The RIIPL lab is a pioneering force in neuroimaging research housed within the Wake Forest University School of Medicine. Our lab research focus extends through diverse areas such as Diabetes, Dyslexia, Aging, Music, Brain Tumors, and Alzheimer's Disease, leveraging our expertise in:
- Medical 3D Printing to create anatomical models for surgical planning and education.
- Immersive Technologies including Virtual Reality (VR), Extended Reality (XR), and Spatial Computing to enhance diagnostic accuracy and therapeutic applications.
- Advanced Artificial Intelligence in medical imaging to improve the precision and speed of image analysis.
At RIIPL, we maintain a robust automated pipeline for both functional and structural image processing, supporting a broad range of scientific research and clinical studies. Our infrastructure ensures seamless management of extensive imaging datasets, aiding studies on Aging Dementia, Parkinson's Disease, traumatic brain injuries related to sports, and substance dependence.
Our team is a multidisciplinary collective of experts from Biomedical Engineering, Computer Science, Neuroradiology, and Medical Physics. We employ sophisticated analysis methods including:
- Diffusion Tensor and Kurtosis Imaging
- Neurite Orientation Dispersion and Density Imaging
- Resting-State BOLD Imaging
- Pseudo-Continuous Arterial Spin Labeling (pCASL) Imaging
- Susceptibility Weighted Imaging
The RIIPL lab is actively expanding its team and collaborations. We are currently accepting applications for graduate students and postdoctoral researchers, offering a select number of internships to undergraduate students over the summer. We welcome discussions with prospective collaborators from academia and industry who are eager to advance the frontiers of medical imaging.
- ASL - Arterial Spin Label MRI is a noninvasive method for measuring perfusion. Unlike traditional contrast-based methods, the method is completely non-invasive, it is repeatable, and provides quantitative measures of blood flow. The RIIPL lab is actively involved in technique development for ASL research and clinical applications.
- DTI - Diffusion Tensor Imaging uses MRI to measure the microscopic movement of water molecules. It provides measures of white matter axonal integrity and can be used in research studies of diseased and normal states, as well as for visualization of white matter tracts for clinical neurosurgical guidance.
- FMRI - Functional MRI uses changes in local blood oxygen content to identify areas in the brain involved in performance of specific tasks. Resting state fMRI can be used to identify underlying brain networks, as well as changes related to disease or state-based modification of brain connectivity.
- Structural MRI - Structural MRI analysis methods use high-resolution volumetric MRI data to identify differences in brain structure (e.g., grey matter, cortical thickness) between individuals or groups of subjects.
- MEG - Magnetoencephalography (MEG) is a non-invasive imaging technique that measures the magnetic fields produced by the electrical currents associated with neuronal function. It provides extremely high temporal resolution, and is complementary to structural and functional MRI methods. The RIIPL lab has developed a high-throughput fully automated MEG analysis pipeline for high density source-space analysis of resting state MEG data, incorporating a variety of head models, forward models, beamformers, and connectivity metrics.
- Machine Learning - Machine learning has emerged as a powerful method of performing spatial pattern analysis and data classification. The multivariate nature of these approaches allows them to take into consideration correlations present in the data, overcoming limitations of standard analytical approaches. The prediction capabilities of machine learning methods are ideal for many clinical applications.
- Network Analysis - Graph Theory based analysis of brain imaging data models the brain as a complex network represented as a collection of nodes and connecting edges. Nodes are typically defined as voxels (or regions of interest) in imaging space, and the edges are identified based on some connectivity measure between nodes (e.g. correlation coefficient). This framework allows the application of a variety of graph-theory based metrics to brain imaging data in order to identify and understand differences in brain connectivity between groups. While this has typically been applied to resting state fMRI data, graph theoretic methods can also be used with structural imaging data (e.g. DTI, as well as T1-weighted MRI).
2024
- Assessing Cerebrovascular Reactivity (CVR) in Rhesus Macaques (Macaca Mulatta) Using a Hypercapnic Challenge and Pseudo-Continuous Arterial Spin Labeling (pCASL).
Brendan J. Johnson, Megan E. Lipford, Richard A. Barcus, John D. Olson, George W. Schaaf, Rachel N. Andrews, Jeongchul Kim.
NeuroImage. 2024 January 1. doi:10.1016/j.neuroimage.2023.120491. https://doi.org/gtnpxs.
- Automated Extraction of Heart Rate Variability From Magnetoencephalography Signals.
Ryan C. Godwin, William C. Flood, Jeremy P. Hudson, Marc D. Benayoun, Michael E. Zapadka, Ryan L. Melvin, Christopher T. Whitlow.
