Robert Englert¹, Balint Kincses², Giuseppe Gallitto², Raviteja Kotikalapudi¹, Kevin Hoffschlag¹, and Tamas Spisak^1
1Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany, Essen, Germany, ²Department of Neurology, University Hospital Essen, Germany, Essen, Germany

We propose a novel method which considers the functional connectome as an already-trained, empirical continuous Hopfield Network, to extract brain states from a population connectome to analyze the dynamics of the so-called attractor states on the subject level. We apply our method to the Human Connectome Project dataset, and we could show, that the mean activation of the participants during different states is a significant predictor of fluid intelligence.

Bradley Fitzgerald¹, Sumra Bari², Nicole Vike², Roy J. Lycke³, Joshua D. Auger⁴, Eric Nauman⁵, Joaquin Goni^6,7, and Thomas M. Talavage^1,8
1Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, United States, ²Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States, ³Department of Electrical and Computer Engineering, Rice University, Houton, TX, United States, ⁴Department of Mechanical Engineering, Boston University, Boston, MA, United States, ⁵School of Mechanical Engineering, Purdue University, West Lafayette, IN, United States, ⁶School of Industrial Engineering, Purdue University, West Lafayette, IN, United States, ⁷Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, IN, United States, ⁸Department of Biomedical Engineering, University of Cincinnati, Cincinnati, OH, United States

We used resting state functional MRI data to investigate whether asymptomatic high school American football athletes undergo functional connectome (FC) changes during exposure to head acceleration events (HAEs). FCs of asymptomatic high school football athletes were shown to undergo significant changes during the season and then return to their pre-season state after the conclusion of collision activities, with changes reflected most profoundly in the somatomotor network. Late-season changes appear to affect all athletes without association with the magnitude of HAEs experienced. These observations demonstrate that participation in American football can cause significant changes in brain functionality without manifesting symptoms.

Morteza Esmaeili^1,2 and Alireza Salami^3,4,5
1Department of Diagnostic Imaging, Akershus University Hospital, Lørenskog, Norway, ²Department of Electrical Engineering and Computer Science, University of Stavanger, Stavanger, Norway, ³Wallenberg Center for Molecular Medicine (WCMM), Umeå University, Umeå, Sweden, ⁴Department of Integrative Medical Biology, Umeå University, Umeå, Sweden, ⁵Aging Research Center, Karolinska Institute and Stockholm University, Solna, Sweden

Machine learning approaches provide convenient autonomous object classification in medical imaging domains. This study examines the utility of convolutional neural networks in predicting individual differences in cognition from the resting-state functional connectome.  We observed significant contributions from the subcortical areas (including hippocampus) and their interactions with the cortical default mode network to the training progress. Our results demonstrate that an AI-based model can predict an individual's EM scores.

Natalie Forde¹, Alberto Llera¹, Jan Buitelaar¹, and Christian Beckmann^1
1Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Netherlands

Understanding brain-behaviour correlates is challenging. Multimodal approaches increase the sensitivity to brain-behaviour relationships. Here we integrated grey matter density maps, structural connectivity metrics and functional connectopic (gradient) maps from 676 participants of the HCP, with the hypothesis that these more advanced measures of both structural and functional organisation would improve sensitivity to brain-behaviour associations. Our results show 1 component significantly associated with various behavioural measures including multiple measures of alcohol use. This component was composed of structural modalities. There was little shared variance between structural measures and functional gradients across the components.

