Han Zhang

• Ph.D.
• Professor (Assistant) at University of North Carolina at Chapel Hill

Resting-state functional magnetic resonance imaging (rs-fMRI) is a commonly used functional neuroimaging technique to investigate the functional brain networks. However, rs-fMRI data are often contaminated with noise and artifacts that adversely affect the results of rs-fMRI studies. Several machine/deep learning methods have achieved impressive pe...

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infa...

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infa...

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infa...

Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infa...

Objectives Our objective was to use deep learning models to identify underlying brain regions associated with depression symptom phenotypes in late-life depression (LLD). Participants Diagnosed with LLD (N = 116) and enrolled in a prospective treatment study. Design Cross-sectional. Measurements Structural magnetic resonance imaging (sMRI) was u...

Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities....

Purpose The aim of this study is to compare the blood oxygen level–dependent (BOLD) fluctuation power in 96 frequency points ranging from 0 to 0.25 Hz between benign and malignant musculoskeletal (MSK) tumors via power spectrum analyses using functional magnetic resonance imaging (fMRI). Materials and methods BOLD-fMRI and T1-weighted imaging (T1W...

Glioblastoma (GBM) is a severe malignant brain tumor with bad prognosis, and overall survival (OS) time prediction is of great clinical value for customized treatment. Recently, many deep learning (DL) based methods have been proposed, and most of them build deep networks to directly map pre-operative images of patients to the OS time. However, suc...

Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory....

Multi-modal structural Magnetic Resonance Image (MRI) provides complementary information and has been used widely for diagnosis and treatment planning of gliomas. While machine learning is popularly adopted to process and analyze MRI images, most existing tools are based on complete sets of multi-modality images that are costly and sometimes imposs...

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most im...

The human brain is not only efficiently but also “redundantly” connected. The redundancy design could help the brain maintain resilience to disease attacks. This paper explores subnetwork-level redundancy dynamics and the potential of such metrics in disease studies. As such, we looked into specific functional subnetworks, including those associate...

Infancy is a dynamic and immensely important period in human brain development. Studies of infant functional development using resting-state fMRI rely on precisely defined cortical parcellation maps. However, available adult-based functional parcellation maps are not applicable for infants due to their substantial differences in functional organiza...

Functional connectome "fingerprint" is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist since infancy is barely investigated despite its importance in ide...

Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cog...

Major depressive disorder (MDD) represents a grand challenge to human health and society, but the underlying pathophysiological mechanisms remain elusive. Previous neuroimaging studies have suggested that MDD is associated with abnormal interactions and dynamics in two major neural systems including the default mode - salience (DMN-SAL) network and...

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneo...

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneo...

Functional connectivity (FC) networks built from resting-state functional magnetic resonance imaging (rs-fMRI) has shown promising results for the diagnosis of Alzheimer's disease and its prodromal stage, that is, mild cognitive impairment (MCI). FC is usually estimated as a temporal correlation of regional mean rs-fMRI signals between any pair of...

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Many studies have shown that aHPSFs, and their respective functional networks, are differentially vulnerable to a variety of disorders. Hippocampal functional networks, particularly during resting state, are generally a...

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting‐state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. Th...

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 (...

Functional segregation and specialization of cortical regions is central to the significant changes that take place during early brain development. We present an automated scheme that harnesses local and long-range connectivity features of the cortex — derived from multiple imaging modalities — for longitudinal parcellation of the early developing h...

Functional brain development in early infancy is a highly dynamic and complex process. Understanding each brain region’s topological role and its development in the brain functional connectivity (FC) networks is essential for early disorder detection. A handful of previous studies have mostly focused on how FC network is changing regarding age. The...

Functional connectome “fingerprint” is a highly characterized brain pattern that distinguishes one individual from others. Although its existence has been demonstrated in adults, an unanswered but fundamental question is whether such individualized pattern emerges since infancy. This problem is barely investigated despites its importance in identif...

Graph theory has been used extensively to investigate information exchange efficiency among brain regions represented as graph nodes. In this work, we propose a new metric to measure how the brain network is robust or resilient to any attack on its nodes and edges. The metric measures redundancy in the sense that it calculates the minimum number of...

The human brains are organized into hierarchically modular networks facilitating efficient and stable information processing and supporting diverse cognitive processes during the course of development. While the remarkable reconfiguration of functional brain network has been firmly established in early life, all these studies investigated the netwo...

Infant cortical surface templates play an essential role in spatial normalization of cortical surfaces across individuals in pediatric neuroimaging analysis. However, existing infant surface templates have two major limitations in functional MRI analysis. First, they are constructed by co-registration of cortical surfaces based on structural attrib...

Significance While adult brains are known to be functionally flexible, the emergence of a functionally flexible brain during early infancy is largely uncharted due the lack of approaches to assess neural flexibility in infants. Using recent advances of multilayer network approaches and a cohort of typically developing children who underwent longitu...

Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy of infant age prediction thanks to the complementary information provided by different imaging modalities. However, functional connectivity measured by fMRI during infancy is largely immature...

