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Barbara Cassone¹, Francesca Saviola¹, Stefano Tambalo¹, Enrico Amico^2,3, Dimitri Van De Ville^3,4, and Jorge Jovicich^1
1CIMeC, University of Trento, Trento, Italy, ²Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland, ³Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland, ⁴Institute of Bioengineering, EPFL, Lausanne, Switzerland

Keywords: Brain Connectivity, fMRI (resting state)

Brain fingerprints reside in timescale-specific functional connectivity of specific regions. However, the effect of acquisition’s temporal resolution on fingerprinting is unknown. Here, we manipulated repetition time (TR) in resting-state fMRI acquisitions, and observed that subject identifiability was maximized when using fast (TR = 0.5 s) or slow (TR = 3 s) protocols, and decreased with TR = 0.7, 1 or 2 s. Moreover, while high-level association areas gave the highest contribution to individual fingerprinting regardless of TR, low-level sensorimotor areas were mostly involved in discriminating between acquisitions with different TRs.

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Ali M Golestani¹ and J Jean Chen^2,3
1University of Calgary, Calgary, AB, Canada, ²Rotman Research Institute at Baycrest, Toronto, ON, Canada, ³Department of Biophysics, University of Toronto, Toronto, ON, Canada

Keywords: Brain Connectivity, Artifacts

Data-driven denoising methods (global-signal regression (GS), white matter and CSF (cerebrospinal fluid) regression, anatomical and temporal CompCor, ICA AROMA) have been shown to remove cardiac and respiratory contributions from the fMRI signal. In this study, we compared the effectiveness of these methods in preserving the signals associated with age-related brain connectivity changes. We show that GS and AROMA resulted in diminished age-related brain connectivity differences, aCompCor and tCompCor retained the most connectivity differences while denoising effectively.  

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Eneko Uruñuela¹, Miguel Ánguel Veganzones², and César Caballero-Gaudes^1
1Basque Center on Cognition, Brain and Language, Donostia - San Sebastián, Spain, ²University of Deusto, Donostia - San Sebastián, Spain

Keywords: Brain Connectivity, fMRI, Tensor Decomposition

Denoising of the blood oxygen level-dependent signal is critical for the study of brain dynamics with functional MRI data. However, disentangling neurobiological signals  from non-neurobiological ones such as head motion-related artifacts, and cardiac-related and respiration-related fluctuations. Multi-echo ICA approaches are often used to denoise the data by exploiting the echo-time dependence of the BOLD signal. Nevertheless, these rely on the optimally combined data and do not employ the information contained in the different echo-time signals. Here, we explore the potential of tensor decomposition techniques, which can simultaneously consider all the information available, as a way to process multi-echo fMRI data.

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Dafa Shi¹, Haoran Zhang¹, Guangsong Wang¹, and Ke Ren^1
1Department of Radiology, Xiang’an Hospital of Xiamen Uneversity,School of Medicine, Xiamen University, Xiamen, China

Keywords: Brain Connectivity, fMRI (resting state), connectome-based predictive modeling

Physiologic significance of the global signal and the use (or omission) global signal regression (GSR) in fMRI data preprocessing remain controversial. Connectome-based predictive modeling(CPM) is one of the most commonly used machine-learning models. The effect of GSR on the performance of the CPM model is not well understood. We performed two preprocessing procedures for fMRI data: GSR and without GSR, and we used different brain atlases to construct CPM models to predict age, full-scale, performance and verbal IQ. We found that GSR can improve the predictive performance of CPM, at least for age, full-scale, performance  and verbal IQ .

