Room 151 AG

16:00 - 18:00

Moderators:

Alberto Bizzi, Kirk Welker

Xia Liang^1,2, Hong Gu¹, Betty Jo Salmeron¹, Yuzheng Hu¹, Yong He², Elliot Stein¹, and Yihong Yang^1
1Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, United States, ²State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, Beijing, China

To examine the alterations in network-level interactions following chronic use of cocaine, we combined modular network analysis based on graph theory and resting-state fMRI technique. We observed significantly decreased inter-module connectivity among the default mode, salience and emotional networks in cocaine dependents. Moreover, we found that the intra-module connectivity within the salience module showed significantly negative correlation with difficulty of describing feelings in cocaine dependents. Our results demonstrate that cocaine addiction is associated with disruptions of network-level interactions, which may provide novel insights into the neurobiological mechanisms of cocaine addiction.

Gopikrishna Deshpande^1,2, Karthik Ramakrishnan Sreenivasan¹, Hrishikesh Deshpande³, and Rajesk K. Kana^4
1AU MRI Research Center, Department of Electrical and Computer Engineering, Auburn University, Auburn, AL, United States, ²Department of Psychology, Auburn University, Auburn, AL, United States, ³Department of Biomedical Engineering, University of Alabama, Birmingham, AL, United States, ⁴Department of Psychology, University of Alabama, Birmingham, AL, United States

The current study focuses on effective connectivity (EC) in autism, demonstrating the use of machine learning for identification of metrics which can be used to predict a novel subject’s group membership. fMRI time-series were de-convolved using a cubature Kalman filter and the resultant neuronal variables were input into a multivariate autoregressive model (MVAR) to obtain the EC paths. These metrics were then input into a recursive cluster elimination based support vector machine (RCE-SVM) classifier which showed a prediction accuracy of 94.3% based only on causal connectivity weights indicating that EC could serve as a potential non-invasive neuroimaging biomarker for autism.

Vadim Zotev¹, Han Yuan¹, Masaya Misaki¹, Raquel Phillips¹, Kymberly D. Young¹, and Jerzy Bodurka^1,2
1Laureate Institute for Brain Research, Tulsa, OK, United States, ²College of Engineering, University of Oklahoma, Tulsa, OK, United States

We have performed the first study utilizing real-time fMRI neurofeedback (rtfMRI-nf) with simultaneous EEG to explore electrophysiological correlates of rtfMRI-nf training. Eleven MDD patients learned to self-regulate their left amygdala activation using rtfMRI-nf during positive emotion induction task based on retrieval of happy autobiographical memories. We observed task-dependent variations in frontal alpha EEG asymmetry that correlated with fMRI activation in left parahippocampal gyrus area including left amygdala. Our results suggest that frontal asymmetry-based EEG neurofeedback may naturally complement rtfMRI-nf in training of emotional self-regulation, and may enhance the therapeutic value of rtfMRI-nf for major neuropsychiatric disorders, particularly depression.

Pierre LeVan¹, Julia Jacobs², Vasumathi Jayakumar¹, and Jürgen Hennig^1
1Radiology, Medical Physics, University Medical Center Freiburg, Freiburg, Baden-Württemberg, Germany, ²Neuropediatrics and Muscular Diseases, University Medical Center Freiburg, Freiburg, Baden-Württemberg, Germany

FMRI hemodynamics are generally assumed to be prolonged and slow-varying at timescales of several seconds and can thus be adequately sampled at conventional fMRI temporal resolutions of 2-3 seconds. In contrast, the current study investigates high-frequency (>0.5Hz) fMRI fluctuations measured by the whole-brain ultra-fast MR-encephalography (MREG) sequence (100ms temporal resolution), following epileptic discharges recorded by EEG. In 67% of the analyzed spike types, a cluster of voxels showing significant high-frequency fluctuations following the spikes could be identified in brain regions concordant with the spike field. MREG opens up new avenues for the localization of neuronal activity with high spatiotemporal resolution.

Kayako Matsuo¹, Chih-Min Liu², Shen-Hsing Annabel Chen³, Hai-Gwo Hwu², and Wen-Yih Isaac Tseng^1
1Advanced Biomedical MRI Lab, Center for Optoelectronic Biomedicine, National Taiwan University College of Medicine, Taipei, Taipei, Taiwan, ²Department of Psychiatry, National Taiwan University Hospital & College of Medicine, Taipei, Taipei, Taiwan, ³Division of Psychology, School of Humanities and Social Sciences, Nanyang Technological University, Singapore, Singapore, Singapore

NRG1 is a susceptibility gene for schizophrenia. Brain activation was compared during fMRI between promoter SNP (NRG1-P3) risk carriers and non-carriers in schizophrenia and controls using a Sternberg verbal working memory task. We performed a group (patients vs. controls) by risk (with or without risk allele) ANOVA in 4 groups (20 each) and found a marked activation decrease in the thalamus and cerebellum only in patients with risk allele. In contrast, risk carriers in general had increased "deeactivation" in DMN. This is the first neuroimaging study showing that a NRG1 promoter genetic variant is associated with working memory in schizophrenia.

