MRI-measured conduction velocity predicts N170 latency as a function of autism diagnosis
Poster No:
347
Submission Type:
Abstract Submission
Authors:
Campbell Coleman1, Madelyn Nance1, Zachary Jacokes1, John Van Horn1, Kevin Pelphrey1, Benjamin Newman1, Meghan Puglia1
Institutions:
1University of Virginia, Charlottesville, VA
First Author:
Co-Author(s):
Introduction:
Our lab previously developed MRI-derived aggregate conduction velocity (ACV) as a voxelwise estimate of action potential speed. ACV captures the contributions of myelin thickness and axonal diameter, which promote faster action potential transmission1. Theoretical latency, a measure of action potential transit time, can be derived from tract length and ACV. In this study, we aim to compare theoretical latency estimates to actual electrical signal transmission, as measured by EEG. By utilizing the face processing network, which includes the primary visual cortex (V1), the fusiform face area (FFA), and the posterior superior temporal sulcus (pSTS)2, we assess if theoretical latency predicts experimental latency of the N170, a marker of face processing in EEG3. Longer N170 latency is associated with Autism Spectrum Disorder (ASD)4, and we explore the ability of theoretical latency to predict N170 latency in a ASD and non-ASD adolescent sample.
Methods:
ASD (n = 62) and control participants (n = 63) viewed faces and objects in a social reward task while undergoing EEG. EEG data processing was completed using the Automated Pipe-Line for the Estimation of Scale-wise Entropy from EEG data5. N170 (experimental) latency was defined as the negative peak occurring between 170 to 270 milliseconds after stimulus onset over electrodes in the right hemisphere. Participants underwent T1-, T2-weighted and diffusion MRI imaging, images were pre-processed and tractography performed according to previous work6,7. ACV was calculated using the axonal volume fraction and myelin-volume fraction8. Masks for right fusiform face area (rFFA), right posterior superior temporal sulcus (rpSTS), and bilateral primary visual cortex (V1) were derived from Neurosynth meta-analyses9. The tracts terminating in both ROIs were identified, and mean tract length and mean ACV within voxels traversed by tracts were measured. Theoretical latency was calculated by dividing length by ACV. An AIC regression analysis identified the best predictors of N170 latency based on phenotypic and imaging metrics along V1 to rFFA and rpSTS pathways, including theoretical latency, age, sex, and brain volume.
·Figure 1: Overlaid tracts from entire cohort showing tractography between bilateral V1 and either right FFA (red) or right pSTS (blue). Areas where tracts going to both regions is colored purple.
Results:
AIC model selection algorithms were applied based on diagnosis (ASD, controls, both), stimulus (faces or objects), and ROI pathway (V1 to rpSTS or rFFA), resulting in 12 models. In each model, theoretical latency and age were always positively and negatively associated with N170 latency, respectively. When all participants were combined, N170 latency was associated with V1 to rFFA theoretical latency(p<0.05) and age (p<0.05, overall model: p<0.05, adj-R2 = .213) and separately with V1 to rpSTS theoretical latency and age (p<0.05, overall model: p<0.05, adj-R2 = .211). Within controls, N170 latency was significantly predicted by V1 to rFFA theoretical latency (p<0.05) and age (p < 0.05; overall model: p<0.05, adj-R2 = 0.291) but not V1 to rpSTS theoretical latency. Within ASD, N170 latency was best fit by a model including an interaction between age and sex (n.s.) and age separately (n.s.), sex separately (p < 0.05, adj-R2 = 0.133), and theoretical latency along V1 to rpSTS (n.s.) (overall model: p < 0.05, adj-R2 = .195). Theoretical latency along either pathway did not predict experimental latency to objects.
·Figure 2: Theoretical latency vs. experimental N170 latency faceted by ROI and stimulus-type. Blue points signify ASD participants and orange points signify control participants.
Conclusions:
MRI-derived theoretical latency was able to predict N170 latency to faces rather than objects along the two socially salient pathways. Neurotypical controls exhibited a markedly stronger relationship between V1 to rFFA theoretical latency and N170 latency than ASD, suggesting that the relationship between action potential transmission speed and axonal diameter/myelination may be altered in ASD. This study further captured a face-specific, ASD diagnosis-dependent utilization of V1 to rFFA white matter pathways in generating the N170, and suggests action potential transmission dynamics may be altered in ASD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
EEG/MEG Modeling and Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 2
Keywords:
Autism
Development
Electroencephaolography (EEG)
MRI
Myelin
Neurological
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
2. Berman, S., Filo, S., & Mezer, A. A. (2019). Modeling conduction delays in the corpus callosum using MRI-measured g-ratio. NeuroImage, 195, 128–139. https://doi.org/10.1016/j.neuroimage.2019.03.025
3. Kang, E., Keifer, C. M., Levy, E. J., Foss-Feig, J. H., McPartland, J. C., & Lerner, M. D. (2018). Atypicality of the N170 Event-Related Potential in Autism Spectrum Disorder: A Meta-Analysis. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(8), 657–666. https://doi.org/10.1016/j.bpsc.2017.11.003
4. Newman, B. T., Dhollander, T., Reynier, K. A., Panzer, M. B., & Druzgal, T. J. (2020). Test-retest reliability and long-term stability of 3-tissue constrained spherical deconvolution methods for analyzing diffusion MRI data. Magnetic Resonance in Medicine, 84(4), 2161–2173. https://doi.org/10.1002/mrm.28242
5. Puglia, M. H., Slobin, J. S., & Williams, C. L. (2022). The automated preprocessing pipe-line for the estimation of scale-wise entropy from EEG data (APPLESEED): Development and validation for use in pediatric populations. Developmental Cognitive Neuroscience, 58, 101163. https://doi.org/10.1016/j.dcn.2022.101163
6. Rushton, W. A. H. (1951). A theory of the effects of fibre size in medullated nerve. The Journal of Physiology, 115(1), 101–122.
7. Skyberg, A. M., Newman, B. T., Graves, A. J., Goldstein, A. M., Brindley, S. R., Kim, M., Druzgal, T. J., Connelly, J. J., & Morris, J. P. (2023). An epigenetic mechanism for differential maturation of amygdala–prefrontal connectivity in childhood socio-emotional development. Translational Psychiatry, 13(1), 91.
8. Wang, X., Zhen, Z., Song, Y., Huang, L., Kong, X., & Liu, J. (2016). The Hierarchical Structure of the Face Network Revealed by Its Functional Connectivity Pattern. The Journal of Neuroscience, 36(3), 890–900. https://doi.org/10.1523/JNEUROSCI.2789-15.2016
9. Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665–670. https://doi.org/10.1038/nmeth.1635