Heliyon. 2024 March 1. doi:10.1016/j.heliyon.2024.e26664. https://doi.org/gtnpxf.
- The Influence of White Matter Lesion Volume on Hippocampal Shape in Patients Along the Alzheimer’s Disease Continuum .
Kawas, M.I, Madi, K.V., Lockhart, S.N., Hudson, J.P., Barcus, R.A., Bateman, J.R., Craft, S., Wolfe, S.Q. and Whitlow, C.T.
Alzheimer's Dement. 2023, 19: e079954. (https://doi.org/10.1002/alz.079954).
- Morphometric Differences in Hippocampal Shape Across the Alzheimer’s Disease Spectrum
Madi, K.V., Kawas, M.I, Lockhart, S.N., Barcus, R.A., Hudson, J.P., Bateman, J.R., Craft, S., Wolfe, S.Q. and Whitlow, C.T. (2023),
Alzheimer's Dement., 19: e079950. (https://doi.org/10.1002/alz.079950).
2023
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Rapid-Onset Dystonia-Parkinsonism is Associated With Reduced Cerebral Blood Flow Without Gray Matter Changes.
Christopher T. Whitlow, Kyle M. Atcheson, Beverly M. Snively, Jared F. Cook, Jeongchul Kim, Ihtsham U. Haq, Kathleen J. Sweadner.
Frontiers in Neurology. 2023 January 26. doi:10.3389/fneur.2023.1116723. https://doi.org/gtnpxp.
- Parametric Cerebral Blood Flow and Arterial Transit Time Mapping Using a 3D Convolutional Neural Network.
Donghoon Kim, Megan E. Lipford, Hongjian He, Qiuping Ding, Vladimir Ivanovic, Samuel N. Lockhart, Suzanne Craft.
Magnetic Resonance in Medicine. 2023 April 24. doi:10.1002/mrm.29674. https://doi.org/gtnpxj.
- Translating Radiology Reports Into Plain Language Using ChatGPT and GPT-4 With Prompt Learning: Results, Limitations, and Potential.
Qing Lyu, Josh Tan, Michael E. Zapadka, Janardhana Ponnatapura, Chuang Niu, Kyle J. Myers, Ge Wang.
Visual Computing for Industry, Biomedicine, and Art. 2023 May 18. doi:10.1186/s42492-023-00136-5. https://doi.org/gtnpxr.
- Total-Body Irradiation Alters White Matter Volume and Microstructural Integrity in Rhesus Macaques.
Brendan J. Johnson, Richard A. Barcus, John D. Olson, Megan E. Lipford, Rachel N. Andrews, Greg O. Dugan, Janet A. Tooze.
International Journal of Radiation Oncology*Biology*Physics. 2023 November 1. doi:10.1016/j.ijrobp.2023.11.014. https://doi.org/gtnpxh.
- Effects of Multiple Anesthetic Exposures on Rhesus Macaque Brain Development: A Longitudinal Structural MRI Analysis.
Jeongchul Kim, Richard Barcus, Megan E. Lipford, Hongyu Yuan, Douglas G. Ririe, Youngkyoo Jung, Roza M. Vlasova.
Cerebral Cortex. 2023 December 23. doi:10.1093/cercor/bhad463. https://doi.org/gtnpxg.
2022
- Corrigendum: Relationship Between Cerebrovascular Reactivity and Cognition Among People With Risk of Cognitive Decline.
Donghoon Kim, Timothy M. Hughes, Megan E. Lipford, Suzanne Craft, Laura D. Baker, Samuel N. Lockhart, Christopher T. Whitlow.
Frontiers in Physiology. 2022 September 23. doi:10.3389/fphys.2022.1020999. https://doi.org/gtnpxm.
- A Transformer-Based Deep-Learning Approach for Classifying Brain Metastases Into Primary Organ Sites Using Clinical Whole-Brain MRI Images.
Qing Lyu, Sanjeev V. Namjoshi, Emory McTyre, Umit Topaloglu, Richard Barcus, Michael D. Chan, Christina K. Cramer.
Patterns. 2022 November 1. doi:10.1016/j.patter.2022.100613. https://doi.org/gtnpxk.
2021
- Relationship Between Cerebrovascular Reactivity and Cognition Among People With Risk of Cognitive Decline.
Donghoon Kim, Timothy M. Hughes, Megan E. Lipford, Suzanne Craft, Laura D. Baker, Samuel N. Lockhart, Christopher T. Whitlow.
