Leighton Barnden¹, Sonya Marshall-Gradisnik¹, Donald Staines¹, and Zack Shan^2
1Griffith University, Southport, QLD, Australia, ²University of Sunshine Coast, Sunshine Coast, QLD, Australia

For 44 ME/CFS and 26 HC, resting state and Stroop task fMRI were spatially normalized with a method optimized for the brainstem. After physiological signal (RETROICOR) denoising in CONN,  BOLD correlations were computed between 12 brainstem reticular activation system (RAS) ROI seeds. A survey of  connections showed fewer were significant in ME/CFS than HC at Rest, indicating impaired RAS function. During Task, although the same number of connections were active for the two groups, many were between different ROIs, suggesting compensatory mechanisms may be involved. Nerve signal conduction in the brainstem RAS was shown to be impaired in ME/CFS.

Ya Guo¹, Haihua Bao¹, and Shaoyu Wang^2
1Affiliated Hospital of Qinghai University, Xining, China, ²Siemens Healthineers, Shanghai, China

Headache caused by paroxysmal vestibular symptoms Known as vestibular migraine (VM), its pathophysiology and related mechanisms are less understood at this stage.  The purpose of this study was to apply Simultaneous Multi-Slice Resting State functional Magnetic Resonance Imaging (SMS-Rs-fMRI) to explore the gray matter volume (GMV), brain function, and brain default in patients with VM..Compared with the control group, patients with VM had lower GMV in multiple brain areas, higher ALFF values in multiple brain areas, and changes in the brain default network function connection in the resting state.

huaguang yang¹, zhi Wen¹, Lanhua Hu¹, Weiyin Vivian Liu², Guoguang Fan³, and Yunfei Zha^1
1Radiology, Renmin Hospital of Wuhan University, Wuhan, China, ²MR Research, GE Healthcare, Wuhan, China, ³The First Affiliated Hospital of China Medical University, Shenyang, China

Depression is a common non-motor system symptom in patients with multiple system atrophy (MSA). Its pathogenesis is unknown. Multimodal functional magnetic resonance imaging (fMRI) is the main tool to explore the mechanism of pure depression and other diseases with depressive symptoms. Applications of fMRI on patients with MSA in accompanied with depressive symptoms is expected to reveal the difference to the MSA-involved depression mechanism different to the pure depression-involved brain network. Our results found altered secondary network in MSA patients with depression and provided a new idea for determination on treatments to MSA with depression. This study provided neuroimaging evidence for a clinical understanding of non-pure depression and possible reference for treatment measures.

Daehun Kang¹, Myung-Ho In¹, Erin Gray¹, Thomas K Foo², Radhika Madhavan², Nolan K Meyer¹, Lydia J Bardwell Speltz¹, Zaki Ahmed¹, Jeffrey Gunter¹, Brice Fernandez³, Joshua D Trzasko¹, John Huston¹, Matt A Bernstein¹, and Yunhong Shu^1
1Radiology, Mayo Clinic, Rochester, MN, United States, ²GE Global Research, Niskayuna, NY, United States, ³GE Healthcare, Buc, France

Using a high-performance gradient system, multi-band multi-echo fMRI with high temporal and spatial resolutions was acquired. The multi-echo images can be combined based on the weighting of local T₂^*. The echo-combination improves both the signal intensity and temporal SNR, particularly in regions suffering from signal dropout due to iron deposition like the basal ganglia. Also, the echo-combined images were demonstrated to be superior to the single-echo images for studying functional connectivity due to increased BOLD contrast sensitivity. Multi-echo fMRI is particularly promising for studies of aging and dementia as iron deposition and brain atrophy confound analyses.

Mu-Lan Jen¹, Laura B. Eisenmenger², Sterling C. Johnson^3,4, Veena A. Nair², Vivek Prabhakaran², and Kevin M. Johnson^1,2
1Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, United States, ⁴Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison, 53705, WI, United States

Recent studies have suggested reduced altered low-frequency oscillations (LFOs) and cerebral blood flow (CBF) in subjects with Alzheimer’s disease (AD). These measures may reflect reduced neural activity and altered waste clearance but are subject to confounding effects from patient motion. This study of a cohort of AD and controls investigates interaction of patient motion with LFO from BOLD and CBF from multi-delay arterial spin labeling. Results demonstrate significantly increased motion in subjects with AD and a correlation of motion estimates with BOLD LFO but not with CBF. These results suggest motion should be considered for studies in neurodegenerative subjects. 

