Assessing Brain Microstructure in Juvenile Myoclonic Epilepsy using NODDI
Poster No:
2111
Submission Type:
Abstract Submission
Authors:
Camille Garcia Ramos1, Veena Nair2, Anusha Adluru1, Daniel Chu2, Andrew Alexander1, Vivek Prabhakaran2, Bruce Hermann1, Nagesh Adluru2, Aaron Struck1
Institutions:
1UW-Madison, Madison, WI, 2University of Wisconsin-Madison, Madison, WI
First Author:
Co-Author(s):
Introduction:
Juvenile Myoclonic Epilepsy (JME) is the most common idiopathic/genetic generalized epilepsy (IGE), comprising 10% of all epilepsies. JME is characterized by generalized epileptiform discharges, generalized tonic-clonic seizures, myoclonus, and seizure onset in adolescence. Even when structural changes are not apparent on routine MRIs it is now evident that brain network can be disrupted in JME relative to healthy controls. Such brain differences may arise from microstructural changes leading to changes at the macrostructural connectivity disruptions. In this study, we used connectome quality multi-shell diffusion weighted imaging (msDWI) and the neurite orientation dispersion and density imaging (NODDI) model to investigate cortical microstructural alterations in JME.
Methods:
Data and pre-processing: MRI images were acquired on GE 3 T wide bore with 48-ch head coil, from n=49 JME participants (20.2±3.4 years) and n=25 healthy controls (HC) (17.8±3.9 years) between 12-25 years. Briefly, msDWI data were acquired using multiband EPI (Moeller et al. 2010) with slice acceleration factor 3 and with opposite phase-encoding polarity (AP and PA). The acquisition of 2 reversed phase-encoding directions makes it possible to eliminate susceptibility distortions to a great extent. Collectively, these factors result in a much-improved characterization of WM connectivity and identification of aberrant patterns (Sotiropoulos et al. 2013). The diffusion-weighted images were acquired at b = 1,000 s·mm-2 and b = 2,000 s·mm-2 in an alternating fashion. There were two sets of protocol: 1) with 38 directions at 1,000 s·mm-2, 37 directions at b = 2,000 s·mm-2, and 9 b=0 volumes; 2) 50 directions at both 1,000 s·mm-2 and b = 2,000 s·mm-2, and 5 b=0 volumes. The data were pre-processed using DESIGNER (Ades-Aron et al., 2018) guidelines and NODDI measures were estimated using DMIPY (Fick et al., 2019).
NODDI measures such as NDI (neurite density index) and ODI (orientation dispersion index) were calculated at the gray-matter (GM) boundary using GBSS (gray-matter based spatial statistics) (see Figure 1 for conceptual overview) framework (Vogt et al. 2020). The NODDI data projected onto the gray matter skeleton were harmonized using NeuroComBat to account for the protocol differences (Garcia-Ramos et al. 2023). Permutation testing with n=10000 permutations were used to conduct the statistical analysis with threshold free cluster enhancement (Winkler et al., 2014) for family-wise error corrections.
NODDI measures such as NDI (neurite density index) and ODI (orientation dispersion index) were calculated at the gray-matter (GM) boundary using GBSS (gray-matter based spatial statistics) (see Figure 1 for conceptual overview) framework (Vogt et al. 2020). The NODDI data projected onto the gray matter skeleton were harmonized using NeuroComBat to account for the protocol differences (Garcia-Ramos et al. 2023). Permutation testing with n=10000 permutations were used to conduct the statistical analysis with threshold free cluster enhancement (Winkler et al., 2014) for family-wise error corrections.
Results:
Neurite density was significantly higher throughout frontal and cingulate regions as well as insular areas (Figure 2, left), along with significantly higher orientation dispersion of neurites (Figure 2, right) in bilateral frontal areas, posterior cingulate and left temporal regions compared to controls. These results support the hypothesis that defective neuronal pruning might be present in JME which enables hyperexcitable synapses in the brain (Meencke and Janz, 1984). Abnormalities in brain function, cortical thickness, positron emission tomography and EEG have been found on JME at the frontal lobe and cingulate areas, which are the most prominent areas of significant differences in this study (see Wolf et al., 2015 for a review). These findings suggest that JME is associated with microstructural deviations compared to controls throughout diverse gray matter brain regions.
Conclusions:
Preliminary results from the JMECP demonstrate significant gray matter microstructural differences in NDI and ODI in patients with JME. The general pattern demonstrates increased NDI and to a lesser extent increased ODI within frontal regions. These suggest a less organized and hyperconnected neural architecture within the frontal gray matter-the region partially related to seizure generation in JME. Further investigation is needed to determine how these changes relate to clinical and cognitive outcomes in JME.
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 1
Keywords:
Cortex
Epilepsy
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
Fick, R.H.J. et al., (2019), The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy, Frontiers in Neuroinformatics, vol. 13, no. 64.
Moeller, S. et al., (2010), Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI, Magnetic Resonance in Medicine, vol. 63, no. 5, pp. 1144-53.
Sotiropoulos, S.N. et al., (2013), Advances in diffusion MRI acquisition and processing in the Human Connectome Project, Neuroimage, vol. 80, pp. 125-43.
Vogt, N.M. et al., (2020), Cortical Microstructural Alterations in Mild Cognitive Impairment and Alzheimer’s Disease Dementia, Cerebral Cortex, vol. 30, no. 5, pp. 2948–2960.
Garcia-Ramos, C. et al., (2023), Multi-shell connectome DWI-based graph theory measures for the prediction of temporal lobe epilepsy and cognition, Cerebral Cortex, vol. 33, no. 12, pp. 8056-8065.
Meencke, H.J. et al., (1984), Neuropathological findings in primary generalized epilepsy: a study of eight cases, Epilepsia, vol. 25, pp. 8-21.
Winkler, A.M. et al., (2014), Permutation inference for the general linear model, Neuroimage, vol. 92, no. 100, pp. 381-97.
Wolf, P. et al. (2015), Juvenile myoclonic epilepsy: A system disorder of the brain. Epilepsy Research, vol. 11, pp. 2-12.