Stefan Holiga¹, Karsten Mueller¹, Harald E. Möller¹, Gabriele Lohmann¹, Tomáš Sieger^2,3, Josef Vymazal⁴, Filip Ruzicka^2,5, Dušan Urgošík⁵, Matthias L. Schroeter^1,6, Evzen Ruzicka², and Robert Jech^2
1Max Planck Institute for Human Cognitive and Bran Sciences, Leipzig, Germany, ²Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine, Charles University in Prague, Prague, Czech Republic, ³Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic, ⁴Department of Radiology, Na Homolce Hospital, Prague, Czech Republic, ⁵Department of Stereotactic and Radiation Neurosurgery, Na Homolce Hospital, Prague, Czech Republic, ⁶Clinic for Cognitive Neurology, University of Leipzig, Leipzig, Germany

In this work, the effect of levodopa was tested on 24 patients suffering from Parkinson’s disease (PD). We used a novel, model-free method based on eigenvector centrality to reveal the changes of connectivity patterns following the dopaminergic challenge, using resting-state functional magnetic resonance imaging. We observed major alterations in connectivity between cerebellum and other key regions responsible for motor control, such as substantia nigra, subthalamic nucleus, putamen, thalamus, but also cerebellum itself. Thus, we demonstrate that levodopa modulates the connectivity in motor networks affected by PD. Additionally to striato-thalamo-cortical system, also cerebello-thalamic loops deserve significant attention in PD research.

Bo Wang¹, Shaodan Zhang^2,3, Y Xie², Ningli Wang², Chun Zhang³, Xiaohong Joe Zhou⁴, and Yan Zhuo^1
1State Key Lab of Brain & Cognitive Science, Institute of Biophysics, Chinese Academy of China, Beijing, Beijing, China, ²Beijing Tongren Hospital affiliated to Capital Medical University, Beijing, Beijing, China, ³Peking University Third Hospital, Beijing, Beijing, China, ⁴University of Illinois Medical Center at Chicago, Chicago, Illinois, United States

Differences were observed in blood flow, morphology and metabolites in the visual cortex between POAG (patients with primary open angle glaucoma) patients and age-matched normal controls, suggesting that rCBF and/or VBM may be used as a specific and sensitive marker in assessing brain injury in glaucoma patients.

Samiul Hayder Choudhury¹, Michael Richard Smith^1,2, Bradley Goodyear², and Fiona Costello^3
1Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada, ²Department of Radiology, University of Calgary, Calgary, Alberta, Canada, ³Department of Clinical Neuroscience, University of Calgary, Calgary, Alberta, Canada

Reliable imaging biomarkers can be developed using Optic Neuritis (ON) as a system model which are essential for advanced treatment and pathology of Multiple Sclerosis. We propose an approach of differentiating healthy persons from ON patients by transfer function analysis between brain regions that are correlated in optical activity during resting state. We provide receiver operator characteristic analysis results on various transfer function metrics and compare the results with existing outcomes. Our analysis suggests that with proper choice of threshold and decision metrics we can better differentiate subject groups for both lower and higher order visual information propagation.

Yongxia Zhou¹, Xi-Nian Zuo², Daniel K. Sodickson¹, Yulin Ge¹, Michael P. Milham³, Robert I. Grossman⁴, and Yvonne W. Lui^1
1Radiology/Center for Biomedical Imaging, New York University Langone Medical Center, New York, NY, United States, ²Psychology, Chinese Academy of Sciences, Beijing, Beijing, China, ³Child Mind Institute, New York University Langone Medical Center, New York, NY, United States, ⁴Radiology/Center for Biomedical Imaging, New York University, New York, NY, United States

We aim to study the structural and functional connectivity changes of two primary sub-segments of inferior frontal gyrus (IFG) which contain the motor neurons and the subcortical caudate regions in children with autism spectrum disorder (ASD). Data were downloaded from a multi-center Functional Connectome Project including structural and functional MRI data; with a total of 79 children with ASD and 105 age-matched typically developing children. Results showed brain atrophy in IFG, and abnormal caudate and IFG functional connectivity and reduced neuronal activity (via fractional amplitude) in children with ASD. There were significant correlations between fMRI metrics and multiple phenotypic data.

Woo Shim¹, Ji-Yeon Suh^1,2, Jeong Kon Kim², Bruce Rosen¹, and Young Kim^1
1Massachusetts General Hospital, Charlestown, Ma, United States, ²Asan Medical Center, Seoul, Seoul, Korea

Resting-state fMRI (rs-fMRI) has emerged as an important method for non-invasively assessing evolution of neural networks. Using rs-fMRI, the current work shows that the concomitant reshaping of the causal connectivity in the frequency-domain accompanies the stroke-recovery process.