Diffusion Magnetic Resonance Imaging (MRI)-Biomarkers for Diagnosis of Parkinson’s Disease

Parkinson’s disease (PD) is a degenerative neurological disorder, the origin of which remains unclear. The efficacy of treatments is limited due to the small number of remaining neurons. Diffusion magnetic resonance imaging (MRI) has revolutionized clinical neuroimaging. This noninvasive and quantitative method gathers in vivo microstructural information to characterize pathological processes that modify nervous tissue integrity. The changes in signal intensity result from the motion of the water molecules; they can be quantified by diffusivity measures. Diffusion MRI has revealed “biomarkers” in several brain regions that could be useful for PD diagnosis. These regions include the olfactory tracts, putamen, white matter, superior cerebellar peduncles, middle cerebellar peduncle, pons, cerebellum, and substantia nigra. There are encouraging preliminary data that differentiate PD from atypical parkinsonian diseases based on these microstructural changes

Potential application of ivim and dwi imaging in parkinson’s disease

Parkinson’s disease (PD) is a progressive degenerative neurological condition, which origin remains unclear. We are interested in proposing the study ofblood flow in the substantia nigra (SN) in PD patients, based on findings that demonstrated relative hypoactivity in PD patients located to subthalamicnucleus and SN. It is believed that this hipoactivity may suggest changes in the blood flow to the SN, where the particular loss of dopaminergic neuronsoccurs.The method used is the Incoherent Motion Intravoxel (IVIM) that allows measurement of blood flow to the microvascular level and recently has been producing high resolution quantitative perfusion maps.This paper proposes to measure the perfusion in PD patients and find any correlation with neural activity and water displacements within thetissue. Assuming decreasing the local perfusion suggests the possible impairments that affect the neural activity in PD causing the progressivedeath of neurons in the SN.Parkinson’s disease (PD) is a progressive degenerative neurological condition, which origin remains unclear. We are interested in proposing the study ofblood flow in the substantia nigra (SN) in PD patients, based on findings that demonstrated relative hypoactivity in PD patients located to subthalamicnucleus and SN. It is believed that this hipoactivity may suggest changes in the blood flow to the SN, where the particular loss of dopaminergic neuronsoccurs.The method used is the Incoherent Motion Intravoxel (IVIM) that allows measurement of blood flow to the microvascular level and recently has been producing high resolution quantitative perfusion maps.This paper proposes to measure the perfusion in PD patients and find any correlation with neural activity and water displacements within thetissue. Assuming decreasing the local perfusion suggests the possible impairments that affect the neural activity in PD causing the progressivedeath of neurons in the SN

Significance of cuproptosis- related genes in the diagnosis and classification of psoriasis

Cuproptosis is a novel form of cell death linked to mitochondrial metabolism and is mediated by protein lipoylation. The mechanism of cuproptosis in many diseases, such as psoriasis, remains unclear. In this study, signature diagnostic markers of cuproptosis were screened by differential analysis between psoriatic and non-psoriatic patients. The differentially expressed cuproptosis-related genes (CRGs) for patients with psoriasis were screened using the GSE178197 dataset from the gene expression omnibus database. The biological roles of CRGs were identified by GO and KEGG enrichment analyses, and the candidates of cuproptosis-related regulators were selected from a nomogram model. The consensus clustering approach was used to classify psoriasis into clusters and the principal component analysis algorithms were constructed to calculate the cuproptosis score. Finally, latent diagnostic markers and drug sensitivity were analyzed using the pRRophetic R package. The differential analysis revealed that CRGs (MTF1, ATP7B, and SLC31A1) are significantly expressed in psoriatic patients. GO and KEGG enrichment analyses showed that the biological functions of CRGs were mainly related to acetyl-CoA metabolic processes, the mitochondrial matrix, and acyltransferase activity. Compared to the machine learning method used, the random forest model has higher accuracy in the occurrence of cuproptosis. However, the decision curve of the candidate cuproptosis regulators analysis showed that patients can benefit from the nomogram model. The consensus clustering analysis showed that psoriasis can be grouped into three patterns of cuproptosis (clusterA, clusterB, and clusterC) based on selected important regulators of cuproptosis. In advance, we analyzed the immune characteristics of patients and found that clusterA was associated with T cells, clusterB with neutrophil cells, and clusterC predominantly with B cells. Drug sensitivity analysis showed that three cuproptosis regulators (ATP7B, SLC31A1, and MTF1) were associated with the drug sensitivity. This study provides insight into the specific biological functions and related mechanisms of CRGs in the development of psoriasis and indicates that cuproptosis plays a non-negligible role. These results may help guide future treatment strategies for psoriasis

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096

Potential application of ivim and dwi imaging in parkinson’s disease

Parkinson’s disease (PD) is a progressive degenerative neurological condition, which origin remains unclear. We are interested in proposing the study ofblood flow in the substantia nigra (SN) in PD patients, based on findings that demonstrated relative hypoactivity in PD patients located to subthalamicnucleus and SN. It is believed that this hipoactivity may suggest changes in the blood flow to the SN, where the particular loss of dopaminergic neuronsoccurs.The method used is the Incoherent Motion Intravoxel (IVIM) that allows measurement of blood flow to the microvascular level and recently has been producing high resolution quantitative perfusion maps.This paper proposes to measure the perfusion in PD patients and find any correlation with neural activity and water displacements within thetissue. Assuming decreasing the local perfusion suggests the possible impairments that affect the neural activity in PD causing the progressivedeath of neurons in the SN.Parkinson’s disease (PD) is a progressive degenerative neurological condition, which origin remains unclear. We are interested in proposing the study ofblood flow in the substantia nigra (SN) in PD patients, based on findings that demonstrated relative hypoactivity in PD patients located to subthalamicnucleus and SN. It is believed that this hipoactivity may suggest changes in the blood flow to the SN, where the particular loss of dopaminergic neuronsoccurs.The method used is the Incoherent Motion Intravoxel (IVIM) that allows measurement of blood flow to the microvascular level and recently has been producing high resolution quantitative perfusion maps.This paper proposes to measure the perfusion in PD patients and find any correlation with neural activity and water displacements within thetissue. Assuming decreasing the local perfusion suggests the possible impairments that affect the neural activity in PD causing the progressivedeath of neurons in the SN

Improved Butler–Reeds–Dawson Algorithm for the Inversion of Two-Dimensional NMR Relaxometry Data

Two-dimensional (2D) NMR relaxometry has been widely used as a powerful new tool for identifying and characterizing molecular dynamics. Various inversion algorithms have been introduced to obtain the versatile relaxation information conveyed by spectra. The inversion procedure is especially challenging because the relevant data are huge in 2D cases and the inversion problem is ill-posed. Here, we propose a new method to process the 2D NMR relaxometry data. Our approach varies from Tikhonov regularization, known previously in CONTIN and Maximum Entropy (MaxEnt) methods, which need additional efforts to compute an appropriate regularization factor. This variety improves Butler–Reeds–Dawson algorithm to optimize the Tikhonov regularization problem and the regularization factor is calculated alongside. The calculation is considerably faster than the mentioned algorithms. The proposed method is compared with some widely used methods on simulated datasets, regarding algorithm efficiency and noise vulnerability. Also, the result of the experimental data is presented to test the practical utility of the proposed algorithm. The results suggest that our approach is efficient and robust. It can meet different application requirements

The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer’s Disease and Mild Cognitive Impairment

A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD