High-resolution Brain Imaging Research Articles

A voxel-based magnetic resonance study of cortical gray matter structures in intermittent exotropia

Objective: To explore the changes in gray matter volume of the cerebral cortex in patients with intermittent exotropia (IXT) using the voxel-based analysis and to analyze the correlation between these changes and clinical manifestations. Methods: This was a cross-sectional study. A collection of 15 consecutive patients diagnosed with IXT at Tianjin Eye Hospital from March 2021 to May 2022 formed the exotropia group, which comprised 8 males and 7 females, with an average age of (23.5±5.2) years. Ten healthy individuals, 3 males and 7 females, with an average age of (27.0±7.5) years, were selected as the control group. All participants underwent assessments of exotropia severity and Titmus stereoacuity. Three-dimensional high-resolution brain images were obtained through MRI scans. Voxel-based morphometry was employed to preprocess the MRI data, and the SPM toolbox in MATLAB was utilized to analyze differences of images between the two groups. Regions of interest (ROI) with structural abnormalities in the gray matter volume analysis were selected, and the ratio of gray matter voxel values in the ROI to the mean gray matter voxel values of the whole brain for each participant was calculated using the MarsBaR software. The correlation between this ratio and exotropia severity as well as the common logarithm of Titmus stereoacuity was analyzed. Results: The differences in age, gender distribution, and refractive error between the two groups were not statistically significant (all P>0.05). However, there were statistically significant differences in the degree of strabismus and Titmus stereoacuity (both P<0.001). Compared to the control group, patients in the strabismus group exhibited decreased gray matter volume in several brain regions, including the wedges of the medial surface of the cerebral hemisphere (decreased by 89 voxels), the left lingual gyrus (decreased by 176 voxels), the left calcarine sulcus V3 area (decreased by 30 voxels), the central anterior gyrus of the right frontal lobe (decreased by 192 voxels), the gray matter of the left hippocampal gyrus (decreased by 20 voxels), and the bilateral lateral geniculate nuclei (decreased by 100 and 40 voxels on the left and right sides, respectively). These differences were all statistically significant (all P<0.001). Additionally, there was an increase in gray matter volume in several brain regions, including the bilateral caudate nuclei (increased by 60 and 76 voxels on the left and right sides, respectively) and the left precentral gyrus (increased by 36 voxels). These differences were also statistically significant (all P<0.001). A group-level analysis identified 10 brain regions with structural differences between the two groups, which were used as ROI. The gray matter volume ratio was negatively correlated with the degree of exotropia (all P<0.05) in the ROI of the left wedges (r=-0.670), left calcarine sulcus V3 area (r=-0.610), and left lingual gyrus (r=-0.684). The gray matter volume ratio was negatively correlated with lgTS (all P<0.05) in the ROI of the left wedges (r=-0.568) and the central anterior gyrus of the right frontal lobe (r=-0.563). Conclusions: Patients with IXT exhibit decreased gray matter volume in the horizontal connection areas between the primary visual cortices V1 and V2. The reduction in gray matter volume of the lingual gyrus and the dorsal visual pathway V3 area becomes more pronounced with increasing exotropia severity, while the gray matter volume of the precentral gyrus (BA6 area) decreases with worsening stereoacuity.

High-resolution anatomical imaging of the fetal brain with a reduced field of view using outer volume suppression.

To achieve high-resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full-FOV fetal brain imaging. Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single-shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip-angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in-vivo experiments were performed to evaluate OVS performance. In-vivo high-resolution SSFSE images acquired using the proposed approach were compared with conventional and high-resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics. Excellent signal suppression in the saturation bands was confirmed in phantom and in-vivo experiments. High-resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method. High-resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero.

In Vivo Intelligent Fluorescence Endo-Microscopy by Varifocal Meta-Device and Deep Learning.

