T1-weighted Brain Magnetic Resonance Research Articles

Patch-Based Surface Morphometry Feature Selection with Federated Group Lasso Regression.

Collectively, vast quantities of brain imaging data exist across hospitals and research institutions, providing valuable resources to study brain disorders such as Alzheimer's disease (AD). However, in practice, putting all these distributed datasets into a centralized platform is infeasible due to patient privacy concerns, data restrictions and legal regulations. In this study, we propose a novel federated feature selection framework that can analyze the data at each individual institution without data-sharing or accessing private patient information. In this framework, we first propose a federated group lasso optimization method based on block coordinate descent. We employ stability selection to determine statistically significant features, by solving the group lasso problem with a sequence of regularization parameters. To accelerate the stability selection, we further propose a federated screening rule, which can identify and exclude the irrelevant features before solving the group lasso. Here, we use this framework for patch based feature selection on hippocampal morphometry. Shape is characterized through two different kinds of local measures, the radial distance and the surface area determined via tensor-based morphometry (TBM). The method is tested on 1,127 T1-weighted brain magnetic resonance images (MRI) of AD, mild cognitive impairment (MCI) and elderly control subjects, randomly assigned to five independent hypothetical institutions for testing purpose. We examine the association of MRI-based anatomical measures with general cognitive assessment and amyloid burden to identify the morphometry changes related to AD deterioration and plaque accumulation. Finally, we visualize the significance of the association on the hippocampal surfaces. Our experimental results successfully demonstrate the efficiency and effectiveness of our method.

Deep Learning on Conventional Magnetic Resonance Imaging Improves the Diagnosis of Multiple Sclerosis Mimics.

The aims of this study were to present a deep learning approach for the automated classification of multiple sclerosis and its mimics and compare model performance with that of 2 expert neuroradiologists. A total of 268 T2-weighted and T1-weighted brain magnetic resonance imagin scans were retrospectively collected from patients with migraine (n = 56), multiple sclerosis (n = 70), neuromyelitis optica spectrum disorders (n = 91), and central nervous system vasculitis (n = 51). The neural network architecture, trained on 178 scans, was based on a cascade of 4 three-dimensional convolutional layers, followed by a fully dense layer after feature extraction. The ability of the final algorithm to correctly classify the diseases in an independent test set of 90 scans was compared with that of the neuroradiologists. The interrater agreement was 84.9% (Cohen κ = 0.78, P < 0.001). In the test set, deep learning and expert raters reached the highest diagnostic accuracy in multiple sclerosis (98.8% vs 72.8%, P < 0.001, for rater 1; and 81.8%, P < 0.001, for rater 2) and the lowest in neuromyelitis optica spectrum disorders (88.6% vs 4.4%, P < 0.001, for both raters), whereas they achieved intermediate values for migraine (92.2% vs 53%, P = 0.03, for rater 1; and 64.8%, P = 0.01, for rater 2) and vasculitis (92.1% vs 54.6%, P = 0.3, for rater 1; and 45.5%, P = 0.2, for rater 2). The overall performance of the automated method exceeded that of expert raters, with the worst misdiagnosis when discriminating between neuromyelitis optica spectrum disorders and vasculitis or migraine. A neural network performed better than expert raters in terms of accuracy in classifying white matter disorders from magnetic resonance imaging and may help in their diagnostic work-up.

Sex Differences in the Association of Household Income with Amygdala Volume

Background: Household income and other socioeconomic position (SEP) indicators are among the most salient social determinants of children’s emotions and behaviors. Some research has shown that income and other SEP indicators may have certain sex-specific effects on the structures and functions of particular brain regions. Objectives: To investigate sex differences in the association of household income with amygdala volumes in US children. Materials &amp; Methods: This is a cross-sectional study using data from the Adolescent Brain Cognitive Development (ABCD) study. The study data was collected between 2016 and 2018 across 21 sites distributed across US states. Wave 1 ABCD included 10262 American children aged between 9 and 10 years old. The independent variable was household income. The primary outcome was the left amygdala volume, which was measured by T1-weighted structural brain Magnetic Resonance Imaging (MRI). We used a data exploration and analysis portal for our data analysis. Results: Overall, the household income was positively associated with left amygdala size in children. Sex showed a statistically significant interaction with household income on children’s left amygdala volume, net of all confounders, indicating a stronger effect of high household income on male children compared to female children. Conclusion: Household income is a more salient determinant of left amygdala volume for male children compared to female American children. Low-income male children remain at the highest risk of a small amygdala.