Frontiers in Physiology. 2021 May 31. doi:10.3389/fphys.2021.645342. https://doi.org/gp39js.
- Dual-Energy Parathyroid 4D-CT: Improved Discrimination of Parathyroid Lesions from Thyroid Tissue Using Noncontrast 40-keV Virtual Monoenergetic Images.
P.M. Bunch, A.A. Pavlina, M.E. Lipford, J.R. Sachs.
American Journal of Neuroradiology. 2021 September 2. doi:10.3174/ajnr.A7265. https://doi.org/gtnpxv.
- Modified Mediterranean Ketogenic Diet Resolves Default Mode Network Connectivity Differences Between Adults With Normal and Impaired Cognition.
Mohammad I. Kawas, Samuel N. Lockhart, Jeongchul Kim, Bryan J. Neth, Richard A. Barcus, Tim M. Hughes, Kiran K. Solingapuram Sai.
Alzheimer's & Dementia. 2021 December 1. doi:10.1002/alz.056711. https://doi.org/gtnpx4.
- Effects Of Diet and Social Status on White Matter Integrity in Female Cynomolgus Monkeys.
Jeongchul Kim, Richard A. Barcus, Brett M. Frye, Samuel N. Lockhart, Suzanne Craft, Tom Register, Rachel N. Andrews.
Alzheimer's & Dementia. 2021 December 1. doi:10.1002/alz.055761. https://doi.org/gtnpx5.
- Cortical Gray Matter Volume and Working Memory in a NHP Model of AD-Like Neuropathology.
Brett M. Frye, Suzanne Craft, Thomas C. Register, Jeongchul Kim, Christopher T. Whitlow, Richard A. Barcus, Samuel N. Lockhart.
Alzheimer's & Dementia. 2021 December 1. doi:10.1002/alz.056364. https://doi.org/gtnpx6.
2020
- Rhesus Macaque Brain Developmental Trajectory: A Longitudinal Analysis Using Tensor-Based Structural Morphometry and Diffusion Tensor Imaging.
Jeongchul Kim, Youngkyoo Jung, Richard Barcus, Jocelyne H. Bachevalier. Mar M. Sanchez, Michael A. Nader, Christopher T. Whitlow.
Cerebral Cortex. 2020 April 2. doi:10.1093/cercor/bhaa015. https://doi.org/gtnpxn.
- Mediterranean Versus Western Diet Effects on Cerebral Cortical Thickness and Volume in Cynomolgus Macaques.
Carol A. Shively, Brett M. Frye, Thomas C. Register, Rachel N. Andrews, Susan E. Appt, Mara Z. Vitolins, Beth Uberseder.
Alzheimer's & Dementia. 2020 December 1. doi:10.1002/alz.044554. https://doi.org/gtnpx3.
- Diet, Psychosocial Stress, and Alzheimer's Disease-Related Neuroanatomy in Female Nonhuman Primates.
Brett M. Frye, Suzanne Craft, Thomas C. Register, Rachel N. Andrews, Susan E. Appt, Mara Z. Vitolins, Beth Uberseder.
Alzheimer's & Dementia. 2020 December 3. doi:10.1002/alz.12232. https://doi.org/gstm58.
2019
- P4-323: Multi-Scale Analysis of White Matter Degeneration in Cognitively Normal and MCI Adults.
Jeongchul Kim, Youngkyoo Jung, Richard Barcus, Samuel N. Lockhart, Laura D. Baker, Suzanne Craft, Christopher T. Whitlow.
Alzheimer's & Dementia. 2019 July 1. doi:10.1016/j.jalz.2019.06.3993. https://doi.org/gtnpxx.
2018
- P3-414: Longitudinal Analysis of Microstructural White Matter Change in MCI Following A 6-Month Aerobic Exercise Intervention.
Jeongchul Kim, Youngkyoo Jung, Richard Barcus, Suzanne Craft, Laura D. Baker, Christopher T. Whitlow.
Alzheimer's & Dementia. 2018 July 1. doi:10.1016/j.jalz.2018.06.1777. https://doi.org/gtnpxz.
2017
- [P3–318]: Biomechanical Characterization of Brain Atrophy in Cognitively Normal and MCI Groups.
Jeongchul Kim, Richard Barcus, Youngkyoo Jung, Laura D. Baker, Suzanne Craft, Christopher T. Whitlow.
Alzheimer's & Dementia. 2017 July 1. doi:10.1016/j.jalz.2017.06.1533. https://doi.org/gtnpx2.
Download past RIPL Laboratory publications (pdf)