Uncovering the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for understanding emerging complex cognitive functions and behaviors. To this end, this paper leveraged a longitudinal resting-state functional magnetic resonance imaging dataset from 51 typically developing infants and, f...

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...

Computational neuroimaging has played a central role in characterizing functional abnormalities in major depressive disorder (MDD). However, most of existing non-invasive analysis tools based on functional magnetic resonance imaging (fMRI) are largely descriptive and superficial, thus cannot offer a deep mechanistic understanding of neural circuit...

In various clinical scenarios, medical image is crucial in disease diagnosis and treatment. Different modalities of medical images provide complementary information and jointly helps doctors to make accurate clinical decision. However, due to clinical and practical restrictions, certain imaging modalities may be unavailable nor complete. To impute...

Brain functional network has been increasingly used in understanding brain functions and diseases. While many network construction methods have been proposed, the progress in the field still largely relies on static pairwise Pearson's correlation‐based functional network and group‐level comparisons. We introduce a “Brain Network Construction and Cl...

The amplitude of low-frequency fluctuation (ALFF) measures resting-state functional magnetic resonance imaging (RS-fMRI) signal of each voxel. However, the unit of blood oxygenation level-dependent (BOLD) signal is arbitrary and hence ALFF is sensitive to the scale of raw signal. A well-accepted standardization procedure is to divide each voxel’s A...

Glioblastoma (GBM) is the most common and deadly malignant brain tumor. For personalized treatment, an accurate pre-operative prognosis for GBM patients is highly desired. Recently, many machine learning-based methods have been adopted to predict overall survival (OS) time based on the pre-operative mono- or multi-modal imaging phenotype. The genot...

Major depressive disorder (MDD) is a serious mental illness characterized by dysfunctional connectivity among distributed brain regions. Previous connectome studies based on functional magnetic resonance imaging (fMRI) have focused primarily on undirected functional connectivity and existing directed effective connectivity (EC) studies concerned mo...

Understanding the moment-to-moment dynamics of functional connectivity (FC) in the human brain during early development is crucial for uncovering neuro-mechanisms of the emerging complex cognitive functions and behaviors. Instead of calculating FC in a static perspective, we leveraged a longitudinal resting-state functional magnetic resonances imag...

Resting-state functional magnetic resonance imaging (rs-fMRI) studies have focused primarily on characterizing functional or effective connectivity of discrete brain regions. A major drawback of this approach is that it does not provide a mechanistic understanding of brain cognitive function or dysfunction at cellular and circuit levels. To overcom...

Spatiotemporal dynamics analysis of the human brain functional connectome and its early development in the first few years of life is tremendously essential for grasping such a shining jewel in life science as such a knowledge shed light on the long-standing mysteries of emerging and fast developing of various high-level cognitive abilities in such...

The infant brain experiences explosive growth in the first few years of life. The developing topology of the functional network mirrors the emergence of complex cognitive functions. However, early development of brain topological properties in infants is still largely unclear due to the dearth of high-quality longitudinal infant functional MRI (fMR...

High-grade glioma (HGG) is a lethal cancer with poor outcome. Accurate preoperative overall survival (OS) time prediction for HGG patients is crucial for treatment planning. Traditional presurgical and noninvasive OS prediction studies have used radiomics features at the local lesion area based on the magnetic resonance images (MRI). However, the h...

Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this me...

Alzheimer’s disease (AD) is a chronic neurodegenerative disease that could cause severe cognitive damage to the patients. Diagnosis of AD at its preclinical stage, i.e., mild cognitive impairment (MCI), could help to prevent or slow down AD progression. With machine learning, automatic MCI diagnosis could be achieved. Most of the previous studies m...

Meaningful division of the human cortex into distinct regions is a longstanding goal in neuroscience. Many of the most widely cited parcellations utilize anatomical priors or depend on functional magnetic resonance imaging (MRI) data while there exists a relative dearth of parcellations that use only structural data based on diffusion MRI. In light...

Multi-modal structural MRI has been widely used for presurgical glioma grading for treatment planning. Despite providing complementary information, a complete set of high-resolution multi-modality data is costly and often impossible to acquire in clinical settings (although T1 MRI is more commonly acquired). To leverage more comprehensive multimoda...

Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, we introduce label distribution learning (LDL), a popular machine learning paradigm focusing on the small sample problem, for infant age prediction. As directly applying LDL yields dramatically increas...

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional imaging technique that has been widely used to investigate brain functional connectome. Noises and artifacts are dominant in the raw rs-fMRI, making effective noise removal a necessity prior to any subsequent analysis. Without requiring any additional biophys...

Glioblastoma (GBM) is the most common and deadly malignant brain tumor with short yet varied overall survival (OS) time. Per request of personalized treatment, accurate pre-operative prognosis for GBM patients is highly desired. Currently, many machine learning-based studies have been conducted to predict OS time based on pre-operative multimodal M...

Decoding visual stimuli from brain activities is an interdisciplinary study of neuroscience and computer vision. With the emerging of Human-AI Collaboration, Human-Computer Interaction, and the development of advanced machine learning models, brain decoding based on deep learning attracts more attention. Electroencephalogram (EEG) is a widely used...

