Human-Technology Symbiosis at work: a brain morphometric investigation of inter-individual differences in smart-tool proneness

Differential Prefrontal White Matter Development in Chimpanzees and Humans

Classification of schizophrenia patients and healthy controls from structural MRI scans in two large independent samples

BackgroundIn the diagnosis of Alzheimer's disease (AD), structural magnetic resonance imaging (MRI) scans have been used primarily to exclude non‐Alzheimer's causes of dementia. However, the pattern and the extent of medial temporal atrophy on structural MRI scans, which correlate strongly with the pathological severity of AD, can be used to support the diagnosis of a degenerative dementia, especially AD, even in its early predementia stage.MethodsElderly subjects (n = 224) were diagnosed with either no cognitive impairment (NCI), amnestic mild cognitive impairment (aMCI), or AD. Hippocampal and hemispheric gray matter volumes were measured on structural MRI scans, and a new visual rating system was used to score the severity of medial temporal atrophy (VRS‐MTA) of the hippocampus (HPC), entorhinal cortex, and perirhinal cortex on a coronal image intersecting the mammillary bodies.ResultsAlthough both VRS‐MTA scores and HPC volumes distinguished between subjects with NCI, aMCI, and AD, subjects with aMCI and NCI could be better distinguished using right VRS‐MTA scores, in comparison with right HPC volumes. VRS‐MTA scores were more highly correlated with episodic memory and Clinical Dementia Rating scores. A combination of left sided VRS‐MTA scores and left sided hippocampal volume was the most predictive measure of diagnostic classification.ConclusionVRS‐MTA is a clinically convenient method or distinguishing aMCI or AD from NCI. As compared with volumetric measures, it provides better discriminatory power and correlates more strongly with memory and functional scores.

Recent Neuroimaging Techniques in Mild Traumatic Brain Injury

The Erasmus Glioma Database (EGD) contains structural magnetic resonance imaging (MRI) scans, genetic and histological features (specifying the WHO 2016 subtype), and whole tumor segmentations of patients with glioma. Pre-operative MRI data of 774 patients with glioma (281 female, 492 male, 1 unknown, age range 19–86 years) treated at the Erasmus MC between 2008 and 2018 is available. For all patients a pre-contrast T1-weighted, post-contrast T1-weighted, T2-weighted, and T2-weighted FLAIR scan are available, made on a variety of scanners from four different vendors. All scans are registered to a common atlas and defaced. Genetic and histological data consists of the IDH mutation status (available for 467 patients), 1p/19q co-deletion status (available for 259 patients), and grade (available for 716 patients). The full WHO 2016 subtype is available for 415 patients. Manual segmentations are available for 374 patients and automatically generated segmentations are available for 400 patients. The dataset can be used to relate the visual appearance of the tumor on the scan with the genetic and histological features, and to develop automatic segmentation methods.

We evaluated the use of a mock scanner training protocol as an alternative for sedation and for preparing young children for (functional) magnetic resonance imaging (MRI). Children with severe mental retardation or developmental disorders were excluded. A group of 90 children (median age 6.5 years, range 3.65–14.5 years) participated in this study. Children were referred to the actual MRI investigation only when they passed the training. We assessed the pass rate of the mock scanner training sessions. In addition, the quality of both structural and functional MRI (fMRI) scans was rated on a semi-quantitative scale. The overall pass rate of the mock scanner training sessions was 85/90. Structural scans of diagnostic quality were obtained in 81/90 children, and fMRI scans with sufficient quality for further analysis were obtained in 30/43 of the children. Even in children under 7 years of age, who are generally sedated, the success rate of structural scans with diagnostic quality was 53/60. FMRI scans with sufficient quality were obtained in 23/36 of the children in this younger age group. The association between age and proportion of children with fMRI scans of sufficient quality was not statistically significant. We conclude that a mock MRI scanner training protocol can be useful to prepare children for a diagnostic MRI scan. It may reduce the need for sedation in young children undergoing MRI. Our protocol is also effective in preparing young children to participate in fMRI investigations.

