T1-weighted MRI Images Research Articles

Generative Adversarial Networks Can Create High Quality Artificial Prostate Cancer Magnetic Resonance Images

The recent integration of open-source data with machine learning models, especially in the medical field, has opened new doors to studying disease progression and/or regression. However, the ability to use medical data for machine learning approaches is limited by the specificity of data for a particular medical condition. In this context, the most recent technologies, like generative adversarial networks (GANs), are being looked upon as a potential way to generate high-quality synthetic data that preserve the clinical variability of a condition. However, despite some success, GAN model usage remains largely minimal when depicting the heterogeneity of a disease such as prostate cancer. Previous studies from our group members have focused on automating the quantitative multi-parametric magnetic resonance imaging (mpMRI) using habitat risk scoring (HRS) maps on the prostate cancer patients in the BLaStM trial. In the current study, we aimed to use the images from the BLaStM trial and other sources to train the GAN models, generate synthetic images, and validate their quality. In this context, we used T2-weighted prostate MRI images as training data for Single Natural Image GANs (SinGANs) to make a generative model. A deep learning semantic segmentation pipeline trained the model to segment the prostate boundary on 2D MRI slices. Synthetic images with a high-level segmentation boundary of the prostate were filtered and used in the quality control assessment by participating scientists with varying degrees of experience (more than ten years, one year, or no experience) to work with MRI images. Results showed that the most experienced participating group correctly identified conventional vs. synthetic images with 67% accuracy, the group with one year of experience correctly identified the images with 58% accuracy, and the group with no prior experience reached 50% accuracy. Nearly half (47%) of the synthetic images were mistakenly evaluated as conventional. Interestingly, in a blinded quality assessment, a board-certified radiologist did not significantly differentiate between conventional and synthetic images in the context of the mean quality of synthetic and conventional images. Furthermore, to validate the usability of the generated synthetic images from prostate cancer MRIs, we subjected these to anomaly detection along with the original images. Importantly, the success rate of anomaly detection for quality control-approved synthetic data in phase one corresponded to that of the conventional images. In sum, this study shows promise that high-quality synthetic images from MRIs can be generated using GANs. Such an AI model may contribute significantly to various clinical applications which involve supervised machine-learning approaches.

Detecting Cortical Thickness Changes in Epileptogenic Lesions Using Machine Learning.

Epilepsy is a neurological disorder characterized by abnormal brain activity. Epileptic patients suffer from unpredictable seizures, which may cause a loss of awareness. Seizures are considered drug resistant if treatment does not affect success. This leads practitioners to calculate the cortical thickness to measure the distance between the brain's white and grey matter surfaces at various locations to perform a surgical intervention. In this study, we introduce using machine learning as an approach to classify extracted measurements from T1-weighted magnetic resonance imaging. Data were collected from the epilepsy unit at King Abdulaziz University Hospital. We applied two trials to classify the extracted measurements from T1-weighted MRI for drug-resistant epilepsy and healthy control subjects. The preprocessing sequence on T1-weighted MRI images was performed using C++ through BrainSuite's pipeline. The first trial was performed on seven different combinations of four commonly selected measurements. The best performance was achieved in Exp6 and Exp7, with 80.00% accuracy, 83.00% recall score, and 83.88% precision. It is noticeable that grey matter volume and white matter volume measurements are more significant than the cortical thickness measurement. The second trial applied four different machine learning classifiers after applying 10-fold cross-validation and principal component analysis on all extracted measurements as in the first trial based on the mentioned previous works. The K-nearest neighbours model outperformed the other machine learning classifiers with 97.11% accuracy, 75.00% recall score, and 75.00% precision.

An Inverse Relationship Between Gray Matter Volume and Speech-in-Noise Performance in Tinnitus Patients with Normal Hearing Sensitivity.

