Transformer-based deep learning denoising of single and multi-delay 3D arterial spin labeling.

PURPOSE: To present a Swin Transformer-based deep learning (DL) model (SwinIR) for denoising single-delay and multi-delay 3D arterial spin labeling (ASL) and compare its performance with convolutional neural network (CNN) and other Transformer-based methods. METHODS: SwinIR and CNN-based spatial denoising models were developed for single-delay ASL. The models were trained on 66 subjects (119 scans) and tested on 39 subjects (44 scans) from three different vendors. Spatiotemporal denoising models were developed using another dataset (6 subjects, 10 scans) of multi-delay ASL. A range of input conditions was tested for denoising single and multi-delay ASL, respectively. The performance was evaluated using similarity metrics, spatial SNR and quantification accuracy of cerebral blood flow (CBF), and arterial transit time (ATT). RESULTS: SwinIR outperformed CNN and other Transformer-based networks, whereas pseudo-3D models performed better than 2D models for denoising single-delay ASL. The similarity metrics and image quality (SNR) improved with more slices in pseudo-3D models and further improved when using M0 as input, but introduced greater biases for CBF quantification. Pseudo-3D models with three slices achieved optimal balance between SNR and accuracy, which can be generalized to different vendors. For multi-delay ASL, spatiotemporal denoising models had better performance than spatial-only models with reduced biases in fitted CBF and ATT maps. CONCLUSIONS: SwinIR provided better performance than CNN and other Transformer-based methods for denoising both single and multi-delay 3D ASL data. The proposed model offers flexibility to improve image quality and/or reduce scan time for 3D ASL to facilitate its clinical use.

Low-threshold 2†µm InAs/InP quantum dash lasers enabled by punctuated growth.

2 µm photonics and optoelectronics is promising for potential applications such as optical communications, LiDAR, and chemical sensing. While the research on 2 µm detectors is on the rise, the development of InP-based 2 µm gain materials with 0D nanostructures is rather stalled. Here, we demonstrate low-threshold, continuous wave lasing at 2 µm wavelength from InAs quantum dash/InP lasers enabled by punctuated growth of the quantum structure. We demonstrate low threshold current densities from the 7.1 µm width ridge-waveguide lasers, with values of 657, 1183, and 1944 A/cm2 under short pulse wave (SPW), quasi-continuous wave (QCW), and continuous wave operation. The lasers also exhibited good thermal stability, with a characteristic temperature T0 of 43 K under SPW mode. The lasing spectra is centered at 1.97 µm, coinciding with the ground-state emission observed from photoluminescence studies. We believe that the InAs quantum dash/InP lasers emitting near 2 µm will be a key enabling technology for 2 µm communication and sensing.

Palsy of Both the Tibial Nerve and Common Peroneal Nerve Caused by a Ganglion Cyst in the Popliteal Area.

A ganglion cyst is a benign mass consisting of high-viscosity mucinous fluid. It can originate from the sheath of a tendon, peripheral nerve, or joint capsule. Compressive neuropathy caused by a ganglion cyst is rarely reported, with the majority of documented cases involving peroneal nerve palsy. To date, cases demonstrating both peroneal and tibial nerve palsies resulting from a ganglion cyst forming on a branch of the sciatic nerve have not been reported. In this paper, we present the case of a 74-year-old man visiting an outpatient clinic complaining of left-sided foot drop and sensory loss in the lower extremity, a lack of strength in his left leg, and a decrease in sensation in the leg for the past month without any history of trauma. Ankle dorsiflexion and great toe extension strength on the left side were Grade I. Ankle plantar flexion and great toe flexion were Grade II. We suspected peroneal and tibial nerve palsy and performed a screening ultrasound, which is inexpensive and rapid. In the operative field, several cysts were discovered, originating at the site where the sciatic nerve splits into peroneal and tibial nerves. After successful surgical decompression and a series of rehabilitation procedures, the patient's neurological symptoms improved. There was no recurrence.

Style transfer generative adversarial networks to harmonize multisite MRI to a single reference image to avoid overcorrection.

