Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer's dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis

Intrusive Thinking unravels allostatic dysregulation of glutamatergic neurometabolism within Anterior Cingulate Cortex in Generalized Anxiety Disorder

Alterations in glutamatergic and GABAergic neurotransmission are posited to be implicated in the pathophysiology of stress-related disorders. However, in humans existing evidence is inconsistent. Indeed, comparisons between pathological and healthy individuals are primarily at rest and not during specific disease states, making it difficult to understand processes underpinning transdiagnostic psychiatric symptoms. The present study applied 1H magnetic resonance spectroscopy in the anterior cingulate cortex to investigate the effects of an experimental induction of intrusive thinking on glutamate (Glx, glutamate/glutamine ratio) and GABA in pathological worriers and controls (n = 33; 15 males). While increases in GABA were elicited in both groups, indicating enhanced inhibitory effort to suppress intrusive thoughts, an opposite pattern emerged for Glx with an increase in controls and a decrease in worriers. Notably, resting levels of GABA and Glx resulted capable of predicting subjective responses to the induction, namely levels of intrusiveness and repetitiveness. The ecological validity of such prediction was supported by an ecological momentary assessment of intrusive thinking on the same participants. These preliminary findings suggest that glutamatergic dysfunctions may contribute to the maintenance of intrusive thinking in pathological worriers and may inform personally-tailored treatments in the framework of precision psychiatry

Enhancement in PDMS-Based Microfluidic Network for On-Chip Thermal Treatment of Biomolecules

In this paper, we present an improved microfluidic network based on polydimethylsiloxane (PDMS) and thin film heaters for thermal treatment of biomolecules in lab-on-chip systems. It relies on the series connection of two thermally actuated valves, at both inlet and outlet of the network, in order to reduce leakage of sample when its process temperature approaches 100, °C. The spatial arrangement of valves and microfluidic channels in between has been optimized using COMSOL Multiphysics, through the investigation of the system thermal behavior. Taking into account the simulation results, the geometries of the heaters have been defined following standard microelectronic technologies and the microfluidic network has been fabricated by soft lithography. The experiments demonstrate that with the proposed configuration the liquid evaporation is strongly reduced since more than 80% of the sample is recovered after a practical thermal treatment experiment

State-Dependent Aberrant Gamma-Aminobutyric Acid Reactivity and Downstream Functional Connectivity of Central Autonomic Network Subserve Pathological Intrusive Thinking

Alterations in neurotransmission mediated by gamma-aminobutyric acid (GABA), the main inhibitory neurotransmitter, are posited to play a pathophysiological role in stress-related disorders. Evidence, however, comes from the comparisons of pathological and healthy samples at rest and not during specic disease states, making it dicult to understand the processes underlying this assumption. The present study used 3T-proton magnetic resonance spectroscopy to investigate the effects of an experimental induction of intrusive thinking (IT; a transdiagnostic psychiatric symptom) on GABAergic and glutamatergic neurometabolic concentration within the bilateral Anterior Cingulate Cortex (ACC) in individuals with a pathological tendency to engage in IT (n = 29; 11 males) and controls (n = 29; 16 males). To assess physiological and functional concomitants of these neurochemical changes, autonomic measures and resting-state functional magnetic resonance imaging were also acquired before and after induction of IT. While engendering levels of IT amplied ACC GABA and GABA to Glx in the pathological group, an opposite trend emerged for controls. Notably, the pre-to post induction increase in GABAergic neurometabolism in the pathological group was accompanied by a dampened autonomic and resting state functional connectivity within nodes of the Central Autonomic Network. Current results are in line with the view of IT as a “better safe than sorry” strategy, which may be maintained in pathological conditions via a negative reinforcement mechanism through which increased GABAergic neurotransmission fosters avoidance of the transition from a relaxed state to a sudden spike of autonomic activation

Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of ‘virtual’ and ‘augmented’ contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media

Ground penetrating radar for the evaluation and monitoring of transport infrastructures

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