Analysis of residual dependencies of independent components extracted from fMRI data

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data can be employed as an exploratory method. The lack in the ICA model of strong a priori assumptions about the signal or about the noise leads to difficult interpretations of the results. Moreover, the statistical independence of the components is only approximated. Residual dependencies among the components can reveal informative structure in the data. A major problem is related to model order selection, that is, the number of components to be extracted. Specifically, overestimation may lead to component splitting. In this work, a method based on hierarchical clustering of ICA applied to fMRI datasets is investigated. The clustering algorithm uses a metric based on the mutual information between the ICs. To estimate the similarity measure, a histogram-based technique and one based on kernel density estimation are tested on simulated datasets. Simulations results indicate that the method could be used to cluster components related to the same task and resulting from a splitting process occurring at different model orders. Different performances of the similarity measures were found and discussed. Preliminary results on real data are reported and show that the method can group task related and transiently task related components

Data processing and wearable systems for effective human monitoring

The last few decades have seen an unrestrained diffusion of smart-integrated technologies that are extremely pervasive and customized based on humans’ environments and habits. Wearable and mobile technologies such as smartphones, smartwatches, lightweight sensors, textile-based support systems, flexible displays, and micro-cameras are now supplied with a significant amount of computational power, low-energy wireless communication, long-life battery, and large-memory storage that make them a valid platform for monitoring the everyday life of humans [1]. In this context, a large variety of new sensors are being developed to equip such well-established wearable and mobile technologies with the aim of continuous monitoring of physical behavior, emotional state, well-being, and health condition. Interestingly, the recently improved computational resources of mobile systems allow us to acquire, process, and communicate a large set of different information. Nevertheless, this confronts us with the chance and challenge of managing an impressive amount of heterogeneous data, including physiological signals, through new ad-hoc processing, synthesis methods, and big data analysis as well as ad-hoc experimental paradigms, system designs, and models

A Smart Region-Growing Algorithm for Single-Neuron Segmentation From Confocal and 2-Photon Datasets

Accurately digitizing the brain at the micro-scale is crucial for investigating brain structure-function relationships and documenting morphological alterations due to neuropathies. Here we present a new Smart Region Growing algorithm (SmRG) for the segmentation of single neurons in their intricate 3D arrangement within the brain. Its Region Growing procedure is based on a homogeneity predicate determined by describing the pixel intensity statistics of confocal acquisitions with a mixture model, enabling an accurate reconstruction of complex 3D cellular structures from high-resolution images of neural tissue. The algorithm’s outcome is a 3D matrix of logical values identifying the voxels belonging to the segmented structure, thus providing additional useful volumetric information on neurons. To highlight the algorithm’s full potential, we compared its performance in terms of accuracy, reproducibility, precision and robustness of 3D neuron reconstructions based on microscopic data from different brain locations and imaging protocols against both manual and state-of-the-art reconstruction tools

Gotta trace ‘em all: A mini-review on tools and procedures for segmenting single neurons toward deciphering the structural connectome

Decoding the morphology and physical connections of all the neurons populating a brain is necessary for predicting and studying the relationships between its form and function, as well as for documenting structural abnormalities in neuropathies. Digitizing a complete and high-fidelity map of the mammalian brain at the micro-scale will allow neuroscientists to understand disease, consciousness, and ultimately what it is that makes us humans. The critical obstacle for reaching this goal is the lack of robust and accurate tools able to deal with 3D datasets representing dense-packed cells in their native arrangement within the brain. This obliges neuroscientist to manually identify the neurons populating an acquired digital image stack, a notably time-consuming procedure prone to human bias. Here we review the automatic and semi-automatic algorithms and software for neuron segmentation available in the literature, as well as the metrics purposely designed for their validation, highlighting their strengths and limitations. In this direction, we also briefly introduce the recent advances in tissue clarification that enable significant improvements in both optical access of neural tissue and image stack quality, and which could enable more efficient segmentation approaches. Finally, we discuss new methods and tools for processing tissues and acquiring images at sub-cellular scales, which will require new robust algorithms for identifying neurons and their sub-structures (e.g., spines, thin neurites). This will lead to a more detailed structural map of the brain, taking twenty-first century cellular neuroscience to the next level, i.e., the Structural Connectome