Recent work within neuroimaging consortia have aimed to identify reproducible, and often subtle, brain signatures of psychiatric or neurological conditions. To allow for high-powered brain imaging analyses, it is often necessary to pool MR images that were acquired with different protocols across multiple scanners. Current retrospective harmonization techniques have shown promise in removing site-related image variation. However, most statistical approaches may over-correct for technical, scanning-related, variation as they cannot distinguish between confounded image-acquisition based variability and site-related population variability. Such statistical methods often require that datasets contain subjects or patient groups with similar clinical or demographic information to isolate the acquisition-based variability. To overcome this limitation, we consider site-related magnetic resonance (MR) imaging harmonization as a style transfer problem rather than a domain transfer problem. Using a fully unsupervised deep-learning framework based on a generative adversarial network (GAN), we show that MR images can be harmonized by inserting the style information encoded from a single reference image, without knowing their site/scanner labels a priori. We trained our model using data from five large-scale multisite datasets with varied demographics. Results demonstrated that our style-encoding model can harmonize MR images, and match intensity profiles, without relying on traveling subjects. This model also avoids the need to control for clinical, diagnostic, or demographic information. We highlight the effectiveness of our method for clinical research by comparing extracted cortical and subcortical features, brain-age estimates, and case-control effect sizes before and after the harmonization. We showed that our harmonization removed the site-related variances, while preserving the anatomical information and clinical meaningful patterns. We further demonstrated that with a diverse training set, our method successfully harmonized MR images collected from unseen scanners and protocols, suggesting a promising tool for ongoing collaborative studies. Source code is released in USC-IGC/style_transfer_harmonization (github.com).

More extensive white matter disruptions present in untreated obstructive sleep apnea than we thought: A large sample diffusion imaging study.

Obstructive sleep apnea (OSA) may lead to white mater (WM) disruptions and cognitive deficits. However, no studies have investigated the full extent of the brain WM, and its associations with cognitive deficits in OSA remain unclear. We thus applied diffusion tensor imaging (DTI) tractography with multi-fiber models and used atlas-based bundle-specific approach to investigate the WM abnormalities for various tracts of the cerebral cortex, thalamus, brainstem, and cerebellum in patients with untreated OSA. We enrolled 100 OSA patients and 63 healthy controls. Fractional anisotropy (FA) and mean diffusivity (MD) values mapped on 33 regions of interest including WM tracts of cortex, thalamus, brainstem, and cerebellum were obtained from tractography-based reconstructions. We compared FA/MD values between groups and correlated FA/MD with clinical data in the OSA group after controlling for age and body mass index. OSA patients showed significantly lower FA values in multiple WM fibers including corpus callosum, inferior fronto-occipital fasciculus, middle/superior longitudinal fasciculi, thalamic radiations, and uncinate (FDR <0.05). Higher FA values were found in medial lemniscus of patients compared to controls (FDR <0.05). Lower FA values of rostrum of corpus callosum correlated with lower visual memory performance in OSA group (p < .005). Our quantitative DTI analysis demonstrated that untreated OSA could negatively impact the integrity of pathways more broadly, including brainstem structures such as medial lemniscus, in comparison to previous findings. Fiber tract abnormalities of the rostral corpus callosum were associated with impaired visual memory in untreated OSA may provide insights into the related pathomechanism.

Obstructive Sleep Apnea and Striatal Dopamine Availability in Parkinson's Disease.

BACKGROUND: Sleep disorders are frequently associated with Parkinson's disease. Obstructive sleep apnea syndrome is one of these sleep disorders and is associated with the severity of motor symptoms in Parkinson's disease. Obstructive sleep apnea can lead to dopaminergic neuronal cell degeneration and may impair the clearance of α-synuclein in Parkinson's disease. Striatal dopamine uptake is a surrogate marker of nigral dopaminergic cell damage. OBJECTIVE: We aimed to investigate the differences in striatal dopamine availability between Parkinson's disease patients with or without obstructive sleep apnea. METHODS: A total of 85 de novo and nonmedicated Parkinson's disease patients were enrolled. Full-night polysomnography was performed for all patients, and obstructive sleep apnea was diagnosed as apnea/hypopnea index ≥5. Positron emission tomography was performed with 18 F-N-(3-fluoropropyl)-2ß-carbon ethoxy-3ß-(4-iodophenyl) nortropane, and the regional standardized-uptake values were analyzed using a volume-of-interest template and compared between groups with or without obstructive sleep apnea. RESULTS: Dopamine availability in the caudate nucleus of the obstructive sleep apnea group was significantly lower than that of the nonobstructive sleep apnea group. On subgroup analysis, such association was found in female but not in male patients. In other structures (putamen, globus pallidus, and thalamus), dopamine availability did not differ between the two groups. CONCLUSION: This study supports the proposition that obstructive sleep apnea can contribute to reduced striatal dopamine transporter availability in Parkinson's disease. Additional studies are needed to assess the causal association between obstructive sleep apnea and the neurodegenerative process in Parkinson's disease. © 2023 International Parkinson and Movement Disorder Society.

Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates.

OBJECTIVES: Dramatic brain morphological changes occur throughout the third trimester of gestation. In this study, we investigated whether the predicted brain age (PBA) derived from graph convolutional network (GCN) that accounts for cortical morphometrics in third trimester is associated with postnatal abnormalities and neurodevelopmental outcome. METHODS: In total, 577 T1 MRI scans of preterm neonates from two different datasets were analyzed; the NEOCIVET pipeline generated cortical surfaces and morphological features, which were then fed to the GCN to predict brain age. The brain age index (BAI; PBA minus chronological age) was used to determine the relationships among preterm birth (i.e., birthweight and birth age), perinatal brain injuries, postnatal events/clinical conditions, BAI at postnatal scan, and neurodevelopmental scores at 30 months. RESULTS: Brain morphology and GCN-based age prediction of preterm neonates without brain lesions (mean absolute error [MAE]: 0.96 weeks) outperformed conventional machine learning methods using no topological information. Structural equation models (SEM) showed that BAI mediated the influence of preterm birth and postnatal clinical factors, but not perinatal brain injuries, on neurodevelopmental outcome at 30 months of age. CONCLUSIONS: Brain morphology may be clinically meaningful in measuring brain age, as it relates to postnatal factors, and predicting neurodevelopmental outcome. CLINICAL RELEVANCE STATEMENT: Understanding the neurodevelopmental trajectory of preterm neonates through the prediction of brain age using a graph convolutional neural network may allow for earlier detection of potential developmental abnormalities and improved interventions, consequently enhancing the prognosis and quality of life in this vulnerable population. KEY POINTS: •Brain age in preterm neonates predicted using a graph convolutional network with brain morphological changes mediates the pre-scan risk factors and post-scan neurodevelopmental outcomes. •Predicted brain age oriented from conventional deep learning approaches, which indicates the neurodevelopmental status in neonates, shows a lack of sensitivity to perinatal risk factors and predicting neurodevelopmental outcomes. •The new brain age index based on brain morphology and graph convolutional network enhances the accuracy and clinical interpretation of predicted brain age for neonates.

Cyto/myeloarchitecture of cortical gray matter and superficial white matter in early neurodevelopment: multimodal MRI study in preterm neonates.

The cerebral cortex undergoes rapid microstructural changes throughout the third trimester. Recently, there has been growing interest on imaging features that represent cyto/myeloarchitecture underlying intracortical myelination, cortical gray matter (GM), and its adjacent superficial whitematter (sWM). Using 92 magnetic resonance imaging scans from 78 preterm neonates, the current study used combined T1-weighted/T2-weighted (T1w/T2w) intensity ratio and diffusion tensor imaging (DTI) measurements, including fractional anisotropy (FA) and mean diffusivity (MD), to characterize the developing cyto/myeloarchitectural architecture. DTI metrics showed a linear trajectory: FA decreased in GM but increased in sWM with time; and MD decreased in both GM and sWM. Conversely, T1w/T2w measurements showed a distinctive parabolic trajectory, revealing additional cyto/myeloarchitectural signature inferred. Furthermore, the spatiotemporal courses were regionally heterogeneous: central, ventral, and temporal regions of GM and sWM exhibited faster T1w/T2w changes; anterior sWM areas exhibited faster FA increases; and central and cingulate areas in GM and sWM exhibited faster MD decreases. These results may explain cyto/myeloarchitectural processes, including dendritic arborization, synaptogenesis, glial proliferation, and radial glial cell organization and apoptosis. Finally, T1w/T2w values were significantly associated with 1-year language and cognitive outcome scores, while MD significantly decreased with intraventricular hemorrhage.

Hippocampal asymmetry of regional development and structural covariance in preterm neonates.

Premature birth is associated with a high prevalence of neurodevelopmental impairments in surviving infants. The hippocampus is known to be critical for learning and memory, yet the putative effects of hippocampal dysfunction remain poorly understood in preterm neonates. In particular, while asymmetry of the hippocampus has been well noted both structurally and functionally, how preterm birth impairs hippocampal development and to what extent the hippocampus is asymmetrically impaired by preterm birth have not been well delineated. In this study, we compared volumetric growth and shape development in the hippocampal hemispheres and structural covariance (SC) between hippocampal vertices and cortical thickness in cerebral cortex regions between two groups. We found that premature infants had smaller volumes of the right hippocampi only. Lower thickness was observed in the hippocampal head in both hemispheres for preterm neonates compared with full-term peers, though preterm neonates exhibited an accelerated age-related change of hippocampal thickness in the left hippocampi. The SC between the left hippocampi and the limbic lobe of the premature infants was severely impaired compared with the term-born neonates. These findings suggested that the development of the hippocampus during the third trimester may be altered following early extrauterine exposure with a high degree of asymmetry.

