Rebuttal to "Comments on 'Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features' ".

Bharadwaj et al. (2023) present a comments paper evaluating the classification accuracy of several state-of-the-art methods using EEG data averaged over random class samples. According to the results, some of the methods achieve above-chance accuracy, while the method proposed in (Palazzo et al.

A Convolutional-Transformer Model for FFR and iFR Assessment From Coronary Angiography.

The quantification of stenosis severity from X-ray catheter angiography is a challenging task. Indeed, this requires to fully understand the lesion's geometry by analyzing dynamics of the contrast material, only relying on visual observation by clinicians. To support decision making for cardiac intervention, we propose a hybrid CNN-Transformer model for the assessment of angiography-based non-invasive fractional flow-reserve (FFR) and instantaneous wave-free ratio (iFR) of intermediate coronary stenosis.

An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans.

COVID-19 infection caused by SARS-CoV-2 pathogen has been a catastrophic pandemic outbreak all over the world, with exponential increasing of confirmed cases and, unfortunately, deaths. In this work we propose an AI-powered pipeline, based on the deep-learning paradigm, for automated COVID-19 detection and lesion categorization from CT scans. We first propose a new segmentation module aimed at automatically identifying lung parenchyma and lobes.

Decoding Brain Representations by Multimodal Learning of Neural Activity and Visual Features.

This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification.

Modeling skeletal bone development with hidden Markov models.

This paper presents a tool for automatic assessment of skeletal bone age according to a modified version of the Tanner and Whitehouse (TW2) clinical method. The tool is able to provide an accurate bone age assessment in the range 0-6 years by processing epiphysial/metaphysial ROIs with image-processing techniques, and assigning TW2 stage to each ROI by means of hidden Markov models. The system was evaluated on a set of 360 X-rays (180 for males and 180 for females) achieving a high success rate in bone age evaluation (mean error rate of 0.

Transcranial magnetic stimulation in the assessment of motor cortex excitability and treatment of drug-resistant major depression.

Major depression is one of the leading causes of disabling condition worldwide and its treatment is often challenging and unsatisfactory, since many patients become refractory to pharmacological therapies. Transcranial magnetic stimulation (TMS) is a noninvasive neurophysiological investigation mainly used to study the integrity of the primary motor cortex excitability and of the cortico-spinal tract. The development of paired-pulse and repetitive TMS (rTMS) paradigms has allowed investigators to explore the pathophysiology of depressive disorders and other neuropsychiatric diseases linked to brain excitability dysfunctions.

An integrated computer-controlled system for assisting researchers in cortical excitability studies by using transcranial magnetic stimulation.

Transcranial magnetic stimulation (TMS) is the most important technique currently available to study cortical excitability. Additionally, TMS can be used for therapeutic and rehabilitation purposes, replacing the more painful transcranial electric stimulation (TES). In this paper we present an innovative and easy-to-use tool that enables neuroscientists to design, carry out and analyze scientific studies based on TMS experiments for both diagnostic and research purposes, assisting them not only in the practicalities of administering the TMS but also in each step of the entire study's workflow.