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Classification of Alzheimer disease using DenseNet-201 based on deep transfer learning technique

Zia-Ur-Rehman, Awang MK, Rashid J, Ali G, Hamid M, Mahmoud SF, et al.

PLoS One, 2024;19(9):e0304995.
PMID: 39240975 DOI: 10.1371/journal.pone.0304995

Alzheimer's disease (AD) is a brain illness that causes gradual memory loss. AD has no treatment and cannot be cured, so early detection is critical. Various AD diagnosis approaches are used in this regard, but Magnetic Resonance Imaging (MRI) provides the most helpful neuroimaging tool for detecting AD. In this paper, we employ a DenseNet-201 based transfer learning technique for diagnosing different Alzheimer's stages as Non-Demented (ND), Moderate Demented (MOD), Mild Demented (MD), Very Mild Demented (VMD), and Severe Demented (SD). The suggested method for a dataset of MRI scans for Alzheimer's disease is divided into five classes. Data augmentation methods were used to expand the size of the dataset and increase DenseNet-201's accuracy. It was found that the proposed strategy provides a very high classification accuracy. This practical and reliable model delivers a success rate of 98.24%. The findings of the experiments demonstrate that the suggested deep learning approach is more accurate and performs well compared to existing techniques and state-of-the-art methods.

Matched MeSH terms: Neuroimaging/methods
Visual brain activity patterns classification with simultaneous EEG-fMRI: A multimodal approach

Ahmad RF, Malik AS, Kamel N, Reza F, Amin HU, Hussain M

Technol Health Care, 2017;25(3):471-485.
PMID: 27935575 DOI: 10.3233/THC-161286

BACKGROUND: Classification of the visual information from the brain activity data is a challenging task. Many studies reported in the literature are based on the brain activity patterns using either fMRI or EEG/MEG only. EEG and fMRI considered as two complementary neuroimaging modalities in terms of their temporal and spatial resolution to map the brain activity. For getting a high spatial and temporal resolution of the brain at the same time, simultaneous EEG-fMRI seems to be fruitful.
METHODS: In this article, we propose a new method based on simultaneous EEG-fMRI data and machine learning approach to classify the visual brain activity patterns. We acquired EEG-fMRI data simultaneously on the ten healthy human participants by showing them visual stimuli. Data fusion approach is used to merge EEG and fMRI data. Machine learning classifier is used for the classification purposes.
RESULTS: Results showed that superior classification performance has been achieved with simultaneous EEG-fMRI data as compared to the EEG and fMRI data standalone. This shows that multimodal approach improved the classification accuracy results as compared with other approaches reported in the literature.
CONCLUSIONS: The proposed simultaneous EEG-fMRI approach for classifying the brain activity patterns can be helpful to predict or fully decode the brain activity patterns.

Matched MeSH terms: Functional Neuroimaging/methods*
Teaching Video NeuroImages: Purposeless groaning in progressive supranuclear palsy

Low SC, Tan AH, Lim SY

Neurology, 2017 01 03;88(1):e9.
PMID: 28025406 DOI: 10.1212/WNL.0000000000003465
Matched MeSH terms: Neuroimaging/methods*
Fulltext Augmented Reality Interface for Complex Anatomy Learning in the Central Nervous System: A Systematic Review

Wirza R, Nazir S, Khan HU, García-Magariño I, Amin R

J Healthc Eng, 2020;2020:8835544.
PMID: 32963749 DOI: 10.1155/2020/8835544

The medical system is facing the transformations with augmentation in the use of medical information systems, electronic records, smart, wearable devices, and handheld. The central nervous system function is to control the activities of the mind and the human body. Modern speedy development in medical and computational growth in the field of the central nervous system enables practitioners and researchers to extract and visualize insight from these systems. The function of augmented reality is to incorporate virtual and real objects, interactively running in a real-time and real environment. The role of augmented reality in the central nervous system becomes a thought-provoking task. Gesture interaction approach-based augmented reality in the central nervous system has enormous impending for reducing the care cost, quality refining of care, and waste and error reducing. To make this process smooth, it would be effective to present a comprehensive study report of the available state-of-the-art-work for enabling doctors and practitioners to easily use it in the decision making process. This comprehensive study will finally summarise the outputs of the published materials associate to gesture interaction-based augmented reality approach in the central nervous system. This research uses the protocol of systematic literature which systematically collects, analyses, and derives facts from the collected papers. The data collected range from the published materials for 10 years. 78 papers were selected and included papers based on the predefined inclusion, exclusion, and quality criteria. The study supports to identify the studies related to augmented reality in the nervous system, application of augmented reality in the nervous system, technique of augmented reality in the nervous system, and the gesture interaction approaches in the nervous system. The derivations from the studies show that there is certain amount of rise-up in yearly wise articles, and numerous studies exist, related to augmented reality and gestures interaction approaches to different systems of the human body, specifically to the nervous system. This research organises and summarises the existing associated work, which is in the form of published materials, and are related to augmented reality. This research will help the practitioners and researchers to sight most of the existing studies subjected to augmented reality-based gestures interaction approaches for the nervous system and then can eventually be followed as support in future for complex anatomy learning.

