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Background: Hospital mortality and admission to the Intensive Care Unit (ICU) are markers of disease severity in COVID-19 patients. Cardiovascular co-morbidities are one of the main determinants of negative outcomes. In this study we investigated the impact of cardiovascular co-morbidities on mortality and admission to the ICU in first-wave COVID-19 patients. Methods: A multicenter, retrospective, cohort study. A total of 1077 patients were analyzed for mortality and ICU admission. Cardiovascular risk factors were explored as determinants of the outcomes after correction for other confounders. Results: In the multivariable model, after correction for age, only a history of heart failure remained independently associated (p = 0.0013) with mortality (hazard ratio 2.22, 95% confidence interval 1.37 to 3.62). Age showed a mortality risk increase of 8% per year (hazard ratio 1.08, 95% confidence interval 1.05 to 1.10, p = 0.001). The transition from ward to the ICU had, as a single determinant, the age, but in a reversed fashion (hazard ratio 0.96, 95% confidence interval 0.94 to 0.98, p = 0.0002). Conclusions: Once adjusted for the main determinant of mortality (age) heart failure only remained independently associated with mortality. Admission to the ICU was less likely for elderly patients. This may reflect the catastrophic impact of the first wave of COVID-19 pandemic in terms of ICU bed availability in Lombardy, leading to a selection process for ICU admission.

The precise delineation of auditory areas in vivo remains problematic. Histological analysis of postmortem tissue indicates that the relation of areal borders to macroanatomical landmarks is variable across subjects. Furthermore, functional parcellation schemes based on measures of, for example, frequency preference (tonotopy) remain controversial. Here, we propose a 7 Tesla magnetic resonance imaging method that enables the anatomical delineation of auditory cortical areas in vivo and in individual brains, through the high-resolution visualization (0.6 x 0.6 x 0.6 mm(3)) of intracortical anatomical contrast related to myelin. The approach combines the acquisition and analysis of images with multiple MR contrasts (T-1, T-2*, and proton density). Compared with previous methods, the proposed solution is feasible at high fields and time efficient, which allows collecting myelin-related and functional images within the same measurement session. Our results show that a data-driven analysis of cortical depth-dependent profiles of anatomical contrast allows identifying a most densely myelinated cortical region on the medial Heschl's gyrus. Analyses of functional responses show that this region includes neuronal populations with typical primary functional properties (single tonotopic gradient and narrow frequency tuning), thus indicating that it may correspond to the human homolog of monkey A1.

Columnar arrangements of neurons with similar preference have been suggested as the fundamental processing units of the cerebral cortex. Within these columnar arrangements, feed-forward information enters at middle cortical layers whereas feedback information arrives at superficial and deep layers. This interplay of feed-forward and feedback processing is at the core of perception and behavior. Here we provide in vivo evidence consistent with a columnar organization of the processing of sound frequency in the human auditory cortex. We measure submillimeter functional responses to sound frequency sweeps at high magnetic fields (7 tesla) and show that frequency preference is stable through cortical depth in primary auditory cortex. Furthermore, we demonstrate that-in this highly columnar cortex-task demands sharpen the frequency tuning in superficial cortical layers more than in middle or deep layers. These findings are pivotal to understanding mechanisms of neural information processing and flow during the active perception of sounds.

Auditory inputs reaching our ears are often incomplete, but our brains nevertheless transform them into rich and complete perceptual phenomena, such as meaningful conversations or pleasurable music. It has been hypothesised that our brains extract regularities in inputs, which enables us to predict the upcoming stimuli, leading to efficient sensory processing. However, it is unclear whether tone predictions are encoded with similar specificity as perceived signals. Here we used high-field functional magnetic resonance imaging (fMRI) to ask whether human auditory regions encode one of the most defining characteristics of auditory perception - the frequency of predicted tones.Two pairs of tone sequences were presented in ascending or descending directions, with the last tone omitted in half of the trials. Every pair of incomplete sequences contained identical sounds, but was associated with different expectations about the last tone (a high or low frequency target). This allowed us to disambiguate predictive signalling from sensory-driven processing. We recorded fMRI responses from eight female participants during passive listening to complete and incomplete sequences. Inspection of specificity and spatial patterns of responses revealed that target frequencies were encoded similarly during their presentations, as well as during omissions. This speaks to frequency-specific encoding of predicted tones in the auditory cortex. Importantly, frequency-specificity of predictive signalling was observed already at the earliest levels of auditory cortical hierarchy - in the primary auditory cortex. Our findings provide evidence for content-specific predictive processing, starting at the earliest cortical levels.Significance statementGiven the abundance of sensory information around us in any given moment, it has been proposed that our brain uses contextual information to prioritise and form predictions about incoming signals. However, there remains surprising lack of underst

