Neurology Commons^™

Insular And Amygdala Origin Seizures: Unmasked With Electrocorticography (Ecog), Younes Motii, Arichenaramanathan Manmatharayan, Parthasarathi Chamiraju, Mona Elsayed

Medical Student Research Symposium

Insular and Amygdala Origin Seizures: Unmasked with Electrocorticography (ECOG)

Introduction:

Insula and amygdala have a large network of connections to other cortical regions. Seizures originating from these structures may become evident only after propagation to other structures. High degree of attention must be paid to auras and seizure semiology in patients with medically intractable epilepsy.

Methodology:

We have described a series of three adult male patients with focal intractable epilepsy who did not have structural lesions on Magnetic Resonance Imaging (MRI). Patients underwent MRI, Positron Emission Tomography (PET) scan, scalp electroencephalography (EEG), and stereo EEG implantation.

Results:

All three patients …

Differentiating Axonal From Demyelinating Neuropathies Using Multiparametric Quantitative Mri Of Peripheral Nerves, Jacob D. Baraz, Stephanie Xuan, Sadaf Saba, Xue Yang, Ryan Castoro, Yang Xuan, Alison Roth, Richard D. Dortch, Jun Li, Yongsheng Chen

Medical Student Research Symposium

Objectives: To develop a multiparametric quantitative MRI (qMRI) method to track pathological changes in the peripheral neuropathies.

Background: Irrespective of the causes or types of polyneuropathies, peripheral nerves are mainly afflicted by two kinds of pathologies – axonal loss and demyelination. It is critical to differentiate between the two as treatments are different for the two conditions. While nerve conduction studies (NCS) have been used to differentiate the two pathologies in the distal nerves, there are no tools to probe the pathologies in the proximal peripheral nerves. This is particularly needed when distal nerves become non-responsive in NCS.

Methods: We …

The Connectivity Domain: Analyzing Resting State Fmri Data Using Feature-Based Data-Driven And Model-Based Methods, Armin Iraji, Vince D. Calhoun, Natalie M. Wiseman, Esmaeil Davoodi-Bojd, Mohammad R. N. Avanaki, Zhifeng Kou

Biomedical Engineering Faculty Research Publications

Spontaneous fluctuations of resting state functional MRI (rsfMRI) have been widely used to understand the macro-connectome of the human brain. However, these fluctuations are not synchronized among subjects, which leads to limitations and makes utilization of first-level model-based methods challenging. Considering this limitation of rsfMRI data in the time domain, we propose to transfer the spatiotemporal information of the rsfMRI data to another domain, the connectivity domain, in which each value represents the same effect across subjects. Using a set of seed networks and a connectivity index to calculate the functional connectivity for each seed network, we transform data into …

Segmentation Of Corpus Callosum Using Diffusion Tensor Imaging: Validation In Patients With Glioblastoma, Mohammad-Reza Nazem-Zadeh, Sona Saksena, Abbas Babajani-Fermi, Quan Jiang, Hamid Soltanian-Zadeh, Mark Rosenblum, Tom Mikkelsen, Rajan Jain

Wayne State University Associated BioMed Central Scholarship

Abstract

Background

This paper presents a three-dimensional (3D) method for segmenting corpus callosum in normal subjects and brain cancer patients with glioblastoma.

Methods

Nineteen patients with histologically confirmed treatment naïve glioblastoma and eleven normal control subjects underwent DTI on a 3T scanner. Based on the information inherent in diffusion tensors, a similarity measure was proposed and used in the proposed algorithm. In this algorithm, diffusion pattern of corpus callosum was used as prior information. Subsequently, corpus callosum was automatically divided into Witelson subdivisions. We simulated the potential rotation of corpus callosum under tumor pressure and studied the reproducibility of the …