Significance During the first 2 postnatal years, the human brain undergoes dynamic growth and shows rapid expansions in behavioral and cognitive abilities. Charting the developmental patterns of cortical thickness in healthy infants is important for understanding many neurodevelopmental disorders, which unfortunately remains unexplored. Therefore,...

While convolutional neural network (CNN) has been demonstrating powerful ability to learn hierarchical spatial features from medical images, it is still difficult to apply it directly to resting-state functional MRI (rs-fMRI) and the derived brain functional networks (BFNs). We propose a novel CNN framework to simultaneously learn embedded features...

Brain functional network has become an increasingly used approach in understanding brain functions and diseases. Many network construction methods have been developed, whereas the majority of the studies still used static pairwise Pearson's correlation-based functional connectivity. The goal of this work is to introduce a toolbox namely "Brain Netw...

Introduction: Olfactory deficits are prevalent in early Alzheimer's disease (AD) and are predictive of progressive memory loss and dementia. However, direct neural evidence to relate AD neurodegeneration to deficits in olfaction and memory is limited. Methods: We combined the University of Pennsylvania Smell Identification Test (UPSIT) with olfa...

Understanding the nature of dynamic neural interactions during development is a critical issue of cognitive neuroscience. However, our knowledge on infants’ functional connectivity (FC) dynamics is still scarce. Leveraging longitudinal infant resting-state fMRI from fifty-one typically developing infants, we, for the first time, charted the develop...

Background: Recent functional connectivity (FC) studies have proved the potential value of resting-state functional magnetic resonance imaging (rs-fMRI) in the study of major depressive disorder (MDD); yet, the rs-fMRI-based individualized diagnosis of MDD is still challenging. Methods: We enrolled 82 treatment-naïve first episode depression (FE...

Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation n...

Objectives: We aimed to investigate whether an inter-voxel diffusivity metric (local diffusion homogeneity, LDH), can provide supplementary information to traditional intra-voxel metrics (i.e., fractional anisotropy, FA) in white matter (WM) abnormality detection for type 2 diabetes mellitus (T2DM). Methods: Diffusion tensor imaging was acquired fr...

Many functional magnetic resonance imaging (fMRI) studies have indicated that Granger causality analysis (GCA) is a suitable method for revealing causal effects between brain regions. The purpose of the present study was to identify neuroimaging biomarkers with a high sensitivity to amnestic mild cognitive impairment (aMCI). The resting-state fMRI...

High-grade gliomas are the most aggressive malignant brain tumors. Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate. Recent radiomics studies have shown better prognosis by using carefully-engineered image feat...

The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such...

Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer’s disease (AD) diagnosis, it is still challenging to perform individualized diagnosis, especially at the very early stage of preclinical stage of AD, i....

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotempor...

Understanding human brain functional development in the very early ages is of great importance for charting normative development and detecting early neurodevelopmental disorders, but it is very challenging. We propose a group-constrained, robust community detection method for better understanding of developing brain functional connectome from neon...

Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective inter...

The human cerebellum has been recognized as a key brain structure for motor control and cognitive function regulation. Investigation of brain functional development in the early life has recently been focusing on both cerebral and cerebellar development. Accurate segmentation of the infant cerebellum into different tissues is among the most importa...

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotempor...

Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotempor...

Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networ...

This is our work orally presented in OHBM 2018. We proposed a “Multi-Layer Inter-Subject-Constrained Modularity Analysis (MLISMA)” method for resting-state fMRI-based connectome analysis, which can detect group consistent modules without losing valuable individual information, thus allowing assessment of individual variability in the brain network...

Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from ear...

Autism spectrum disorder (ASD) is an age- and sex-related neurodevelopmental disorder that alters the brain's functional connectivity (FC). The changes caused by ASD are associated with different age- and sex-related patterns in neuroimaging data. However, most contemporary computer-assisted ASD diagnosis methods ignore the aforementioned age-/sex-...

Synopsis Increasing studies focus on delineating the development of brain networks of infants by detecting the corresponding modular organization. However, in these papers, the adopted module detection method, i.e., maximizing modularity, has a resolution limit, such that modular structures could only be investigated at a particular scale. To addre...

Functional brain networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for Autism Spectrum Disorder (ASD) diagnosis. Typically, these networks are constructed by calculating functional connectivity (FC) between any pair of brain regions of interest (ROIs), i.e., using Pearson's correlation between...

Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly dem...

Population studies of brain function with resting-state functional magnetic resonance imaging (rs-fMRI) rely on accurate inter-subject registration of functional areas. This is typically achieved through registration using high-resolution structural images with more spatial details and better tissue contrast. However, accumulating evidence has sugg...

The O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> -methylguanine-DNA methyltransferase (MGMT) promoter methylation and isocitrate dehydrogenase 1 (IDH1) mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditiona...

Accurate delineation of gliomas from the surrounding normal brain areas helps maximize tumor resection and improves outcome. Blood-oxygen-level-dependent (BOLD) functional MRI (fMRI) has been routinely adopted for presurgical mapping of the surrounding functional areas. For completely utilizing such imaging data, here we show the feasibility of usi...