Elizabeth G. Keeling^1,2, Maurizio Bergamino¹, Lori Steffes¹, Anna Burke³, and Ashley M. Stokes^1
1Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States, ²School of Life Sciences, Arizona State University, Tempe, AZ, United States, ³Division of Neurology, Barrow Neurological Institute, Phoenix, AZ, United States

The purpose of this study was to apply a previously developed multi-contrast, multi-echo sequence using spin- and gradient-echo (SAGE) for fMRI in the context of Alzheimer’s disease (AD). We acquired pilot SAGE-fMRI data with five echoes (2 gradient-echoes, 2 asymmetric spin-echoes, 1 spin-echo) using episodic memory encoding and retrieval tasks in healthy aging and cognitively impaired (mild cognitive impairment (MCI) or mild AD) cohorts. Group differences in task-related activation were evaluated across single echo and multi-echo combined dynamic SAGE signals. SAGE-fMRI contrasts showed promise for more robust distinction between HC and CI groups than single-echo fMRI in AD-relevant brain regions. 

Kaio Felippe Secchinato¹, Pedro Henrique Rodrigues da Silva¹, Júlia Palaretti¹, and Renata Ferranti Leoni^1
1Departamento de Física, University of São Paulo, Ribeirão Preto, Brazil

Early detection of Alzheimer's disease (AD) increases the treatment benefits. However, it is still a challenging question which biomarkers are useful for early diagnosis. Then, we aimed to classify cognitively normal elderly regarding the possibility to develop AD based on resting-state cerebral vasoreactivity (CVR) values and neuropsychological (NP) scores. We used supervised machine learning algorithms. Our results suggest that Random Forest and K-Nearest Neighbors classifiers trained with CVR values of the vermis.7 (part of the cerebellum), and left parahippocampal gyrus, and Mini-Mental State Examination (MMSE), and Trail Making Test A scores can be useful on the early detection of AD.

Qian Chen¹, Jilei Zhang², and Bing Zhang^1
1Department of Radiology, Drum Tower Hospital, Clinical College of Nanjing Medical University, Nanjing, China, ²Philips Healthcare, Shanghai, China

Individuals with subjective cognitive decline (SCD) were at higher risk of Alzheimer’s disease. Based on community-informed connectome analysis, we observed decreased intra-module connectivity in superior occipital gyrus of the visual network and decreased inter-module connectivity in supramarginal gyrus of the dorsal attention network in SCD subjects. The SCD group also showed increased intra-module connectivity in the dorsolateral prefrontal cortex and middle frontal gyrus of the frontoparietal network. The altered network measures showed significant correlations with cognitive performance on memory and language. Our findings may benefit a better understanding of the neural basis underlying early cognitive decline in the SCD stage. 

Mohsen Mazrooyisebdani^1,2, BARBARA B BENDLIN³, Shi-Jiang Li⁴, and Vivek Prabhakaran^1,5,6
1Radiology Department, University of Wisconsin Madison, Madison, WI, United States, ²Electrical and computer Engineering, University of Wisconsin Madison, Madison, WI, United States, ³Department of Medicine, University of Wisconsin Madison, Madison, WI, United States, ⁴Department of Biophysics, University of Wisconsin Madison, Madison, WI, United States, ⁵Neuroscience Training program, University of Wisconsin Madison, Madison, WI, United States, ⁶Medical Scientist Training Program, University of Wisconsin Madison, Madison, WI, United States

This abstract presents an evaluation of alteration in the functional brain network of patients with Alzheimer’s disease (AD) with a focus on the application of graph theory as a pure data-driven analysis approach. The main goal in this work is to diagnose any potential biomarker of Alzheimer’s disease in the brain connectivity network of patients.