Speech-in-noise (SiN) recognition difficulties are often reported in patients with tinnitus. Although brain structural changes such as reduced gray matter (GM) volume in auditory and cognitive processing regions have been reported in the tinnitus population, it remains unclear how such changes influence speech understanding, such as SiN performance. In this study, pure-tone audiometry and Quick Speech-in-Noise test were conducted on individuals with tinnitus and normal hearing and hearing-matched controls. T1-weighted structural MRI images were obtained from all participants. After preprocessing, GM volumes were compared between tinnitus and control groups using whole-brain and region-of-interest analyses. Further, regression analyses were performed to examine the correlation between regional GM volume and SiN scores in each group. The results showed decreased GM volume in the right inferior frontal gyrus in the tinnitus group relative to the control group. In the tinnitus group, SiN performance showed a negative correlation with GM volume in the left cerebellum (Crus I/II) and the left superior temporal gyrus; no significant correlation between SiN performance and regional GM volume was found in the control group. Even with clinically defined normal hearing and comparable SiN performance relative to controls, tinnitus appears to change the association between SiN recognition and regional GM volume. This change may reflect compensatory mechanisms utilized by individuals with tinnitus who maintain behavioral performance.

Brainstem volume changes in myalgic encephalomyelitis/chronic fatigue syndrome and long COVID patients.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and long COVID patients have overlapping neurological, autonomic, pain, and post-exertional symptoms. We compared volumes of brainstem regions for 10 ME/CFS (CCC or ICC criteria), 8 long COVID (WHO Delphi consensus), and 10 healthy control (HC) subjects on 3D, T1-weighted MRI images acquired using sub-millimeter isotropic resolution using an ultra-high field strength of 7 Tesla. Group comparisons with HC detected significantly larger volumes in ME/CFS for pons (p = 0.004) and whole brainstem (p = 0.01), and in long COVID for pons (p = 0.003), superior cerebellar peduncle (p = 0.009), and whole brainstem (p = 0.005). No significant differences were found between ME/CFS and long COVID volumes. In ME/CFS, we detected positive correlations between the pons and whole brainstem volumes with "pain" and negative correlations between the midbrain and whole brainstem volumes with "breathing difficulty." In long COVID patients a strong negative relationship was detected between midbrain volume and "breathing difficulty." Our study demonstrated an abnormal brainstem volume in both ME/CFS and long COVID consistent with the overlapping symptoms.

Shaveless focused-ultrasound-induced blood-brain barrier opening in mice

To demonstrate that focused ultrasound-induced blood-brain barrier opening (FUS-BBBO) in mice can be achieved without shaving hairs. We performed FUS-BBBO in mice by using oil as the coupling medium without shaving hairs. The hydrophobic nature of oil leads to a higher affinity to hair than water-based ultrasound gel. FUS-BBBO outcome was compared under three conditions: “oil + hairs,” “gel + hairs,” and “gel + no hairs.” T2-weighted, T1-weighted MRI, and fluorescence imaging of the ex vivo brain slices were performed to measure the quality of coupling and outcome of FUS-BBBO. Results showed that “oil + hairs” consistently achieved high-quality acoustic coupling without trapping air bubbles (Figures A & B). FUS-BBBO outcome was not significantly different between the “oil + hairs” group and the “gel + no hairs” group based on T1-weighted MRI (Figures C & D) and ex vivo fluorescence imaging (Figure E). The FUS-BBBO efficiencies for both the “oil + hairs” and “gel + no hairs” groups were significantly higher than the “gel + hairs” group. This study demonstrated that FUS-BBBO in mice could be achieved without shaving hairs. Oil provides a simple solution for achieving effective acoustic coupling for transcranial FUS procedures.

Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery.

To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED. 13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution. There was a significant increase (p < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (p < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (p < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution. ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.

Different neuroanatomical correlates for temporal and spectral supra-threshold auditory tasks and speech in noise recognition in older adults with hearing impairment.

Varying degrees of pure-tone hearing loss in older adults are differentially associated with cortical volume (CV) and thickness (CT) within and outside of the auditory pathway. This study addressed the question to what degree supra-threshold auditory performance (i.e., temporal compression and frequency selectivity) as well as speech in noise (SiN) recognition are associated with neurostructural correlates in a sample of 59 healthy older adults with mild to moderate pure-tone hearing loss. Using surface-based morphometry on T1-weighted MRI images, CT, CV, and surface area (CSA) of several regions-of-interest were obtained. The results showed distinct neurostructural patterns for the different tasks in terms of involved regions as well as morphometric parameters. While pure-tone averages (PTAs) positively correlated with CT in a right hemisphere superior temporal sulcus and gyrus cluster, supra-threshold auditory perception additionally extended significantly to CV and CT in left and right superior temporal clusters including Heschl's gyrus and sulcus, the planum polare and temporale. For SiN recognition, we found significant correlations with an auditory-related CT cluster and furthermore with language-related areas in the prefrontal cortex. Taken together, our results show that different auditory abilities are differently associated with cortical morphology in older adults with hearing impairment. Still, a common pattern is that greater PTAs and poorer supra-threshold auditory performance as well as poorerSiN recognition are all related to cortical thinning and volume loss but not to changes in CSA. These results support the hypothesis that mostly CT undergoes alterations in the context of auditory decline, while CSA remains stable.

