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Brain multimodal co-alterations related to delay discounting: a multimodal MRI fusion analysis in persons with and without cocaine use disorder

Christina S. Meade, Xiang Li, Sheri L. Towe, Ryan P. Bell, Vince D. Calhoun, Jing Sui
2021 BMC Neuroscience  
This study implemented a supervised multimodal fusion approach to reveal neural networks associated with delay discounting that distinguish persons with and without cocaine use disorder (CUD).  ...  Importantly, these multimodal networks were weaker in persons with CUD, indicating less cognitive control that may contribute to impulsive behaviors.  ...  This innovative multimodal fusion analysis has the potential to uncover biomarkers of CUD, and an exciting future direction is to incorporate longitudinal multimodal imaging and clinical data into the  ... 
doi:10.1186/s12868-021-00654-z pmid:34416865 pmcid:PMC8377830 fatcat:4l6qujig55altevaynsruz3abm

Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness

Vince D. Calhoun, Jing Sui
2016 Biological Psychiatry: Cognitive Neuroscience and Neuroimaging  
The complexity of the human brain coupled with the incomplete measurement provided by existing imaging technology makes multimodal fusion essential in order to mitigate against misdirection and hopefully  ...  We then provide a review of the diverse studies that have used multimodal data fusion (primarily focused on psychosis) as well as provide an introduction to some of the existing analytic approaches.  ...  There has been a rapid growth in the use of multimodal fusion approaches.  ... 
doi:10.1016/j.bpsc.2015.12.005 pmid:27347565 pmcid:PMC4917230 fatcat:nwzossoddfgohd4z2sjivd5i7e

A Selective Review of Multimodal Fusion Methods in Schizophrenia

Jing Sui, Qingbao Yu, Hao He, Godfrey D. Pearlson, Vince D. Calhoun
2012 Frontiers in Human Neuroscience  
It is becoming increasingly clear that multimodal fusion, a technique which takes advantage of the fact that each modality provides a limited view of the brain/gene and may uncover hidden relationships  ...  In this review paper, we survey a number of multimodal fusion applications which enable us to study the schizophrenia macro-connectome, including brain functional, structural, and genetic aspects and may  ...  -superior longitudinal fasciculus, and (4) parietal/frontal -thalamus (Xu et al., 2009) , which include a large number of brain regional networks and reflecting the widespread nature of the disease.  ... 
doi:10.3389/fnhum.2012.00027 pmid:22375114 pmcid:PMC3285795 fatcat:oyme4vbhvjh3rhy3gdk54wmoti

Function–structure associations of the brain: Evidence from multimodal connectivity and covariance studies

Jing Sui, Rene Huster, Qingbao Yu, Judith M. Segall, Vince D. Calhoun
2014 NeuroImage  
In particular, we believe that multimodal fusion approaches will shed further light on the neuronal mechanisms underlying the major structural and functional pathophysiological features of both the healthy  ...  Multimodal brain studies can be used to understand the complex interplay of anatomical, functional and physiological brain alterations or development, and to better comprehend the biological significance  ...  Acknowledgments This work was partially supported by the "100 Talent Plan" of Chinese Academy of Sciences (to Sui J), National Institutes of Health grants R01EB 006841 and R01EB005846 (to Calhoun VD),  ... 
doi:10.1016/j.neuroimage.2013.09.044 pmid:24084066 pmcid:PMC3969780 fatcat:tngvfiyrdreyvmcg55qyqs6o6u

Multimodal MR Images-Based Diagnosis of Early Adolescent Attention-Deficit/Hyperactivity Disorder Using Multiple Kernel Learning

Xiaocheng Zhou, Qingmin Lin, Yuanyuan Gui, Zixin Wang, Manhua Liu, Hui Lu
2021 Frontiers in Neuroscience  
diagnosis of brain diseases.  ...  The results indicate that the kernel-level fusion of multimodal features achieves 0.698 of AUC (area under the receiver operating characteristic curves) and 64.3% of classification accuracy for ADHD diagnosis  ...  This technique is appropriate for the highly correlated network of human brain structures.  ... 
doi:10.3389/fnins.2021.710133 pmid:34594183 pmcid:PMC8477011 fatcat:ckfesvvq6jbjfauz56q42ehe5q