Dietary diversity is associated with longitudinal changes in hippocampal volume among Japanese community dwellers

Background/ObjectivesDietary habits are known to affect health, including the rate of brain ageing and susceptibility to diseases. This study examines the longitudinal relationship between dietary diversity and hippocampal volume, which is a key structure of memory processing and is known to be impaired in dementia.Subjects/MethodsSubjects were aged 40–89 years (n = 1683, men: 50.6%) and participated in a 2-year follow-up study of the National Institute for Longevity Sciences-Longitudinal Study of Aging. Dietary intake was calculated from 3-day dietary records, and dietary diversity was determined using the Quantitative Index for Dietary Diversity at baseline. Longitudinal changes in hippocampal and total grey matter volumes were estimated by T1-weighted brain magnetic resonance imaging and FreeSurfer software. Estimated mean brain volume change in relation to dietary diversity score quintiles was assessed by the general linear model, adjusted for age, sex, education, smoking status, alcohol intake, physical activity, and comorbidities.ResultsThe mean (± standard deviation) % decreases in hippocampal and total grey matter volume during the 2-year follow-up were 1.00% (±2.27%) and 0.78% (±1.83%), respectively. Multivariate-adjusted decreases in total grey matter volume were associated with dietary diversity score (p = 0.065, p for trend = 0.017), and the % decrease in hippocampal volume was more strongly associated with the dietary diversity score: the estimated mean (± standard error) values were 1.31% (±0.12%), 1.07% (±0.12%), 0.98% (±0.12%), 0.81% (±0.12%), and 0.85% (±0.12%), according to dietary diversity quintiles in ascending order (p = 0.030, p for trend = 0.003).ConclusionsAmong community dwellers, increased dietary diversity may be a new nutritional strategy to prevent hippocampal atrophy.

The Essentials of Brain Anatomy for Physiatrists: Magnetic Resonance Imaging Findings.

Detailed knowledge of the brain anatomy is important for the treatment of patients with brain disorders. In this study, we conducted a review of essential parts of human brain anatomy based on magnetic resonance imaging of the brain. Using T2-weighted brain magnetic resonance imaging, we explained how to recognize several structures in each brain lobe (the frontal, parietal, temporal, and occipital lobes). We depicted the boundary of each structure on brain magnetic resonance imaging and described their functions. The limbic system controls various functions such as emotion, motivation, behavior, memory, and olfaction. Broca's and Wernicke's areas and arcuate fasciculus are important structures for human language functions. Emotion, memory, and language function are one of the main functions of human. Therefore, the anatomical knowledge of the limbic system and language-related structures is important for physiatrists. We described the anatomical location and function of each substructure of the limbic system and language centers. In addition, we indicated the exact points of motor- and sensory-related neural tracts (corticospinal tract, corticoreticular pathway, medial lemniscus, and spinothalamic tract) on brain magnetic resonance imaging. We believe that our review on brain anatomy would be helpful for physiatrists to accurately identify the damage of each function from brain disorders and elucidate proper plan for rehabilitative treatment.

Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure

BackgroundEmerging findings have increased concern that exposure to fine particulate matter air pollution (aerodynamic diameter ≤ 2.5 μm; PM2.5) may be neurotoxic, even at lower levels of exposure. Yet, additional studies are needed to determine if exposure to current PM2.5 levels may be linked to hemispheric and regional patterns of brain development in children across the United States. ObjectivesWe examined the cross-sectional associations between geocoded measures of concurrent annual average outdoor PM2.5 exposure, regional- and hemisphere-specific differences in brain morphometry and cognition in 10,343 9- and 10- year-old children. MethodsHigh-resolution structural T1-weighted brain magnetic resonance imaging (MRI) and NIH Toolbox measures of cognition were collected from children at ages 9–10 years. FreeSurfer was used to quantify cortical surface area, cortical thickness, as well as subcortical and cerebellum volumes in each hemisphere. PM2.5 concentrations were estimated using an ensemble-based model approach and assigned to each child’s primary residential address collected at the study visit. We used mixed-effects models to examine regional- and hemispheric- effects of PM2.5 exposure on brain estimates and cognition after considering nesting of participants by familial relationships and study site, adjustment for socio-demographic factors and multiple comparisons. ResultsAnnual residential PM2.5 exposure (7.63 ± 1.57 µg/m3) was associated with hemispheric specific differences in gray matter across cortical regions of the frontal, parietal, temporal and occipital lobes as well as subcortical and cerebellum brain regions. There were hemispheric-specific associations between PM2.5 exposures and cortical surface area in 9/31 regions; cortical thickness in 22/27 regions; and volumes of the thalamus, pallidum, and nucleus accumbens. We found neither significant associations between PM2.5 and task performance on individual measures of neurocognition nor evidence that sex moderated the observed associations. DiscussionEven at relatively low-levels, current PM2.5 exposure across the U.S. may be an important environmental factor influencing patterns of structural brain development in childhood. Prospective follow-up of this cohort will help determine how current levels of PM2.5 exposure may affect brain development and subsequent risk for cognitive and emotional problems across adolescence.

Articulation rehabilitation induces cortical plasticity in adults with non-syndromic cleft lip and palate.

In this study, we investigated brain morphological changes in adults with non-syndromic cleft lip and palate (NSCLP) after articulation rehabilitation (AR). High-resolution T1 weighted brain magnetic resonance imaging data were analyzed from 45 adults with NSCLP after palatoplasty: 24 subjects were assessed before AR (bNSCLP) and 21 subjects were assessed after AR (aNSCLP). In addition, there were 24 age and sex matched controls. Intergroup differences of grey matter volume were evaluated as a comprehensive measure of the cortex; cortical thickness and cortical complexity (gyrification and fractal dimensions) were also analyzed. As compared to controls, the bNSCLP subjects exhibited altered indexes in frontal, temporal, and parietal lobes; these morphological changes are characteristic for adults with NSCLP. Importantly, as compared to the bNSCLP and control subjects, the aNSCLP subjects exhibited cortical plasticity in the regions involved in language, auditory, pronunciation planning, and execution functions. The AR-mediated cortical plasticity in aNSCLP subjects may be caused by AR-induced cortical neurogenesis, which might reflect the underlying neural mechanism during AR.

A longitudinal study of brain volume changes in rhesus macaque model infected with SIV.

Given the current lack of understanding of brain volume changes caused by HIV infection, this study aimed to longitudinally assess the changes in regional brain tissue volume following HIV infection and to explore its relationship with peripheral blood absolute CD4+ lymphocyte count (CD4+), the percentage of monocytes in plasma(MON%) and cerebrospinal fluid viral load (CFVL).Four adult male rhesus monkeys were examined in healthy status and following infection with simian immunodeficiency virus using high-resolution 3D T1-weighted sagittal whole brain magnetic resonance imaging. DPABI and SPM were used to process and record changes in brain tissue volume. Correlation analyses were then used to explore the above relationships. Compared with brain tissue volume during the healthy stage, there was no change at 12 and 24weeks postinoculation (12wpi, 24wpi). At 36wpi, 48wpi, and 60wpi, basal ganglia, left inferior temporal gyrus, left occipital gyrus, and left superior frontal gyrus exhibited varying degrees of atrophy. There was no association found between CD4+, MON%, CFVL, and brain volume loss in any brain region. Our research demonstrated that in the early stage of HIV infection, local brain tissue atrophy can be demonstrated by MRI technique; furthermore, MRI can identify the earliest site of atrophy as well as the most severely affected site. Although there was no significant correlation between brain tissue volume loss and CD4+, MON%, and CFVL, our findings provided some evidence in the application of volumetric MR imaging in the early diagnosis and treatment follow-up of patients with HIV infection.