Endo-microscopy is crucial for real-time 3D visualization of internal tissues and subcellular structures. Conventional methods rely on axial movement of optical components for precise focus adjustment, limiting miniaturization and complicating procedures. Meta-device, composed of artificial nanostructures, is an emerging optical flat device that can freely manipulate the phase and amplitude of light. Here, an intelligent fluorescence endo-microscope is developed based on varifocal meta-lens and deep learning (DL). The breakthrough enables in vivo 3D imaging of mouse brains, where varifocal meta-lens focal length adjusts through relative rotation angle. The system offers key advantages such as invariant magnification, a large field-of-view, and optical sectioning at a maximum focal length tuning range of ≈2mm with 3µm lateral resolution. Using a DL network, image acquisition time and system complexity are significantly reduced, and in vivo high-resolution brain images of detailed vessels and surrounding perivascular space are clearly observed within 0.1 s (≈50 times faster). The approach will benefit various surgical procedures, such as gastrointestinal biopsies, neural imaging, brain surgery, etc.

Immunolabeling-compatible PEGASOS tissue clearing for high-resolution whole mouse brain imaging.

Novel brain clearing methods revolutionize imaging by increasing visualization throughout the brain at high resolution. However, combining the standard tool of immunostaining targets of interest with clearing methods has lagged behind. We integrate whole-mount immunostaining with PEGASOS tissue clearing, referred to as iPEGASOS (immunostaining-compatible PEGASOS), to address the challenge of signal quenching during clearing processes. iPEGASOS effectively enhances molecular-genetically targeted fluorescent signals that are otherwise compromised during conventional clearing procedures. Additionally, we demonstrate the utility of iPEGASOS for visualizing neurochemical markers or viral labels to augment visualization that transgenic mouse lines cannot provide. Our study encompasses three distinct applications, each showcasing the versatility and efficacy of this approach. We employ whole-mount immunostaining to enhance molecular signals in transgenic reporter mouse lines to visualize the whole-brain spatial distribution of specific cellular populations. We also significantly improve the visualization of neural circuit connections by enhancing signals from viral tracers injected into the brain. Last, we show immunostaining without genetic markers to selectively label beta-amyloid deposits in a mouse model of Alzheimer's disease, facilitating the comprehensive whole-brain study of pathological features.

Sex-, strain and lateral differences in brain cytoarchitecture across a large mouse population.

The mouse brain is by far the most intensively studied among mammalian brains, yet basic measures of its cytoarchitecture remain obscure. For example, quantifying cell numbers, and the interplay of sex-, strain-, and individual variability in cell density and volume is out of reach for many regions. The Allen Mouse Brain Connectivity project produces high-resolution full brain images of hundreds of brains. Although these were created for a different purpose, they reveal details of neuroanatomy and cytoarchitecture. Here, we used this population to systematically characterize cell density and volume for each anatomical unit in the mouse brain. We developed a deep neural network-based segmentation pipeline that uses the auto-fluorescence intensities of images to segment cell nuclei even within the densest regions, such as the dentate gyrus. We applied our pipeline to 507 brains of males and females from C57BL/6J and FVB.CD1 strains. Globally, we found that increased overall brain volume does not result in uniform expansion across all regions. Moreover, region-specific density changes are often negatively correlated with the volume of the region, therefore cell count does not scale linearly with volume. Many regions, including layer 2/3 across several cortical areas, showed distinct lateral bias. We identified strain-specific or sex-specific differences. For example, males tended to have more cells in extended amygdala and hypothalamic regions (MEA, BST, BLA, BMA, and LPO, AHN) while females had more cells in the orbital cortex (ORB). Yet, inter-individual variability was always greater than the effect size of a single qualifier. We provide the results of this analysis as an accessible resource for the community.

Super-resolution of brain MRI images based on denoising diffusion probabilistic model

Super-resolution of brain magnetic resonance imaging (MRI) generates high resolution brain images as opposed to low-resolution ones, thus providing more detailed anatomical information for diagnosis of neurodegenerative diseases. Although denoising diffusion probabilistic model (DDPM) has displayed remarkable performance in super-resolution of face and natural images, its application to producing high-resolution brain MRI images has not been explored. This study proposed a new deep-learning super-resolution framework for brain MRI images based on DDPM, via incorporating self-attention mechanism into DDPM. The main improvements are as follows: (a) Only one input channel is preserved. (b) The number of baseline channels is reduced from 64 to 32, to improve training speed. (c) Self-attention mechanism is added to 32 × 32, 16 × 16, and 8 × 8 resolution layers rather than only to 16 × 16 layer. (d) Feature-wise affine transformation is added to residual block. Experimental results on open T1- and diffusion-weighted brain MRI datasets show that our DDPM model outperformed the state-of-the-art super-resolution methods. In terms of learned perceptual image patch similarity (LPIPS) metric, the proposed DDPM model achieved the least distortion of generated super-resolution brain MRI images. This framework can also be conveniently used to reconstruct high-resolution MRI images of other body parts such as spinal and knee in future.

Integrating Nutrient Biomarkers, Cognitive Function, and Structural MRI Data to Build Multivariate Phenotypes of Healthy Aging

BackgroundResearch in the emerging field of nutritional cognitive neuroscience demonstrates that many aspects of nutrition—from entire diets to specific nutrients—affect cognitive performance and brain health. ObjectivesAlthough previous research has primarily examined the bivariate relationship between nutrition and cognition or nutrition and brain health, this study sought to investigate the joint relationship between these essential and interactive elements of human health. MethodsWe applied a state-of-the-art data fusion method, coupled matrix tensor factorization, to characterize the joint association between measures of nutrition (52 nutrient biomarkers), cognition (Wechsler Abbreviated Test of Intelligence and Wechsler Memory Scale), and brain health (high-resolution MRI measures of structural brain volume) within a cross-sectional sample of 111 healthy older adults, with an average age of 69.1 y, 62% being female, and an average body mass index of 26.0 kg/m2. ResultsData fusion uncovered latent factors that capture the joint association between specific nutrient profiles, cognitive measures, and cortical volumes, demonstrating the respects in which these health domains are coupled. A hierarchical cluster analysis further revealed systematic differences between a subset of variables contributing to the underlying latent factors, providing evidence for multivariate phenotypes that represent high and low levels of performance across multiple health domains. The primary features that distinguish between each phenotype were as follows: 1) nutrient biomarkers for monounsaturated and polyunsaturated fatty acids; 2) cognitive measures of immediate, auditory, and delayed memory; and 3) brain volumes within frontal, temporal, and parietal cortexes. ConclusionsBy incorporating innovations in nutritional epidemiology (nutrient biomarker analysis), cognitive neuroscience (high-resolution structural brain imaging), and statistics (data fusion), this study provides an interdisciplinary synthesis of methods that elucidate how nutrition, cognition, and brain health are integrated through lifestyle choices that affect healthy aging.

High-resolution magnetization-transfer imaging of post-mortem marmoset brain: Comparisons with relaxometry and histology

Cell membranes and macromolecules or paramagnetic compounds interact with water proton spins, which modulates magnetic resonance imaging (MRI) contrast providing information on tissue composition. For a further investigation, quantitative magnetization transfer (qMT) parameters (at 3T), including the ratio of the macromolecular and water proton pools, F, and the exchange-rate constant as well as the (observed) longitudinal and the effective transverse relaxation rates (at 3T and 7T), R1obs and R2*, respectively, were measured at high spatial resolution (200 µm) in a slice of fixed marmoset brain and compared to histology results obtained with Gallyas’ myelin stain and Perls’ iron stain. R1obs and R2* were linearly correlated with the iron content for the entire slice, whereas distinct differences were obtained between gray and white matter for correlations of relaxometry and qMT parameters with myelin content. The combined results suggest that the macromolecular pool interacting with water consists of myelin and (less efficient) non-myelin contributions. Despite strong correlation of F and R1obs, none of these parameters was uniquely specific to myelination. Due to additional sensitivity to iron stores, R1obs and R2* were more sensitive for depicting microstructural differences between cortical layers than F.