Multimodal Neuroimaging Fusion in Nonsubsampled Shearlet Domain Using Location-scale Distribution by Maximizing the High Frequency Subband Energy

Emimal Jabason, M. Omair Ahmad, M.N.S. Swamy
2019 IEEE Access  
In this paper, we propose a novel multimodal fusion algorithm for brain imaging data based on the statistical properties of nonsubsampled shearlet transform (NSST) coefficients and a novel energy maximization  ...  It is seen from the subjective and objective results that the proposed multimodal neuroimaging fusion method significantly outperforms the state-of-the-art methods including under noisy scenarios, and  ...  Objective fusion results of 2-D neuroimages in 3) FUSION OF AN MR IMAGE WITH SPECT OR PET IMAGE TAKEN FROM WBA DATABASE We now consider the fusion of four pairs of multimodal brain images taken from  ... 
doi:10.1109/access.2019.2930225 fatcat:r3h7jeugubgrtheckudarvtmb4

Multimodal neuromarkers in schizophrenia via cognition-guided MRI fusion

Jing Sui, Shile Qi, Theo G. M. van Erp, Juan Bustillo, Rongtao Jiang, Dongdong Lin, Jessica A. Turner, Eswar Damaraju, Andrew R. Mayer, Yue Cui, Zening Fu, Yuhui Du (+13 others)
2018 Nature Communications  
A supervised learning strategy is used to guide three-way multimodal magnetic resonance imaging (MRI) fusion in two independent cohorts including both healthy individuals and individuals with schizophrenia  ...  These modality-specific brain regions define-in three separate cohorts-promising co-varying multimodal signatures that can be used as predictors of multi-domain cognition.  ...  Results Multimodal networks associated with cognitive composite scores. We aim to identify multimodal co-varying and modalityspecific brain networks associated with composite cognitive scores.  ... 
doi:10.1038/s41467-018-05432-w pmid:30072715 pmcid:PMC6072778 fatcat:4w3zoj4y7vg6vm3bpcxhl6q4ue

Modern Views of Machine Learning for Precision Psychiatry [article]

Zhe Sage Chen, Prathamesh Kulkarni, Isaac R. Galatzer-Levy, Benedetta Bigio, Carla Nasca, Yu Zhang
2022 arXiv   pre-print
We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion.  ...  Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment.  ...  Declaration of interests The authors declare no competing financial interests. References  ... 
arXiv:2204.01607v2 fatcat:coo557v2jzh6debycy3mhccfze

Adolescent development of multiscale cortical wiring and functional connectivity in the human connectome [article]

Bo-yong Park, Casey Paquola, Richard A.I. Bethlehem, Oualid Benkarim, Bratislav Misic, Jonathan Smallwood, Edward T. Bullmore, Boris Bernhardt, Neuroscience in Psychiatry Network (NSPN) Consortium
2021 bioRxiv   pre-print
Our findings provide new insights into adolescent brain network development, illustrating how the maturation of structural wiring interacts with the development of macroscale network function.  ...  Here, we analyzed structural and functional brain network development in an accelerated longitudinal cohort spanning 14-25 years (n = 199).  ...  Our multimodal framework, thus, provides novel insights into structural and functional brain development in adolescence, and points to an inherent coupling of developmental trajectories across both domains  ... 
doi:10.1101/2021.08.16.456455 fatcat:w5yswllbczfirjbp4b67pdb2km

Advances in Multimodal Data Fusion in Neuroimaging: Overview, Challenges, and Novel Orientation