Novel neuroelectrophysiological age index associated with imaging features of brain aging and sleep disorders.

Sleep architecture and microstructures alter with aging and sleep disorder-led accelerated aging. We proposed a sleep EEG based brain age prediction model using convolutional neural networks. We then associated the estimated brain age index with brain structural aging features, sleep disorders and various sleep parameters. Our model also showed a higher BAI (predicted brain age minus chronological age) is associated with cortical thinning in various functional areas. We found a higher BAI for sleep disorder groups compared to healthy sleepers, as well as significant differences in the spectral pattern of EEG among different sleep disorders (lower power in slow and ϑ waves for sleep apnea vs. higher power in ß and σ for insomnia), suggesting sleep disorder-dependent pathomechanisms of aging. Our results demonstrate that the new EEG-BAI can be a biomarker reflecting brain health in normal and various sleep disorder subjects, and may be used to assess treatment efficacy.

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke.

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Morphological Development Trajectory and Structural Covariance Network of the Human Fetal Cortical Plate during the Early Second Trimester.

During the early second trimester, the cortical plate, or "the developing cortex", undergoes immensely complex and rapid development to complete its major complement of neurons. However, morphological development of the cortical plate and the precise patterning of brain structural covariance networks during this period remain unexplored. In this study, we used 7.0 T high-resolution magnetic resonance images of brain specimens ranging from 14 to 22 gestational weeks to manually segment the cortical plate. Thickness, area expansion, and curvature (i.e., folding) across the cortical plate regions were computed, and correlations of thickness values among different cortical plate regions were measured to analyze fetal cortico-cortical structural covariance throughout development of the early second trimester. The cortical plate displayed significant increases in thickness and expansions in area throughout all regions but changes of curvature in only certain major sulci. The topological architecture and network properties of fetal brain covariance presented immature and inefficient organizations with low degree of integration and high degree of segregation. Altogether, our results provide novel insight on the developmental patterning of cortical plate thickness and the developmental origin of brain network architecture throughout the early second trimester.

Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs.

Despite the proliferation of innovative technologies during the Fourth Industrial Revolution (4IR), there is a severe lack of quantitative and empirical studies that deal with the effectiveness of recently emerging technologies. This study examines the impact of employing core technologies of the 4IR on small and medium enterprises (SMEs). We used the firm-level cross-sectional data on Korean manufacturing SMEs, including the information on technology utilization. The stochastic production frontier estimation with selectivity correction is employed, besides matching technique to obtain unbiased estimates on technology efficiency. The empirical analysis finds that adopting emerging technologies enhances the productivity of SMEs. After observed and unobserved factors are controlled, the technical efficiency of adopters is higher by more than 26% on average, compared to non-adopters. Moreover, if the gap among production frontiers is considered, the difference in productivity would rise further. Additionally, a strategic alliance is a crucial route for SMEs to accept new technologies.

White matter hyperintensities segmentation using the ensemble U-Net with multi-scale highlighting foregrounds.

White matter hyperintensities (WMHs) are abnormal signals within the white matter region on the human brain MRI and have been associated with aging processes, cognitive decline, and dementia. In the current study, we proposed a U-Net with multi-scale highlighting foregrounds (HF) for WMHs segmentation. Our method, U-Net with HF, is designed to improve the detection of the WMH voxels with partial volume effects. We evaluated the segmentation performance of the proposed approach using the Challenge training dataset. Then we assessed the clinical utility of the WMH volumes that were automatically computed using our method and the Alzheimer's Disease Neuroimaging Initiative database. We demonstrated that the U-Net with HF significantly improved the detection of the WMH voxels at the boundary of the WMHs or in small WMH clusters quantitatively and qualitatively. Up to date, the proposed method has achieved the best overall evaluation scores, the highest dice similarity index, and the best F1-score among 39 methods submitted on the WMH Segmentation Challenge that was initially hosted by MICCAI 2017 and is continuously accepting new challengers. The evaluation of the clinical utility showed that the WMH volume that was automatically computed using U-Net with HF was significantly associated with cognitive performance and improves the classification between cognitive normal and Alzheimer's disease subjects and between patients with mild cognitive impairment and those with Alzheimer's disease. The implementation of our proposed method is publicly available using Dockerhub (https://hub.docker.com/r/wmhchallenge/pgs).

Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions.

Head motion during MRI acquisition presents significant challenges for neuroimaging analyses. In this work, we present a retrospective motion correction framework built on a Fourier domain motion simulation model combined with established 3D convolutional neural network (CNN) architectures. Quantitative evaluation metrics were used to validate the method on three separate multi-site datasets. The 3D CNN was trained using motion-free images that were corrupted using simulated artifacts. CNN based correction successfully diminished the severity of artifacts on real motion affected data on a separate test dataset as measured by significant improvements in image quality metrics compared to a minimal motion reference image. On the test set of 13 image pairs, the mean peak signal-to-noise-ratio was improved from 31.7 to 33.3 dB. Furthermore, improvements in cortical surface reconstruction quality were demonstrated using a blinded manual quality assessment on the Parkinson's Progression Markers Initiative (PPMI) dataset. Upon applying the correction algorithm, out of a total of 617 images, the number of quality control failures was reduced from 61 to 38. On this same dataset, we investigated whether motion correction resulted in a more statistically significant relationship between cortical thickness and Parkinson's disease. Before correction, significant cortical thinning was found to be restricted to limited regions within the temporal and frontal lobes. After correction, there was found to be more widespread and significant cortical thinning bilaterally across the temporal lobes and frontal cortex. Our results highlight the utility of image domain motion correction for use in studies with a high prevalence of motion artifacts, such as studies of movement disorders as well as infant and pediatric subjects.

Cortical reorganization following auditory deprivation predicts cochlear implant performance in postlingually deaf adults.

Long-term hearing loss in postlingually deaf (PD) adults may lead to brain structural changes that affect the outcomes of cochlear implantation. We studied 94 PD patients who underwent cochlear implantation and 37 patients who were MRI-scanned within 2 weeks after the onset of sudden hearing loss and expected with minimal brain structural changes in relation to deafness. Compared with those with sudden hearing loss, we found lower gray matter (GM) probabilities in bilateral thalami, superior, middle, inferior temporal cortices as well as the central cortical regions corresponding to the movement and sensation of the lips, tongue, and larynx in the PD group. Among these brain areas, the GM in the middle temporal cortex showed negative correlation with disease duration, whereas the other areas displayed positive correlations. Left superior, middle temporal cortical, and bilateral thalamic GMs were the most accurate predictors of post-cochlear implantation word recognition scores (mean absolute error [MAE] = 10.1, r = .82), which was superior to clinical variables used (MAE: 12.1, p < .05). Using the combined brain morphological and clinical features, we achieved the best prediction of the outcome (MAE: 8.51, r = .90). Our findings suggest that the cross-modal plasticity allowing the superior temporal cortex and thalamus to process other modal sensory inputs reverses the initially lower volume when deafness becomes persistent. The middle temporal cortex processing higher-level language comprehension shows persistent negative correlations with disease duration, suggesting this area's association with degraded speech comprehensions due to long-term deafness. Morphological features combined with clinical variables might play a key role in predicting outcomes of cochlear implantation.

ENIGMA-Sleep: Challenges, opportunities, and the road map.

Neuroimaging and genetics studies have advanced our understanding of the neurobiology of sleep and its disorders. However, individual studies usually have limitations to identifying consistent and reproducible effects, including modest sample sizes, heterogeneous clinical characteristics and varied methodologies. These issues call for a large-scale multi-centre effort in sleep research, in order to increase the number of samples, and harmonize the methods of data collection, preprocessing and analysis using pre-registered well-established protocols. The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium provides a powerful collaborative framework for combining datasets across individual sites. Recently, we have launched the ENIGMA-Sleep working group with the collaboration of several institutes from 15 countries to perform large-scale worldwide neuroimaging and genetics studies for better understanding the neurobiology of impaired sleep quality in population-based healthy individuals, the neural consequences of sleep deprivation, pathophysiology of sleep disorders, as well as neural correlates of sleep disturbances across various neuropsychiatric disorders. In this introductory review, we describe the details of our currently available datasets and our ongoing projects in the ENIGMA-Sleep group, and discuss both the potential challenges and opportunities of a collaborative initiative in sleep medicine.

Disruption and Compensation of Sulcation-based Covariance Networks in Neonatal Brain Growth after Perinatal Injury.

