3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI

Duc, Nguyen Thanh; Ryu, Seungjun; Qureshi, Muhammad Naveed Iqbal; Choi, Min Sung; Lee, Kun Ho; Lee, Boreom

doi:10.1007/s12021-019-09419-w

Cited by 143 publications

(71 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we have exploited Gwangju Alzheimer Research Data (GARD) [34][35][36] and Alzheimer Neuroimaging Initiative Data. ADNI was exploited for comparison with stat-of-the-art methods while extensive analysis was done for GARD database.…”

Section: Data Setmentioning

confidence: 99%

“…The sMRI scans were acquired from the registered subjects at the NRCD during the time period of 2014 to March 2018. The subject selection, MRI acquisition and exclusion criteria are mentioned in [34][35][36].…”

Section: Gard Datasetmentioning

confidence: 99%

“…The sMRI scans were processed using the FreeSurfer software (FSS) version 5.3.0 [37] with an automated reconstruction protocol described in [38][39][40]. Pure volume (P), percentile of intracranial volume (V) and cortical thickness (T) of 101 ROIs for each scan were assessed using the measurement techniques described in [34][35][36]. The test-retest reproducibility of each quantitative measure was assessed.…”

Section: Gard Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

Self Cite

View full text Add to dashboard Cite

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

show abstract

Section: Data Setmentioning

confidence: 99%

Section: Gard Datasetmentioning

confidence: 99%

Section: Gard Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep learning is a powerful tool for solving numerous complex problems in various disciplines, such as pattern recognition, speech recognition and medical imaging. Various complicated medical imaging problems have already been addressed using deep learning-based algorithms [9], [37]- [41]. Wachinger et al [9] proposed the deep learningbased DeepNAT method to segment the neuroanatomy.…”

Section: A Prior Workmentioning

confidence: 99%

Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network

et al. 2020

Self Cite

View full text Add to dashboard Cite

Automatic hippocampal volume measurement from brain magnetic resonance imaging (MRI) is a crucial task and an important research area, especially in the study of neurodegenerative diseases; hippocampal volume atrophy is known to be connected with Alzheimer's disease. In this research work, we propose a deep learning-based method to automatically measure the discrete hippocampal volume without prior segmentation of the volumetric MRI scans. We constructed a 2-D convolutional neural network (CNN) model that uses 3-channel 2-D patches to predict the number of voxels attributed to the hippocampus; the number of estimated hippocampal voxels is multiplied by the voxel volume to measure the discrete volume of the hippocampus. In addition, we demonstrate a preprocessing scheme to prepare the data using a relatively small number of MRI scans. The average errors in the measured volumes of the proposed approach and the compared atlas-based system were 4.3173 ± 3.5436 (avg. error% ± STD) and 4.1562 ±3.5262 (avg. error % ± STD) for the left and right hippocampi, respectively. The correlation coefficients of the proposed approach with atlas-based volume measurement were statistically significant (p-value < 0.01, R 2 = 0.834 (left hippocampus), and R 2 = 0.848 (right hippocampus) based on 0.05 significance level), which suggests that the proposed approach can be used as a proxy method for the atlas-based system. Furthermore, the proposed approach is computationally efficient and requires less than 2 seconds to calculate the number of voxels for an MRI scan. Moreover, our method outperforms the state-of-the-art deep learning approach, such as 2-D U-Net and SegNet in the context of voxel/volume estimation errors% for the left and right hippocampi.

show abstract

“…State-of-the-art neuroimaging and machine learning in computational neurosciences have offered novel strategies to study brain mechanisms [50][51][52][53]. This paper introduced a new framework which, when applied to multivariate high temporal resolution EEG, revealed microstate source generators and functional connectomics coordinated in speech perception.…”

Section: A Brain Dynamics In Speech Perceptionmentioning

confidence: 99%

Decoding Brain Dynamics in Speech Perception Based on EEG Microstates Decomposed by Multivariate Gaussian Hidden Markov Model

Duc

Lee

2020

IEEE Access

Self Cite

View full text Add to dashboard Cite

This study aims to reveal dynamic brain networks during speech perception. All male subjects were presented five English vowel [a], [e], [i], [o], and [u] stimuli. Brain dynamics were decoded using multivariate Gaussian hidden Markov model (MGHMM), which trained on spatiotemporal patterns of broadband multivariate event-related potential amplitudes to identify distinct broadband EEG microstates (MS), microstate source imaging, and microstate functional connectivity (μFC). Obtained results showed fluctuated cortical generators and μFC in eight microstates throughout the perception. Microstate source imaging revealed involvements of bilateral (left-side dominance) posterior superior temporal cortex (TC), inferior frontal gyrus (IFG), and supramarginal regions in perception. Precentral cortex where primary motor cortex located was also significantly activated. These regions were early appeared at 96-151 ms (left-side dominance) and at 186-246 ms (left hemisphere only) after the stimuli onset. Results from μFC revealed significant increases in delta (2.5-4.5 Hz), theta (4.5-8.5 Hz), alpha (12.5-14.5 Hz), beta (22.5-24.5 Hz), low gamma (30.5-32.5, 38.5-40.5 Hz) but decreases in high gamma (42.5-46.5 Hz) bands in perception. Increased FC were observed mainly at; (1) microstate segments 34-95 ms (MS2) and 96-151 ms (MS3) in early stages, (2) microstate intervals 186-246 ms (MS5) and 297-449 ms (MS6) in subsequent stages of perception. We found that stronger statistical FC differences in perception at TCs, with respect to left IFG (Broca' area), left TC, and precentral areas. Furthermore, by conducting a comparative protocol measuring FC distinction degree, we showed performance improvements of 8.01% (p-value=0.0162), 14.41% (p-value=0.006) when compared MGHMM to well established Lehmann-based modified K-means, Atomize and Agglomerative Hierarchical Clustering and 8.791% (p-value=0.0097) over the combination of K-means and sliding window methods, respectively. This study indicates the usefulness of EEG microstates to investigate broadband brain dynamics in speech perception. The current findings based on male subjects would be generalized more by future studies with a larger appropriate sample size including female subjects.

show abstract

3D-Deep Learning Based Automatic Diagnosis of Alzheimer’s Disease with Joint MMSE Prediction Using Resting-State fMRI

Cited by 143 publications

References 73 publications

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging

Automatic Localization and Discrete Volume Measurements of Hippocampi From MRI Data Using a Convolutional Neural Network

Decoding Brain Dynamics in Speech Perception Based on EEG Microstates Decomposed by Multivariate Gaussian Hidden Markov Model

Contact Info

Product

Resources

About