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Faculty Research Labs

Wang Lab

The Laboratory of Nian Wang, PhD, is focused on developing novel quantitative MRI techniques and analysis methods on CNS and musculoskeletal system. The research group has developed advanced MRI techniques to detect the early change of Multiple Sclerosis, Alzheimer’s disease, and ASD, including UTE, multi-component T2*, quantitative susceptibility mapping (QSM) and compressed sensing for high-resolution diffusion MRI.

Research Updates

To stay current on the medical research at IU School of Medicine’s statewide campuses, follow the IU School of Medicine research blog, where investigators throughout the school’s academic departments post updates about their work.


Cover Articles


News/Press Releases

  • 2022 Gill Imaging Award

    Sept. 14, 2022:  High-resolution Tract Density Imaging was awarded the 2022 Gill Imaging Award at the 2022 Gill Symposium & Awards.

  • Gill Symposium Presentation

    Sept. 14, 2022:  Dr. Surendra Maharjan presented his work, "High-resolution MRI in a novel mouse model of Alzheimer’s disease," at the 2022 Gill Symposium & Awards.

  • NeuroImage Manuscript 
    "Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain" This manuscript has just been accepted by NeuroImage. Congratulations to Stephanie, Suren and other contributors!

  • BioMed Central Best Science Research Image
    Rainbow Mouse Kidney Named Year's Best Science Research Image: Nian Wang won the annual 'Research in Progress' photo competition from BioMed Central (BMC), a scientific publisher.

  • September 2021 ISMRM Member Spotlight
    ISMRM & SMRT Member Spotlight for September 2021: Dr. Surendra Maharjan 
    Dr. Surendra Maharjan, postdoctoral fellow for the Nian Lab, was selected as the September 2021 Member Spotlight for the International Society for Magnetic Resonance in Medicine (ISMRM).


HUman Brain Tractography

Human Brain tractography using diffusion MRI. 

LIGament Tractography

Rat Ligaments tractography using high-resolution diffusion MRI.

Mouse Brain Tractography

Mouse Brain tractography using high-resolution diffusion MRI

Mouse Heart Tractography

Mouse Heart tractography using high-resolution diffusion MRI

Active Research

  • Compressed sensing for high-resolution diffusion MRI (Wang et al., BS&F, 2018)
    Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics showed consistently low error across the whole brain even with 8.0 times acceleration. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.

  • Advanced diffusion MRI models to probe the tissue microstructure (Wang et al., NeuroImage, 2020)

    In the present study, we acquired high spatial resolution MRI datasets, including diffusion MRI (dMRI) at 25 μm isotropic resolution and quantitative susceptibility mapping (QSM) at 21.5 μm isotropic resolution to validate with conventional mouse brain histology. Diffusion weighted images (DWIs) show better delineation of cortical layers and glomeruli in the olfactory bulb than fractional anisotropy (FA) maps. However, among all the image contrasts, including quantitative susceptibility mapping (QSM), T1/T2 images and DTI metrics, FA maps highlight unique laminar architecture in sub-regions of the hippocampus, including the strata of the dentate gyrus and CA fields of the hippocampus. Our findings demonstrate that MRI at microscopic resolution deliver a three-dimensional, non-invasive and non-destructive platform for characterization of fine structural detail in both gray matter and white matter of the mouse brain.

  • Brain connectivity analysis for neurodegenerative diseases (Johnson et al., J. Comp. Neuro., 2019)
    Methods have been developed to allow quantitative connectivity of the whole fixed mouse brain by means of magnetic resonance imaging (MRI). Compressed sensing has been integrated into high performance reconstruction and post processing pipelines allowing acquisition of a whole mouse brain connectome in <12 hr. The methods have been streamlined to provide high-fidelity, whole mouse brain connectomes as a routine study. The data package provides holistic insight into the mouse brain with anatomic definition at the meso-scale, quantitative volumes of subfields, scalar DTI metrics, and quantitative tractography.

  • Heritability study of different mouse strains (Wang et al., NeuroImage, 2020)
    Genome-wide association studies have demonstrated significant links between human brain structure and common DNA variants. Similar studies with rodents have been challenging because of smaller brain volumes. Using high field MRI (9.4 T) and compressed sensing, we have achieved microscopic resolution and sufficiently high throughput for rodent population studies. We generated whole brain structural MRI and diffusion connectomes for four diverse isogenic lines of mice (C57BL/6J, DBA/2J, CAST/EiJ, and BTBR) at spatial resolution 20,000 times higher than human connectomes. We measured narrow sense heritability (h2) I.e. the fraction of variance explained by strains in a simple ANOVA model for volumes and scalar diffusion metrics, and estimates of residual technical error for 166 regions in each hemisphere and connectivity between the regions. Nearly 150 of the connection profiles were statistically different between the C57BL/6J, DBA/2J, and CAST/EiJ lines. Microscopic whole brain MRI/DTI has allowed us to identify significant heritable phenotypes in brain volume, scalar DTI metrics, and quantitative connectomes.

