A scoping review of resting-state brain functional alterations in Type 2 diabetes.

Resting-state functional magnetic resonance imaging (rs-fMRI) has been actively used in the last decade to investigate brain functional connectivity alterations in Type 2 Diabetes Mellitus (T2DM) to understand the neuropathophysiology of T2DM in cognitive degeneration. Given the emergence of new analysis techniques, this scoping review aims to map the rs-fMRI analysis techniques that have been applied in the literature and reports the latest rs-fMRI findings that have not been covered in previous reviews. Graph theory, the contemporary rs-fMRI analysis, has been used to demonstrate altered brain topological organisations in people with T2DM, which included altered degree centrality, functional connectivity strength, the small-world architecture and network-based statistics. These alterations were correlated with T2DM patients' cognitive performances. Graph theory also contributes to identify unbiased seeds for seed-based analysis. The expanding rs-fMRI analytical approaches continue to provide new evidence that helps to understand the mechanisms of T2DM-related cognitive degeneration.

Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.

BACKGROUND: MRI magnetization-prepared rapid acquisition (MPRAGE) is an easily available imaging modality for dementia diagnosis. Previous studies suggested that volumetric analysis plays a crucial role in various stages of dementia classification. In this study, volumetry, radiomics and demographics were integrated as inputs to develop an artificial intelligence model for various stages, including Alzheimer's disease (AD), mild cognitive decline (MCI) and cognitive normal (CN) dementia classifications. METHOD: The Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset was separated into training and testing groups, and the Open Access Series of Imaging Studies (OASIS) dataset was used as the second testing group. The MRI MPRAGE image was reoriented via statistical parametric mapping (SPM12). Freesurfer was employed for brain segmentation, and 45 regional brain volumes were retrieved. The 3D Slicer software was employed for 107 radiomics feature extractions from within the whole brain. Data on patient demographics were collected from the datasets. The feed-forward neural network (FFNN) and the other most common artificial intelligence algorithms, including support vector machine (SVM), ensemble classifier (EC) and decision tree (DT), were used to build the models using various features. RESULTS: The integration of brain regional volumes, radiomics and patient demographics attained the highest overall accuracy at 76.57% and 73.14% in ADNI and OASIS testing, respectively. The subclass accuracies in MCI, AD and CN were 78.29%, 89.71% and 85.14%, respectively, in ADNI testing, as well as 74.86%, 88% and 83.43% in OASIS testing. Balanced sensitivity and specificity were obtained for all subclass classifications in MCI, AD and CN. CONCLUSION: The FFNN yielded good overall accuracy for MCI, AD and CN categorization, with balanced subclass accuracy, sensitivity and specificity. The proposed FFNN model is simple, and it may support the triage of patients for further confirmation of the diagnosis.

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis.

BACKGROUND: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60-70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). METHOD: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. RESULTS: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. CONCLUSION: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.

Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry.

This study aimed to build automated detection models-one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)-to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer's Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. METHOD: 582 MRI T1-weighted images were retrieved from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. RESULTS: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. CONCLUSION: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.

Level of Amyloid-ß (Aß) Binding Leading to Differential Effects on Resting State Functional Connectivity in Major Brain Networks.

INTRODUCTION: Amyloid-ß protein (Aß) is one of the biomarkers for Alzheimer's disease (AD). The recent application of interhemispheric functional connectivity (IFC) in resting-state fMRI has been used as a non-invasive diagnostic tool for early dementia. In this study, we focused on the level of Aß accumulated and its effects on the major functional networks, including default mode network (DMN), central executive network (CEN), salience network (SN), self-referential network (SRN) and sensory motor network (SMN). METHODS: 58 participants (27 Hi Aß (HiAmy) and 31 low Aß (LowAmy)) and 25 healthy controls (HC) were recruited. [18F]flutemetamol PET/CT was performed for diseased groups, and MRI scanning was done for all participants. Voxel-by-voxel correlation analysis was done for both groups in all networks. RESULTS: In HiAmy, IFC was reduced in all networks except SN. A negative correlation in DMN, CEN, SRN and SMN suggests high Aß related to IFC reduction; However, a positive correlation in SN suggests high Aß related to an increase in IFC. In LowAmy, IFC increased in CEN, SMN, SN and SRN. Positive correlation in all major brain networks. CONCLUSION: The level of Aß accumulated demonstrated differential effects on IFC in various brain networks. As the treatment to reduce Aß plaque deposition is available in the market, it may be an option for the HiAmy group to improve their IFC in major brain networks.

Multi-Planar VMAT Plans for High-Grade Glioma and Glioblastoma Targeting the Hypothalamic-Pituitary Axis Sparing.

