Biology subjects

De Feo, R.

Publications and source records attributed to De Feo, R..

3 recordsLinked to original sources

bioRxiv2025

EEG-to-fMRI Prediction for Neurofeedback: Evaluating Regularized Regression and Clustering Approaches

While functional Magnetic Resonance Imaging (fMRI) can provide detailed information regarding the functional activity of the whole brain, its cumbersome experimental setting and high cost prevent its application in ecological conditions. This represents a challenge in the context of neurofeedback therapy. To address this challenge one possible approach is to train a predictor for the localized functional activity using multimodal EEG-fMRI records and utilize that predictor in real-time EEG sessions. Using publicly available multimodal real-time EEG-fMRI data, we present a detailed evaluation of regularized linear regressors both in the context of predicting localized fMRI activity, and to characterize the properties of individual EEG-fMRI runs. Our results indicate that while it is possible to find clusters of similar time-series in which localized brain activity is highly predictable (Pearsons r=0.43), the capacity to train regularized linear regressors able to generalize to new subjects remains limited (r=0.24), highlighting two distinct strategies in EEG-fMRI neurofeedback settings. We characterize the fundamental role for the clustering distance used to identify similar time-series, based both on theoretically-grounded considerations and experimental observations. Our results suggest a clear preference for the cosine metric, instead of the Pearson-based metric utilized in current literature. We further suggest that highly-predictable clusters regular offline paradigms would correspond to high-performing subjects in the context of real-time neurofeedback, and evaluate a regressor-free clustering strategy based on the Log-Euclidean distance of covariance matrices.

bioinformatics↗

bioRxiv2021

Automatic segmentation of the rat brain hippocampus in MRI after traumatic brain injury

AO_SCPLOWBSTRACTC_SCPLOWRegistration-based methods are commonly used in the anatomical segmentation of magnetic resonance (MR) brain images. However, they are sensitive to the presence of deforming brain pathologies that may interfere with the alignment of the atlas image with the target image. Our goal was to develop an algorithm for automated segmentation of the normal and injured rat hippocampus. We implemented automated segmentation using a U-Net-like Convolutional Neural Network (CNN). of sham-operated experimental controls and rats with lateral-fluid-percussion induced traumatic brain injury (TBI) on MR images and trained ensembles of CNNs. Their performance was compared to three registration-based methods: single-atlas, multi-atlas based on majority voting and Similarity and Truth Estimation for Propagated Segmentations (STEPS). Then, the automatic segmentations were quantitatively evaluated using six metrics: Dice score, Hausdorff distance, precision, recall, volume similarity and compactness using cross-validation. Our CNN and multi-atlas -based segmentations provided excellent results (Dice scores > 0.90) despite the presence of brain lesions, atrophy and ventricular enlargement. In contrast, the performance of singe-atlas registration was poor (Dice scores < 0.85). Unlike registration-based methods, which performed better in segmenting the contralateral than the ipsilateral hippocampus, our CNN-based method performed equally well bilaterally. Finally, we assessed the progression of hippocampal damage after TBI by applying our automated segmentation tool. Our data show that the presence of TBI, time after TBI, and whether the location of the hippocampus was ipsilateral or contralateral to the injury explained hippocampal volume (p = 0.029, p < 0.001, and p < 0.001 respectively).

bioinformatics↗

bioRxiv2020

Automated skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 seconds and no pre-processing requirements. We evaluated the performance of our network in the presence of skip connections and recently proposed framing connections, finding the simplest network to be the most effective. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1,782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net.

bioinformatics↗