Contactless measurements of liquid sample electrical conductivity for estimating specific absorption rate in MR applications

Specific Absorption Rate (SAR) is the dosimetric parameter currently used as standard in the safety recommendation reports [1] for Magnetic Resonance Imaging (MRI) procedures. With the employment of MR systems with high field strengths (from 3T up to 8T), the study of the potential radiofrequency (RF) effects on the biological tissues due to higher radiofrequency, has a particular relevance [2]. Bottomley et al. [3] described a theoretical method to estimate the radiofrequency power deposition during MR exams, based on the sample geometry, the magnetic field radiofrequency, the MR sequence used (its pulse width, repetition time and flip angle) and, finally, the sample electrical conductivity. In this work we develop a liquid sample dielectric properties measurement system based on the evaluation of the resonance frequency and quality factor of a resonant circuit composed by a home-made coil. The major advantage of this method is the contactless between the liquid sample and the measurement electrode. We perform the measurement at 63.85MHz, corresponding to a 1.5T clinical MR environment, but this method can be used for measurements in the whole RF range, tuning the resonant circuit on the desired frequency

Emotion and value : a phenomenological approach.

In this thesis I argue that the affective component of emotional experience plays an essential explanatory role in the acquisition of evaluative knowledge. I call this the notion of affect as a disclosure of value. The thesis is divided into two parts. In the first part I critically assess three contemporary accounts which, I argue, are motivated either implicitly or explicitly by the notion of affect as a disclosure of value. I argue that all three accounts fail due to the theoretical assumptions they inherit from the respective underlying theories of perceptual experience they rely on to theorise the relation between affect and evaluation in emotional experience. Nevertheless, out of the critical assessment I extract three criteria that an account of affect as a disclosure of value ought to satisfy and I clarify the theoretical positions that we ought to avoid. In the second part of the thesis I build on these three criteria to provide an account of affect as a disclosure of value. I argue that at the core of this account is the constitutive thesis that the formation of rationally intelligible motivational states towards an object is constitutive of the disclosure of value. I then argue that a defence of the constitutive thesis commits us to a response-dependent notion of the objectivity of value of the sort defended by David Wiggins and John McDowell. Finally, I rely on the work of John Campbell to clarify both the sort of evaluative knowledge at stake and the role of affect in its acquisition. I argue that the sort of knowledge at stake is the sort that grounds our evaluative concepts and affect provides us with the epistemic access to the evaluative concepts’ semantic value

Assessing the quality of heart rate variability estimated from wrist and finger PPG: A novel approach based on cross-mapping method

The non-invasiveness of photoplethysmographic (PPG) acquisition systems, together with their cost-effectiveness and easiness of connection with IoT technologies, is opening up to the possibility of their widespread use. For this reason, the study of the reliability of PPG and pulse rate variability (PRV) signal quality has become of great scientific, technological, and commercial interest. In this field, sensor location has been demonstrated to play a crucial role. The goal of this study was to investigate PPG and PRV signal quality acquired from two body locations: finger and wrist. We simultaneously acquired the PPG and electrocardiographic (ECG) signals from sixteen healthy subjects (aged 28.5 ± 3.5, seven females) who followed an experimental protocol of affective stimulation through visual stimuli. Statistical tests demonstrated that PPG signals acquired from the wrist and the finger presented different signal quality indexes (kurtosis and Shannon entropy), with higher values for the wrist-PPG. Then we propose to apply the cross-mapping (CM) approach as a new method to quantify the PRV signal quality. We found that the performance achieved using the two sites was significantly different in all the experimental sessions (p < 0.01), and the PRV dynamics acquired from the finger were the most similar to heart rate variability (HRV) dynamics

Parasympathetic-sympathetic causal interactions assessed by time-varying multivariate autoregressive modeling of electrodermal activity and heart-rate-variability