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Smitha Karavallil Achuthan¹, Despina Stavrinos², Haley B Holm³, Sheeba Arnold Anteraper⁴, and Rajesh K Kana^1
1Department of Psychology & The Center for Innovative Research in Autism., University of Alabama, Tuscaloosa, AL, United States, ²University of Alabama at Birmingham, Birmingham, AL, United States, ³Children’s Healthcare of Atlanta, Atlanta, GA, United States, ⁴Carle Illinois Advanced Imaging Center, Urbana, IL, United States

Keywords: Brain Connectivity, Brain Connectivity, Multivoxel Pattern Analysis

This study examined whole-brain resting state fMRI connectivity patterns in autistic and attention deficit hyperactivity disorder (ADHD) adults in comparison with neurotypical adults using multivoxel pattern analysis. Results highlight alterations in cerebellar-cortical functional connectivity (FC) in autistic participants and the involvement of cerebellum and inferior frontal gyrus in ADHD. 

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Fanghui Dong¹, Tongpeng Chu¹, Ning Mao¹, and Haizhu Xie^1
1Yantai Yuhuangding Hospital, Yantai, China

Keywords: Brain Connectivity, fMRI (resting state), Major depressive disorder, edge-centric functional connectivity, gene expression

This study aims to use the combination of the edge-centric functional network (eFC) model with transcribed gene datasets to explore the molecular basis of depression. In our study, we for the first time reported the correlation between eFC and transcriptional profiles in MDD patients. These findings revealed eFC phenotypes in MDD and bridged the gap between transcriptome and neuroimaging. It advanced our understanding of the neurobiological mechanism underlying depression and provided potential biomarkers for the evaluation of MDD treatment.

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Farzad V Farahani¹, Cristina Sadowsky^2,3, James J Pekar^4,5, Martin Lindquist¹, and Ann S Choe^4,5
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States, ²International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States, ³Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD, United States, ⁴F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States, ⁵Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States

Keywords: Brain Connectivity, Spinal Cord, graph theory, spinal cord injury, plasticity, reorganization

Cortical plasticity contributes to neurological recovery in persons with spinal cord injury (SCI) and can be studied using resting-state fMRI. Global graph measures offer insufficient detail to investigate local changes. Here, we investigated whether mesoscale graph measures provide additional insight. Changes in sensorimotor, visual, and ventral attention networks were revealed. Notably, decreased communication between lower body SMN with the rest of the functional networks across the brain and increased communication within the upper body SMN were observed. This suggests a potential for the mesoscale graph measures' utility in understanding the complex brain functional reorganizations in persons with chronic SCI.

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Apoorva Safai¹, Jeffrey Katz^2,3,4,5, Barbara Bondy⁶, and Gopikrishna Deshpande^{2,3,4,5,7,8,9,10
1}Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Pune, India, ²Department of Psychological Sciences, Auburn University, Auburn, AL, United States, ³Department of Electrical & Computer Engineering, Auburn University, Auburn, AL, United States, ⁴Alabama Advanced Imaging Consortium, Birmingham, AL, United States, ⁵Center for Neuroscience, Auburn University, Auburn, AL, United States, ⁶Department of Art and Art History, Auburn University, Auburn, AL, United States, ⁷Key Laboratory for Learning and Cognition, Capital Normal University, Beijing, China, ⁸Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India, ⁹Centre for Brain Research, Indian Institute of Science, Bangalore, India, ¹⁰Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad, India

Keywords: Brain Connectivity, Neuroscience, Neuroplasticity

Observational drawing is representation of observed 3D real-life objects into 2D drawings on paper, which involves engaging complex skills like visual perception, spatial encoding, memory and decision making. This study investigates longitudinal functional connectivity (FC) in art students who underwent a 16-week long drawing course, in comparison to students who took unrelated courses. The art group showed improved drawing skills and demonstrated enhanced FC between DMN and salience network and in cerebellar regions post-art class, in comparison, with healthy non-art students. These findings imply positive changes in brain functioning on learning and practicing art, thereby highlighting its potential therapeutic applications.