Nicholas Maurice Simard^1,2, Dinesh A Kumbhare^3,4, Stephan Ulmer^5,6, and Michael D Noseworthy^1,2,7,8
1Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada, ²St. Joseph's Healthcare Hamilton, Imaging Research Centre, Hamilton, ON, Canada, ³Toronto Rehabilitation Institute, Toronto, ON, Canada, ⁴Department of Medicine, University of Toronto, Toronto, ON, Canada, ⁵neurorad.ch, Zurich, Switzerland, ⁶Department of Radiology and Neuroradiology, University hospital of Schleswig-Holstein, Kiel, Germany, ⁷School of Biomedical Engineering, McMaster University, Hamilton, ON, Canada, ⁸Radiology, McMaster University, Hamilton, ON, Canada

Understanding the process of aging and the differences in sex with regards to large data repositories can help improve the implementation of machine learning and artificial intelligence paradigms in neuroimaging. The following research presents data that identifies a relationship between aging and sex in resting state functional magnetic resonance imaging (rs-fMRI) data. Using over 10,000 age and sex matched healthy controls and performing a homebuilt processing pipeline for rs-fMRI data, significant relationships between aging and reduced temporal complexity (TC) was found (p=0.03058), along with women having a higher TC than men (p=0.000623).

Xue Li^1,2, Xin Xu², Jiang Xi¹, Xiaoqi Huang^2,3, and Haoyang Xing^1,2
1College of Physics, Sichuan University, Chengdu, China, ²Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, Department of Radiology, West China Hospital, Sichuan University,, Chengdu, China, ³Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China

This study explored the seed-based functional connectivity (FC) and topological organization alteration applied different TBS protocols over precuneus in healthy adults. Increased FC were found in sensorimotor network and insula after application of iTBS or cTBS, decreased FC in left thalamus was found after cTBS. Only cTBS induced significant topological alterations in whole-brain network characterized by small-worldness on FC and functional topology organization, The findings suggested distal effects were complicated and could not been simply summarized as inhibition/facilitation like as proximal rTMS effects. This study may help to elucidate the underlying mechanisms of information transmission through brain function connectivity.

S Senthil Kumaran¹, A Ankeeta¹, Himanshu Singh¹, and Nisha Chauhan^1
1Department of NMR and MRI Facility, All India Institute of Medical Sciences, New Delhi, India

The effects of cumulative lifetime noise exposure on ascending auditory pathway function in audio metrically normal individuals is explored by sustained and transient fMRI responses and related auditory brainstem response (ABR). The experiment included continuous (scanner noise) and deviant noise (additional pink noise) levels that affected mental workload and auditory/visual attention. Our results exhibited reduced attention and mental workload in central auditory regions in people exposed to higher noise (>85dBA) in comparison with those exposed to lesser noise (<85dBA).

Francesca Saviola¹, Stefano Tambalo¹, Donna Gift Cabalo¹, Lisa Novello¹, Enrica Pierotti¹, Alessandra Dodich¹, Costanza Papagno¹, Luca Turella¹, Dimitri Van De Ville^2,3, and Jorge Jovicich^1
1University of Trento, Center for Mind/Brain Sciences, Rovereto, Italy, ²Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, ³Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland

An open discussion in functional connectivity (FC) studies is the mitigation of motion-related artifacts. Data-driven denoising such as Independent Component Analysis (ICA) could help in improving the reproducibility of results, however, the definition of pipelines to deal with mild to high motion cases is still controversial. We estimate the effect of different workflows optimized for best-controlling head motion both in healthy and Parkinson’s Disease cohorts. Regardless of baseline head motion level, ICA-based control of motion confounds affects functional connectivity metrics, with non-negligible impact on static connectivity and most severe effects on temporal and spatial features of dynamic functional connectivity measures.

Wen Zhang¹, Jilei Zhang², and Bing Zhang^1
1Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, Nanjing, China, ²Philips Healthcare, Shanghai, China, Shanghai, China

Patients with type 2 diabetes mellitus (T2DM) often have pathologically altered lipid metabolism. Evidence has confirmed that dyslipidemia is linked to cognitive decline. We explored the large-scale functional network connectivity alterations and their potential relationships with serum lipids and cognitive performance in T2DM patients. Our results suggested lower inter and intra-network functional connectivity in T2DM patients and the possibility of improving cognitive function through lipids management.