Smaller Cerebellar Lobule VIIb is Associated with Tremor Severity in Parkinson's Disease.

Alterations in the cerebellum's morphology in Parkinson's disease (PD) point to its pathophysiological involvement in this movement disorder. Such abnormalities have previously been attributed to different PD motor subtypes. The aim of the study was to relate volumes of specific cerebellar lobules to motor symptom severity, in particular tremor (TR), bradykinesia/rigidity (BR), and postural instability and gait disorders (PIGD) in PD. We performed a volumetric analysis based on T1-weighted MRI images of 55 participants with PD (22 females, median age 65years, Hoehn and Yahr stage 2). Multiple regression models were fitted to investigate associations between volumes of cerebellar lobules with clinical symptom severity based on MDS-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part III score and sub-scores for TR, BR, and PIGD; adjusted for age, sex, disease duration, and intercranial volume as cofactors. Smaller volume of lobule VIIb was associated with higher tremor severity (P = 0.004). No structure-function relationships were detected for other lobules or other motor symptoms. This distinct structural association denotes the involvement of the cerebellum in PD tremor. Characterizing morphological features of the cerebellum leads to a better understanding of its role in the spectrum of motor symptoms in PD and contributes further to identifying potential biological markers.

Computer assisted diagnosis of Alzheimer's disease using statistical likelihood-ratio test.

The purpose of this work is to present a computer assisted diagnostic tool for radiologists in their diagnosis of Alzheimer's disease. A statistical likelihood-ratio procedure from signal detection theory was implemented in the detection of Alzheimer's disease. The probability density functions of the likelihood ratio were constructed by using medial temporal lobe (MTL) volumes of patients with Alzheimer's disease (AD) and normal controls (NC). The volumes of MTL as well as other anatomical regions of the brains were calculated by the FreeSurfer software using T1 weighted MRI images. The MRI images of AD and NC were downloaded from the database of Alzheimer's disease neuroimaging initiative (ADNI). A separate dataset of minimal interval resonance imaging in Alzheimer's disease (MIRIAD) was used for diagnostic testing. A sensitivity of 89.1% and specificity of 87.0% were achieved for the MIRIAD dataset which are better than the 85% sensitivity and specificity achieved by the best radiologists without input of other patient information.

Preoperative meningioma vascularity index is associated with significantly increased intraoperative blood loss and greater risk of subtotal resection.

Surgical recovery of meningiomas relies on a variety of factors, including tumor volume, vascularity, embolization status, and blood loss during excision. Although hypervascular meningiomas can potentially be amendable to embolization, methods for determining optimal vascularity for this procedure are currently lacking. Our group previously established the meningioma vascularity index (MVI) as a marker of tumor vascularity. In this study, we aim to build on our previous work and further examine the relationship between MVI and intraoperative estimated blood loss (EBL). A retrospective data extraction was conducted between August 2010 and October 2019 from patients undergoing craniotomy for meningioma. Of the 85 intracranial meningiomas included, 39 were embolized. Demographic data, extent of resection, embolization status, and EBL were among the extracted variables. Flow void volumes were measured on T2-weighted MRI images using a segmentation software with a voxel-based segmentation method. MVI was a predictor of EBL within the entire cohort, when controlling for tumor volume (R2 adjusted = 0.26; P = 0.027). A high MVI (> 2.01 cm3) was associated with higher likelihood of receiving subtotal resection (STR) (OR 4.07, 95% CI 1.17-14.15; P = 0.035). Although the mean MVI and tumor volume were higher in the embolized cohort (P = 0.009 and P = 0.005), there were no significant differences in EBL, or blood transfusion rates regardless of embolization status. MVI may be used as a non-invasive radiological marker to gauge meningioma vascularity, predict EBL, and guide the decision-making process when it comes to embolization and surgical planning.