An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans

Four electric field modeling methods of Dosing Prefrontal Transcranial Magnetic Stimulation (TMS): Introducing APEX MT dosimetry

A 67‐Year‐Old Woman Receiving Tumor Necrosis Factor α Inhibitor Therapy Presenting With Neurologic Dysfunction

In this study of cytomegalovirus (CMV)-infected fetuses with first-trimester seroconversion, we aimed to evaluate the detection of brain abnormalities using magnetic resonance imaging (MRI) and neurosonography (NSG) in the third trimester, and compare the grading systems of the two modalities. We also evaluated the feasibility of routine use of diffusion-weighted imaging (DWI) fetal MRI and compared the regional apparent diffusion coefficient (ADC) values between CMV-infected fetuses and presumed normal, non-infected fetuses in the third trimester. This was a retrospective review of MRI and NSG scans in fetuses with confirmed first-trimester CMV infection performed between September 2015 and August 2019. Brain abnormalities were recorded and graded using fetal MRI and NSG grading systems to compare the two modalities. To investigate feasibility of DWI, a four-point rating scale (poor, suboptimal, good, excellent) was applied to assess the quality of the images. Quantitative assessment was performed by placing a freehand drawn region of interest in the white matter of the frontal, parietal, temporal and occipital lobes and the basal ganglia, pons and cerebellum to calculate ADC values. Regional ADC measurements were obtained similarly in a control group of fetuses with negative maternal CMV serology in the first trimester, normal brain findings on fetal MRI and normal genetic testing. Fifty-three MRI examinations of 46 fetuses with confirmed first-trimester CMV infection were included. NSG detected 24 of 27 temporal cysts seen on MRI scans, with a sensitivity of 78% and an accuracy of 83%. NSG did not detect abnormal gyration visible on two (4%) MRI scans. Periventricular calcifications were detected on two MRI scans compared with 10 NSG scans. While lenticulostriate vasculopathy was detected on 11 (21%) NSG scans, no fetus demonstrated this finding on MRI. MRI grading correlated significantly with NSG grading of brain abnormalities (P < 0.0001). Eight (15%) of the DWI scans in the CMV cohort were excluded from further analysis because of insufficient quality. The ADC values of CMV-infected fetuses were significantly increased in the frontal (both sides, P < 0.0001), temporal (both sides, P < 0.0001), parietal (left side, P = 0.0378 and right side, P = 0.0014) and occipital (left side, P = 0.0002 and right side, P < 0.0001) lobes and decreased in the pons (P = 0.0085) when compared with non-infected fetuses. The ADC values in the basal ganglia and the cerebellum were not significantly different in CMV-infected fetuses compared with normal controls (all P > 0.05). Temporal and frontal ADC values were higher in CMV-infected fetuses with more severe brain abnormalities compared to fetuses with mild abnormalities. Ultrasound and MRI are complementary during the third trimester in the assessment of brain abnormalities in CMV-infected fetuses, with a significant correlation between the grading systems of the two modalities. On DWI in the third trimester, the ADC values in several brain regions are abnormal in CMV-infected fetuses compared with normal controls. Furthermore, they seem to correlate in the temporal area and, to a lesser extent, frontal area with the severity of brain abnormalities associated with CMV infection. Larger prospective studies are needed for further investigation of the microscopic nature of diffusion abnormalities and correlation of different imaging findings with postnatal outcome. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

ECT in Patients With Intracranial Masses

Currently available non-human primate templates typically require input of a skull-stripped brain for structural processing. This can be a manually intensive procedure, and considerably limits their utility. The purpose of this study was to create a vervet MRI population template, associated tissue probability maps (TPM), and a label atlas to facilitate true fully automated Magnetic Resonance Imaging (MRI) structural analyses for morphometric analyses. Structural MRI scans of ten vervet monkeys (Chlorocebus aethiops) scanned at three time points were used in this study. An unbiased population average template was created using a symmetric diffeomorphic registration (SyN) procedure. Skull stripping, segmentation, and label map generation were performed using the publically available rhesus INIA19 MRI template and NeuroMap label atlas. A six-class TPM and a six-layer two-class normalization template was created from the vervet segmentation for use within the Statistical Parametric Mapping (SPM) framework. Fully automated morphologic processing of all of the vervet MRI scans was then performed using the vervet TPM and vervet normalization template including skull-stripping, segmentation and normalization. The vervet template creation procedure resulted in excellent skull stripping, segmentation, and NeuroMap atlas labeling with 720 structures successfully registered. Fully automated processing was accomplished for all vervet scans, demonstrating excellent skull-stripping, segmentation, and normalization performance. We describe creation of an unbiased vervet structural MRI population template and atlas. The template includes an associated six-class TPM and DARTEL six-layer two-class normalization template for true fully automated skull-stripping, segmentation, and normalization of vervet structural T1-weighted MRI scans. We provide the most detailed vervet label atlas currently available based on the NeuroMaps atlas with 720 labels successfully registered. We additionally describe a novel method for atlas label generation that capitalizes on previous work in this area using high-dimensional highly accurate image matching procedures for inter-species morphologic normalization.

