Wanyong Shin¹ and Mark J. Lowe^1
1Radiology, Cleveland Clinic, Cleveland, OH, United States

We investigated the impact of physiologic noise correction on PCC seeded co-activation pattern (CAP) analysis varying the cluster number. We found that patterns from PCC seeded CAP analysis were best classified as 5 sub-patterns of default mode, sensory visual and motor, salience, central executive networks and noise. Also we observed that physiologic noise correction resulted in less frequent salience and central executive networks from PCC-related co-activations than uncorrected data.

Ajay Nemani¹ and Mark Lowe^1
1Imaging Sciences, Cleveland Clinic, Cleveland, OH, United States

Most connectivity-based parcellations of rsfMRI are synthesized from the dense connectome.  However, the mean parcel signal is typically used for data reduction prior to network analysis.  This results in representative parcel time series that are poorly correlated to their member voxels (poor parcel cohesion).  We propose an adjacency-constrained, agglomerative hierarchical clustering framework that uses parcel cohesion as a linkage criteria.  This results in parcels with mean time series that are significantly more representative of their member voxels.  This parcellation is easily interpretable and well-suited for downstream analyses.

Hiroyuki Ueda¹, Yosuke Ito¹, Takenori Oida¹, Yo Taniguchi², and Tetsuo Kobayashi^1
1Electrical Engineering, Kyoto University, Kyoto, Japan, ²Hitachi, Ltd., Kokubunji, Japan

To detect neural activities using a low-field MRI, we focused on spin-lock techniques, which are potentially effective tools for low-field functional MRI (fMRI). First, we estimated the magnetic field strength generated from neural activities using an equivalent current dipole model. Subsequently, we compared two spin-lock techniques by simulation and phantom experiments. It was found that the lowest detectable field strength was several tens pT in the simulation, whereas the experimental one was 2.34 nT. In future work, we plan to improve SNR using noise-reduction processing to realize the low-field fMRI.

Alexandra Reardon^1,2, Kaiming Li^1,2, Jason Langley², and Xiaoping Hu^1,2
1Bioengineering, University of California, Riverside, Riverside, CA, United States, ²Center for Advanced Neuroimaging, University of California, Riverside, Riverside, CA, United States

Multisite databases allow for increased statistical power and enhanced reproducibility, however data pooled across sites are often acquired with different scanners and/or protocols, leading to significant site-dependent variances and thereby reduced effects in group analyses.  We quantified the functional connectivity variance associated with the Autism Brain Imaging Data Exchange, a multisite resting state functional MRI database, and demonstrate how normalization functional connectivity results led to increased effect size when measuring established functional connectivity changes in autistic subjects as compared to healthy controls.

Francesca Saviola¹, Lisa Novello¹, Domenico Zacà^1,2, Luciano Annicchiarico^3,4, Luca Zigiotto^3,4, Silvio Sarubbo^3,4, and Jorge Jovicich^1
1CIMeC, Center for Mind/Brain Sciences, University of Trento, Rovereto, Italy, ²Siemens Healthcare, Milano, Italy, ³Department of Neuroscience,, Division of Neurosurgery, S.Chiara Hospital, Trento, Italy, ⁴Structural and Functional Connectivity Lab, S.Chiara Hospital, Trento, Italy

The recent brain connectomic framework has introduced a shift in the study of brain tumors. Previously considered as a focal brain disease, brain tumors are nowadays also studied in the context of structural and functional neural networking disruptions and their cognitive consequences. In this longitudinal behavior and resting state functional MRI study, we investigate how tumor grade, behavior and functional connectivity in brain network hubs and their connections to non-hubs are affected following surgical tumor resection. 

