Junhao Wen

Junhao Wen
University of Pennsylvania | UP · Department of Radiology

PhD
#LookForJobs

About

53
Publications
7,816
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
533
Citations
Additional affiliations
September 2019 - present
University of Pennsylvania
Position
  • PostDoc Position
Description
  • Working on clustering problems in brain diseases
July 2019 - October 2019
University College London
Position
  • Visiting scholar
Description
  • Visiting scholar
Education
October 2015 - July 2019
Sorbonne Université
Field of study
  • Computer science
September 2008 - May 2015
Beihang University (BUAA)
Field of study
  • Electronic engineering

Publications

Publications (53)
Preprint
Full-text available
Autism spectrum disorder (ASD) is associated with high structural heterogeneity in magnetic resonance imaging (MRI). This work uncovers three neuroanatomical dimensions of ASD ( N = 307) using machine learning methods and constructs their characteristic MRI signatures. The presence of these signatures, along with their clinical profiles and genetic...
Preprint
Full-text available
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to a broader family called generative methods, which generate new data with a probabilistic model by learning sample distribution from real examples. In the clinical context, GANs have shown enhan...
Preprint
Full-text available
A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurological and neuropsychiatric diseases. Oftentimes, such methods don't explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover, u...
Article
Full-text available
Objective: The prevalence and significance of schizophrenia-related phenotypes at the population level is debated in the literature. Here, the authors assessed whether two recently reported neuroanatomical signatures of schizophrenia-signature 1, with widespread reduction of gray matter volume, and signature 2, with increased striatal volume-could...
Article
Full-text available
Background Identifying neurobiologically based transdiagnostic categories of depression and psychosis may elucidate heterogeneity, and provide better candidates for predictive modelling. We aimed to identify clusters across patients with recent onset depression (ROD) and recent onset psychosis (ROP) based on structural neuroimaging data. We hypothe...
Preprint
Full-text available
Normal and pathologic neurobiological processes influence brain morphology in coordinated ways that give rise to structural covariance patterns across brain regions and individuals. We present a mega-analysis of structural covariance with magnetic resonance imaging of 50,699 healthy and diseased individuals (12 studies, 130 sites, and 12 countries)...
Article
Full-text available
Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associa...
Chapter
Full-text available
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases,...
Preprint
Full-text available
The imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases,...
Conference Paper
Full-text available
A plethora of machine learning methods have been applied to imaging data, enabling the construction of clinically relevant imaging signatures of neurologi-cal and neuropsychiatric diseases. Oftentimes, such methods do not explicitly model the heterogeneity of disease effects, or approach it via nonlinear models that are not interpretable. Moreover,...
Preprint
Full-text available
The prevalence and significance of schizophrenia-related phenotypes at the population-level are debated in the literature. Here we assess whether two recently reported neuroanatomical signatures of schizophrenia, signature 1 with widespread reduction of gray matter volume and signature 2 with increased striatal volume, could be replicated in an ind...
Article
Full-text available
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a semi-supervised deep-clustering method, which examines neuroanatomical heterogeneity contrasted against normal brain structure, to identify disease subtypes through neuro...
Article
Heterogeneity of neurodegenerative diseases, including Alzheimer’s disease (AD), has hampered precision diagnosis and prognosis. Machine learning methods are able to dissect neuroanatomical heterogeneity and enable identification of disease subtypes via their imaging signatures. We apply a novel semi‐supervised deep‐learning clustering method to de...
Article
Full-text available
Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by...
Preprint
Full-text available
Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to ne...
Article
Full-text available
We present Clinica ( www.clinica.run ), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their inst...
Preprint
Full-text available
We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to i) spend less time on data management and processing, ii) perform reproducible evaluations of their methods, and iii) easily share data and results within their instituti...
Preprint
Full-text available
Disease heterogeneity is a significant obstacle to understanding pathological processes and delivering precision diagnostics and treatment. Clustering methods have gained popularity for stratifying patients into subpopulations (i.e., subtypes) of brain diseases using imaging data. However, unsupervised clustering approaches are often confounded by...
Preprint
Full-text available
Heterogeneity of brain diseases is a challenge for precision diagnosis/prognosis. We describe and validate Smile-GAN (SeMI-supervised cLustEring-Generative Adversarial Network), a novel semi-supervised deep-clustering method, which dissects neuroanatomical heterogeneity, enabling identification of disease subtypes via their imaging signatures relat...