  • Tractography in Cartilage (Wang et al., Mag. Res. Med., 2020)
    To evaluate the complex fiber orientations and 3D collagen fiber network of knee joint connective tissues, including ligaments, muscle, articular cartilage, and meniscus using high spatial and angular resolution diffusion imaging. To better resolve the crossing fibers, the b value should be great than or equal to 1000 s/mm2. The tractography exhibited apparent difference between DTI and GQI in connective tissues with more complex collagen fibers network, such as cartilage and meniscus. High-resolution diffusion imaging with GQI method can trace the complex collagen fiber orientations and architectures of the knee joint at microscopic resolution.

  • 3D collagen fiber structure in ligaments (Wang et al., Cartilage, 2021)
    Fractional anisotropy (FA) and mean diffusivity (MD) were compared at different angular resolution. Tractography was quantitatively evaluated at different b values and angular resolutions in cartilage, ligament, meniscus, and growth plate. Compared with FA, MD showed less sensitivity to the angular resolution. The quantitative measurements of tract length and track volume were strongly dependent on angular resolution and b value. To obtain consistent DTI outputs and tractography in knee joint, the scan may require a proper b value (ranging from 500 to 1500 s/mm2) and sufficient angular resolution (>14) with signal-to-noise ratio >10.

Research Team

PRincipal Investigator
48212-Wang, Nian

Nian Wang, PhD

Assistant Professor of Radiology & Imaging Sciences

Read Bio

Megan Jewett

Lab Technician

Abigail Wallace

Research Assistant

60024-Maharjan, Surendra

Surendra Maharjan, MSC, PhD

Postdoctoral Fellow in Radiology & Imaging Sciences

Read Bio

faculty image

Samuel Olubakinde

Research Assistant

Research Funding/Grants

AD Pre-Clinical Translational Science Grant, Wang (PI), 2022-2023
Quantify ALZ-801 Effect on Whole Brain Beta-amyloid Removal and Connectivity Recovery in 5xFAD and SAA Mice Using High-resolution MRI

R01 NS079635, NIH, Liu (PI), 2014-2019
Susceptibility MRI as a marker for diagnosis and clinical disability in MS P41 EB015897, NIH, Johnson (PI), 2016-2018
Integrated Center for In Vivo Microscopy

R01 NS096720, NIH, Johnson (PI), 2016-2020
Waxholm Space for Rodent Neuroinformatics

Research Project, Duke University, Wang (PI), 2019-2020
Quantification of Knee Degradation using High Resolution Diffusion MRI

R01 NS125020, NIH, Wang (PI), 2022-2027
Characterization of Whole Brain Demyelination and Axon Damage Using High-resolution Magnetic Resonance Imaging

Indiana Center for Diabetes and Metabolic Diseases Pilot and Feasibility Grant,  Wang (PI), 2022-2023 
Whole Brain Connectivity Alterations in Metabolic Syndrome Swine Using High-resolution Magnetic Resonance Imaging

Recent Publications

  • 2022

    Maharjan S, Tsai PA, Lin BP, Ingraham C, Jewett RM, Landreth EG, Oblak LA, Wang N*. “Age-dependent microstructure alterations in 5xFAD mice by high-resolution diffusion tensor imaging”, Frontiers in Neuroscience, 2022, 16:964654

    Wang N*, Wen QT, Maharjan S, Mirando JA, Qi Y, Hilton JM, Spritzer EC. “Magic angle effect on diffusion tensor imaging in ligament and brain”, Magnetic Resonance Imaging, 2022, in press.

    Shen JK, Zhao Q, Qi Y, Cofer G, Johnson GA, Wang N*.  “Tractography of Porcine Meniscus Microstructure using High-resolution Diffusion Magnetic Resonance Imaging”, Frontiers in Endocrinology, 2022, 13:876784

    Crater S, Maharjan S, Qi Y, Zhao Q, Cofer G, Cook JJ, Johnson GA, Wang N*. “Resolution and b value dependent Structural Connectome in ex vivo Mouse Brain”, NeuroImage, 2022, 119199. 