BACKGROUND: This study aimed to identify the better arc configuration of volumetric modulated arc therapy (VMAT) for high-grade glioma and glioblastoma, focusing on a dose reduction to the hypothalamic-pituitary axis through an analysis of dose-volumetric parameters, as well as a correlation analysis between the planned target volume (PTV) to organs at risk (OAR) distance and the radiation dose. METHOD: Twenty-four patients with 9 high-grade glioma and 15 glioblastomas were included in this study. Identical CT, MRI and structure sets of each patient were used for coplanar VMAT (CO-VMAT), dual planar VMAT (DP-VMAT) and multi-planar VMAT (MP-VMAT) planning. The dose constraints adhered to the RTOG0825 and RTOG9006 protocols. The dose-volumetric parameters of each plan were collected for statistical analysis. Correlation analyses were performed between radiation dose and PTV-OARs distance. RESULTS: The DP-VMAT and MP-VMAT achieved a significant dose reduction to most nearby OARs when compared to CO-VMAT, without compromising the dose to PTV, plan homogeneity and conformity. For centrally located OARs, including the hypothalamus, pituitary, brain stem and optic chiasm, the dose reductions ranged from 2.65 Gy to 3.91 Gy (p < 0.001) in DP-VMAT and from 2.57 Gy to 4 Gy (p < 0.001) in MP-VMAT. Similar dose reduction effects were achieved for contralaterally located OARs, including the hippocampus, optic nerve, lens and retina, ranging from 1.06 Gy to 4.37 Gy in DP-VMAT and from 0.54 Gy to 3.39 Gy in MP-VMAT. For ipsilaterally located OARs, DP-VMAT achieved a significant dose reduction of 1.75 Gy to Dmax for the optic nerve. In the correlation analysis, DP-VMAT and MP-VMAT showed significant dose reductions to centrally located OARs when the PTV-OAR distance was less than 4 cm. In particular, DP-VMAT offered better sparing to the optic chiasm when it was located less than 2 cm from the PTV than that of MP-VMAT and CO-VMAT. DP-VMAT and MP-VMAT also showed better sparing to the contralateral hippocampus and retina when they were located 3-8 cm from the PTV. CONCLUSION: The proposed DP-VMAT and MP-VMAT demonstrated significant dose reductions to centrally located and contralateral OARs and maintained the high plan qualities to PTV with good homogeneity and conformity when compared to CO-VMAT for high-grade glioma and glioblastoma. The benefit in choosing DP-VMAT and MP-VMAT over CO-VMAT was substantial when the PTV was located near the hypothalamus, pituitary, optic chiasm, contralateral hippocampus and contralateral retina.

Diagnostic Efficacy of Voxel-Mirrored Homotopic Connectivity in Vascular Dementia as Compared to Alzheimer's Related Neurodegenerative Diseases-A Resting State fMRI Study.

Previous studies have demonstrated that functional connectivity (FC) of different brain regions in resting state function MRI were abnormal in patients suffering from mild cognitive impairment (MCI) and Alzheimer's disease (AD) when comparing to healthy controls (HC) using seed based, independent component analysis (ICA) or small world network techniques. A new technique called voxel-mirrored homotopic connectivity (VMHC) was used in the current study to evaluate the value of interhemispheric functional connectivity (IFC) as a diagnostic tool to differentiate vascular dementia (VD) from other Alzheimer's related neurodegenerative diseases. Eighty-three participants were recruited from the university hospital memory clinic. A multidisciplinary panel formed by a neuroradiologist and two geriatricians classified the participants into VD (13), AD (16), MCI (29), and HC (25) based on clinical history, Montreal Cognitive Assessment Hong Kong version (HK­MoCA) neuropsychological score, structural MRI, MR perfusion, and 18-F Flutametamol (amyloid) PET-CT findings of individual subjects. We adopted the calculation method used by Kelly et al. (2011) and Zuo et al. (2010) in obtaining VMHC maps. Specific patterns of VMHC maps were obtained for VD, AD, and MCI to HC comparison. VD showed significant reduction in VMHC in frontal orbital gyrus and gyrus rectus. Increased VMHC was observed in default mode network (DMN), executive control network (ECN), and the remaining salient network (SN) regions. AD showed a reduction of IFC in all DMN, ECN, and SN regions; whereas MCI showed VMHC reduction in vSN, and increased VMHC in DMN and ECN. When combining VMHC values of relevant brain regions, the accuracy was improved to 87%, 92%, and 83% for VD, AD, and MCI from HC, respectively, in receiver operating characteristic (ROC) analysis. Through studying the VMHC maps and using VMHC values in relevant brain regions, VMHC can be considered as a reliable diagnostic tool for VD, AD, and MCI from HC.

Modified VMAT Plans for Locally Advanced Centrally Located Non-Small Cell Lung Cancer (NSCLC).