Pineal gland volume is associated with prevalent and incident isolated rapid eye movement sleep behavior disorder.

We aimed to investigate the association of pineal gland volume with the risk of isolated rapid eye movement (REM) sleep behavior disorder (RBD). We enrolled 245 community-dwelling cognitively normal elderly individuals without major psychiatric or neurological disorders at the baseline evaluation, of whom 146 completed the 2-year follow-up evaluation. We assessed RBD symptoms using the REM Sleep Behavior Disorder Screening Questionnaire (RBDSQ) and defined probable RBD (pRBD) as an RBDSQ score of ≥ 5. We manually segmented the pineal gland on 3T T1-weighted brain magnetic resonance imaging and estimated its volume. The smaller the baseline pineal gland volume, the more severe the RBD symptoms at baseline. The individuals with isolated pRBD showed smaller pineal gland volumes than those without isolated pRBD. The larger the baseline pineal gland volume, the lower the risks of prevalent isolated pRBD at the baseline evaluation and incident isolated pRBD at the 2-year follow-up evaluation. Pineal gland volume showed good diagnostic accuracy for prevalent isolated pRBD and predictive accuracy for incident isolated pRBD in the receiver operator characteristic analysis. Our findings suggest that pineal gland volume may be associated with the severity of RBD symptoms and the risk of isolated RBD in cognitively normal elderly individuals.

A Classification Algorithm by Combination of Feature Decomposition and Kernel Discriminant Analysis (KDA) for Automatic MR Brain Image Classification and AD Diagnosis.

Magnetic resonance (MR) imaging is a widely used imaging modality for detection of brain anatomical variations caused by brain diseases such as Alzheimer's disease (AD) and mild cognitive impairment (MCI). AD considered as an irreversible neurodegenerative disorder with progressive memory impairment moreover cognitive functions, while MCI would be considered as a transitional phase amongst age-related cognitive weakening. Numerous machine learning approaches have been examined, aiming at AD computer-aided diagnosis through employing MR image analysis. Conversely, MR brain image changes could be caused by different effects such as aging and dementia. It is still a challenging difficulty to extract the relevant imaging features and classify the subjects of different groups. This paper would propose an automatic classification technique based on feature decomposition and kernel discriminant analysis (KDA) for classifications of progressive MCI (pMCI) vs. normal control (NC), AD vs. NC, and pMCI vs. stable MCI (sMCI). Feature decomposition would be based on dictionary learning, which is used for separation of class-specific components from the non-class-specific components in the features, while KDA would be applied for mapping original nonlinearly separable feature space to the separable features that are linear. The proposed technique would be evaluated by employing T1-weighted MR brain images from 830 subjects comprising 198 AD patients, 167 pMCI, 236 sMCI, and 229 NC from the Alzheimer's disease neuroimaging initiative (ADNI) dataset. Experimental results demonstrate that classification accuracy (ACC) of 90.41%, 84.29%, and 65.94% can be achieved for classification of AD vs. NC, pMCI vs. NC, and pMCI vs. sMCI, respectively, indicating the promising performance of the proposed method.

Association of Copy Number Variation of the 15q11.2 BP1-BP2 Region With Cortical and Subcortical Morphology and Cognition

Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities. To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance. In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019. The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort. Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = -0.41; SE, 0.08; P = 4.9 × 10-8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10-7), and a smaller nucleus accumbens (Cohen d = -0.27; SE, 0.07; P = 7.3 × 10-5). There was also a significant negative dose response on cortical thickness (β = -0.24; SE, 0.05; P = 6.8 × 10-7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks. These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders.

Telomere Length Is Associated with Disability Progression in Multiple Sclerosis.