Qianlin Ding¹, Guanqiao Jin¹, Lidong Liu¹, Danhui Fu¹, Wenjuan Deng¹, Yang Zhao ¹, Long Qian², Weiyin Vivian Liu², and Danke Su^1
1GuangXi Medical University Cancer Hospital, Nanning, China, ²MR Research, GE Healthcare, Beijin, China

Frascati criteria for diagnosis of HIV-associated neurocognitive disorder lacks sensitivity, specificity, susceptible to learning effect, socioeconomic position and other confounding factors. fMRI can sensitively discover pathophysiological changes in HIV-infected central nervous system. In our study, ALFF and fALFF separately well classified HIV-positive and HIV-negative groups with AUC = 0.65 and 0.62, respectively. Our findings of injured frontal,parietal and occipital cortex associated with execution and attention function were consistent with previous studies, indicating activations reduced in the frontal lobe but increased in occipital lobe among patients with HAND. Both ALFF and fALFF could be biomarkers for objectively classifying HIV individuals.

Kyle Murray¹, Martin Cole², Etienne St-Onge³, Maxime Descouteaux³, Jianhui Zhong^1,4, Giovanni Schifitto^4,5, and Zhengwu Zhang^2
1Physics and Astronomy, University of Rochester, Webster, NY, United States, ²Biostatistics and Computational Biology, University of Rochester, Rochester, NY, United States, ³Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada, ⁴Imaging Sciences, University of Rochester, Rochester, NY, United States, ⁵Neurology, University of Rochester, Rochester, NY, United States

The integration of structural and functional connectivity thus far has been limited to atlas-based parcellation studies. We present a novel atlas-free processing pipeline to explore the integration of structural and functional connectivity at high spatial resolution. This pipeline has been reliably replicated in other research subjects. We also introduce a dual-modality imaging feature that can be mapped to the gray-white matter surfaces. Early results demonstrate that structural and functional connectivity are likely strongly linked. This pipeline allows for the first time to perform connectivity analyses on individual white surfaces, opening up many more possibilities to future connectivity studies. 

Zhizheng Zhuo¹, Haiyun Li², Yingjie Mei³, and Yaou Liu^1
1Department of Radiology, Beijing Tiantan Hospital, Beijing, China, ²School of Biomedical Engineering, Capital Medical University, Beijing, China, ³Clinical Science, Philips Healthcare, Guangzhou, China

Different information flow patterns were found within low and high signal frequency bands based on resting-state fMRI.
 

Mukesh Kumar¹, Poonam Rana¹, Tarun Sekhri², Subash Khushu¹, Rajanikant Panda³, Ratnesh Kanwar², Shilpi Modi¹, and Maria Dsouza^1
1NMR Research Center, INMAS, Delhi, India, ²Thyroid research Center, INMAS, Delhi, India, ³Cognitive Neuroscience Center, NIMHANS, Bangalore, India

The aim of this study is to explore the alteration RSNs connectivity and its association with underlying neurobehavioral symptoms in hypothyroid as compared to controls and consequences of therapeutic intervention using graph theory-based network analysis. Our finding revealed significantly reduced clustering coefficient, small world connectivity and local network efficiency in hypothyroid as compared to controls in sub-regions FPN, DMN, cingulo-opercular network, SMN and got reversed back in euthyroid patients in the network sub-regions of frontoparietal network, cingulo-opercular network, SMN after thyroxin therapy. These findings of reduced clustering coefficient suggest abated activities of motor, attention, working memory and cognition in hypothyroid.

Saba Amiri¹, Jafar Mehvari Habibabadi², Seyed Sohrab Hashemi-Fesharaki³, Neda Mohammadi Mobarakeh¹, Mehdi Mirbagheri¹, and Mohammad-Reza Nazem-Zadeh^1,4
1Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ²Isfahan Neuroscience Research Center, Isfahan University of Medical Sciences, Isfahan, Iran (Islamic Republic of), ³Pars Advanced Medical Research Center, Pars Hospital, Tehran, Iran (Islamic Republic of), ⁴Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Inst, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

Temporal lobe epilepsy (TLE) is a disorder of altered brain networks. We evaluated the functional connectivity of the brain limbic system believed to be affected by TLE, based on resting state functional MRI and the graph network analysis. Our results showed that insula, posterior cingulate gyrus, and thalamus may undergo abnormal functional connectivity in terms of local degree in both right and left TLE compared to the healthy subjects. Furthermore, our results suggest that functional connectivity, as evaluated by local degree in insula and thalamus may have a potential application to help determine the laterality in cases of TLE.

