Enrico Kaden¹, Nathaniel D. Kelm², Robert P. Carson³, Mark D. Does², and Daniel C. Alexander^1
1Centre for Medical Image Computing, University College London, London, United Kingdom, ²Institute of Imaging Science, Vanderbilt University, Nashville, TN, United States, ³Departments of Neurology and Pediatrics, Vanderbilt University, Nashville, TN, United States

This work introduces a multi-compartment model for microscopic diffusion anisotropy imaging using an off-the-shelf pulse sequence achievable on standard clinical scanners. In particular, we will provide estimates of microscopic features specific to the intra- and extra-neurite compartments unconfounded by the effects of fibre crossings and orientation dispersion, which are ubiquitous in the brain. The new imaging technique is demonstrated in a large cohort of healthy young adults as well as for the detection of microstructural tissue alterations in a preclinical animal model of tuberous sclerosis complex.

Jiaying Zhang¹, Aurobrata Ghosh¹, Daniel C Alexander¹, and Gary Hui Zhang^1
1Computer Science and Centre for medical image computing, University College London, London, United Kingdom

The structure and function of human brain evolve across the lifespan. The microstructural white matter changes across lifespan have been studied using Diffusion tensor imaging. Whilst sensitive, DTI parameters have no direct tissue specificity. Here, given the availability of high-quality HCP lifespan dataset, we aim to study the lifespan trajectory of microstructural WM changes using NODDI and evaluate another NODDI fitting framework - Accelerated microstructure imaging via convex optimization (AMICO). We found U-shaped neurite density changes across lifespan and feasibility of AMICO NODDI parameters in capturing the similar lifespan trajectory as the standard fitting. 

Nicholas G Dowell¹, Simon L Evans², Sarah L King², Naji Tabet³, and Jennifer M Rusted^2
1Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom, ²Psychology, University of Sussex, Brighton, United Kingdom, ³Centre for Dementia Studies, Brighton and Sussex Medical School, Brighton, United Kingdom

The APOE-e4 gene is the best known genetic risk factor for late-onset Alzheimer's Disease. However, carriers of this gene have demonstrated behavioural differences compared to non-carriers on a number of cognitive tasks at young age. In this study, we demonstrate for the first time using the advanced diffusion imaging technique, NODDI, that there are detectable genotype-dependent structural differences in the brain of young healthy volunteers. The strongest differences are in the measure of orientation dispersion (ODI) of neurites, where e4 carriers show higher ODI than non-e4 carriers in the white matter.

Filip Szczepankiewicz¹, Carl-Fredrik Westin², Freddy Ståhlberg¹, Jimmy Lätt³, and Markus Nilsson^4
1Dept. of Medical Radiation Physics, Lund University, Lund, Sweden, ²Dept. of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States, ³Center for Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden, ⁴Lund University Bioimaging Center, Lund University, Lund, Sweden

Diffusion MRI that goes beyond DTI is challenging at 7T due to the short transverse relaxation time. We address this inherent limitation of 7T by employing asymmetric gradient waveforms for diffusion encoding, and demonstrate that imaging of microscopic diffusion anisotropy is feasible at a 7T system.

Ahmad Raza Khan¹, Andery Chuhutin¹, Ove Wiborg², Christopher D Kroenke³, Jens R Nyengaard⁴, Brian Hansen¹, and Sune Nørhøj Jespersen^1,5
1Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark, ²Centre for Psychiatric Research, Aarhus University, Aarhus, Denmark, ³Advanced Imaging Research Center, Portland, OR, United States, ⁴Stereology and Electron Microscopy Laboratory, Aarhus University, Aarhus, Denmark, ⁵Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark

Biophysical modelling of diffusion MRI data allows detection of specific tissue microstructures such as neurite density. However, histological validation of MR-derived indication of microstructural alteration is limited due extensive time labour and invasive character, even though histological validation is crucial because it remains the gold standard. The present study applies Matlab based image processing and analysis tools to compute histological neurite density to validate diffusion MRI based neurite density changes in the amygdala of chronic mild stress rat brains. The image processing and analyses provides novel tools to validate diffusion data robustly. 