To assess whether biological aging as measured by leukocyte telomere length (LTL) is associated with clinical disability and brain volume loss in multiple sclerosis (MS). Adults with MS/clinically isolated syndrome in the University of California, San Francisco EPIC cohort study were included. LTL was measured on DNA samples by quantitative polymerase chain reaction and expressed as telomere to somatic DNA (T/S) ratio. Expanded Disability Status Scale (EDSS) and 3-dimensional T1-weighted brain magnetic resonance imaging were performed at baseline and follow-up. Associations of baseline LTL with cross-sectional and longitudinal outcomes were assessed using simple and mixed effects linear regression models. A subset (n = 46) had LTL measured over time, and we assessed the association of LTL change with EDSS change with mixed effects models. Included were 356 women and 160 men (mean age = 43 years, median disease duration = 6 years, median EDSS = 1.5 [range = 0-7], mean T/S ratio = 0.97 [standard deviation = 0.18]). In baseline analyses adjusted for age, disease duration, and sex, for every 0.2 lower LTL, EDSS was 0.27 higher (95% confidence interval [CI] = 0.13-0.42, p < 0.001) and brain volume was 7.4mm3 lower (95% CI = 0.10-14.7, p = 0.047). In longitudinal adjusted analyses, those with lower baseline LTL had higher EDSS and lower brain volumes over time. In adjusted analysis of the subset, LTL change was associated with EDSS change over 10 years; for every 0.2 LTL decrease, EDSS was 0.34 higher (95% CI = 0.08-0.61, p = 0.012). Shorter telomere length was associated with disability independent of chronological age, suggesting that biological aging may contribute to neurological injury in MS. Targeting aging-related mechanisms is a potential therapeutic strategy against MS progression. ANN NEUROL 2019;86:671-682.

MRI Signal Intensity and Parkinsonism in Manganese-Exposed Workers.

T1-weighted brain magnetic resonance imaging (MRI) of the basal ganglia provides a noninvasive measure of manganese (Mn) exposure, and may also represent a biomarker for clinical neurotoxicity. We acquired T1-weighted MRI scans in 27 Mn-exposed welders, 12 other Mn-exposed workers, and 29 nonexposed participants. T1-weighted intensity indices were calculated for four basal ganglia regions. Cumulative Mn exposure was estimated from work history data. Participants were examined using the Unified Parkinson's Disease Rating Scale motor subsection 3 (UPDRS3). We observed a positive dose-response association between cumulative Mn exposure and the pallidal index (PI) (β = 2.33; 95% confidence interval [CI], 0.93 to 3.74). There was a positive relationship between the PI and UPDRS3 (β = 0.15; 95% CI, 0.03 to 0.27). The T1-weighted pallidal signal is associated with occupational Mn exposure and severity of parkinsonism.

Anatomical context improves deep learning on the brain age estimation task

Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4–96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1–97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them.

Altered Brain Morphology In Women With History Of ACL Rupture: A Structural MRI Study

Anterior cruciate ligament ruptures (ACLR) are among the most common musculoskeletal injuries in young women. Despite the presence of supraspinal alterations after ACLR, the global and localized morphological underpinnings have yet to be elucidated. PURPOSE: This study aimed to determine whether brain morphology differs in individuals with a history of ACLR compared to healthy controls with no history of injury. METHODS: Twenty (10 ACL, 10 controls) age- and physical activity-matched women (age: 20.9±2.9yr, weight: 65.9±8.8kg, height: 165.2±6.2cm) underwent 3T T1-weighted structural brain magnetic resonance imaging. Mean cortical thickness, grey matter and white matter volume were measured globally and within 70 and 95 anatomically defined regions of interest (ROIs), respectively. A two-way analysis of variance was used to determine differences in global and regional brain structure between ACL and controls, while correcting for multiple comparisons by controlling the false discovery rate. RESULTS: Mean duration since the completion of rehabilitation after ACLR was 3.1±1.1yr. Five injured the left leg and all but two participants were right foot dominant. Cortical thickness was significantly greater for controls in the left precentral gyrus (3.62±0.22 vs. 3.19±0.39mm, respectively P=0.019) and left paracentral lobule (3.70±0.23 vs. 3.27±0.26mm respectively; P=0.025). No differences in gray or white matter volume were seen for any of the ROIs between groups. CONCLUSION: Three years after ACL rupture, young women demonstrated persistent alterations in cortical thickness relative to individuals without a history of injury. Together with evidence of other supraspinal and neuromuscular deficits, this suggests cortical involvement in the ACLR pathological process. Thus, neurophysiological assessments should be considered in addition to traditional musculoskeletal measurements. Supported by a doctoral grant from the National Strength and Conditioning Association (SDF)

