506 Hits in 6.7 sec

A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [article]

Mengjia Xu, David Lopez Sanz, Pilar Garces, Fernando Maestu, Quanzheng Li, Dimitrios Pantazis
2020 arXiv   pre-print
We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network  ...  Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression  ...  Briefly, the MG2G model learns non-linear node embeddings from original high-dimensional brain networks into a stochastic latent space.  ... 
arXiv:2005.05784v2 fatcat:77x3jlz7njgxxdzbuuzj4kzfki

Bayesian recurrent state space model for rs-fMRI [article]

Arunesh Mittal, Scott Linderman, John Paisley, Paul Sajda
2020 arXiv   pre-print
In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI.  ...  We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data.  ...  State-space model with deep learning for functional dynamics estimation in resting-state fmri. NeuroImage, 129:292-307, 2016.  ... 
arXiv:2011.07365v1 fatcat:3i24euyqrze7hexohvfq2znzqe

Attention-Guided Autoencoder for Automated Progression Prediction of Subjective Cognitive Decline with Structural MRI [article]

Hao Guan, Ling Yue, Pew-Thian Yap, Andrea Bozoki, Mingxia Liu
2022 arXiv   pre-print
discriminative brain regions of interest defined in brain atlases, 3) a decoding module for reconstructing the original input, 4) a classification module for identification of brain diseases.  ...  Progressive SCD will convert to MCI with the potential of further evolving to AD.  ...  [27] reveal that training a deep convolutional neural network with AD/NC samples is beneficial for MCI conversion prediction.  ... 
arXiv:2206.12480v1 fatcat:ybfx4gwczbejpjfhgtqzcfjrxq

Interpretable Fusion Analytics Framework for fMRI Connectivity: Self-Attention Mechanism and Latent Space Item-Response Model [article]

Jeong-Jae Kim, Yeseul Jeon, SuMin Yu, Junggu Choi, Sanghoon Han
2022 arXiv   pre-print
Finally, in order to interpret the classification results, we employ a latent space item-response interaction network model to identify the significant functions that exhibit distinct connectivity patterns  ...  There have been several attempts to use deep learning based on brain fMRI signals to classify cognitive impairment diseases.  ...  In order to avoid redundancy and noise while maintaining core latent features in the data to represent functional brain network with the goal of embedding brain functions to estimate their connections,  ... 
arXiv:2207.01581v1 fatcat:pljf6lksrzamlhh3gwefbcaypa

State-space model with deep learning for functional dynamics estimation in resting-state fMRI

Heung-Il Suk, Chong-Yaw Wee, Seong-Whan Lee, Dinggang Shen
2016 NeuroImage  
with the network modelling.  ...  In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along  ...  B0101-15-0307, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)).  ... 
doi:10.1016/j.neuroimage.2016.01.005 pmid:26774612 pmcid:PMC5437848 fatcat:b5fhyiv7lzb5zegq2jptwhj7ve

Medical imaging diagnosis of early Alzheimer rsquo s disease

Ayman El-Baz
2018 Frontiers in Bioscience  
-The system achieved an identification accuracy of 92.78% with the latent symmetry of the brain, linear kernel, and a leave-one-out cross-validation strategy.  ...  (88) analyzed the importance of the latent brain symmetry and asymmetry parts in the AD subjects' identification.  ...  Dependency upon the enrolled data also represents a source of limitations in the context of applying computerized methods/techniques with AD.  ... 
doi:10.2741/4612 pmid:28930568 fatcat:6f5gzcdyireuro3ylaswz2p734

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI [chapter]

Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
2015 Lecture Notes in Computer Science  
In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification.  ...  By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label.  ...  Conclusion In this paper, we proposed a novel method to model functional dynamics in rs-fMRI for MCI identification.  ... 
doi:10.1007/978-3-319-24553-9_70 pmid:27054199 pmcid:PMC4820012 fatcat:nle66mvdb5gdjhtqszpkyhjgku

Automated identification of dementia using medical imaging: a survey from a pattern classification perspective

Chuanchuan Zheng, Yong Xia, Yongsheng Pan, Jinhu Chen
2015 Brain Informatics  
We also compare the reported performance of many recently published dementia identification algorithms.  ...  In this review paper, we summarized the automated dementia identification algorithms in the literature from a pattern classification perspective.  ...  Artificial neural network (ANN) ANNs are a family of models inspired by biological neural networks and are used to estimate or approximate functions that depend on a large number of inputs and are generally  ... 
doi:10.1007/s40708-015-0027-x pmid:27747596 pmcid:PMC4883162 fatcat:yefc226j4za75afkkkolg4m5n4