Objective: Most of the bodily functions are regulated by multiple interactions between the parasympathetic (PNS) and sympathetic (SNS) nervous system. In this study, we propose a novel framework to quantify the causal flow of information between PNS and SNS through the analysis of heart rate variability (HRV) and electrodermal activity (EDA) signals. Methods: Our method is based on a time-varying (TV) multivariate autoregressive model of EDA and HRV time-series and incorporates physiologically inspired assumptions by estimating the Directed Coherence in a specific frequency range. The statistical significance of the observed interactions is assessed by a bootstrap procedure purposely developed to infer causalities in the presence of both TV model coefficients and TV model residuals (i.e., heteroskedasticity). We tested our method on two different experiments designed to trigger a sympathetic response, i.e., a hand-grip task (HG) and a mental-computation task (MC). Results: Our results show a parasympathetic driven interaction in the resting state, which is consistent across different studies. The onset of the stressful stimulation triggers a cascade of events characterized by the presence or absence of the PNS-SNS interaction and changes in the directionality. Despite similarities between the results related to the two tasks, we reveal differences in the dynamics of the PNS-SNS interaction, which might reflect different regulatory mechanisms associated with different stressors. Conclusion: We estimate causal coupling between PNS and SNS through MVAR modeling of EDA and HRV time-series. Significance: Our results suggest promising future applicability to investigate more complex contexts such as affective and pathological scenarios

Valence, Arousal, and Gender Effect on Olfactory Cortical Network Connectivity: a study using Dynamic Causal Modeling for EEG

The cortical network including the piriform (PC), orbitofrontal (OFC), and entorhinal (EC) cortices allows the complex processing of behavioral, cognitive, and context-related odor information and represents an access gate to the subcortical limbic regions. Among the several factors that influence odor processing, their hedonic content and gender differences play a relevant role. Here, we investigated how these factors influence EEG effective connectivity among the mentioned brain regions during emotional olfactory stimuli. To this aim, we acquired EEG data from twenty-one healthy volunteers, during a passive odor task of odorants with different valence. We used Dynamic Causal Modeling (DCM) for EEG and Parametric Empirical Bayes (PEB) to investigate the modulatory effects of odors’ valence on the connectivity strengths of the PC-EC-OFC network. Moreover, we controlled for the influence of arousal and gender on such modulatory effects. Our results highlighted the relevant role of the forward and backward PC-EC connections in odor’s brain processing. On the one hand, the EC-to-PC connection was inhibited by both pleasant and unpleasant odors, but not by the neutral one. On the other hand, the PC-to-EC forward connection was found to be modulated (posterior probability (Pp)>0.95) by the arousal level associated with an unpleasant odor. Finally, the whole network dynamics showed several significant gender-related differences (Pp>0.95) suggesting a better ability in odor discrimination for the female gender

Automatic analysis of speech F0 contour for the characterization of mood changes in bipolar patients

da inserireBipolar disorders are characterized by a mood swing, ranging from mania to depression. A system that could monitor and eventually predict these changes would be useful to improve therapy and avoid dangerous events. Speech might convey relevant information about subjects' mood and there is a growing interest to study its changes in presence of mood disorders. In this work we present an automatic method to characterize fundamental frequency (F0) dynamics in voiced part of syllables. The method performs a segmentation of voiced sounds from running speech samples and estimates two categories of features. The first category is borrowed from Taylor's Tilt intonational model. However, the meaning of the proposed features is different from the meaning of Taylor's ones since the former are estimated from all voiced segments without performing any analysis of intonation. A second category of features takes into account the speed of change of F0. In this work, the proposed features are first estimated from an emotional speech database. Then, an analysis on speech samples acquired from eleven psychiatric patients experiencing different mood states, and eighteen healthy control subjects is introduced. Subjects had to perform a text reading task and a picture commenting task. The results of the analysis on the emotional speech database indicate that the proposed features can discriminate between high and low arousal emotions. This was verified both at single subject and group level. An intra-subject analysis was performed on bipolar patients and it highlighted significant changes of the features with different mood states, although this was not observed for all the subjects. The directions of the changes estimated for different patients experiencing the same mood swing, were not coherent and were task-dependent. Interestingly, a single-subject analysis performed on healthy controls and on bipolar patients recorded twice with the same mood label, resulted in a very small number of significant differences. In particular a very good specificity was highlighted for the Taylor-inspired features and for a subset of the second category of features, thus strengthening the significance of the results obtained with patients. Even if the number of enrolled patients is small, this work suggests that the proposed features might give a relevant contribution to the demanding research field of speech-based mood classifiers. Moreover, the results here presented indicate that a model of speech changes in bipolar patients might be subject-specific and that a richer characterization of subject status could be necessary to explain the observed variability