Gerhard Drenthen¹, Walter Backes¹, Whitney Freeze¹, and Jacobus Jansen^1
1Maastricht University Medical Center, Maastricht, Netherlands

A reduced rich-club connectivity of the structural covariance network was found in individuals with MCI and AD compared to controls. Moreover, we show that a loss of connections in the rich-club subnetwork may form a mechanism underlying memory loss in the context of MCI and dementia.

Catarina Domingos¹, Amparo Ruiz-Tagle¹, Ana Fouto¹, Inês Esteves¹, Raquel Gil-Gouveia², and Patrícia Figueiredo^1
1Institute for Systems and Robotics - Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, ²Neurology Department, Hospital da Luz, Lisbon, Portugal

We investigated habituation in migraine, by measuring repetition effects to emotional faces using BOLD-fMRI, during all phases of the migraine cycle. We first performed simulations to determine the sensitivity of the proposed fMRI paradigm and respective BOLD deconvolution approach to detect repetition effects, and then applied this to real data. Simulations showed that a commonly used deconvolution approach was able to correctly estimate repetition effects provided that response delays did not significantly deviate from default. When applied to real data, we found that controls exhibited the normal repetition suppression, but patients exhibited response maintenance or even potentiation. 

Unal Sakoglu¹, Lohit Ravi Teja Bhupati², Olexandra Petrenko¹, and Vince D Calhoun^3
1Computer Engineering, University of Houston - Clear Lake, Houston, TX, United States, ²Computer Science, University of Houston - Clear Lake, Houston, TX, United States, ³Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States

In this work, we develop a 3D to 1D ordering methodology for fMRI data, using a new space filling curve (SFC), which is adaptive to brain's shape based on T1 MRI. We apply this SFC ordering to fMRI activation maps from a schizophrenia study, compress/bin the data, obtain features, and perform classification of schizophrenia vs normal controls. The classification results using SFC ordering are superior to those using linear ordering, the traditional method.

Mei-Yu Yeh^1,2, Ping Hou², and Ho-Ling Liu ^2
1Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan, ²Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Houston, TX, United States

Cerebrovascular reactivity (CVR) mapping can be achieved by BOLD MRI during hypercapnia challenges, such as a breath-hold (BH) task. It has wide applications for the assessment of various neurological deficits and neurovascular uncoupling. The BH MRI is typically analyzed by modeling with a hemodynamic or respiratory response function and account for latencies in signal time courses. Accurate modeling requires physiological monitoring and/or consistent patient performance during the scan. Because the BOLD signal response to the BH is a global effect, this study proposes to utilize the individual’s whole-brain signal average as a regressor for the analysis of BH MRI.   

Yanyao Du¹, Ru Yang¹, Wenhan Yang¹, Huiting Zhang², and Jun Liu^1
1Department of Radiology, the Second Xiangya Hospital of Central South University, Changsha, China, ²MR Scientific Marking, Siemens Healthcare Ltd., Wuhan, China

This study aimed to evaluate the correlation of longitudinal changes of amplitude low-frequency fluctuation (ALFF) and regional homogeneity (ReHo) values based on Harvard-Oxford atlas (HOA) and Barratt Impulsivity Scale 11 (BIS-11) in subjects with methamphetamine abstinence. The results suggested that there were significant positively correlations between right middle frontal gyrus (8) and BIS total scores, BIS attention scores as well as BIS non-planning score.

Kaundinya Gopinath¹, Elissar Andari¹, and Larry Young^1
1Emory University, Atlanta, GA, United States

Autism spectrum disorders (ASD) are characterized by dysregulation of endogenous oxytocin (OXT) function. In this study, we employed complexity of resting state fMRI signal as a tool to examine: 1) brain function impairments in adult ASD, 2) the relationship between DNA methylation of OXT receptor gene and severity of cognitive impairment, and 3) the effects of intranasal OXT treatment (IN-OXT).  ASD patients exhibited abnormally increased fMRI signal complexity in a number of brain function networks. The severity of these impairments were inversely related to DNA methylation levels. IN-OXT attenuated impairments in these networks. Thus, OXT has neuroprotective effects in ASD.