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Yakun Zhang¹, Shichun Chen¹, Zongpai Zhang¹, Wenna Duan¹, George Weinschenk¹, Li Zhao², Brandon Gibb³, Adam Anderson⁴, Wenming Luh⁵, and Weiying Dai^1
1Department of Computer Science, State University of New York at Binghamton, Binghamton, NY, United States, ²College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China, ³Department of Psychology, State University of New York at Binghamton, Binghamton, NY, United States, ⁴Department of Psychology, Cornell University, Ithaca, NY, United States, ⁵Cornell MRI Facility, Cornell University, Ithaca, NY, United States

Keywords: Brain Connectivity, Arterial spin labelling

We evaluated the relationship between attention levels and brain global efficiencies. This relationship was compared with brain global efficiencies measured with dynamic ASL (dASL) and with multi-echo (ME) BOLD fMRI. We found significantly greater correlation between attention levels (reflected by P3 properties when performing an attention task) and brain global efficiencies based on rsFC using dASL than those using ME BOLD fMRI, indicating that dASL can offer more accurate global neural signal fluctuations.

Yuanqiang Zhu¹, Fan Guo¹, and Yingjuan Chang^1
1Air Force Medical University, Xi'an, China

Keywords: Data Analysis, fMRI (task based)

Our pilot study validated the effectiveness of rTMS after a brief nap in terms of improving sustained attention in the context of SD. An investigation of the dynamic changes of PVT task-related cerebral responses across the three conditions showed that the middle frontal gyrus recovered least after the nap and was selected as the stimulation target. Through modulating the functional connectivity within the FPN and DMN, individualized, 10-Hz rTMS showed promise in terms of improving the vigilance of military academy cadets accepting real stimulation. 

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Priyanka Bhat¹, S Senthil Kumaran², Vinay Goyal³, Achal K Srivastava⁴, S N Dwivedi⁵, and Madhuri Behari^6
1Dept. of Neurology, AIIMS, Delhi, India, ²Dept of Nuc. Mag. Res, AIIMS, Delhi, India, ³Medanta, The Medicity, Delhi, India, ⁴Dept of Neurology, AIIMS, Delhi, India, ⁵Dept. of Biostatistics, AIIMS, Delhi, India, ⁶Dept. of Neurology, Fortis Hospitals, Vasant Kunj, Delhi, India

Keywords: Brain Connectivity, fMRI (task based), Parkinson's Disease, Supplementary Motor Area, Transcranial Magnetic Stimulation

Transcranial Magnetic stimulation alters cortical connectivity in Parkinson’s disease (PD) imparting clinical benefits. The mechanisms by which this occurs remain unexplored. In this study, Multivariate pattern analyses of task-based connectivity was carried out to understand the effects of rTMS in PD. Visuospatial task based functional MRI was acquired before and after stimulation sessions. Correlation matrices revealed an increased connectivity between left supplementary motor area (SMA) and left primary motor area (M1), suggesting an improvement in the motor network.

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Tzu-chen Yeh^1,2, Chou-ming Cheng³, and Chi-che Chou^3
1Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan, ²Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei, Taiwan, ³Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan

Keywords: Brain Connectivity, PET/MR, metabolic connectivity mapping

Metabolic connectivity mapping (MCM) is a unique application of simultaneous PET/fMRI for solving the effective connectivity (EC) of brain circuits. Reproducibility of MCM was critical after the initial report. By applying the released database of MONASH (Melbourne, Australia), consistent EC among bilateral inferior parietal lobules (IPL) and posterior cingulate cortices (PCC), as nodes within default mode network (DMN), was identified using MCM with the statistic-based approach. One-way EC using dedicated ROI (regions of interest) derived from grouped ICA (independent component analysis) outperformed the atlas-based ROIs of DMN.

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Dafa Shi¹, Haoran Zhang¹, Guangsong Wang¹, and Ke Ren^1
1Department of Radiology, Xiang’an Hospital of Xiamen Uneversity,School of Medicine, Xiamen University, Xiamen, China

Keywords: Brain Connectivity, fMRI (resting state)

We used  Brainnetome 246 atlas to construct FC matrices and extract image features from ALFF, DC, ReHo and VMHC maps.The classical CPM method using FC data and the combination model using multimodal data were constructed to predict FIQ scores. We found the combination model outperformed either classical models. Similar results were found with the Shen 268 atlas. The functional networks and regions related to intelligence mainly included intrafrontal, frontoparietal, frontotemporal and temporoparietal networks.The models constructed with the intelligence-related networks and regions could predict PIQ, VIQ, and CRT scores well, but not BDI, SAT, and TAT scores.