Abstract WP233: Sex Differences In The Impact Of Focal Ischemia On Cognitive Impairment In Middle-aged Rats And The Effect Of Iv Mir20a-3p Treatment

Introduction: Our previous studies show that the small non-coding RNA, mir20a-3p, is neuroprotective for stroke in the acute phase (Branyan et al., 2021). Here we used a battery of tests to assess the integrated functioning of affective and cognitive circuits in the chronic phase of stroke and the impact of mir20a-3p. Methods: Middle-aged (12-month-old) female and male Sprague-Dawley rats were subject to middle cerebral artery occlusion (MCAo) using endothelin-1. Mir20a-3p mimic or scrambled oligo was administered i.v. 4h, 24h, and 60d after stroke. Cognitive changes (30-100d after stroke) were assessed by contextual fear conditioning (CFC) and the novel object recognition test (NORT). Blood samples were collected at 60 and 100d to assess cytokines and short chain fatty acids. Results: MCAo impaired sensory motor performance in the acute phase, which was ameliorated by Mir20a-3p treatment. At 30, 60 and 100d after stroke, retrieval of fear memory was significantly reduced over time in sham and stroke males and females. However, the rate of decline of fear memory was significantly accelerated in the scramble treated female stroke group, but not in the scramble treated male stroke group. Similarly, declarative memory (NORT) declined in all female groups at 30 days post stroke and continued to worsen only in the stroke+ scrambled treated group. Male rats did not show any change in performance due to stroke or its treatment at comparable time points of testing. Hyperintensities in the forebrain of T2 weighted MRI images were decreased in mir20a-3p treated stroke females compared to males. Correlation analysis showed that in females at 60d post stroke, the extent of Remote Fear memory recall was highly positively correlated with the levels of the SCFA butyric acid (+0.81) and valeric acid (+0.61), as well as the pro-inflammatory cytokine IL-12p70 (+0.64), and the serine hydrolase, butyrylcholinesterase (+0.71). Conclusion: Although acute sensory-motor impairment is seen to a similar extent in males and females, stroke significantly affected cognitive function in females but not males, and this was associated with alterations in inflammatory mediators. Mir20a-3p, which is predicted to repress inflammatory analytes, abrogated stroke effects in females.

Differential association of endothelial function with brain structure in youth with versus without bipolar disorder

BackgroundMood symptoms and disorders are associated with impaired endothelial function, a marker of early atherosclerosis. Given the increased vascular burden and neurostructural differences among individuals with mood disorders, we investigated the endothelial function and brain structure interface in relation to youth bipolar disorder (BD). MethodsThis cross-sectional case-controlled study included 115 youth, ages 13–20 years (n = 66 BD; n = 49 controls [CG]). Cortical thickness and volume for regions of interest (ROI; insular cortex, ventrolateral prefrontal cortex [vlPFC], temporal lobe) were acquired from FreeSurfer processed T1-weighted MRI images. Endothelial function was assessed using pulse amplitude tonometry, yielding a reactive hyperemia index (RHI). ROI and vertex-wise analyses controlling for age, sex, obesity, and intracranial volume investigated for RHI-neurostructural associations, and RHI-by-diagnosis interactions. ResultsIn ROI analyses, higher RHI (i.e., better endothelial function) was associated with lower thickness in the insular cortex (β = −0.19, pFDR = 0.03), vlPFC (β = −0.30, pFDR = 0.003), and temporal lobe (β = −0.22, pFDR = 0.01); and lower temporal lobe volume (β = −0.16, pFDR = 0.01) in the overall sample. In vertex-wise analyses, higher RHI was associated with lower cortical thickness and volume in the insular cortex, prefrontal cortex (e.g., vlPFC), and temporal lobe. Additionally, higher RHI was associated with lower vlPFC and temporal lobe volume to a greater extent in youth with BD vs. CG. ConclusionsBetter endothelial function was associated with lower regional brain thickness and volume, contrasting the hypothesized associations. Additionally, we found evidence that this pattern was exaggerated in youth with BD. Future studies examining the direction of the observed associations and underlying mechanisms are warranted.

Deformable medical image registration based on CNN.

This study aims to develop and test a new image registration method in which full-scale skip connections in the encoding process is added into the registration network, which can predict the deformation field more accurately by retaining more features and information in the decoding process. Two improved registration networks are connected in series in the registration framework. Each registration network uses the unsupervised learning registration method to predict a small deformation field, and the last two small deformation fields are superimposed to obtain the final deformation field. The model is evaluated by the oasis datasets (brain T1-weighted MRI images), one image is selected as the fixed image, while 383 images are used as training images and 30 images are used as test images. Wavelet decomposition and reconstruction are also used to enhance the image. Compared with the affine method, the voxelmorph-1 method and the voxelmorph-2 method, applying the new registration network was proposed by this study improves the registration accuracy by 28.6%, 1.2% and 0.2%, respectively. The experimental results demonstrate that the method proposed in this study can improve the accuracy of image registration effectively.