Article
Full-text available
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer’s disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant sel...
Article
Full-text available
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer’s disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we rep...
Article
Full-text available
Subjective cognitive decline (SCD) is a high-risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1-weighted structural MRI. A machine learning-based model was trained based on 198 baseline neuroimaging (...
Conference Paper
Full-text available
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimer’s Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing tar...
Preprint
Full-text available
We performed a systematic review of studies focusing on the automatic prediction of the progression of mild cognitive impairment to Alzheimer's disease (AD) dementia, and a quantitative analysis of the methodological choices impacting performance. This review included 172 articles, from which 234 experiments were extracted. For each of them, we rep...
Preprint
Full-text available
There is a growing amount of clinical, anatomical and functional evidence for the heterogeneous presentation of neuropsychiatric and neurodegenerative diseases such as schizophrenia and Alzheimers Disease (AD). Elucidating distinct subtypes of diseases allows a better understanding of neuropathogenesis and enables the possibility of developing targ...
Preprint
Full-text available
Machine learning methods applied to complex biomedical data has enabled the construction of disease signatures of diagnostic/prognostic value. However, less attention has been given to understanding disease heterogeneity. Semi-supervised clustering methods can address this problem by estimating multiple transformations from a (e.g. healthy) control...
Preprint
Full-text available
Subjective cognitive decline (SCD) is a high-risk yet less understood status years before Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and seven years later) neuropsychological evaluations and a baseline T1-MRI. A machine learning-based model was trained based on 198 baseline neuroimage features and a battery o...
Article
Full-text available
Numerous machine learning (ML) approaches have been proposed for automatic classification of Alzheimer's disease (AD) from brain imaging data. In particular, over 30 papers have proposed to use convolutional neural networks (CNN) for AD classification from anatomical MRI. However, the classification performance is difficult to compare across studie...
Thesis
Full-text available
Biomarker identification and tracking in dementia are essential to better understand the pathological mechanism and disease trajectory. The current PhD aims has two main objectives. First, we aim to identify the most promising biomarkers at the presymptomatic stage of dementia. More specifically, we studied this in the case of genetic frontotempora...
Preprint
Full-text available
In the past two years, over 30 papers have proposed to use convolutional neural network (CNN) for AD classification. However, the classification performances across studies are difficult to compare. Moreover, these studies are hardly reproducible because their frameworks are not publicly accessible. Lastly, some of these papers may reported biased...
Preprint
Full-text available
Diffusion MRI is the modality of choice to study alterations of white matter. In the past years, various works have used diffusion MRI for automatic classification of Alzheimers disease. However, the performances obtained with different approaches are difficult to compare because of variations in components such as input data, participant selection...
Article
Full-text available
We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical s...
Article
Full-text available
Objective To assess the added value of neurite orientation dispersion and density imaging (NODDI) compared with conventional diffusion tensor imaging (DTI) and anatomical MRI to detect changes in presymptomatic carriers of chromosome 9 open reading frame 72 ( C9orf72 ) mutation. Methods The PREV-DEMALS (Predict to Prevent Frontotemporal Lobar Dege...
Article
Full-text available
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of AD. However, they are difficult to reproduce because key components of the validation are often not readily available. These components include selected participants and input data, image preprocessing and cross-validation...
Preprint
Full-text available
A large number of papers have introduced novel machine learning and feature extraction methods for automatic classification of Alzheimer’s disease (AD). However, while the vast majority of these works use the public dataset ADNI for evaluation, they are difficult to reproduce because different key components of the validation are often not readily...
Article
Full-text available
Importance: Presymptomatic carriers of chromosome 9 open reading frame 72 (C9orf72) mutation, the most frequent genetic cause of frontotemporal lobar degeneration and amyotrophic lateral sclerosis, represent the optimal target population for the development of disease-modifying drugs. Preclinical biomarkers are needed to monitor the effect of ther...

Network

Cited By

Projects

Projects (3)
Project
Unveil de novo IDP and genomic loci to understand their functional pathway and pathology mechanism.
Project
Reproducible evaluation of machine learning methods and neuroimaging for computer-aided diagnosis of Alzheimer's disease.
Project
Disentangling disease heterogeneity and purify targeted treatment population.