Perinatal brain injuries in preterm neonates are associated with alterations in structural neurodevelopment, leading to impaired cognition, motor coordination, and behavior. However, it remains unknown how such injuries affect postnatal cortical folding and structural covariance networks, which indicate functional parcellation and reciprocal brain connectivity. Studying 229 magnetic resonance scans from 158 preterm neonates (n = 158, mean age = 28.2), we found that severe injuries including intraventricular hemorrhage, periventricular leukomalacia, and ventriculomegaly lead to significantly reduced cortical folding and increased covariance (hyper-covariance) in only the early (<31 weeks) but not middle (31-35 weeks) or late stage (>35 weeks) of the third trimester. The aberrant hyper-covariance may drive acceleration of cortical folding as a compensatory mechanism to "catch-up" with normal development. By 40 weeks, preterm neonates with/without severe brain injuries exhibited no difference in cortical folding and covariance compared with healthy term neonates. However, graph theory-based analysis showed that even after recovery, severely injured brains exhibit a more segregated, less integrated, and overall inefficient network system with reduced integration strength in the dorsal attention, frontoparietal, limbic, and visual network systems. Ultimately, severe perinatal injuries cause network-level deviations that persist until the late stage of the third trimester and may contribute to neurofunctional impairment.

Deep Learning Detection of Penumbral Tissue on Arterial Spin Labeling in Stroke.

Background and Purpose- Selection of patients with acute ischemic stroke for endovascular treatment generally relies on dynamic susceptibility contrast magnetic resonance imaging or computed tomography perfusion. Dynamic susceptibility contrast magnetic resonance imaging requires injection of contrast, whereas computed tomography perfusion requires high doses of ionizing radiation. The purpose of this work was to develop and evaluate a deep learning (DL)-based algorithm for assisting the selection of suitable patients with acute ischemic stroke for endovascular treatment based on 3-dimensional pseudo-continuous arterial spin labeling (pCASL). Methods- A total of 167 image sets of 3-dimensional pCASL data from 137 patients with acute ischemic stroke scanned on 1.5T and 3.0T Siemens MR systems were included for neural network training. The concurrently acquired dynamic susceptibility contrast magnetic resonance imaging was used to produce labels of hypoperfused brain regions, analyzed using commercial software. The DL and 6 machine learning (ML) algorithms were trained with 10-fold cross-validation. The eligibility for endovascular treatment was determined retrospectively based on the criteria of perfusion/diffusion mismatch in the DEFUSE 3 trial (Endovascular Therapy Following Imaging Evaluation for Ischemic Stroke). The trained DL algorithm was further applied on twelve 3-dimensional pCASL data sets acquired on 1.5T and 3T General Electric MR systems, without fine-tuning of parameters. Results- The DL algorithm can predict the dynamic susceptibility contrast-defined hypoperfusion region in pCASL with a voxel-wise area under the curve of 0.958, while the 6 ML algorithms ranged from 0.897 to 0.933. For retrospective determination for subject-level endovascular treatment eligibility, the DL algorithm achieved an accuracy of 92%, with a sensitivity of 0.89 and specificity of 0.95. When applied to the GE pCASL data, the DL algorithm achieved a voxel-wise area under the curve of 0.94 and a subject-level accuracy of 92% for endovascular treatment eligibility. Conclusions- pCASL perfusion magnetic resonance imaging in conjunction with the DL algorithm provides a promising approach for assisting decision-making for endovascular treatment in patients with acute ischemic stroke.

Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults.

Brain structural morphology differs with age. This study examined age-differences in surface-based morphometric measures of cortical thickness, volume, and surface area in a well-defined sample of 8137 generally healthy UK Biobank participants aged 45-79 years. We illustrate that the complexity of age-related brain morphological differences may be related to the laminar organization and regional evolutionary history of the cortex, and age of about 60 is a break point for increasing negative associations between age and brain morphology in Alzheimer's disease (AD)-prone areas. We also report novel relationships of age-related cortical differences with individual factors of sex, cognitive functions of fluid intelligence, reaction time and prospective memory, cigarette smoking, alcohol consumption, sleep disruption, genetic markers of apolipoprotein E, brain-derived neurotrophic factor, catechol-O-methyltransferase, and several genome-wide association study loci for AD and further reveal joint effects of cognitive functions, lifestyle behaviors, and education on age-related cortical differences. These findings provide one of the most extensive characterizations of age associations with major brain morphological measures and improve our understanding of normal structural brain aging and its potential modifiers.