La scrittura del filosofo italiano Giorgio Colli è caratterizzata, questo è noto, da uno stile molto vivido e personale. Questa relazione ha l'obiettivo di ripercorrere la sua produzione scritta per cercare di rintracciare le costanti che ne hanno definito l'alto valore anche sul piano espositivo e letterario. Si dimostrerà, quindi, che questo tipo di stile riflette nella forma un'indagine filosofica sul valore comunicativo della filosofia, in particolare attraverso forme non sistematiche come l'aforisma, che è alla base delle ricerche più prolifiche di Colli.

To date, the functional organization of human auditory subcortical structures can only be inferred from animal models. Here we use high-resolution functional magnetic resonance imaging at ultra-high magnetic fields (7T) to map the organization of spectral responses in the human inferior colliculus, a subcortical structure fundamental for sound processing. We reveal a tonotopic map with a spatial gradient of preferred frequencies approximately oriented from dorsolateral (low frequencies) to ventromedial (high frequencies) locations. Furthermore, we observe a spatial organization of spectral selectivity (tuning) of functional magnetic resonance imaging responses in the human inferior colliculus. Along isofrequency contours, functional magnetic resonance imaging tuning is narrowest in central locations and broadest in the surrounding regions. Finally, by comparing subcortical and cortical auditory areas we show that functional magnetic resonance imaging tuning is narrower in human inferior colliculus than on the cortical surface. Our findings pave the way to noninvasive investigations of sound processing in human subcortical nuclei and for studying the interplay between subcortical and cortical neuronal populations.

Ultra high fields (UHF) permit unprecedented explorations of functional organizations and insight into basic neuronal processes. Increases in the signal and contrast to noise ratios have allowed increases in the spatial resolution of T(2)() weighted gradient echo (GE) echo planar imaging (EPI). Furthermore, while the use of T(2) weighted imaging methods at UHF (e.g. spin echo (SE) EPI, gradient and spin echo (GRASE) EPI) can also permit higher resolution images, they in addition allow for increased spatial specificity of functional responses, permitting the in-vivo study of functional organizations down to the columnar level of the cortex. The study of the visual cortex has, thus far, benefitted the most from higher resolution T(2) weighted studies as achieving the required transmit B(1) magnitude at 7T is more challenging in other brain regions, such as the auditory cortex. As such, auditory fMRI studies at UHF have been limited to T(2)* weighted GE sequences. Recent advances in multi-channel RF transmission (e.g. B(1) shimming) have enabled procedures to efficiently address deficiencies in transmit B(1) profiles. However, these techniques, shown to be advantageous in anatomical imaging at UHF, are not generally utilized to facilitate T(2) weighted fMRI studies. Here we investigate the feasibility of applying B(1) shimming to achieve efficient RF transmission in the human auditory cortex. We demonstrate that, with B(1) shimming, functional responses to simple tones and to complex sounds (i.e. voices, speech, animal cries, tools and nature) can be efficiently measured with T(2) weighted SE-EPI in the bilateral human auditory cortex at 7T without exceeding specific absorption rate (SAR) limits.