Multi-Classification of Brain Tumors on Magnetic Resonance Images Using an Ensemble of Pre-Trained Convolutional Neural Networks.

Automatic classification of brain tumors is an important issue in computeraided diagnosis (CAD) for medical applications since it can efficiently improve the clinician's diagnostic performance and the current study focused on the CAD system of the brain tumors. Existing studies mainly focused on a single classifier either based on traditional machinelearning algorithms or deep learning algorithms with unsatisfied results. In this study, we proposed an ensemble of pre-trained convolutional neural networks to classify brain tumors into three types from their T1-weighted contrast-enhanced MRI (CE-MRI) images, which are meningioma, glioma, and pituitary tumor. Three pre-trained convolutional neural networks (Inception-v3, Resnet101, Densenet201) with the best classification performance (i.e. accuracy of 96.21%, 97.00%, 96.54%, respectively) on the CE-MRI benchmark dataset were selected as backbones of the ensemble model. The features extracted by backbone networks in the ensemble model were further classified by a support vector machine. The ensemble system achieved an average classification accuracy of 98.14% under a five-fold cross-validation process, outperforming any single deep learning model in the ensemble system and other methods in the previous studies. Performance metrics for each brain tumor type, including area under the curve, sensitivity, specificity, precision, and F-score, were calculated to show the ensemble system's performance. Our work addressed a practical issue by evaluating the model with fewer training samples. The classification accuracy was reduced to 97.23%, 96.87%, and 93.96% when 75%, 50%, and 25% training data was used to train the ensemble model, respectively. Our ensemble model has a great capacity and achieved the best performance in any single convolutional neural networks for brain tumors classification and is potentially applicable in real clinical practice.

Tumor-Attentive Segmentation-Guided GAN for Synthesizing Breast Contrast-Enhanced MRI Without Contrast Agents.

Breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a sensitive imaging technique critical for breast cancer diagnosis. However, the administration of contrast agents poses a potential risk. This can be avoided if contrast-enhanced MRI can be obtained without using contrast agents. Thus, we aimed to generate T1-weighted contrast-enhanced MRI (ceT1) images from pre-contrast T1 weighted MRI (preT1) images in the breast. We proposed a generative adversarial network to synthesize ceT1 from preT1 breast images that adopted a local discriminator and segmentation task network to focus specifically on the tumor region in addition to the whole breast. The segmentation network performed a related task of segmentation of the tumor region, which allowed important tumor-related information to be enhanced. In addition, edge maps were included to provide explicit shape and structural information. Our approach was evaluated and compared with other methods in the local (n = 306) and external validation (n = 140) cohorts. Four evaluation metrics of normalized mean squared error (NRMSE), Pearson cross-correlation coefficients (CC), peak signal-to-noise ratio (PSNR), and structural similarity index map (SSIM) for the whole breast and tumor region were measured. An ablation study was performed to evaluate the incremental benefits of various components in our approach. Our approach performed the best with an NRMSE 25.65, PSNR 54.80 dB, SSIM 0.91, and CC 0.88 on average, in the local test set. Performance gains were replicated in the validation cohort. We hope that our method will help patients avoid potentially harmful contrast agents. Clinical and Translational Impact Statement-Contrast agents are necessary to obtain DCE-MRI which is essential in breast cancer diagnosis. However, administration of contrast agents may cause side effects such as nephrogenic systemic fibrosis and risk of toxic residue deposits. Our approach can generate DCE-MRI without contrast agents using a generative deep neural network. Thus, our approach could help patients avoid potentially harmful contrast agents resulting in an improved diagnosis and treatment workflow for breast cancer.

