Hyunjin Park

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Verified

Hyunjin verified their affiliation via an institutional email.

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• Doctor of Philosophy
• Professor at Sungkyunkwan University

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder, with its progression influenced by aberrant gene expression and alterations in the brain network topology. Although APOE has been extensively studied in relation to AD, the role of APOC1 remains relatively underexplored. This study investigated the impact of APOC1 on changes in cor...

Autism Spectrum Disorder (ASD) is a multifaceted neurodevelopmental condition characterized by a spectrum of behavioral and cognitive traits. As the characteristics of ASD are highly heterogeneous across individuals, a dimensional approach that overcomes the limitation of the categorical approach is preferred to reveal the symptomatology of ASD. Pr...

Background Autism spectrum disorder (ASD) is marked by disruptions in low-level sensory processing and higher-order sociocognitive functions, suggesting a complex interplay between different brain regions across the cortical hierarchy. However, the developmental trajectory of this hierarchical organization in ASD remains underexplored. Herein, we i...

The brain undergoes profound structural and functional transformations from childhood to adolescence. Convergent evidence suggests that neurodevelopment proceeds in a hierarchical manner, characterized by heterogeneous maturation patterns across brain regions and networks. However, the maturation of the intrinsic spatiotemporal propagations of brai...

The study of large-scale brain connectivity is increasingly adopting unsupervised approaches that derive low-dimensional spatial representations from high-dimensional connectomes, referred to as gradient analysis. When translating this approach to study interindividual variations in connectivity, one technical issue pertains to the selection of an...

Background Early detection of Alzheimer’s disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between ind...

The scarcity of annotated medical images is a major bottleneck in developing learning models for medical image analysis. Hence, recent studies have focused on pretrained models with fewer annotation requirements that can be fine-tuned for various downstream tasks. However, existing approaches are mainly 3D adaptions of 2D approaches ill-suited for...

Objective: Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities a...

Alzheimer’s disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each...

Motivated by the question, "Can we generate tumors with desired attributes?'' this study leverages radiomics features to explore the feasibility of generating synthetic tumor images. Characterized by its low-dimensional yet biologically meaningful markers, radiomics bridges the gap between complex medical imaging data and actionable clinical insigh...

Background This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure. Methods We retrospectively reviewe...

Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical imaging diagnosis, particularly when utilizing multiple imaging modalities to enhance accuracy. This study prese...

The effect of arterial tortuosity on intracranial atherosclerosis (ICAS) is not well understood. This study aimed to evaluate the effect of global intracranial arterial tortuosity on intracranial atherosclerotic burden in patients with ischemic stroke. We included patients with acute ischemic stroke who underwent magnetic resonance angiography (MRA...

Previous studies on Alzheimer’s disease-type cognitive impairment (ADCI) and subcortical vascular cognitive impairment (SVCI) has rarely explored spatiotemporal heterogeneity. This study aims to identify distinct spatiotemporal cortical atrophy patterns in ADCI and SVCI. 1,338 participants (713 ADCI, 208 SVCI, and 417 cognitively unimpaired elders)...

Autism spectrum disorder is one of the most common neurodevelopmental conditions associated with sensory and social communication impairments. Previous neuroimaging studies reported that atypical nodal- or network-level functional brain organization in individuals with autism was associated with autistic behaviors. Although dimensionality reduction...

Introduction We aimed to predict platinum sensitivity using routine baseline multimodal magnetic resonance imaging (MRI) and established clinical data in a radiomics framework. Methods We evaluated 96 patients with ovarian cancer who underwent multimodal MRI and routine laboratory tests between January 2016 and December 2020. The patients underwen...

Eating behavior is highly heterogeneous across individuals and cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors measured by a self‐assessment instrument using 424 healthy adults (mean ± standard deviation [SD] a...

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is u...

BACKGROUND Previous studies have assessed the relationship between vessel tortuosity and atherosclerosis in long vessel segments. We evaluated the relationship between tortuosity and parent artery atherosclerotic disease (PAD) in short vessel segments, and the differences between the PAD and branch atheromatous disease (BAD). METHODS Computerized...

Background Waiting impulsivity in progressive supranuclear palsy-Richardson’s syndrome (PSP-RS) is difficult to assess, and its regulation is known to involve nucleus accumbens (NAc) subregions. We investigated waiting impulsivity using the “jumping the gun” (JTG) sign, which is defined as premature initiation of clapping before the start signal in...

Radical prostatectomy (RP) is the main treatment of prostate cancer (PCa). Biochemical recurrence (BCR) following RP remains the first sign of aggressive disease; hence, better assessment of potential long-term post-RP BCR-free survival is crucial. Our study aimed to evaluate a combined clinical-deep learning (DL) model using multiparametric magnet...