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Carolyn B McNabb¹, Eirini Messaritaki¹, Kristin Koller¹, and Derek K Jones^1
1Psychology, Cardiff University, Cardiff, United Kingdom

Keywords: Brain Connectivity, Brain Connectivity

We aimed to identify potential redundancies in microstructural measures between major white matter tracts and asked which microstructural metrics correlate most within a tract. Using a combination of microstructural imaging techniques, including diffusion imaging, relaxometry and quantitative magnetisation transfer imaging, we identified strong correlations between homologous left and right fasciculi in the cingulum bundles, inferior longitudinal fasciculi, uncinate fasciculi and arcuate fasciculi as well as similar patterns of tissue microstructure between heterologous tracts. However, corticospinal tracts showed weak correlations with other tracts and a unique pattern of inter-metric correlations, suggesting that summarising metrics across all tracts would be inappropriate.

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Ana Matoso¹, Ana R Fouto¹, A. Ruiz-Tagle¹, I. Esteves¹, Gina Caetano¹, Nuno A. Silva², Pedro Vilela³, Raquel Gil-Gouveia^4,5, Patrícia Figueiredo¹, and Rita G Nunes^1
1Institute for Systems and Robotics- Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal, ²Learning Health, Hospital da Luz, Lisbon, Portugal, ³Imaging Department, Hospital da Luz, Lisbon, Portugal, ⁴Neurology Department, Hospital da Luz, Lisbon, Portugal, ⁵Center for Interdisciplinary Research in Health, Universidade Católica Portuguesa, Lisbon, Portugal

Keywords: Brain Connectivity, Data Analysis, Migraine

When studying structural brain connectivity, there are many normalization strategies that can be employed, however, there is still no consensus on which one is best to use. Hence, in this study, we investigated the impact of normalizing by the region volumes in the context of migraine as this is sometimes done to avoid bias towards higher connectivity in bigger regions. Several metrics showed significant differences between normalization strategies and, more critically, nodal metrics displayed different behaviours when compared between controls and migraineurs.

Tong Fu¹, Yujia Gao¹, Xiaobin Huang¹, Di Zhang¹, Lindong Liu¹, Xindao Yin¹, Xinying Wu ¹, Hai Lin², and Yongming Dai^2
1Department of Radiology, Nanjing first hospital，Nanjing Medical University, Nanjing, China, ²Central Research Institute, United Imaging Healthcare, Shanghai, China

Keywords: Brain Connectivity, Machine Learning/Artificial Intelligence

In comparison with migraine without aura (MwoA), migraine with aura (MwA) has its own characteristics in symptom, pathological mechanism, treatment and prognosis. In this study, we conducted connectome-based analysis to capture brain connectivity markers that would show identifiable signature of MwA and MwoA, using diffusion tensor imaging and resting-state functional MRI. We found that the alterations of structural and functional connectivity strength contributed to migraine patient subtyping. The whole brain connectome-based imaging markers might provide possible evidence in understanding the heterogeneity of migraine with aura and help for patient-specific decision.

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Gerhard Drenthen¹, Amir Far², Catharina Faber², Jaymin Upadhyay³, David Linden⁴, Raquel van Gool⁴, Walter Backes¹, Janneke Hoeijmakers², and Jacobus Jansen^1
1Department of Radiology & Nuclear Medicine, School for Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands, ²Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands, ³Boston Children's Hospital, Boston, MA, United States, ⁴School for Mental Health & Neuroscience, Maastricht University Medical Center, Maastricht, Netherlands

Keywords: Brain Connectivity, Diffusion Tensor Imaging, Chornic pain

Patients with small fiber neuropathy (SFN) suffer from chronic pain, which may lead to cerebral changes. Here, we studied structural network changes in idiopathic- and genetic-SFN compared to controls using diffusion-MRI. We found that for the genetic-SFN group pain-associated regions take a more prominent place in the network (in terms of nodal importance). Furthermore, in the genetic-SFN group, a higher nodal importance of pain-associated regions related to lower self-reported pain. This shows that genetic-SFN has a distinct structural pain pathway, which may be indicative of a compensatory mechanism where the structural organization is altered to inhibit the response to pain.