Multiatlas Fusion with a Hybrid CT Number Correction Technique for Subject-Specific Pseudo-CT Estimation in the Context of MRI-Only Radiation Therapy

ObjectiveTo propose a hybrid multiatlas fusion and correction approach to estimate a pseudo–computed tomography (pCT) image from T2-weighted brain magnetic resonance (MR) images in the context of MRI-only radiotherapy. Materials and MethodsA set of eleven pairs of T2-weighted MR and CT brain images was included. Using leave-one-out cross-validation, atlas MR images were registered to the target MRI with multimetric, multiresolution deformable registration. The subsequent deformations were applied to the atlas CT images, producing uncorrected pCT images. Afterward, a three-dimensional hybrid CT number correction technique was used. This technique uses information about MR intensity, spatial location, and tissue label from segmented MR images with the fuzzy c-means algorithm and combines them in a weighted fashion to correct Hounsfield unit values of the uncorrected pCT images. The corrected pCT images were then fused into a final pCT image. ResultsThe proposed hybrid approach proved to be performant in correcting Hounsfield unit values in terms of qualitative and quantitative measures. Average correlation was 0.92 and 0.91 for the proposed approach by taking the mean and the median, respectively, compared with 0.86 for the uncorrected unfused version. Average values of dice similarity coefficient for bone were 0.68 and 0.72 for the fused corrected pCT images by taking the mean and the median, respectively, compared with 0.65 for the uncorrected unfused version indicating a significant bone estimation improvement. ConclusionA hybrid fusion and correction method is presented to estimate a pCT image from T2-weighted brain MR images.

Evaluation of intracerebral ventricles volume of patients with Parkinson's disease using the atlas-based method: A methodological study

Knowing the volumetric changes in brain can allow for the estimation of the disease progression of various neurodegenerative disorders. Many studies have been shown that the volumetric changes in the some brain structures especially including the dopaminergic neurons, in patients with Parkinson’s disease (PD). The objective of this study was to compare intracerebral ventricles volume in patients with PD and healthy subjects to compare an automated atlas-based method (MRIStudio software) and a manual method (ImageJ). T1-weighted brain Magnetic Resonance Imaging (MRI) data of 21 patients with PD and 20 healthy individuals were used to calculate the intracerebral ventricle volumes. Measurement results obtained by ImageJ were considered as the gold standard. We found a significant increase in the left occipital part of the lateral ventricle volume in the patients with PD compared to the control subjects (p < 0.05). Also, no significant difference was found between the two methods of measurement (p > 0.05), meaning that a substantial agreement was found between the results obtained with the atlas-based analysis and manual method.The present study showed that MRIStudio can be performed easily and accurately on routine MRI scans for which the total intracerebral ventricles volume is to be estimated in PD. We suggest that, the attained volume values of intracerebral ventricles may provide a precious data for volumetric dependences of the anatomical structures in several clinical conditions.

Three-Dimensional Surface Reconstruction of the Human Cochlear Nucleus: Implications for Auditory Brain Stem Implant Design.