Michelle Karker¹, Scott Peltier², Allison Moll³, Julia Laing³, and Benjamin M. Hampstead^3,4
1Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States, ²Functional MRI Laboratory, University of Michigan, Ann Arbor, MI, United States, ³Psychiatry, University of Michigan, Ann Arbor, MI, United States, ⁴Mental Health Service, VA Ann Arbor Healthcare System, Ann Arbor, MI, United States

Connectome predictive modeling was applied to task data. Leveraging a MCI-relevant face-name task illuminates relationships with measures of total cognition (RBANS) and memory (Free Recall). This provides support for the use of clinically-relevant tasks in cases where a “driven” connectivity network may elucidate pathological changes.

pengfei zhang¹, jun wang¹, shaoyu wang², and jing zhang^1
1LanZhou University Second Hospital, LanZhou, China, ²MR Scientific Marketing, ShangHai, China

Based on rest-state fMRI, we investigated the changes of regional neural activity in trigeminal Neuralgia (TN) patients via analysis of regional homogeneity (ReHo). Furthermore, the alterations of dynamic functional connectivity was explored. Compared with healthy controls , brain regions with significant ReHo value changes in TN patients are mainly concentrated in default mode network (DMN) and Ventral attention Network(VAN). Dynamic analysis suggested four distinct connectivity ‘States’ across the entire group. Our results demonstrated that TN is associated with abnormal neuronal activities in different areas of the brain and dynamic functional connectivity between functional networks.

James David Wilson¹, Skyler Cranmer², and Zhong-Lin Lu^3
1Mathematics and Statistics, University of San Francisco, San Anselmo, CA, United States, ²Political Science, The Ohio State University, Columbus, OH, United States, ³Center for Neuroscience and Department of Psychology, New York University, New York, NY, United States

Functional connectivity scans are hierarchical – heterogeneity differentiates people according to clinical diagnosis and stage of disease. Hierarchy furthermore dictates connectivity of an individual - functional networks manifest as a hierarchy of subnetworks, each with their own unique biological function. To model functional connectivity across populations of individuals, we develop the hierarchical latent space model (HLSM), a statistical model that accounts for hierarchy as a function of clinical and connectivity features describing functional images. The HLSM reveals differences in the connectivity patterns between healthy and schizophrenia groups when applied to data from the Center for Biomedical Research Excellence (COBRE) project.

Mark J Lowe¹, Anna Crawford¹, and Stephen E Jones^1
1Imaging Institute, Cleveland Clinic, Cleveland, OH, United States

We compare whole brain functional connectivity to the Vim thalamic nucleus at 3T and 7T, and demonstrate that the higher spatial resolution and higher contrast-to-noise ratio at 7T results in maps that are more specific to the function of that thalamic region and the connectivity maps from the 3T data are likely lacking in specificity due to partial volume of signal from neighboring thalamic regions.

Robert Cary Welsh¹ and Martin A Lindquist^2
1Department of Psychiatry, University of Utah, Salt Lake City, UT, United States, ²Biostatistics, The Johns Hopkins University, Baltimore, MD, United States

Temporal filtering is a typical step in preprocessing of BOLD time-series for calculation of resting-state correlations, which are the basis of a connectome calculation. Bandpass filtering is a usual step in preprocessing of resting-state BOLD time-series data. Temporal filtering can introduce bias in the correlation calculations resulting in artifact in the connectome affecting the reliability in a connectome. We demonstrate this effect in simulations and then show this in data from the Human Connectome Project. We also show how to mitigate this issue for high reliability of the connectome as measured by image intraclass correlation coefficient (I2C2).