3D Soma Detection in Large-Scale Whole Brain Images via a Two-Stage Neural Network.

3D soma detection in whole brain images is a critical step for neuron reconstruction. However, existing soma detection methods are not suitable for whole mouse brain images with large amounts of data and complex structure. In this paper, we propose a two-stage deep neural network to achieve fast and accurate soma detection in large-scale and high-resolution whole mouse brain images (more than 1TB). For the first stage, a lightweight Multi-level Cross Classification Network (MCC-Net) is proposed to filter out images without somas and generate coarse candidate images by combining the advantages of the multi convolution layer's feature extraction ability. It can speed up the detection of somas and reduce the computational complexity. For the second stage, to further obtain the accurate locations of somas in the whole mouse brain images, the Scale Fusion Segmentation Network (SFS-Net) is developed to segment soma regions from candidate images. Specifically, the SFS-Net captures multi-scale context information and establishes a complementary relationship between encoder and decoder by combining the encoder-decoder structure and a 3D Scale-Aware Pyramid Fusion (SAPF) module for better segmentation performance. The experimental results on three whole mouse brain images verify that the proposed method can achieve excellent performance and provide the reconstruction of neurons with beneficial information. Additionally, we have established a public dataset named WBMSD, including 798 high-resolution and representative images ( 256 ×256 ×256 voxels) from three whole mouse brain images, dedicated to the research of soma detection, which will be released along with this paper.

Machine-Learning-Based Inversion Scheme for Super-Resolution Three-Dimensional Microwave Human Brain Imaging

To realize efficient three-dimensional (3-D) super-resolution whole brain microwave imaging, a new machine- learning-based inversion method with a resolution enhancement technique is proposed. It consists of three parts: a parallel semiconnected backpropagation neural network (SJ-BPNN) scheme, a U-Net scheme, and a modified Akima piecewise cubic Hermite interpolation (MAPCHI) scheme. The parallel SJ-BPNN scheme is first employed to map the measured scattered field data to the preliminary electrical properties’ distribution of human brain. Then, U-Net is used to improve the quality of these preliminary reconstruction results. Finally, the MAPCHI scheme is adopted to greatly improve the resolution of reconstruction results with a very low computational cost. Numerical examples of a normal human brain and a human brain with abnormal scatterers show that the proposed method can achieve accurate high-resolution human brain imaging with 1024 × 1024 × 1024 voxels with a very low computational cost.

Interdigitated Columnar Representation of Personal Space and Visual Space in Human Parietal Cortex.

Personal space (PS) is the space around the body that people prefer to maintain between themselves and unfamiliar others. Intrusion into personal space evokes discomfort and an urge to move away. Physiologic studies in nonhuman primates suggest that defensive responses to intruding stimuli involve the parietal cortex. We hypothesized that the spatial encoding of interpersonal distance is initially transformed from purely sensory to more egocentric mapping within human parietal cortex. This hypothesis was tested using 7 Tesla (7T) fMRI at high spatial resolution (1.1 mm isotropic), in seven subjects (four females, three males). In response to visual stimuli presented at a range of virtual distances, we found two categories of distance encoding in two corresponding radially-extending columns of activity within parietal cortex. One set of columns (P columns) responded selectively to moving and stationary face images presented at virtual distances that were nearer (but not farther) than each subject's behaviorally-defined personal space boundary. In most P columns, BOLD response amplitudes increased monotonically and nonlinearly with increasing virtual face proximity. In the remaining P columns, BOLD responses decreased with increasing proximity. A second set of parietal columns (D columns) responded selectively to disparity-based distance cues (near or far) in random dot stimuli, similar to disparity-selective columns described previously in occipital cortex. Critically, in parietal cortex, P columns were topographically interdigitated (nonoverlapping) with D columns. These results suggest that visual spatial information is transformed from visual to body-centered (or person-centered) dimensions in multiple local sites within human parietal cortex.SIGNIFICANCE STATEMENT Recent COVID-related social distancing practices highlight the need to better understand brain mechanisms which regulate "personal space" (PS), which is defined by the closest interpersonal distance that is comfortable for an individual. Using high spatial resolution brain imaging, we tested whether a map of external space is transformed from purely visual (3D-based) information to a more egocentric map (related to personal space) in human parietal cortex. We confirmed this transformation and further showed that it was mediated by two mutually segregated sets of columns: one which encoded interpersonal distance and another that encoded visual distance. These results suggest that the cortical transformation of sensory-centered to person-centered encoding of space near the body involves short-range communication across interdigitated columns within parietal cortex.