Multi-robot systems have been used in many fields by utilizing parallel working robots to perform missions by allocating tasks and cooperating. For task planning, multi-robot systems need to solve complex problems that simultaneously consider the movement of the robots and the influence of each robot. For this purpose, researchers have proposed var...

A bstract Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with in...

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and its fast variant, ultrafast DCE-MRI, are useful for the management of breast cancer. Segmentation of breast lesions is necessary for automatic clinical decision support. Despite the advantage of acquisition time, existing segmentation studies on ultrafast DCE-MRI are scarce, and the...

The use of multimodal images generally improves segmentation. However, complete multimodal datasets are often unavailable due to clinical constraints. To address this problem, we propose a novel multimodal segmentation framework that is robust to missing modalities by using a region-of-interest (ROI) attentive modality completion. We use ROI attent...

A bstract Autism spectrum disorder is a common neurodevelopmental condition that manifests as a disruption in sensory and social skills. Although it has been shown that the brain morphology of individuals with autism is asymmetric, how this differentially affects the structural connectome organization of each hemisphere remains under-investigated....

Background The “motor reserve” is an emerging concept based on the discrepancy between the severity of parkinsonism and dopaminergic degeneration; however, the related brain structures have not yet been elucidated. Objective We investigated brain structures relevant to the motor reserve in Parkinson’s disease (PD) in this study. Methods Patients...

Migraine is a type of headache with multiple neurological symptoms. Prior neuroimaging studies in patients with migraine based on functional magnetic resonance imaging have found regional as well as network-level alterations in brain function. Here, we expand on prior studies by establishing whole-brain functional connectivity patterns in patients...

COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperin...

Objective: Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weig...

Background and Objective Gliomas are graded using multimodal magnetic resonance imaging, which provides important information for treatment and prognosis. When modalities are missing, the grading is degraded. We propose a robust brain tumor grading model that can handle missing modalities. Methods Our method was developed and tested on Brain Tumor...

Background Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) showed potency as a non-invasive therapeutic approach in pure ground-glass opacity nodule (pGGN) lung adenocarcinoma. However, optimal methods of extracting information about EGFR mutation from pGGN lung adenocarcinoma images remain uncertain. We aimed to develop, va...

Advanced research in robotics has allowed robots to navigate diverse environments autonomously. However, conducting complex tasks while handling unpredictable circumstances is still challenging for robots. The robots should plan the task by understanding the working environments beyond metric information and need countermeasures against various sit...

The human auditory cortex around Heschl’s gyrus (HG) exhibits diverging patterns across individuals owing to the heterogeneity of its substructures. In this study, we investigated the subregions of the human auditory cortex using data-driven machine-learning techniques at the individual level and assessed their structural and functional profiles. W...

Several studies suggested the association of migraine with deep white matter hyperintensities (WMHs). We aimed to explore the cerebrovascular reactivity (CVR), deep WMH burden, and their association in patients with migraine using a state-of-the-art methodology. A total of 31 patients with migraine without aura and 31 age/sex-matched controls under...

Objectives: Prognostic models of lung adenocarcinoma (ADC) can be built using radiomics features from various categories. The size-zone matrix (SZM) features have a strong biological basis related to tumor partitioning, but their incremental benefits have not been fully explored. In our study, we aimed to evaluate the incremental benefits of SZM f...

Background Despite the clinical impact of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD), the mechanism, especially the role of basal ganglia (BG), is not fully elucidated yet. We investigated the BG structural changes related to LID in PD using a surface-based shape analysis technique. Methods We recruited patients with PD who deve...

Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence (AI). If the AI understands the mental state behind a user’s decision, it can learn more appropriate decisions even in unclear situations. We introduce the Brain–AI Closed-Loop System (BACLoS), a wireless interaction platform that...

The purpose of this study was to identify perfusional subregions sharing similar kinetic characteristics from dynamic contrast-enhanced magnetic resonance imaging (MRI) using data-driven clustering, and to evaluate the effect of perfusional heterogeneity based on those subregions on patients’ survival outcomes in various risk models. From two hospi...

Clinical heterogeneity has been one of the main barriers to develop effective biomarkers and therapeutic strategies in autism spectrum disorder (ASD). Recognizing this challenge, much effort has been made in recent neuroimaging studies to find biologically more homogeneous subgroups (called ‘neurosubtypes’) in autism. However, most approaches have...

A bstract Eating behavior is highly heterogeneous across individuals, and thus, it cannot be fully explained using only the degree of obesity. We utilized unsupervised machine learning and functional connectivity measures to explore the heterogeneity of eating behaviors. This study was conducted on 424 healthy adults. We generated low-dimensional r...