Multi-Modal Semantic Segmentation Model using Encoder Based Link-Net Architecture for BraTS 2020 Challenge

Glioma, which is a malignant tumor, is present in the glial tissue region of the human brain. Segmentation of such tumor cells in the brain region is still challenging and needs experts. Because of the overlap between the intensity distributions of tissue with edema, non-edema, and enhancing features, the segmentation process is a significant challenge for neurosurgeons and radiologists. As per the current state of the art in medicine and surgery, artificial intelligence is gaining attention in effective detection and segmentation in the area of medical diagnosis. In MICCAI 2020, the authors prepare an algorithm for the semantic segmentation of brain tumors from multimodal MRI images for further treatments such as observing treatment, monitoring recovery, and evaluating the effects of the treatment on patients. This paper's objective is to develop an efficient deep learning model which performs semantic segmentation using a multi-modal modified Link-Net model which uses Squeeze and Excitation ResNet152 model is used as a backbone for the segmentation. A model developed by Manipal Hospital in Bangalore is compared with the traditional state-of-the-art models, and its accuracy is verified by neurosurgeons there. This model imbibes the multi-modal MRI dataset which includes T1 weighted images, Flair images, and T2-weighted MRI images of the human brain, model perform comparably well, which shows that our model is robust for tumor segmentation. The accuracy of this model is 99.2.

Motor symptoms in Parkinson’s disease are related to the interplay between cortical curvature and thickness

IntroductionBrain atrophy in Parkinson’s disease occurs to varying degrees in different brain regions, even at the early stage of the disease. While cortical morphological features are often considered independently in structural brain imaging studies, research on the co-progression of different cortical morphological measurements could provide new insights regarding the progression of PD. This study’s aim was to examine the interplay between cortical curvature and thickness as a function of PD diagnosis, motor symptoms, and cognitive performance. MethodsA total of 359 de novo PD patients and 159 healthy controls (HC) from the Parkinson’s Progression Markers Initiative (PPMI) database were included in this study. Additionally, an independent cohort from four databases (182 PD, 132 HC) with longer disease durations was included to assess the effects of PD diagnosis in more advanced cases. Pearson correlation was used to determine subject-specific associations between cortical curvature and thickness estimated from T1-weighted MRI images. General linear modeling (GLM) was then used to assess the effect of PD diagnosis, motor symptoms, and cognitive performance on the curvature-thickness association. Next, longitudinal changes in the curvature-thickness correlation as well as the predictive effect of the cortical curvature-thickness association on changes in motor symptoms and cognitive performance across four years were investigated. Finally, Akaike information criterion (AIC) was used to build a GLM to model PD motor symptom severity cross-sectionally. ResultsA significant interaction effect between PD motor symptoms and age on the curvature-thickness correlation was found (βstandardized = 0.11; t(350) = 2.12; p = 0.03). This interaction effect showed that motor symptoms in older patients were related to an attenuated curvature-thickness association. No significant effect of PD diagnosis was observed for the PPMI database (β = 0.03; t(510) = 0.35; p = 0.72). However, in patients with a longer disease duration, a significant effect of diagnosis on the curvature-thickness association was found (βstandardized = 0.31; t(306.7) = 3.49; p = 0.0006). Moreover, rigidity, but not tremor, in PD was significantly related to the curvature-thickness correlation (βstandardized = 0.11, t(350) = 2.24, p = 0.03; βstandardized = -0.03, t(350) = -0.58, p = 0.56, respectively). The curvature-thickness association was attenuated over time in both PD and HC, but the two groups did not show a significantly different effect (βstandardized = 0.03, t(184.7) = 0.78, p = 0.44). No predictive effects of the CC-CT correlation on longitudinal changes in cognitive performance or motor symptoms were observed (all p-values > 0.05). The best cross-sectional model for PD motor symptoms included the curvature-thickness correlation, cognitive performance, and putamen dopamine transporter (DAT) binding, which together explained 14 % of variance. ConclusionThe association between cortical curvature and thickness is related to PD motor symptoms and age. This research shows the potential of modeling the curvature-thickness interplay in PD.

Cortical and subcortical morphological alterations in motor subtypes of Parkinson’s disease