Language networks associated with computerized semantic indices

Serguei V.S. Pakhomov, David T. Jones, David S. Knopman
2015 NeuroImage  
We found that semantic clustering indices were associated with brain network connectivity in distinct areas including fronto-temporal, fronto-parietal and fusiform gyrus regions.  ...  We computed semantic clustering indices and compared them to brain network connectivity measures obtained with task-free fMRI in a sample consisting of healthy participants and those differentially affected  ...  34.2 for patients with MCI diagnosis.  ... 
doi:10.1016/j.neuroimage.2014.10.008 pmid:25315785 pmcid:PMC4402216 fatcat:sv4a4pq6krhqloczumzrhg2spi

A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis [article]

Li Zhang and Mingliang Wang and Mingxia Liu and Daoqiang Zhang
2020 arXiv   pre-print
This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis.  ...  Deep learning has been recently used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and has achieved significant  ...  The fMRI-methods extracted discriminative features from rs-fMRI brain images with functional connectivity networks.  ... 
arXiv:2005.04573v1 fatcat:64ze55onzfemhgpebvsewe3fki

Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

Buhari Ibrahim, Subapriya Suppiah, Normala Ibrahim, Mazlyfarina Mohamad, Hasyma Abu Hassan, Nisha Syed Nasser, M Iqbal Saripan
2021 Human Brain Mapping  
Resting-state fMRI (rs-fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI).  ...  FC of the default mode network (DMN) is commonly impaired in AD and MCI.  ...  The functional connectivity (FC) of brain networks refers to inter-regional synchrony, as detected from low-frequency fluctuations in the blood oxygen level dependent (BOLD) fMRI sequence (L.  ... 
doi:10.1002/hbm.25369 pmid:33942449 pmcid:PMC8127155 fatcat:7xtbqaupmbfjja5cuvc6kgu7su

Patterns of effective connectivity during memory encoding and retrieval differ between patients with mild cognitive impairment and healthy older adults

B.M. Hampstead, M. Khoshnoodi, W. Yan, G. Deshpande, K. Sathian
2016 NeuroImage  
level-dependent (BOLD) time series data, in order to examine the effective connectivity between brain regions during successful encoding and/or retrieval of object location associations in MCI patients  ...  In contrast, in the MCI patients, the right frontal eye field drove activation in  ...  Emory Alzheimer's Disease Research Center, National Institute on Aging (Grant 2P50AG025688) and Department of Veterans Affairs (B6366W to BMH), and was presented at the 2013 Annual meeting of the Society for  ... 
doi:10.1016/j.neuroimage.2015.10.002 pmid:26458520 pmcid:PMC5619652 fatcat:abegwp3lzrdvhmlvxir2rx5mzq

N6-methyladenosine (m6A) modification and its clinical relevance in cognitive dysfunctions

Bingying Du, Yanbo Zhang, Meng Liang, Zengkan Du, Haibo Li, Cunxiu Fan, Hailing Zhang, Yan Jiang, Xiaoying Bi
2021 Aging  
, and mild cognitive impairment (MCI).  ...  Collectively, these findings suggest that m6A methylations as potential biomarkers and therapeutic targets for cognitive dysfunction.  ...  China for his technical support. CONFLICTS OF INTEREST The authors declare no conflicts of interest related to this study.  ... 
doi:10.18632/aging.203457 pmid:34461609 pmcid:PMC8436914 fatcat:lysv2mqdjfbdzm4rrx6s6w3n7i

XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via Clinically-guided Prototype Learning [article]

Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
2022 arXiv   pre-print
These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging.  ...  We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning.  ...  ReLU was employed as the intermediate activation function in both networks, with a softmax for the last layer for the clinical stage.  ... 
arXiv:2207.13223v1 fatcat:hzkh2fb7gjh7romhk3ufqctssm

A Survey on Deep Learning for Neuroimaging-Based Brain Disorder Analysis

Li Zhang, Mingliang Wang, Mingxia Liu, Daoqiang Zhang
2020 Frontiers in Neuroscience  
This paper reviews the applications of deep learning methods for neuroimaging-based brain disorder analysis.  ...  Deep learning has recently been used for the analysis of neuroimages, such as structural magnetic resonance imaging (MRI), functional MRI, and positron emission tomography (PET), and it has achieved significant  ...  The fMRI-methods extracted discriminative features from rs-fMRI brain images with functional connectivity networks.  ... 
doi:10.3389/fnins.2020.00779 pmid:33117114 pmcid:PMC7578242 fatcat:tzdcq3kyyrefvn7vxgdj5lnhju
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