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General General

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

In Human brain mapping

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr  = .012) and lower functioning on the Children's Global Assessment Scale (pcorr  = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr  = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Luna Alex, Bernanke Joel, Kim Kakyeong, Aw Natalie, Dworkin Jordan D, Cha Jiook, Posner Jonathan


biomarkers, brain age, connectome, diffusion tensor imaging, machine learning

Radiology Radiology

Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute.

In Magnetic resonance in medicine ; h5-index 66.0

PURPOSE : To develop and evaluate a novel and generalizable super-resolution (SR) deep-learning framework for motion-compensated isotropic 3D coronary MR angiography (CMRA), which allows free-breathing acquisitions in less than a minute.

METHODS : Undersampled motion-corrected reconstructions have enabled free-breathing isotropic 3D CMRA in ~5-10 min acquisition times. In this work, we propose a deep-learning-based SR framework, combined with non-rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16-fold increase in spatial resolution is achieved by reconstructing a high-resolution (HR) isotropic CMRA (0.9 mm3 or 1.2 mm3 ) from a low-resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm3 or 1.2 × 4.8 × 4.8 mm3 ). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch-level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR-CMRA in ~50 s under free-breathing. Vessel sharpness and length of the coronary arteries from the SR-CMRA is compared against the HR-CMRA.

RESULTS : SR-CMRA showed statistically significant (P < .001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR-CMRA. Good generalization to input resolution and image/patch-level processing was found. SR-CMRA enabled recovery of coronary stenosis similar to HR-CMRA with comparable qualitative performance.

CONCLUSION : The proposed SR-CMRA provides a 16-fold increase in spatial resolution with comparable image quality to HR-CMRA while reducing the predictable scan time to <1 min.

Küstner Thomas, Munoz Camila, Psenicny Alina, Bustin Aurelien, Fuin Niccolo, Qi Haikun, Neji Radhouene, Kunze Karl, Hajhosseiny Reza, Prieto Claudia, Botnar René


3D whole-heart, coronary MR angiography, deep learning, super resolution

General General

The power of ECG in multimodal patient-specific seizure monitoring: Added value to an EEG-based detector using limited channels.

In Epilepsia

OBJECTIVE : Wearable seizure detection devices could provide more reliable seizure documentation outside the hospital compared to seizure self-reporting by patients, which is the current standard. Previously, during the SeizeIT1 project, we studied seizure detection based on behind-the-ear electroencephalography (EEG). However, the obtained sensitivities were too low for practical use, because not all seizures are associated with typical ictal EEG patterns. Therefore, in this paper, we aim to develop a multimodal automated seizure detection algorithm integrating behind-the-ear EEG and electrocardiography (ECG) for detecting focal seizures. In this framework, we quantified the added value of ECG to behind-the-ear EEG.

METHODS : This study analyzed three multicenter databases consisting of 135 patients having focal epilepsy and a total of 896 seizures. A patient-specific multimodal automated seizure detection algorithm was developed using behind-the-ear/temporal EEG and single-lead ECG. The EEG and ECG data were processed separately using machine learning methods. A late integration approach was applied for fusing those predictions.

RESULTS : The multimodal algorithm outperformed the EEG-based algorithm in two of three databases, with an increase of 11% and 8% in sensitivity for the same false alarm rate.

SIGNIFICANCE : ECG can be of added value to an EEG-based seizure detection algorithm using only behind-the-ear/temporal lobe electrodes for patients with focal epilepsy.

Vandecasteele Kaat, De Cooman Thomas, Chatzichristos Christos, Cleeren Evy, Swinnen Lauren, Macea Ortiz Jaiver, Van Huffel Sabine, Dümpelmann Matthias, Schulze-Bonhage Andreas, De Vos Maarten, Van Paesschen Wim, Hunyadi Borbála


ECG, behind-the-ear EEG, epilepsy, multimodal algorithms, reduced electrode montage, seizure detection, wearable sensors

General General

Micro-Nano Processing of Active Layers in Flexible Tactile Sensors via Template Methods: A Review.