Parkinson’s disease (PD) can be classified into an akinetic-rigid (AR) and a tremor-dominant (TD) subtype based on predominant motor symptoms. Patients with different motor subtypes often show divergent clinical manifestations; however, the underlying neural mechanisms remain unclear. This study aimed to characterize the cortical and subcortical morphological alterations in motor subtypes of PD. T1-weighted MRI images were obtained for 90 patients with PD (64 with the AR subtype and 26 with the TD subtype) and 56 healthy controls (HCs). Cortical surface area, sulcal depth (measured by Freesurfer’s Sulc index), and subcortical volume were computed to identify the cortical and subcortical morphological alterations in the two motor subtypes. Compared with HCs, we found widespread surface area reductions in the AR subtype yet sparse surface area reductions in the TD subtype. We found no significant Sulc change in the AR subtype yet increased Sulc in the right supramarginal gyrus in the TD subtype. The hippocampal volumes in both subtypes were lower than those of HCs. In PD patients, the surface area of left posterior cingulate cortex was positively correlated with Mini-Mental State Examination (MMSE) score, while the Sulc value of right middle frontal gyrus was positively correlated with severity of motor impairments. Additionally, the hippocampal volumes were positively correlated with MMSE and Montreal Cognitive Assessment scores and negatively correlated with severity of motor impairments and Hoehn & Yahr scores. Taken together, these findings may contribute to a better understanding of the neural substrates underlying the distinct symptom profiles in the two PD subtypes.

The human immune cell simulated anti-breast cancer nanorobot: the efficient, traceable, and dirigible anticancer bio-bot

BackgroundVarious types of cancer therapy strategies have been investigated and successfully applied so far. There are a few modern strategies for improving drug selectivity and biocompatibility, such as nanoparticle-based drug delivery systems. Herein, we designed the traceable enzyme-conjugated magnetic nanoparticles to target human breast cancer cells by simulating the innate immune cell’s respiratory explosion response.MethodsThe human immune cell simulated anti-breast cancer-nanorobot (hisABC-NB) was produced by conjugating the mouse-derived iNOS and human-originated MPO enzymes on the folate-linked chitosan-coated Fe3O4 nanoparticles. The synthesized nanoparticles were functionalized with folic acid as the breast cancer cell detector. Then, the hisABC-NB’s stability and structural properties were characterized by studying Zeta-potential, XRD, FTIR, VSM, FESEM, and DLS analysis. Next, the selectivity and anti-tumor activity of the hisABC-NB were comparatively analyzed on both normal (MCF-10) and cancerous (MCF-7) human breast cells by analyzing the cells’ survival, apoptotic gene expression profile (P53, BAX, BCL2), and flow cytometry data. Finally, the hisABC-NB’s traceability was detected by T2-weighted MRI imaging on the balb-c breast tumor models.ResultsThe hisABC-NB significantly reduced the MCF-7 human breast cancer cells by inducing apoptosis response and arresting the cell cycle at the G2/M phase compared with the normal cell type (MCF-10). Moreover, the hisABC-NB exhibited a proper MRI contrast at the tumor region of treated mice compared with the non-treated type, which approved their appropriate MRI-mediated traceability.ConclusionThe hisABC-NB’s traceability, dirigibility, and selective cytotoxicity were approved, which are the three main required factors for an efficient anticancer compound. Therefore, it has the potential to be used as an intelligent safe anticancer agent for human breast cancer treatment. However, several in vitro and in vivo studies are required to clarify its selectivity, stability, and safety.

Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body composition

IntroductionChanges in brain structure and function occur with aging. However, there is substantial heterogeneity both in terms of when these changes begin, and the rate at which they progress. Understanding the mechanisms and/or behaviors underlying this heterogeneity may allow us to act to target and slow negative changes associated with aging. MethodsUsing T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. We also assessed maximal aerobic capacity with a graded exercise test, physical activity and sleep with accelerometers, and body composition with dual energy x-ray absorptiometry. Associations were explored both linearly and logistically using categorical groupings. ResultsVisceral Adipose Tissue (VAT), Total Sleep Time (TST) and maximal aerobic capacity all showed significant associations with BrainPAD. Greater VAT was associated with higher (i.e,. older than chronological) BrainPAD (r = 0.149 p = 0.001)Greater TST was associated with higher BrainPAD (r = 0.087 p = 0.042) and greater aerobic capacity was associated with lower BrainPAD (r = −0.088 p = 0.040). With linear regression, both VAT and TST remained significant (p = 0.036 and 0.008 respectively). Each kg of VAT predicted a 0.741 year increase in BrainPAD, and each hour of increased TST predicted a 0.735 year increase in BrainPAD. Maximal aerobic capacity did not retain statistical significance in fully adjusted linear models. DiscussionAccumulation of visceral adipose tissue and greater total sleep time, but not aerobic capacity, total daily physical activity, or sleep quantity and/or quality are associated with brains that are physiologically older than would be expected based upon chronological age alone (BrainPAD).