Alireza Fallahi¹, Fatemeh Salimi², Fatemeh Eyvazi³, Narges Hosseini Tabatabaei⁴, Mohammad-Reza Ay², and Mohammad-Reza Nazem-Zadeh^2
1Shahed University, Tehran, Iran (Islamic Republic of), ²Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ³Institute for cognitive science studies, Shahid Beheshti University, Tehran, Iran (Islamic Republic of), ⁴Brain and Spinal Cord Injury Research Centre, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

In this study, dynamic causal modeling (DCM) was applied to resting state fMRI data of mesial temporal lobe epilepsy (mTLE) patients to determine the directional alternations in effective connectivity in large scale brain networks. We hypothesized that mTLE alters the functional connectivity in both temporal and extra-temporal functional brain networks. We further hypothesized that the altered connectivity is directional (The direction between two network nodes matters). The results showed significantly different effective connectivity in default mode, limbic and salience networks. 

Betty Chinda^1,2, Simon Liang³, William Siu⁴, George Medvedev⁵, and Xiaowei Song^1,2
1Department of Biomedical Physiology & Kinesiology, Simon Fraser University, Burnaby, BC, Canada, ²Health Research and Innovation, Fraser Health Authority, Surrey, BC, Canada, ³Department of Medicine, University of British Columbia, Vancouver, BC, Canada, ⁴Department of Radiology, Royal Columbian Hospital, New Westminster, BC, Canada, ⁵Department of Neurology, Royal Columbian Hospital, New Westminster, BC, Canada

This longitudinal task-phase fMRI study aims to provide direct evidence of possible effects of Carotid angioplasty and stenting (CAS) on cognition. The initial phase enrolled patients with severe carotid stenosis (≥70% stenosis) who had MRI scans pre-CAS and two-month post-CAS. At each scan, patients completed two fMRI sessions while performing a working memory task. Improved cerebral perfusion to areas supplied by carotid arteries following CAS was correlated with improved cognitive function in working memory performance along with increased fMRI activations in the re-perfused vascular territory especially in frontal and temporal lobes and reduced in the contralateral hemisphere.

David Mattie¹, Zihang Fang², Emi Takahashi^2,3, and Jacob Levman^1
1Bioinformatics Group, St. Francis Xavier Unversity, Antigonish, NS, Canada, ²Department of Medicine, Boston Children’s Hospital, Boston, MA, United States, ³Department of Pediatrics, Harvard Medical School, Boston, MA, United States

Identifying regional neural pathways with magnetic resonance imaging (MRI) based diffusion characteristics across all possible regions of interest (ROI) is computationally expensive and demands a concerted effort to ensure reliable extraction of tractography statistics from the diffusion tensor imaging data associated with identifiable fiber tracts from a patient’s brain MRI examination.  Here we present a software tool called Connectome Reporting User Shell (CRUSH) that processes DTI data using pluggable modules that can be tailored to research specific outcomes.  We demonstrate the value of such a tool by conducting a whole-brain tractography-based connectomics analysis on a clinically imaged neurologically healthy population.

Hyunsil Cha¹, Sang Won Lee², Heajeong Choi¹, Eunji Kim¹, Seungho Kim¹, Yunheung Kim¹, Seung Jae Lee², and Yongmin Chang^3
1Medical and Biological Engineering, Kyungpook National University, Daegu, Korea, Republic of, ²Department of Psychiatry, Kyungpook National University Hospital, Daegu, Korea, Republic of, ³Radiology and Molecular Medicine, Kyungpook National University, Daegu, Korea, Republic of

In this fMRI study, we aimed to investigate the neural circuits related to positive and negative TAF by using a modified TAF task, wherein individuals were asked to read the name of a close person within positive or negative statements.

Thuy Ha Duy Dinh¹, Koji Fujimoto¹, Thai Akasaka¹, Tadashi Isa¹, and Tomohisa Okada^1
1Human Brain Research Center, Kyoto University, Kyoto, Japan

Head motion during the acquisition of fMRI data can significantly contaminate the neural signal and induce spurious, distance-dependent changes in signal correlations. Framewise displacement (FD) has often been used as a cut-off threshold for removing bad fMRI datasets related with high motion. Here we investigated the influence of head motion on the output of ICA-based denoising analysis. The results showed a strong correlation between the number of total ICA components with head mean motion and FD, which indicated that the number of ICA components can be an index for detecting high motion-related fMRI datasets.