In Small (Weinheim an der Bergstrasse, Germany)

Template methods are regarded as an important method for micro-nano processing in the active layer of flexible tactile sensors. These template methods use physical/chemical processes to introduce micro-nano structures on the active layer, which improves many properties including sensitivity, response/recovery time, and detection limit. However, since the processing process and applicable conditions of the template method have not yet formed a perfect system, the development and commercialization of flexible tactile sensors based on the template method are still at a relatively slow stage. Despite the above obstacles, advances in microelectronics, materials science, nanoscience, and other disciplines have laid the foundation for various template methods, enabling the continuous development of flexible tactile sensors. Therefore, a comprehensive and systematic review of flexible tactile sensors based on the template method is needed to further promote progress in this field. Here, the unique advantages and shortcomings of various template methods are summarized in detail and discuss the research progress and challenges in this field. It is believed that this review will have a significant impact on many fields of flexible electronics, which is beneficial to promote the cross-integration of multiple fields and accelerate the development of flexible electronic devices.

Niu Hongsen, Zhang Huiyun, Yue Wenjing, Gao Song, Kan Hao, Zhang Chunwei, Zhang Congcong, Pang Jinbo, Lou Zheng, Wang Lili, Li Yang, Liu Hong, Shen Guozhen


artificial intelligence, micro-nano processing, micro-nano structure, sensing array, tactile sensors, template method

General General

Topology Evaluation of Models for Difficult Targets in the 14th Round of the Critical Assessment of Protein Structure Prediction (CASP14).

In Proteins

This report describes the tertiary structure prediction assessment of difficult modeling targets in the 14th round of the Critical Assessment of Structure Prediction (CASP14). We implemented an official ranking scheme that used the same scores as the previous CASP topology-based assessment, but combined these scores with one that emphasized physically realistic models. The top performing AlphaFold2 group outperformed the rest of the prediction community on all but two of the difficult targets considered in this assessment. They provided high quality models for most of the targets (86% over GDT_TS 70), including larger targets above 150 residues, and they correctly predicted the topology of almost all the rest. AlphaFold2 performance was followed by two manual Baker methods, a Feig method that refined Zhang-server models, two notable automated Zhang server methods (QUARK and Zhang-server), and a Zhang manual group. Despite the remarkable progress in protein structure prediction of difficult targets, both the prediction community and AlphaFold2, to a lesser extent, faced challenges with flexible regions and obligate oligomeric assemblies. The official ranking of top-performing methods was supported by performance generated PCA and heatmap clusters that gave insight into target difficulties and the most successful state-of-the-art structure prediction methodologies. This article is protected by copyright. All rights reserved.

Kinch Lisa N, Pei Jimin, Kryshtafovych Andriy, Schaeffer R Dustin, Grishin Nick V


CASP14, free modeling, homology modeling, machine learning, protein structure prediction, structural bioinformatics, topology structure modeling evaluation

General General

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

In Journal of digital imaging

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, with nearly 200,000 premature infants at risk. Early detection through screening and treatment can prevent this blindness. The automatic screening systems developed so far can detect "severe ROP" or "plus disease," but this information does not help schedule follow-up. Identifying vascularized retinal zones and detecting the ROP stage is essential for follow-up or discharge from screening. There is no automatic system to assist these crucial decisions to the best of the authors' knowledge. The low contrast of images, incompletely developed vessels, macular structure, and lack of public data sets are a few challenges in creating such a system. In this paper, a novel method using an ensemble of "U-Network" and "Circle Hough Transform" is developed to detect zones I, II, and III from retinal images in which macula is not developed. The model developed is generic and trained on mixed images of different sizes. It detects zones in images of variable sizes captured by two different imaging systems with an accuracy of 98%. All images of the test set (including the low-quality images) are considered. The time taken for training was only 14 min, and a single image was tested in 30 ms. The present study can help medical experts interpret retinal vascular status correctly and reduce subjective variation in diagnosis.

Agrawal Ranjana, Kulkarni Sucheta, Walambe Rahee, Kotecha Ketan


Artificial Intelligence, Automatic zone detection, Machine learning, Retinopathy of prematurity(ROP), Segmentation, U-Net