    Yu ZY, Zhou X, Liu ZY, Pastrana-Gomez V, Liu Y, Guo MZ, Tian L, Nelson JT, Wang N, Mital S, Chitayat D, X Wu CJ, Rabinovitch M, Wu MS, Snyder PM, Miao YF, Gu MX. “KMT2D-NOTCH Mediates Coronary Abnormalities in Hypoplastic Left Heart Syndrome”, Circulation Research, 2022, 131

  • 2021
    1. Garrett A, Rakhilin N, Wang N, Mckey J, Cofer G, Anderson R, Capel B, Johnson GA, Shen X*. “Mapping the peripheral nervous system in the whole mouse via compressed sensing tractography”, Journal of Neural Engineering, 2021, 18, 044002
    2. Zhao Q, Ridout R, Shen JK, Wang N*. “Effects of Angular Resolution and b Value on Diffusion Tensor Imaging in Knee Joint”, Cartilage, 2021 (In Press)
  • 2020
    1. Wang N, Anderson R, Ashbrook David, Gopalakrishnan V, Park Y, Priebe C, Qi Y, Laoprasert R, Vogelstein J, Williams RW, Johnson GA*. “Variability and heritability of mouse brain structure: microscopic MRI atlases and connectomes for diverse strains”, NeuroImage, 2020, 222, 117274 (Cover Article)
    2. Wang N*, White L, Cofer G, Qi Y, Anderson R, Johnson GA*. “Cytoarchitecture of the mouse brain by high resolution diffusion magnetic resonance imaging”, NeuroImage, 2020, 216, 116876
    3. Wang N, Farid B, Xia Y*. “Resolution-dependent Influences of Compressed Sensing in Quantitative T2 Mapping of Articular Cartilage”, NMR in Biomedicine, 2020, 10, e4260
    4. Wang N*, Mirando JA, Cofer G, Qi Y, Hilton JM, Johnson GA. “Characterization Complex Collagen Fiber Architecture in Knee Joint Using High Resolution Diffusion Imaging”, Magnetic Resonance in Medicine, 2020, 84, 908-919.
  • 2019
    1. Wang N*, Zhang J, Cofer G, Qi Y, Anderson R, Johnson GA*. “Neurite orientation dispersion and density imaging of mouse brain microstructure”, Brain Structure and Function, 2019, 224, 1797-1813
    2. Wang N, Zhuang J, Wei HJ, Dibb R, Qi Y, Liu CL*. “Probing demyelination and remyelination of the cuprizone mouse model using multimodality MRI”, Journal of Magnetic Resonance Imaging, 2019, 50, 1852-1865
    3. Guan X, Huang P, Zeng Q, Liu C, Wei H, Xuan M, Gu Q, Xu X, Wang N, Yu X, Luo X, Zhang M*. “Quantitative susceptibility mapping as a biomarker for evaluating white matter alterations in Parkinson’s disease”, Brain Imaging and Behavior, 2019, 13, 220-231
    4. Johnson A#*, Wang N#, Anderson R, Chen M, Cofer G, Gee J, Pratson F, Tustison N, Whilte L. “Whole mouse brain connectivity”, Journal of comparative Neurology, 2019, 527, 2146-2157 (# equal contribution)
    5. Wang N*, Mirando JA, Cofer G, Qi Y, Hilton JM, Johnson GA. “Diffusion Tractography of the Rat Knee at Microscopic Resolution”, Magnetic Resonance in Medicine, 2019, 81, 3775-3786 (Cover Article)
    6. Wang N, Farid B, Xia Y. “Experimental Influences to the Accurate Measurement of Cartilage Thickness in MRI”, Cartilage, 2019, 10, 278-287
  • 2018
    1. Wang N, Anderson R, Cofer G, Qi Y, Liu CL, Johnson GA*. “Accelerating 3D high resolution whole brain quantitative susceptibility mapping using compressed sensing”, Physics in Medicine and Biology, 2018, 63, 245002
    2. Wang N, Anderson R, Badea A, Cofer G, Dibb R, Qi Y, Johnson GA*. “Whole mouse brain structural connectomics using magnetic resonance histology”, Brain Structure and Function, 2018, 223, 4323-4335
    3. Wang N, Badar F, Xia Y*. “Quantitative determination of GAG concentration in cartilage by microscopic MRI”, Magnetic Resonance in Medicine, 2018, 79, 3163-3171
    4. Privratksy J, Wang N, Qi Y, Ren JF, Morris B, Hunting J, Johnson GA, Crowley S*. “Dynamic contrast-enhanced (DCE) MRI promotes early detection of toxin-induced acute kidney injury (AKI)”, American Journal of Physiology-Renal Physiology, 2018, 316, F351-359


We are actively looking for candidates who are self-motivated, passionate about brain structure and function, interested in Neuroimaging and Neurodegenerative diseases. For rotation students and PhD student, experience in MRI and/or image processing is useful but not essential. Postdoc applicants must have (or expect to receive) a PhD, MD, or MD/PhD degree. Prior experience in either 1) MRI pulse sequence development, image reconstruction, or imaging data analysis; or 2) neurodegenerative disease models, such as Alzheimer’s disease, Multiple Sclerosis, and autism spectrum disorder. To apply, please send a single email containing you CV, a short statement describing your qualifications for this position and research interests, as well as contact information for three references to Dr. Nian Wang at