Sonno e funzioni cognitive: ruolo della microstruttura del sonno NREM

STUDY OBJECTIVE: Cyclic Alternating Pattern (CAP) is a fluctuation of the arousal level during NREM sleep and consists of the alternation between two phases: phase A (divided into three subtypes A1, A2, and A3) and phase B. A1 is thought to be generated by the frontal cortex and is characterized by the presence of K complexes or delta bursts; additionally, CAP A1 seems to have a role in the involvement of sleep slow wave activity in cognitive processing. Our hypothesis was that an overall CAP rate would have a negative influence on cognitive performance due to excessive fluctuation of the arousal level during NREM sleep. However, we also predicted that CAP A1 would be positively correlated with cognitive functions, especially those related to frontal lobe functioning. For this reason, the objective of our study was to correlate objective sleep parameters with cognitive behavioral measures in normal healthy adults. METHODS: 8 subjects (4 males; 4 females; mean age 27.75 years, range 2334) were recruited for this study. Two nocturnal polysomnography (night 2 and 3 = N2 and N3) were carried out after a night of adaptation. A series of neuropsychological tests were performed by the subjects in the morning and afternoon of the second day (D2am; D2pm) and in the morning of the third day (D3am). Raw scores from the neuropsychological tests were used as dependent variables in the statistical analysis of the results. RESULTS: We computed a series of partial correlations between sleep microstructure parameters (CAP, A1, A2 and A3 rate) and a number of indices of cognitive functioning. CAP rate was positively correlated with visuospatial working memory (Corsi block test), Trial Making Test Part A (planning and motor sequencing) and the retention of words from the Hopkins Verbal Learning Test (HVLT). Conversely, CAP was negatively correlated with visuospatial fluency (Ruff Figure Fluency Test). CAP A1 were correlated with many of the tests of neuropsychological functioning, such as verbal fluency (as measured by the COWAT), working memory (as measured by the Digit Span – Backward test), and both delay recall and retention of the words from the HVLT. The same parameters were found to be negatively correlated with CAP A2 subtypes. CAP 3 were negatively correlated with the Trial Making Test Parts A and B. DISCUSSION: To our knowledge this is the first study indicating a role of CAP A1 and A2 on behavioral cognitive performance of healthy adults. The results suggest that high rate of CAP A1 might be related to an improvement whereas high rate of CAP A2 to a decline of cognitive functions. Further studies need to be done to better determine the role of the overall CAP rate and CAP A3 on cognitive behavioral performances.

A machine learning based method to detect genomic imbalances exploiting X chromosome exome reads

Whole Exome Sequencing (WES) is rapidly becoming the first-tier test in clinics, both thanks to its declining costs and the development of new platforms that help clinicians in the analysis and interpretation of SNV and InDels. However, we still know very little on how CNV detection could increase WES diagnostic yield. A plethora of exome CNV callers have been published over the years, all showing good performances towards specific CNV classes and sizes, suggesting that the combination of multiple tools is needed to obtain an overall good detection performance. Here we present TrainX, a ML-based method for calling heterozygous CNVs in WES data using EXCAVATOR2 Normalized Read Counts. We select males and females’ non pseudo-autosomal chromosome X alignments to construct our dataset and train our model, make predictions on autosomes target regions and use HMM to call CNVs. We compared TrainX against a set of CNV tools differing for the detection method (GATK4 gCNV, ExomeDepth, DECoN, CNVkit and EXCAVATOR2) and found that our algorithm outperformed them in terms of stability, as we identified both deletions and duplications with good scores (0.87 and 0.82 F1-scores respectively) and for sizes reaching the minimum resolution of 2 target regions. We also evaluated the method robustness using a set of WES and SNP array data (n=251), part of the Italian cohort of Epi25 collaborative, and were able to retrieve all clinical CNVs previously identified by the SNP array. TrainX showed good accuracy in detecting heterozygous CNVs of different sizes, making it a promising tool to use in a diagnostic setting.

Development and characterization of deep learning techniques for neuroimaging data

Deep learning methods are extremely promising machine learning tools to analyze neuroimaging data. However, their potential use in clinical settings is limited because of the existing challenges of applying these methods to neuroimaging data. In this study, first a data leakage type caused by slice-level data split that is introduced during training and validation of a 2D CNN is surveyed and a quantitative assessment of the model’s performance overestimation is presented. Second, an interpretable, leakage-fee deep learning software written in a python language with a wide range of options has been developed to conduct both classification and regression analysis. The software was applied to the study of mild cognitive impairment (MCI) in patients with small vessel disease (SVD) using multi-parametric MRI data where the cognitive performance of 58 patients measured by five neuropsychological tests is predicted using a multi-input CNN model taking brain image and demographic data. Each of the cognitive test scores was predicted using different MRI-derived features. As MCI due to SVD has been hypothesized to be the effect of white matter damage, DTI-derived features MD and FA produced the best prediction outcome of the TMT-A score which is consistent with the existing literature. In a second study, an interpretable deep learning system aimed at 1) classifying Alzheimer disease and healthy subjects 2) examining the neural correlates of the disease that causes a cognitive decline in AD patients using CNN visualization tools and 3) highlighting the potential of interpretability techniques to capture a biased deep learning model is developed. Structural magnetic resonance imaging (MRI) data of 200 subjects was used by the proposed CNN model which was trained using a transfer learning-based approach producing a balanced accuracy of 71.6%. Brain regions in the frontal and parietal lobe showing the cerebral cortex atrophy were highlighted by the visualization tools.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.