Morphometric analyses in the early foetal phase (9-13 postconceptional week) are critical for evaluating normal brain growth. In this study, we assessed sequential morphological and morphometric changes in the foetal brain during this period using high-resolution T1-weighted magnetic resonance imaging (MRI) scans from 21 samples preserved at Kyoto University. MRI sectional views (coronal, mid-sagittal, and horizontal sections) and 3D reconstructions of the whole brain revealed sequential changes in its external morphology and internal structures. The cerebrum's gross external view, lateral ventricle and choroid plexus, cerebral wall, basal ganglia and thalamus, and corpus callosum were assessed. The development of the cerebral cortex, white matter microstructure, and basal ganglia can be well-characterized using MRI scans. The insula became apparent and deeply impressed as brain growth progressed. A thick, densely packed cellular ventricular/subventricular zone and ganglionic eminence became apparent at high signal intensity. We detected the emergence of important landmarks which may be candidates in the subdivision processes during the early foetal period; the corpus callosum was first detected in the sample with crown-rump length (CRL) 62mm. A primary sulcus on the medial part of the cortex (cingulate sulcus) was observed in the sample with CRL 114mm. In the cerebellum, the hemispheres, posterolateral fissure, union of the cerebellar halves, and definition of the vermis were observed in the sample with CRL 43.5mm, alongside the appearance of a primary fissure in the sample with CRL 56mm and the prepyramidal fissure in the sample with CRL 75mm. The volumetric, linear, and angle measurements revealed the comprehensive and regional development, growth, and differentiation of brain structures during the early foetal phase. The early foetal period was neither morphologically nor morphometrically uniform. The cerebral proportion (length/height) and the angle of cerebrum to the standard line at the lateral view of the cerebrum, which may reflect the growth and C-shape formation of the cerebrum, may be a candidate for subdividing the early foetal period. Future precise analyses must establish a staging system for the brain during the early foetal period. This study provides insights into brain structure, allowing for a correlation with functional maturation and facilitating the early detection of brain damage and abnormal development.

MRI Noise and Hearing Loss

The brain tumor segmentation is one of the crucial tasks nowadays among other directions and domains where daily clinical workflow requires to put a lot of efforts while studying computer tomography (CT) or structural magnetic resonance imaging (MRI) scans of patients with various pathologies. MRI is the most common method of primary detection, non-invasive diagnostics and a source of recommendations for further treatment of brain diseases. The brain is a complex structure, different areas of which have different functional significance.In this paper, we extend the previous research work on the robust pre-processing methods which allow to consider all available information from MRI scans by the composition of T1, T1C, T2 and T2-Flair sequences in the unique input. Such approach enriches the input data for the segmentation process and helps to improve the accuracy of the segmentation and associated uncertainty evaluation performance.Proposed in this paper method also demonstrates strong improvement on the segmentation problem. This conclusion was done with respect to Dice metric, Sensitivity and Specificity compare to identical training/validation procedure based only on any single sequence and regardless of the chosen neural network architecture.Obtained results demonstrate significant performance improvement while combining three MRI sequences in the 3-channel RGB like image for considered tasks of brain tumor segmentation. In this work we provide the comparison of various gradient descent optimization methods and of the different backbone architectures.KeywordsMedical imagingDeep learningNeural networkSegmentationBrainMRI