Whole-brain functional magnetic resonance imaging (fMRI) allows measuring brain dynamics at all brain regions simultaneously and is widely used in research and clinical neuroscience to observe both stimulus-related and spontaneous neural activity. Ultrahigh magnetic fields (7T and above) allow functional imaging with high contrast-to-noise ratios and improved spatial resolution and specificity compared to clinical fields (1.5T and 3T). High-resolution 7T fMRI, however, has been mostly limited to partial brain coverage with previous whole-brain applications sacrificing either the spatial or temporal resolution. Here we present whole-brain high-resolution (1, 1.5 and 2mm isotropic voxels) resting state fMRI at 7T, obtained with parallel imaging technology, without sacrificing temporal resolution or brain coverage, over what is typically achieved at 3T with several fold larger voxel volumes. Using Independent Component Analysis we demonstrate that high resolution images acquired at 7T retain enough sensitivity for the reliable extraction of typical resting state brain networks and illustrate the added value of obtaining both single subject and group maps, using cortex based alignment, of the default-mode network (DMN) with high native resolution. By comparing results between multiple resolutions we show that smaller voxels volumes (1 and 1.5mm isotropic) data result in reduced partial volume effects, permitting separations of detailed spatial features within the DMN patterns as well as a better function to anatomy correspondence.

The combination of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Integration through EEG-fMRI trial-by-trial coupling has been proposed as a method to combine the different data sets and achieve temporal expansion of the fMRI data (Eichele et al., 2005). To fully benefit of this type of analysis simultaneous EEG-fMRI acquisitions are necessary (Debener et al., 2006). Here we address the issue of predicting the signal in one modality using information from the other modality. We use multivariate Relevance Vector Machine (RVM) regression to "learn" the relation between fMRI activation patterns and simultaneously acquired EEG responses in the context of a complex cognitive task entailing an auditory cue, visual mental imagery and a control visual target. We show that multivariate regression is a valuable approach for predicting evoked and induced oscillatory EEG responses from fMRI time series. Prediction of EEG from fMRI is largely influenced by the overall filtering effects of the hemodynamic response function. However, a detailed analysis of the auditory evoked responses shows that there is a small but significant contribution of single trial modulations that can be exploited for linking spatially-distributed patterns of fMRI activation to specific components of the simultaneously-recorded EEG signal. (PsycINFO Database Record (c) 2011 APA, all rights reserved) (journal abstract)

The combination of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) has been proposed as a tool to study brain dynamics with both high temporal and high spatial resolution. Multimodal imaging techniques rely on the assumption of a common neuronal source for the different recorded signals. In order to maximally exploit the combination of these techniques, one needs to understand the coupling (i.e., the relation) between electroencephalographic (EEG) and fMRI blood oxygen level-dependent (BOLD) signals. Recently, simultaneous EEG-fMRI measurements have been used to investigate the relation between the two signals. Previous attempts at the analysis of simultaneous EEG-fMRI data reported significant correlations between regional BOLD activations and modulation of both event-related potential (ERP) and oscillatory EEG power, mostly in the alpha but also in other frequency bands. Beyond the correlation of the two measured brain signals, the relevant issue we address here is the ability of predicting the signal in one modality using information from the other modality. Using multivariate machine learning-based regression, we show how it is possible to predict EEG power oscillations from simultaneously acquired fMRI data during an eyes-open/eyes-closed task using either the original channels or the underlying cortically distributed sources as the relevant EEG signal for the analysis of multimodal data.

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temp

We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each FMRI-IC is associated with an IC-fingerprint, i.e., a representation of the component in a multidimensional space of parameters. These parameters are post hoc estimates of global properties of the ICs and are largely independent of a specific experimental design and stimulus timing. In the second step a machine learning algorithm automatically separates the IC-fingerprints into six general classes after preliminary training performed on a small subset of expert-labeled components. We illustrate this approach in a multisubject fMRI study employing visual structure-from-motion stimuli encoding faces and control random shapes. We show that: (1) IC-fingerprints are a valuable tool for the inspection, characterization and selection of fMRI-ICs and (2) automatic classifications of FMRI-ICs in new subjects present a high correspondence with those obtained by expert visual inspection of the components. Importantly, our classification procedure highlights several neuro-physiologically interesting processes. The most intriguing of which is reflected, with high intra- and inter-subject reproducibility, in one IC exhibiting a transiently task-related activation in the 'face' region of the primary sensorimotor cortex. This suggests that in addition to or as part of the mirror system, somatotopic regions of the sensorimotor cortex are involved in disambiguating the perception of a moving body part. Finally, we show that the same classification algorithm can be successfully applied, without re-training, to fMRI collected using acquisition parameters, stimulation modality and timing considerably different from those used for training.