Matched MeSH terms: Neuroimaging/methods*
Fulltext Automatic Region-Based Brain Classification of MRI-T1 Data

Yazdani S, Yusof R, Karimian A, Mitsukira Y, Hematian A

PLoS One, 2016;11(4):e0151326.
PMID: 27096925 DOI: 10.1371/journal.pone.0151326

Image segmentation of medical images is a challenging problem with several still not totally solved issues, such as noise interference and image artifacts. Region-based and histogram-based segmentation methods have been widely used in image segmentation. Problems arise when we use these methods, such as the selection of a suitable threshold value for the histogram-based method and the over-segmentation followed by the time-consuming merge processing in the region-based algorithm. To provide an efficient approach that not only produce better results, but also maintain low computational complexity, a new region dividing based technique is developed for image segmentation, which combines the advantages of both regions-based and histogram-based methods. The proposed method is applied to the challenging applications: Gray matter (GM), White matter (WM) and cerebro-spinal fluid (CSF) segmentation in brain MR Images. The method is evaluated on both simulated and real data, and compared with other segmentation techniques. The obtained results have demonstrated its improved performance and robustness.

Matched MeSH terms: Neuroimaging/methods*
Characteristics of Perfusion Computed Tomography Imaging in Patients with Seizures Mimicking Acute Stroke

Khoo CS, Kim SE, Lee BI, Shin KJ, Ha SY, Park J, et al.

Eur Neurol, 2020;83(1):56-64.
PMID: 32320976 DOI: 10.1159/000506591

INTRODUCTION: Seizures as acute stroke mimics are a diagnostic challenge.
OBJECTIVE: The aim of the study was to characterize the perfusion patterns on perfusion computed tomography (PCT) in patients with seizures masquerading as acute stroke.
METHODS: We conducted a study on patients with acute seizures as stroke mimics. The inclusion criteria for this study were patients (1) initially presenting with stroke-like symptoms but finally diagnosed to have seizures and (2) with PCT performed within 72 h of seizures. The PCT of seizure patients (n = 27) was compared with that of revascularized stroke patients (n = 20) as the control group.
RESULTS: Among the 27 patients with seizures as stroke mimics, 70.4% (n = 19) showed characteristic PCT findings compared with the revascularized stroke patients, which were as follows: (1) multi-territorial cortical hyperperfusion {(73.7% [14/19] vs. 0% [0/20], p = 0.002), sensitivity of 73.7%, negative predictive value (NPV) of 80%}, (2) involvement of the ipsilateral thalamus {(57.9% [11/19] vs. 0% [0/20], p = 0.007), sensitivity of 57.9%, NPV of 71.4%}, and (3) reduced perfusion time {(84.2% [16/19] vs. 0% [0/20], p = 0.001), sensitivity of 84.2%, NPV of 87%}. These 3 findings had 100% specificity and positive predictive value in predicting patients with acute seizures in comparison with reperfused stroke patients. Older age was strongly associated with abnormal perfusion changes (p = 0.038), with a mean age of 66.8 ± 14.5 years versus 49.2 ± 27.4 years (in seizure patients with normal perfusion scan).
CONCLUSIONS: PCT is a reliable tool to differentiate acute seizures from acute stroke in the emergency setting.

Matched MeSH terms: Neuroimaging/methods*
Alzheimer's disease prediction algorithm based on de-correlation constraint and multi-modal feature interaction

Cheng J, Wang H, Wei S, Mei J, Liu F, Zhang G

Comput Biol Med, 2024 Mar;170:108000.
PMID: 38232453 DOI: 10.1016/j.compbiomed.2024.108000

Alzheimer's disease (AD) is a neurodegenerative disease characterized by various pathological changes. Utilizing multimodal data from Fluorodeoxyglucose positron emission tomography(FDG-PET) and Magnetic Resonance Imaging(MRI) of the brain can offer comprehensive information about the lesions from different perspectives and improve the accuracy of prediction. However, there are significant differences in the feature space of multimodal data. Commonly, the simple concatenation of multimodal features can cause the model to struggle in distinguishing and utilizing the complementary information between different modalities, thus affecting the accuracy of predictions. Therefore, we propose an AD prediction model based on de-correlation constraint and multi-modal feature interaction. This model consists of the following three parts: (1) The feature extractor employs residual connections and attention mechanisms to capture distinctive lesion features from FDG-PET and MRI data within their respective modalities. (2) The de-correlation constraint function enhances the model's capacity to extract complementary information from different modalities by reducing the feature similarity between them. (3) The mutual attention feature fusion module interacts with the features within and between modalities to enhance the modal-specific features and adaptively adjust the weights of these features based on information from other modalities. The experimental results on ADNI database demonstrate that the proposed model achieves a prediction accuracy of 86.79% for AD, MCI and NC, which is higher than the existing multi-modal AD prediction models.