OBJECTIVES: This study aimed to find the optimal radiotherapy VMAT plans, that achieved high conformity and homogeneity to the planned target volume (PTV), and minimize the dose to nearby organs at risk including the non-PTV lung, heart and oesophagus for patients with centrally located non-small Cell Lung Cancer. METHODS: A total of 18 patients who were treated for stage III centrally located non-small Cell Lung Cancer were selected retrospectively for this study. Identical CT datasets, 4D CT and structure dataset were used for radiotherapy planning based on single-planar VMAT (SP-VMAT), dual-planar VMAT (DP-VMAT) and Hybrid VMAT (H-VMAT). For SP-VMAT, one full arc and two half arcs were created on single-plane with couch at 0°. For DP-VMAT, one full arc was created with couch at 0°, and two half arcs with couch rotation of 330° or 30°. For H-VMAT, anterior-posterior opposing fixed beam and two half arcs were planned at couch at 0°. Dose constraints were adhered to the RTOG0617. Dose volumetric parameters were collected for statistical analysis. RESULTS: There were no significant differences for the PTV, HI, CI between the SP-VMAT, DP-VMAT and H-VMAT. For the non-PTV lungs, Dmean, V20, V10, V5, D1500 and D1000 were significantly lower (2.05 Gy, 6.47%, 15.89%, 11.66% 4.17 Gy and 5.47 Gy respectively) in H-VMAT than that of SP-VMAT (all p < 0.001). For the oesophagus, Dmax, Dmean, V30 and V18.8 of H-VMAT were 0.08 Gy, 1.73 Gy, 5.54% and 7.17% lower than that of the SP-VMAT plan. For the heart, Dmean, V34, V28, V20 and V10 of DP-VMAT were lower than that of SP-VMAT by 1.45 Gy, 0.65%, 1.74%, 4.8% and 7.11% respectively. CONCLUSION: The proposed H-VMAT showed more favourable plan quality than the SP-VMAT for centrally located stage III NSCLC, in particular for non-PTV lungs and the oesophagus. It will benefit patients, especially those who planned for immunotherapy (Durvalumab) after standard chemo-irradiation. The proposed DP-VMAT plan showed significant dose reduction to the heart when compared to the H-VMAT plan.

Impaired cerebral blood flow in type 2 diabetes mellitus - A comparative study with subjective cognitive decline, vascular dementia and Alzheimer's disease subjects.

The link between non-demented type 2 diabetes mellitus (T2DM) and different types of cognitive impairment is controversial. By controlling for co-morbidities such as cerebral macrovascular and microvascular changes, cerebral atrophy, amyloid burden, hypertension or hyperlipidemia, the current study investigated the cerebral blood flow of T2DM individuals as compared to cognitively impaired subjects recruited from a memory clinic. 15 healthy control (71.8 ± 6.1 years), 18 T2DM (62.5 ± 3.7 years), as well as 8 Subjective Cognitive Decline (69.5 ± 7.5 years), 12 Vascular Dementia (79.3 ± 4.2 years) and 17 Alzheimer's Disease (75.1 ± 8.2 years) underwent multi-parametric MRI brain scanning. Subjects with T2DM and from the memory clinic also had 18-F Flutametamol PET-CT scanning to look for any amyloid burden. Pseudocontinuous Arterial Spin Labeling (PCASL), MR Angiography Head, 3D FLAIR and 3D T1-weighted sequences were used to quantify cerebral blood flow, cerebrovascular changes, white matter hyperintensities and brain atrophy respectively. Vascular risk factors were retrieved from the medical records. The 37 subjects from memory clinic were classified into subjective cognitive decline (SCD), vascular dementia (VD) and Alzheimer's disease (AD) subgroups by a multi-disciplinary panel consisting of a neuroradiologist, and 2 geriatricians. Absolute cortical CBF in our cohort of T2DM, SCD, VD and AD was significantly decreased (p < 0.01) as compared to healthy controls (HC) in both whole brain and eight paired brain regions, after age, normalized grey matter volume and gender adjustment and Bonferroni correction. Subgroup analysis between T2DM, SCD, VD, and AD revealed that CBF of T2DM was not significantly different from AD, VD or SCD. By controlling for co-morbidities, impaired cortical CBF in T2DM was not related to microangiopathy or amyloid deposition, but to the interaction of triple risk factors (such as diabetes mellitus, hypertension, and hyperlipidemia). There was statistically significant negative correlation (p ≤ 0.05) between adjusted CBF and HbA1c in all brain regions of T2DM and HC (with partial correlation ranging from -0.30 to -0.46). Taken together, altered cerebral blood flow in T2DM might be related to disruption of cerebrovascular autoregulation related to vascular risk factors, and such oligemia occurred before clinical manifestation due to altered glycemic control.