Objective The auditory brain stem implant (ABI) is a neuroprosthesis placed on the surface of the cochlear nucleus (CN) to provide hearing sensations in children and adults who are not candidates for cochlear implantation. Contemporary ABI arrays are stiff and do not conform to the curved brain stem surface. Recent advancements in microfabrication techniques have enabled the development of flexible surface arrays, but these have only been applied in animal models. Herein, we measure the surface curvature of the human CN and adjoining regions to assist in the design and placement of next-generation conformable clinical ABI arrays. Three-dimensional (3D) reconstructions from ultrahigh T1-weighted brain magnetic resonance imaging (MRI) sequences and histologic reconstructions based on postmortem adult human brain stem specimens were used. Design This is a retrospective review of radiologic data and postmortem histologic axial sections. Setting This is set at the tertiary referral center. Participants Data were acquired from healthy adults. Main Outcome Measures The main outcome measures are principal curvature values (Kmin and Kmax) and global radius of curvature. Results The CN was successfully extracted and rendered as a 3D surface in all cases. Significant curvatures of the CN in both histologic and radiographic reconstructions were found with global radius of curvature ranging from 2.08 to 8.5 mm. In addition, local curvature analysis revealed that the surface is highly complex. Conclusion Detailed rendering of the human CN is feasible using histology and 3D MRI reconstruction and highlights complex surface topography that is not recapitulated by contemporary stiff ABI arrays.

Convolution neural network–based Alzheimer's disease classification using hybrid enhanced independent component analysis based segmented gray matter of T2 weighted magnetic resonance imaging with clinical valuation

In recent times, accurate and early diagnosis of Alzheimer's disease (AD) plays a vital role in patient care and further treatment. Predicting AD from mild cognitive impairment (MCI) and cognitive normal (CN) has become popular. Neuroimaging and computer-aided diagnosis techniques are used for classification of AD by physicians in the early stage. Most of the previous machine learning techniques work on handpicked features. In the recent days, deep learning has been applied for many medical image applications. Existing deep learning systems work on raw magnetic resonance imaging (MRI) images and cortical surface as an input to the convolution neural network (CNN) to perform classification of AD. AD affects the brain volume and changes the gray matter texture. In our work, we used 1820 T2-weighted brain magnetic resonance volumes including 635 AD MRIs, 548 MCI MRIs, and 637 CN MRIs, sliced into 18,017 voxels. We proposed an approach to extract the gray matter from brain voxels and perform the classification using the CNN. A Gaussian filter is used to enhance the voxels, and skull stripping algorithm is used to remove the irrelevant tissues from enhanced voxels. Then, those voxels are segmented by hybrid enhanced independent component analysis. Segmented gray matter is used as an input to the CNN. We performed clinical valuation using our proposed approach and achieved 90.47% accuracy, 86.66% of recall, and 92.59% precision.

Cross-sectional and longitudinal associations between total and regional white matter hyperintensity volume and cognitive and motor function in Parkinson's disease.

BackgroundWhite matter hyperintensities (WMH) are frequently observed on T2-weighted brain magnetic resonance imaging studies of healthy older adults and have been linked with impairments in balance, gait, and cognition. Nonetheless, few studies have investigated the longitudinal effects of comorbid WMH on cognition and motor function in Parkinson's disease.MethodsThe Lesion Segmentation Tool for Statistical Parametric Mapping was used to obtain total lesion volume and map regional WMH probabilities in 29 PD and 42 control participants at two study visits 18 months apart. Both cross-sectional and longitudinal comparisons were made between composite scores in the domains of executive function, memory, and language, and Unified Parkinson's Disease Rating Scale (UPDRS) scores.ResultsWe found no difference between disease and control groups in total WMH volume or progression during the study. Greater regional and global WMH at baseline was more strongly associated with lower executive function in PD subjects than in controls. Increased regional WMH was also more strongly associated with impaired memory performance in PD relative to controls. Longitudinally, no associations between cognitive change and total or regional WMH progression were detected in either group. A positive relationship between baseline regional WMH and total UPDRS scores was present in the control group, but not PD. However, greater WMH increase was associated with a greater increase in UPDRS motor sub-scores in PD.ConclusionsThese findings suggest that although PD patients do not experience greater mean WMH load than normal aged adults, comorbid WMH do exacerbate cognitive and motor symptoms in PD.