Matched MeSH terms: Neuroimaging/methods
Fulltext Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism

Usman OL, Muniyandi RC, Omar K, Mohamad M

PLoS One, 2021;16(2):e0245579.
PMID: 33630876 DOI: 10.1371/journal.pone.0245579

Achieving biologically interpretable neural-biomarkers and features from neuroimaging datasets is a challenging task in an MRI-based dyslexia study. This challenge becomes more pronounced when the needed MRI datasets are collected from multiple heterogeneous sources with inconsistent scanner settings. This study presents a method of improving the biological interpretation of dyslexia's neural-biomarkers from MRI datasets sourced from publicly available open databases. The proposed system utilized a modified histogram normalization (MHN) method to improve dyslexia neural-biomarker interpretations by mapping the pixels' intensities of low-quality input neuroimages to range between the low-intensity region of interest (ROIlow) and high-intensity region of interest (ROIhigh) of the high-quality image. This was achieved after initial image smoothing using the Gaussian filter method with an isotropic kernel of size 4mm. The performance of the proposed smoothing and normalization methods was evaluated based on three image post-processing experiments: ROI segmentation, gray matter (GM) tissues volume estimations, and deep learning (DL) classifications using Computational Anatomy Toolbox (CAT12) and pre-trained models in a MATLAB working environment. The three experiments were preceded by some pre-processing tasks such as image resizing, labelling, patching, and non-rigid registration. Our results showed that the best smoothing was achieved at a scale value, σ = 1.25 with a 0.9% increment in the peak-signal-to-noise ratio (PSNR). Results from the three image post-processing experiments confirmed the efficacy of the proposed methods. Evidence emanating from our analysis showed that using the proposed MHN and Gaussian smoothing methods can improve comparability of image features and neural-biomarkers of dyslexia with a statistically significantly high disc similarity coefficient (DSC) index, low mean square error (MSE), and improved tissue volume estimations. After 10 repeated 10-fold cross-validation, the highest accuracy achieved by DL models is 94.7% at a 95% confidence interval (CI) level. Finally, our finding confirmed that the proposed MHN method significantly outperformed the normalization method of the state-of-the-art histogram matching.

Matched MeSH terms: Neuroimaging/methods*
Fulltext A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank

Shen X, Howard DM, Adams MJ, Hill WD, Clarke TK, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, et al.

Nat Commun, 2020 05 08;11(1):2301.
PMID: 32385265 DOI: 10.1038/s41467-020-16022-0

Depression is a leading cause of worldwide disability but there remains considerable uncertainty regarding its neural and behavioural associations. Here, using non-overlapping Psychiatric Genomics Consortium (PGC) datasets as a reference, we estimate polygenic risk scores for depression (depression-PRS) in a discovery (N = 10,674) and replication (N = 11,214) imaging sample from UK Biobank. We report 77 traits that are significantly associated with depression-PRS, in both discovery and replication analyses. Mendelian Randomisation analysis supports a potential causal effect of liability to depression on brain white matter microstructure (β: 0.125 to 0.868, pFDR

Matched MeSH terms: Neuroimaging/methods
Noncoding CGG repeat expansions in neuronal intranuclear inclusion disease, oculopharyngodistal myopathy and an overlapping disease

Ishiura H, Shibata S, Yoshimura J, Suzuki Y, Qu W, Doi K, et al.

Nat Genet, 2019 08;51(8):1222-1232.
PMID: 31332380 DOI: 10.1038/s41588-019-0458-z

Noncoding repeat expansions cause various neuromuscular diseases, including myotonic dystrophies, fragile X tremor/ataxia syndrome, some spinocerebellar ataxias, amyotrophic lateral sclerosis and benign adult familial myoclonic epilepsies. Inspired by the striking similarities in the clinical and neuroimaging findings between neuronal intranuclear inclusion disease (NIID) and fragile X tremor/ataxia syndrome caused by noncoding CGG repeat expansions in FMR1, we directly searched for repeat expansion mutations and identified noncoding CGG repeat expansions in NBPF19 (NOTCH2NLC) as the causative mutations for NIID. Further prompted by the similarities in the clinical and neuroimaging findings with NIID, we identified similar noncoding CGG repeat expansions in two other diseases: oculopharyngeal myopathy with leukoencephalopathy and oculopharyngodistal myopathy, in LOC642361/NUTM2B-AS1 and LRP12, respectively. These findings expand our knowledge of the clinical spectra of diseases caused by expansions of the same repeat motif, and further highlight how directly searching for expanded repeats can help identify mutations underlying diseases.

Matched MeSH terms: Neuroimaging/methods