MBrainAligner-Web: a web server for cross-modal coherent registration of whole mouse brains.

Recent whole-brain mapping projects are collecting increasingly larger sets of high-resolution brain images using a variety of imaging, labeling and sample preparation techniques. Both mining and analysis of these data require reliable and robust cross-modal registration tools. We recently developed the mBrainAligner, a pipeline for performing cross-modal registration of the whole mouse brain. However, using this tool requires scripting or command-line skills to assemble and configure the different modules of mBrainAligner for accommodating different registration requirements and platform settings. In this application note, we present mBrainAligner-Web, a web server with a user-friendly interface that allows to configure and run mBrainAligner locally or remotely across platforms. mBrainAligner-Web is available at http://mbrainaligner.ahu.edu.cn/ with source code at https://github.com/reaneyli/mBrainAligner-web. Supplementary data are available at Bioinformatics online.

Multi-Coil MRI Reconstruction Challenge-Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations.

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.

Structural Covariance of the Ipsilesional Primary Motor Cortex in Subcortical Stroke Patients with Motor Deficits.

The analysis of structural covariance has emerged as a powerful tool to explore the morphometric correlations among broadly distributed brain regions. However, little is known about the interactions between the damaged primary motor cortex (M1) and other brain regions in stroke patients with motor deficits. This study is aimed at investigating the structural covariance pattern of the ipsilesional M1 in chronic subcortical stroke patients with motor deficits. High-resolution T1-weighted brain images were acquired from 58 chronic subcortical stroke patients with motor deficits (29 with left-sided lesions and 29 with right-sided lesions) and 50 healthy controls. Structural covariance patterns were identified by a seed-based structural covariance method based on gray matter (GM) volume. Group comparisons between stroke patients (left-sided or right-sided groups) and healthy controls were determined by a permutation test. The association between alterations in the regional GM volume and motor recovery after stroke was investigated by a multivariate regression approach. Structural covariance analysis revealed an extensive increase in the structural interactions between the ipsilesional M1 and other brain regions in stroke patients, involving not only motor-related brain regions but also non-motor-related brain regions. We also identified a slightly different pattern of structural covariance between the left-sided stroke group and the right-sided stroke group, thus indicating a lesion-side effect of cortical reorganization after stroke. Moreover, alterations in the GM volume of structural covariance brain regions were significantly correlated to the motor function scores in stroke patients. These findings indicated that the structural covariance patterns of the ipsilesional M1 in chronic subcortical stroke patients were induced by motor-related plasticity. Our findings may help us to better understand the neurobiological mechanisms of motor impairment and recovery in patients with subcortical stroke from different perspectives.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains.