Yu-Dong Zhang, Zhengchao Dong, Shui-Hua Wang, Xiang Yu, Xujing Yao, Qinghua Zhou, Hua Hu, Min Li, Carmen Jiménez-Mesa, Javier Ramirez, Francisco J. Martinez, Juan Manuel Gorriz
2020 Information Fusion  
We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations  ...  Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities.  ...  Wang, Zhao [265] used NSCT and simplified-spatial frequency-pulse coupled neural network to develop a multi-modal functional/anatomical medical image fusion framework.  ... 
doi:10.1016/j.inffus.2020.07.006 pmid:32834795 pmcid:PMC7366126 fatcat:3cmhcplb5bf2fgpx3kukifbj74

Cognitive Implications of Correlated Structural Network Changes in Schizophrenia

Dawn M. Jensen, Elaheh Zendrehrouh, Vince Calhoun, Jessica A. Turner
2022 Frontiers in Integrative Neuroscience  
While the relationship of each of these image modalities and their links to schizophrenia status and cognitive impairment has been investigated separately, a multimodal fusion via parallel independent  ...  of the middle temporal gyrus, precuneus cortex, postcentral gyrus, cingulate gyrus/cingulum, lingual gyrus, and brain stem.ConclusionThe results of this multimodal analysis adds to our understanding of  ...  Multimodal fusion of brain imaging data: a key to finding the missing link(s) in complex mental illness. Biol. Psychiatry Cogn. Neurosci.  ... 
doi:10.3389/fnint.2021.755069 pmid:35126065 pmcid:PMC8811375 fatcat:hiylxt3n5jbolc7glkkpjdztyq

iBEAT: A Toolbox for Infant Brain Magnetic Resonance Image Processing

Yakang Dai, Feng Shi, Li Wang, Guorong Wu, Dinggang Shen
2012 Neuroinformatics  
The performance of iBEAT has been comprehensively evaluated with hundreds of infant brain images. A Linux-based standalone package of iBEAT is freely available at http://www. nitrc.org/projects/ibeat.  ...  Second, a level-sets-based tissue segmentation algorithm that utilizes multimodality information, cortical thickness constraint, and longitudinal consistency constraint was also included in iBEAT for segmentation  ...  2005) , and network-based (Shi et al. 2012b) development of the infant brain.  ... 
doi:10.1007/s12021-012-9164-z pmid:23055044 fatcat:7fbxuzcyqfbt7ftaueoos34nhq

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Neural Networks 567 Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases 568 3D Deep Convolutional Neural Network Revealed the Value of Brain Network Overlap  ...  Epileptic Seizure Detection Using Coupled Hidden Markov Models 668 Efficient Groupwise Registration for MR Brain Images via Hierarchical Graph Set Shrinkage 669 Conditional Generative Adversarial Networks  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

DeepAD: A Robust Deep Learning Model of Alzheimer's Disease Progression for Real-World Clinical Applications [article]

Somaye Hashemifar, Claudia Iriondo, Evan Casey, Mohsen Hejrati
2022 arXiv   pre-print
We propose a novel multimodal multi-task deep learning model to predict AD progression by analyzing longitudinal clinical and neuroimaging data from multiple cohorts.  ...  learning with high dimensional images.  ...  AD is a slowly progressing disease caused by the degeneration of brain cells, with patients showing clinical symptoms years after the onset of the disease.  ... 
arXiv:2203.09096v3 fatcat:w63ascaoqnd6tbigxuzcc5cksa

Editorial: Identifying Neuroimaging-Based Markers for Distinguishing Brain Disorders

Yuhui Du, Jing Sui, Dongdong Lin
2020 Frontiers in Neuroscience  
Using both DTI and fMRI network measures, Park et al. reported the changes of individuals with eating disorder and found the brain regions associated with the behaviors.  ...  In the topic, Acar et al. applied an advanced coupled matrix and tensor factorizations (CMTF) method to the data of EEG, fMRI, and sMRI collected from patients with schizophrenia and healthy controls to  ...  Conflict of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  ... 
doi:10.3389/fnins.2020.00327 pmid:32322189 pmcid:PMC7156887 fatcat:ubeyljn6aza4bczsb544o2able
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