Wei-Tang Chang^1,2, Stephanie Langella³, Khoi Huynh², Min Sung Seo⁴, Pew-Thian Yap², Weili Lin², and Kelly Sullivan Giovanello^4
1Radiology, UNIV OF NORTH CAROLINA AT CHAPEL HILL, Chapel Hill, NC, United States, ²BRIC, UNIV OF NORTH CAROLINA AT CHAPEL HILL, Chapel Hill, NC, United States, ³Martinos center, Massachusetts General Hospital, Charlestown, MA, United States, ⁴Psychology, UNIV OF NORTH CAROLINA AT CHAPEL HILL, Chapel Hill, NC, United States

The interactions between the hippocampal subfields (HCSFs) and cerebral cortex during memory are unclear due to the inherently-low SNR, insufficient spatial resolution or limited spatial coverage. This study is the first fMRI study to examine how the HCSFs interact with the entire cortical surface during the memory encoding and retrieval process by addressing the above-mentioned issues successfully. Our results suggested that 1) the cortical-HCSF functional connectivity (FC) during encoding generally decreased compared to resting, 2) the cortical-HCSF FC increased compared to encoding during retrieval; 3) stronger FC between posterior CA1 and dorsal attention network are correlated with better behavioural performance.

Jeff Sharpe^1,2, Bruno Sa de la Rocque Guimaraes^1,3, and Stefan Posse^1,4
1Neurology Department, University of New Mexico, Albuquerque, NM, United States, ²Computer Science Department, University of New Mexico, Albuquerque, NM, United States, ³Nuclear Engineering Department, University of New Mexico, Albuquerque, NM, United States, ⁴Physics and Astronomy Department, University of New Mexico, Albuquerque, NM, United States

In the present study we develop a real-time seed-based correlation analysis (SBC) pipeline with online regression to compute a connectome fingerprint matrix and characterize the performance of this methodology for quantifying intra- and inter-network connectivity dynamics across major resting state networks (RSNs) in healthy subjects. We assess the association between connectivity in the limbic system and intensity of self-induced mood states with neurofeedback based on the connectome matrix.

Kristina Sabaroedin^1,2,3,4, Will Wilson^1,5, Daniel J Pittman^1,5,6, Paolo Federico^1,3,5,6, and Pierre LeVan^1,2,3,4
1Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada, ²Department of Paediatrics, University of Calgary, Calgary, AB, Canada, ³Department of Radiology, University of Calgary, Calgary, AB, Canada, ⁴Alberta Children’s Hospital Research Institute, University of Calgary, Calgary, AB, Canada, ⁵Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada, ⁶Seaman Family MR Research Centre, University of Calgary, Calgary, AB, Canada

Functional connectivity (FC) of resting-state fMRI (rs-fMRI) is a popular method for characterizing brain connectivity. rs-fMRI indirectly measures neuronal fluctuations through blood-oxygenation-level dependent (BOLD) signal and is susceptible to various physiological noise which lead to spurious correlations. We investigated the relationship between rs-fMRI FC and band-limited power (BLP) coupling measured using intracranial EEG (icEEG). Both static and dynamic connectivity analyses revealed a relationship between BOLD FC and icEEG BLP coupling across multiple frequency bands. However, this relationship depends on distance and electrode type. BOLD FC is distinct in fluctuations and spatial variability, suggesting non-neural signal sources in rs-fMRI connectivity.

Yingying Wang¹, Caihong Wang¹, Peifang Miao¹, Ying Wei¹, Luobing Wu¹, Jingliang Cheng¹, and Jingxia Guo^2
1The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²GE Healthcare MR Research, Beijing, China

To explore the mechanisms of cognitive impairment and reorganization in patients with left- and right-sided capsular stroke (CS_L, CS_R), we collected resting-state fMRI data from 62 CS_L, 56 CS_R patients and 57 normal controls (NC). Based on the triple-network model [involving the default mode, central executive and salience networks], static (SFNC) and dynamic functional network connectivities (DFNC) were performed to calculate inter-network functional connectivities and estimate temporal features in each state among the three groups. We found more significant SFNC and DFNC changes in CS_R group, suggesting more extensive cognitive impairment and reorganization in CS_R patients than those with CS_L.