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

High-throughput volumetric adaptive optical imaging using compressed time-reversal matrix

Deep-tissue optical imaging suffers from the reduction of resolving power due to tissue-induced optical aberrations and multiple scattering noise. Reflection matrix approaches recording the maps of backscattered waves for all the possible orthogonal input channels have provided formidable solutions for removing severe aberrations and recovering the ideal diffraction-limited spatial resolution without relying on fluorescence labeling and guide stars. However, measuring the full input–output response of the tissue specimen is time-consuming, making the real-time image acquisition difficult. Here, we present the use of a time-reversal matrix, instead of the reflection matrix, for fast high-resolution volumetric imaging of a mouse brain. The time-reversal matrix reduces two-way problem to one-way problem, which effectively relieves the requirement for the coverage of input channels. Using a newly developed aberration correction algorithm designed for the time-reversal matrix, we demonstrated the correction of complex aberrations using as small as 2% of the complete basis while maintaining the image reconstruction fidelity comparable to the fully sampled reflection matrix. Due to nearly 100-fold reduction in the matrix recording time, we could achieve real-time aberration-correction imaging for a field of view of 40 × 40 µm2 (176 × 176 pixels) at a frame rate of 80 Hz. Furthermore, we demonstrated high-throughput volumetric adaptive optical imaging of a mouse brain by recording a volume of 128 × 128 × 125 µm3 (568 × 568 × 125 voxels) in 3.58 s, correcting tissue aberrations at each and every 1 µm depth section, and visualizing myelinated axons with a lateral resolution of 0.45 µm and an axial resolution of 2 µm.

Deep robust residual network for super-resolution of 2D fetal brain MRI

Spatial resolution is a key factor of quantitatively evaluating the quality of magnetic resonance imagery (MRI). Super-resolution (SR) approaches can improve its spatial resolution by reconstructing high-resolution (HR) images from low-resolution (LR) ones to meet clinical and scientific requirements. To increase the quality of brain MRI, we study a robust residual-learning SR network (RRLSRN) to generate a sharp HR brain image from an LR input. Due to the Charbonnier loss can handle outliers well, and Gradient Difference Loss (GDL) can sharpen an image, we combined the Charbonnier loss and GDL to improve the robustness of the model and enhance the texture information of SR results. Two MRI datasets of adult brain, Kirby 21 and NAMIC, were used to train and verify the effectiveness of our model. To further verify the generalizability and robustness of the proposed model, we collected eight clinical fetal brain MRI 2D data for evaluation. The experimental results have shown that the proposed deep residual-learning network achieved superior performance and high efficiency over other compared methods.

Machine Learning Algorithms for Classification of First-Trimester Fetal Brain Ultrasound Images.

To evaluate the feasibility of machine learning (ML) tools for segmenting and classifying first-trimester fetal brain ultrasound images. Two image segmentation methods processed high-resolution fetal brain images obtained during the nuchal translucency scan: "Statistical Region Merging" (SRM) and "Trainable Weka Segmentation" (TWS), with training and testing sets in the latter. Measurement of the fetal cerebral cortex in original and processed images served to evaluate the performance of the algorithms. Mean absolute percentage error (MAPE) was used as an accuracy index of the segmentation processing. The SRM plugin revealed a total MAPE of 1.71% ± 1.62 SD (standard deviation) and a MAPE of 1.4% ± 1.32 SD and 2.72% ± 2.21 SD for the normal and increased NT groups, respectively. The TWS plugin displayed a MAPE of 1.71% ± 0.59 SD (testing set). There were no significant differences between the training and testing sets after 5-fold cross-validation. The images obtained from normal NT fetuses and increased NT fetuses revealed a MAPE of 1.52% ± 1.02 SD and 2.63% ± 1.98 SD. Our study demonstrates the feasibility of using ML algorithms to classify first-trimester fetal brain ultrasound images and lay the foundation for earlier diagnosis of fetal brain abnormalities.