Manisha Aggarwal¹, Olli Gröhn², and Alejandra Sierra^2
1Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ²Department of Neurobiology, A. I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland

We investigate changes in the temporal diffusion spectrum sampled using oscillating gradient spin-echo (OGSE) acquisitions at increasing gradient frequencies in the epileptogenic rat brain. PGSE and OGSE data at discrete oscillation frequencies were acquired from pilocarpine-treated and control rat brains (n=5 each) with a spectral resolution of 60 Hz (f = 60 Hz, 120 Hz, 180 Hz). Our findings reveal significant changes in the frequency-dependent modulation of apparent diffusion coefficient (ADC) in specific areas of the pilocarpine brain, which were found to correspond to region-specific gliosis and neuronal loss respectively. Using comparison with histological findings, our results show unique sensitivity of OGSE diffusion MRI to probe specific cellular-level alterations in the epileptogenic brain.

Nan-Jie Gong¹, Russell Dibbs², Kyle Decker², Mikhail V. Pletnikov³, and Chunlei Liu^1
1Brain Imaging and Analysis Center, Duke University, Durham, NC, United States, ²Center for In Vivo Microscopy, Duke University, Durham, NC, United States, ³Behavioral Neurobiology and Neuroimmunology Laboratory, Johns Hopkins University, Baltimore, MD, United States

DKI method provided sensitive metrics for reflecting microstructural changes in not only the anterior commissure but also relatively isotropic gray matter regions of hippocampus, cerebral cortex and caudate putamen. Further relating DKI findings to molecular compositions measured by QSM enabled clearer interpretations of myelin content and cellular density related mechanisms. Further validations that establish the relationship between imaging metrics and histological measurements such as neuronal cell body density, myelin thickness and g-ratio are needed. 

Bing Yu¹, Na Chang¹, Xiaoxue Ge¹, Yueren Wang¹, and Qiyong Guo^1
1Shengjing Hospital of China Medical University, Shenyang, China, People's Republic of

In the present study, we reconstructed the white matter skeleton of the brain using tract-based spatial statistics (TBSS) and compared differences in diffusion kurtosis imaging (DKI) parameters within the skeleton between patients sufferde from postoperative cognitive dysfunction (POCD) and healthy controls to detect white matter abnormalities in POCD.

Nathan P Skinner^1,2,3, Shekar N Kurpad^3,4, Brian D Schmit⁵, L Tugan Muftuler³, and Matthew D Budde^3,4
1Biophysics Graduate Program, Medical College of Wisconsin, Milwaukee, WI, United States, ²Medical Scientist Training Program, Medical College of Wisconsin, Milwaukee, WI, United States, ³Department of Neurosurgery, Medical College of Wisconsin, Milwaukee, WI, United States, ⁴Clement J. Zablocki Veteran's Affairs Medical Center, Milwaukee, WI, United States, ⁵Department of Biomedical Engineering, Marquette University, Milwaukee, WI, United States

Diffusion tensor imaging (DTI) is frequently applied to spinal cord injury, yet suffers from poor detection of axonal integrity changes caused by conflicting extracellular processes.  A double diffusion encoding (DDE) sequence was developed for the spinal cord to remove non-neuronal signal contribution by applying a strong diffusion weighting perpendicular to the spinal cord. A parallel diffusion gradient then sampled diffusivity along the spinal cord.  Application in a rat model showed DDE parameters outperformed DTI in sensitivity to injury severity with substantially reduced acquisition and post-processing time.  Thus, this technique shows potential for rapid, sensitive determination of spinal cord injury severity.

Benoit Scherrer¹, Damien Jacobs², Maxime Taquet^1,2, Anne des Rieux³, Benoit Macq², Sanjay P Prabhu¹, and Simon K Warfield^1
1Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States, ²ICTEAM, Universite catholique de Louvain, Louvain-La-Neuve, Belgium, ³LDRI, Université catholique de Louvain, Brussels, Belgium

The DIAMOND model has been recently proposed to model the heterogeneity of tissue compartments in diffusion compartment imaging. However, it did not enable the characterization of the intra-axonal volume fraction (IAVF), a critical measure to more accurately characterizing axonal loss in abnormal tissues. In this work we investigated mathematical extensions to DIAMOND that model both the IAVF and the heterogeneous nature of in-vivo tissue. We validated our approach using both Monte-Carlo simulations and histological microscopy with an animal model of Wallerian degeneration. We show that our novel model better predicts the signal and provides additional parameters to further describe tissues.