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Daniel Suite¹, Thomas Hagan¹, Chuan Huang^1,2, Minos Kritikos³, Sean Clouston³, Megan Horton⁴, Roman Kotov⁵, Roberto G Lucchini^6,7, Evelyn J Bromet⁵, and Benjamin Luft^8
1Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, United States, ²Department of Radiology, Stony Brook Medicine, Stony Brook, NY, United States, ³Program in Public Health and Department of Family, Population, and Preventative Medicine, Stony Brook Medicine, Stony Brook, NY, United States, ⁴Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ⁵Department of Psychiatry, Stony Brook Medicine, Stony Brook, NY, United States, ⁶Environmental Health Sciences, School of Public Health, Florida International University, Miami, FL, United States, ⁷Occupational Medicine, Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia, Brescia, Italy, ⁸Department of Medicine, Stony Brook Medicine, Stony Brook, NY, United States

Keywords: Brain Connectivity, Psychiatric Disorders

Many World Trade Center (WTC) responders continue to suffer from chronic Post-Traumatic Stress Disorder (PTSD). Multimodal imaging techniques have shown potential as putative markers for PTSD but still lie in the developmental stages. Network connectivity techniques are showing promise for investigating neuropathology influencing PTSD symptom maintenance and course. This work utilizes a graph theory approach with brain diffusion images to probe the network alterations in WTC responders with PTSD. We identified a significant difference in Characteristic Path Length (CPL) between responders with and without chronic PTSD.

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Koji Kamagata¹, Wataru Uchida¹, Christina Andica^1,2, Yasuhito Nagai³, Masaki Nishioka³, Mana Owaki^1,4, Yuya Saito¹, Kaito Takabayashi¹, Akifumi Hagiwara¹, Akihiko Wada¹, Toshiaki Akashi¹, Shigeki Aoki¹, and Tadafumi Kato^3
1Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan, ²Faculty of Health Science, Juntendo University, Chiba, Japan, ³Department of Psychiatry, Juntendo University, Tokyo, Japan, ⁴Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan

Keywords: Brain Connectivity, Brain Connectivity

We attempted to identify the paraventricular thalamic nucleus (PVT), which is known to regulate emotion, motivation, stress, and drug- and alcohol-related behaviors in humans. We used data-driven connectivity profiles obtained using probabilistic tractography and a k-means clustering method with diffusion-weighted imaging data. We consistently identified an anatomical connectivity-based parcellation of the PVT in two independent cohorts that included 601 healthy subjects. Furthermore, we discerned the specific structural pattern of the PVT, which agreed with findings from animal studies. Finally, we noted significant correlations between PVT structural and functional connectivity with the limbic structures and drug-, nicotine-, or alcohol-related scores.

 

 

 

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Majd Alkhalily¹, Laura Currey², Michael Piper², and Nyoman D Kurniawan^1
1Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, ²The School of Biomedical Sciences, The University of Queensland, Brisbane, Australia

Keywords: Brain Connectivity, Genetic Diseases, Tractography, Connectome, EED, Neurodevelopment, MRI, HARDI

Mutation in embryonic ectoderm development (EED) gene in mice have been shown to exhibit microcephaly at birth, however, the adult phenotype is unknown. This study investigates brain connectivity changes arising from EED ablation in adult mice using high angular resolution diffusion imaging. Three groups of adult mice (homozygotes, heterozygotes, controls) were scanned using 16.4 T to acquire diffusion data. Data was then used to compute iFOD2 maps, probabilistic whole brain tractograms, and the connectomes. Connectomic were compared both connectivity- and network-wise. Homozygotes had abnormal connectivity and network metrics suggesting brain under-development, highlighting the importance of EED’s role in brain development.