Malformations of cortical development as models of altered brain excitability

The developmental organisation of the human cerebral cortex is among the most complex processes known in biology. The process includes the migration of neuronal precursors from the cortical endplate to the future outer boundary of the brain, a consistent organisation in layers from the inside to the outside, the differentiation of principal neurons and a wide spectrum of inhibitory neuronal types focusing information processing and controlling excitability, and the precision wiring of neurons within cortical columns and in extended projections. This process results in an efficient, stable, and functional small-world brain network. Unsurprisingly, the complex sequences and interactions contributing to this cortical development are liable to minor and major disturbances. Progress in brain imaging and in the histological analysis of brain specimens, particularly from humans undergoing neurosurgical resections of brain areas considered pathological, has considerably increased knowledge from the spectrum of malformations of cortical development. Such malformations can be extensive (eg, the formation of a double cortex or absent cortical layering, or altered gyrification, such as in pachygyria, or can be circumscribed (as with focal cortical dysplasia), or can be space occupying (as with a developmental tumour). Abnormal neuronal migration, differentiation, and wiring will often affect the intricate balance of excitability and inhibition in the affected brain region, resulting in epilepsy. Improved high resolution brain imaging, including computer-assisted postprocessing of 3D data sets, 1 Gill RS Lee HM Caldairou B et al. Multicenter validation of a deep learning detection algorithm for focal cortical dysplasia. Neurol. 2021; (published online Sep 14)https://doi.org/10.1212/WNL.0000000000012698 Crossref Scopus (10) Google Scholar , 2 Demerath T Rubensdörfer L Schwarzwald R et al. Morphometric MRI analysis: improved detection of focal cortical dysplasia using the MP2RAGE sequence. AJNR Am J Neuroradiol. 2020; 41: 1009-1014 Crossref PubMed Scopus (12) Google Scholar has provided evidence that discrete focal brain malformations are the most common cause of focal epilepsy in people, and particularly of focal epilepsy that responds insufficiently to anti-seizure medication and thus requires surgical treatment. 3 Fauser S Essang C Altenmüller DM et al. Long-term seizure outcome in 211 patients with focal cortical dysplasia. Epilepsia. 2015; 56: 66-76 Crossref PubMed Scopus (113) Google Scholar , 4 Blümcke I Spreafico R Haaker G et al. EEBB Consortium. Histopathological findings in brain tissue obtained during epilepsy surgery. N Engl J Med. 2017; 377: 1648-1656 Crossref PubMed Scopus (370) Google Scholar Neocortical development and epilepsy: insights from focal cortical dysplasia and brain tumoursDuring the past decade, there have been considerable advances in understanding of the genetic and morphogenic processes underlying cortical malformations and developmental brain tumours. Focal malformations are caused by somatic (postzygotic) variants in genes related to cell growth (ie, in the mTOR pathway in focal cortical dysplasia type 2), which are acquired in neuronal progenitors during neurodevelopment. In comparison, developmental brain tumours result from somatic variants in genes related to cell proliferation (eg, in the MAP-kinase pathway in ganglioglioma), which affect proliferating glioneuronal precursors. Full-Text PDF

Poor Sleep Quality Linked to Decreased Brain Gray Matter Density in Adults with Type 2 Diabetes

Poor sleep is common in adults with Type 2 Diabetes Mellitus (T2DM), which may contribute to brain tissue changes. However, the impact of sleep quality on brain tissue in T2DM individuals is unclear. We aimed to evaluate differential sleep quality with brain changes, and brain tissue integrity in T2DM patients. Data were collected from 34 patients with T2DM and included sleep quality (assessed by the Pittsburgh Sleep Quality Index [PSQI], and high-resolution T1-weighted brain images using a 3.0-Tesla MRI scanner. Gray matter density (GMD) maps were compared between subjects with good vs poor sleep quality as assessed by PSQI (covariates: age, sex, BMI). Of 34 T2DM patients, 17 showed poor sleep quality. Multiple brain sites, including the hippocampus, cerebellum, prefrontal, amygdala, thalamus, hypothalamus, insula, cingulate, and temporal areas, showed reduced gray matter in T2DM patients with poor sleep quality over patients with good sleep quality. Negative associations emerged between PSQI scores and gray matter density in multiple areas. T2DM patients with poor sleep quality show brain tissue changes in sites involved in sleep regulation. Findings indicate that improving sleep may help mitigate brain tissue damage, and thus, improve brain function in T2DM patients.