Neuroimaging genetics is a powerful approach to jointly explore genetic features with rich brain imaging phenotypes for neurodegenerative diseases. Conventional imaging genetics approaches based on canonical correlation analysis cannot accommodate multimodal inputs effectively and have limited interpretability. We propose a novel imaging genetics a...

Functional hierarchy establishes core axes of the brain, and overweight individuals show alterations in the networks anchored on these axes, particularly in those involved in sensory and cognitive control systems. However, quantitative assessments of hierarchical brain organization in overweight individuals are lacking. Capitalizing stepwise functi...

Background Many studies have successfully identified radiomics features reflecting macroscale tumor features and tumor microenvironment for various organs. There is an increased interest in applying these radiomics features found in a given organ to other organs. Here, we explored whether common radiomics features could be identified over target or...

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hund...

Recent advancements in imaging technology and analysis methods have led to an analytic framework known as radiomics. This framework extracts comprehensive high-dimensional features from imaging data and performs data mining to build analytical models for improved decision support. Its features include many categories spanning texture and shape; thu...

Deep learning (DL) is a breakthrough technology for medical imaging with high sample size requirements and interpretability issues. Using a pretrained DL model through a radiomics-guided approach, we propose a methodology for stratifying the prognosis of lung adenocarcinomas based on pretreatment CT. Our approach allows us to apply DL with smaller...

Narrative comprehension involves a constant interplay of the accumulation of incoming events and their integration into a coherent structure. This study characterizes cognitive states during narrative comprehension and the network-level reconfiguration occurring dynamically in the functional brain. We presented movie clips of temporally scrambled s...

Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear conne...

Background and aim: Tumor staging in non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging involves expert interpretation of imaging, which we aim to automate with deep learning (DL). We proposed a cascaded DL method comprised of two steps to classification between early- and advanced-stage NSCLC using pretreatment co...

Background: Prognostic considerations for non-predominant patterns are necessary because most lung adenocarcinomas (ADCs) have a mixed histologic pattern, and the spectrum of actual prognosis varies widely even among lung ADCs with the same most predominant pattern. We aimed to identify prognostic stratification by second most predominant pattern...

Introduction To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). Methods We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features...

Spectral-domain optical coherence tomography (SD-OCT) images inevitably suffer from multiplicative speckle noise caused by random interference. This study proposes an unsupervised domain adaptation approach for noise reduction by translating the SD-OCT to the corresponding high-quality enhanced depth imaging (EDI)-OCT. We propose a structure-persev...

We aimed to investigate the relationship between tumor radiomic margin characteristics and prognosis in patients with lung cancer. We enrolled 334 patients who underwent complete resection for lung adenocarcinoma. A quantitative computed tomography analysis was performed, and 76 radiomic margin characteristics were extracted. The radiomic margin ch...

Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. T...

The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper propos...

Sustained pain is a major characteristic of clinical pain disorders, but it is difficult to assess in isolation from co-occurring cognitive and emotional features in patients. In this study, we developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain inten...

Although a substantial decrease in 2-[fluorine-18]fluoro-2-deoxy-d-glucose (FDG) uptake on positron emission tomography-computed tomography (PET-CT) indicates a promising metabolic response to treatment, predicting the pathologic status of lymph nodes (LN) remains challenging. We investigated the potential of a CT radiomics approach to predict the...

Simple Summary Contact between a tumor and the adjacent fat is a potential biomarker to predict the therapy response in breast cancer, but it has not been quantitatively explored. In this study, we measured the direct contact between the tumor and adjacent fat using breast magnetic resonance imaging with machine learning and found that patients wit...

BACKGROUNDS We aimed to evaluate the potential of radiomics as an imaging biomarker for GBM patients and explore the molecular rationale behind radiomics by radio-genomics approach. METHODS A total of 144 primary GBM patients were included in this study as a training cohort. Using multi-parametric MR images, radiomics features were extracted from...

Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can se...

Obesity is often associated with cardiovascular complications. Adolescent obesity is a risk factor for cardiovascular disease in adulthood; thus, intensive management is warranted in adolescence. The brain state contributes to the development of obesity in addition to metabolic conditions, and hence neuroimaging is an important tool for accurately...

The connectivity analysis is a powerful technique for investigating a hard-wired brain architecture as well as flexible, functional dynamics tied to human cognition. Recent multi-modal connectivity studies had the challenge of combining functional and structural connectivity information into one integrated network. In this paper, we proposed a simp...

Classification of headache disorders is dependent on a subjective self-report from patients and its interpretation by physicians. We aimed to apply objective data-driven machine learning approaches to analyze patient-reported symptoms and test the feasibility of the automated classification of headache disorders. The self-report data of 2162 patien...