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. Our approach predicts whether a coronary artery stenosis is hemodynamically significant and provides direct FFR and iFR estimates. This is achieved through a combination of regression and classification branches that forces the model to focus on the cut-off region of FFR (around 0.8 FFR value), which is highly critical for decision-making. We also propose a spatio-temporal factorization mechanisms that redesigns the transformer's self-attention mechanism to capture both local spatial and temporal interactions between vessel geometry, blood flow dynamics, and lesion morphology. The proposed method achieves state-of-the-art performance on a dataset of 778 exams from 389 patients. Unlike existing methods, our approach employs a single angiography view and does not require knowledge of the key frame; supervision at training time is provided by a classification loss (based on a threshold of the FFR/iFR values) and a regression loss for direct estimation. Finally, the analysis of model interpretability and calibration shows that, in spite of the complexity of angiographic imaging data, our method can robustly identify the location of the stenosis and correlate prediction uncertainty to the provided output scores.

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. Repetitive transcranial magnetic stimulation has also therapeutic and rehabilitative capabilities since it is able to induce changes in the excitability of inhibitory and excitatory neuronal networks that may persist in time. However, the therapeutic effects of rTMS on major depression have been demonstrated by analyzing only the improvement of neuropsychological performance. The aim of this study was to investigate cortical excitability changes on 12 chronically-medicated depressed patients (test group) after rTMS treatment and to correlate neurophysiological findings to neuropsychological outcomes. In detail, we assessed different parameters of cortical excitability before and after active rTMS in the test group, then compared to those of 10 age-matched depressed patients (control group) who underwent sham rTMS. In line with previous studies, at baseline both groups exhibited a significant interhemispheric difference of motor cortex excitability. This neurophysiological imbalance was then reduced in the patients treated with active rTMS, resulting also in a clinical benefit as demonstrated by the improvement in neuropsychological test scores. On the contrary, after sham rTMS, the interhemispheric difference was still evident in the control group. The reported clinical benefits in the test group might be related to the plastic remodeling of synaptic connection induced by rTMS treatment.

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. One important aspect of this tool is that it allows neuroscientists to specify research designs at will, enabling them to define any parameter of a TMS study starting from data acquisition and sample group definition to automated statistical data analysis and RDF data storage. It also supports the diagnosing process by using on-line support vector machines able to learn incrementally from the diseases instances that are continuously added into the system. The proposed system is a neuroscientist-centred tool where the protocols being followed in TMS studies are made explicit, leaving to the users flexibility in exploring and sharing the results, and providing assistance in managing the complexity of the final diagnosis. This type of tool can make the results of medical experiments more easily exploitable, thus accelerating scientific progress.