identification of children... more Suicide is among the leading causes of death in youth worldwide. Early identification of children with high risk for suicide is key to effective screening and prevention strategies. Brain imaging can show functional or structural abnormalities related to youth suicidality, but literature is scarce. Here we tested the extent to which brain imaging is useful in predicting suicidal risk in children. In the largest to date, multi-site, multi-ethnic, epidemiological developmental samples in the US (N = 6,172; the ABCD study), we trained and validated machine learning models and deep neural networks on the multimodal brain imaging derived phenotypes (morphometry, white matter connectivity, functional activation, and connectivity) along with behavioral and self-reported psychological questionnaire data. The model trained on diffusion white matter connectomes showed the best performance (test AUC-ROC = 74.82) with a one percentage increase compared with the baseline model trained on behavio...

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Gene, the Sexual Brain, and Cognition: Machine-Predicted Brain Sex Score Explains Individual Differences in Cognitive Development and Genetic Influence

Sex impacts the development of the brain and cognition differently across individuals. We investi... more Sex impacts the development of the brain and cognition differently across individuals. We investigated the biological underpinnings of the individual variability of sexual dimorphism in the brain and its impact on cognitive development. In prepubertal children (N=9,658, ages 9~10 years old; the Adolescent Brain Cognitive Development study), we tested whether the individual difference in brain sex development was related to that in cognitive development, known to be influenced by genetic factors. We estimated an individual’s brain sex score from machine learning models trained on brain morphometry and diffusion white matter connectomes that accurately classified the biological sex with a test ROC-AUC of 93.32%. A greater brain sex score correlated significantly with greater intelligence (Pfdr<0.001, ηp2=0.034~0.050; adjusted for covariates) and higher cognitive genome-wide polygenic scores (GPSs) (Pfdr<0.001, ηp2<0.005). Structural equation models revealed that the GPS-intel...

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Molecular epidemiology of an outbreak of imipenem-resistant Acinetobacter baumannii carrying the ISAba1-bla OXA-51-like Genes in a Korean hospital

Japanese Journal of Infectious Diseases, 2012

affected 77 patients. A case-control study revealed that the time spent in the hospital and mecha... more affected 77 patients. A case-control study revealed that the time spent in the hospital and mechanical ventilation practices were risk factors. IRAB was isolated from the hands of 4z (5/124) of healthcare workers, and 27.3z (21/77) of the samples obtained from the ICU environment. A pulsed-field gel electrophoresis analysis showed that 82.1z (23/28) of clinical IRAB isolates and 85.7z (6/7) of environmental IRAB isolates were type A. The ISAba1F/OXA-51-likeR PCR showed that 93.7z (30/32) of IRAB strains had the ISAba1 gene upstream of the bla OXA-51-like gene. Two ISAba1F/OXA-51-likeR PCR-negative IRAB strains were bla IMP-1 positive. All of the IRAB strains tested by PCR were negative for bla VIM , bla SIM , bla GIM-1 , bla SPM-1 , bla GES , bla OXA-23-like , bla OXA-24-like , and bla OXA-58-like carbapenemase genes. After implementing an infection control strategy, a steady reduction in the attack rate of IRAB infection was observed.

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Striatal Astrocytes Influence Dopamine Dynamics and Behavioral State Transitions

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Striosomes control dopamine via dual pathways paralleling canonical basal ganglia circuits

by Gun Ahn and Ara Mahar

Current Biology, 2024

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Deep learning-based long-term risk evaluation of incident type 2 diabetes using electrocardiogram in a non-diabetic population: a retrospective, multicentre study

eClinicalMedicine, 2024

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The sexual brain, genes, and cognition: A machine‐predicted brain sex score explains individual differences in cognitive intelligence and genetic influence in young children

Human Brain Mapping