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

Towards a Contactless Stress Classification Using Thermal Imaging

Thermal cameras capture the infrared radiation emitted from a body in a contactless manner and can provide an indirect estimation of the autonomic nervous system (ANS) dynamics through the regulation of the skin temperature. This study investigates the contribution given by thermal imaging for an effective automatic stress detection with the perspective of a contactless stress recognition system. To this aim, we recorded both ANS correlates (cardiac, electrodermal, and respiratory activity) and thermal images from 25 volunteers under acute stress induced by the Stroop test. We conducted a statistical analysis on the features extracted from each signal, and we implemented subject-independent classifications based on the support vector machine model with an embedded recursive feature elimination algorithm. Particularly, we trained three classifiers using different feature sets: the full set of features, only those derived from the peripheral autonomic correlates, and only those derived from the thermal images. Classification accuracy and feature selection results confirmed the relevant contribution provided by the thermal features in the acute stress detection task. Indeed, a combination of ANS correlates and thermal features achieved 97.37% of accuracy. Moreover, using only thermal features we could still successfully detect stress with an accuracy of 86.84% in a contact-free manne

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

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

Cortical network and connectivity underlying hedonic olfactory perception

Objective. The emotional response to olfactory stimuli implies the activation of a complex cascade of events triggered by structures lying in the limbic system. However, little is known about how this activation is projected up to cerebral cortex and how different cortical areas dynamically interact each other. Approach. In this study, we acquired EEG from human participants performing a passive odor-perception task with odorants conveying positive, neutral and negative valence. A novel methodological pipeline integrating global field power (GFP), independent component analysis (ICA), dipole source localization was applied to estimate effective connectivity in the challenging scenario of single-trial low-synchronized stimulation. Main results. We identified the brain network and the neural paths, elicited at different frequency bands, i.e. θ (4-7Hz), α (8-12Hz) and β (13-30Hz), involved in odor valence processing. This brain network includes the orbitofrontal cortex (OFC), the cingulate gyrus (CgG), the superior temporal gyrus (STG), the posterior cingulate cortex/precuneus (PCC/PCu) and the parahippocampal gyrus (PHG). It was analyzed using a time-varying multivariate autoregressive model to resolve time-frequency causal interactions. Specifically, the OFC acts as the main node for odor perception and evaluation of pleasant and unpleasant stimuli, whereas no specific path was observed for a neutral stimulus. Significance. The results introduce new evidences on the role of the OFC during hedonic perception and underpin its specificity during the odor valence assessment. Our findings suggest that, after the odor onset different, bidirectional interactions occur between the OFC and other brain regions associated with emotion recognition/categorization and memory according to the stimulus valence. This outcome unveils how the hedonic olfactory network dynamically changes based on odor valence

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

Odor valence modulates cortico-cortical interactions: A preliminary study using DCM for EEG

Olfaction and emotions share common networks in the brain. However, little is known on how the emotional content of odors modulate dynamically the cortico-cortical interactions within these networks. In this preliminary study, we investigated the effect of odor valence on effective connectivity through the use of Dynamic Causal Modeling (DCM). We recorded electroencephalographic (EEG) data from healthy subjects performing a passive odor task of odorants with different valence. Once defined a fully-connected a priori network comprising the pyriform cortex (PC), orbitofrontal cortex (OFC), and entorhinal cortex (EC), we tested the modulatory effect of odor valence on their causal interactions at the group level using the parametric empirical bayes (PEB) framework. Results show that both pleasant and the unpleasant odors have an inhibitory effect on the connection from EC to PC, whereas we did not observe any effect for the neutral odor. Moreover, the odor with positive valence has a stronger influence on connectivity dynamics compared to the negative odor. Although preliminary, our results suggest that odor valence can modulate brain connectivity

Linear and nonlinear quantitative EEG analysis during neutral hypnosis following an opened/closed eye paradigm

Hypnotic susceptibility is a major factor influencing the study of the neural correlates of hypnosis using EEG. In this context, while its effects on the response to hypnotic suggestions are undisputed, less attention has been paid to “neutral hypnosis” (i.e., the hypnotic condition in absence of suggestions). Furthermore, although an influence of opened and closed eye condition onto hypnotizability has been reported, a systematic investigation is still missing. Here, we analyzed EEG signals from 34 healthy subjects with low (LS), medium (MS), and (HS) hypnotic susceptibility using power spectral measures (i.e., TPSD, PSD) and Lempel-Ziv-Complexity (i.e., LZC, fLZC). Indeed, LZC was found to be more suitable than other complexity measures for EEG analysis, while it has been never used in the study of hypnosis. Accordingly, for each measure, we investigated within-group differences between rest and neutral hypnosis, and between opened-eye/closed-eye conditions under both rest and neutral hypnosis. Then, we evaluated between-group differences for each experimental condition. We observed that, while power estimates did not reveal notable differences between groups, LZC and fLZC were able to distinguish between HS, MS, and LS. In particular, we found a left frontal difference between HS and LS during closed-eye rest. Moreover, we observed a symmetric pattern distinguishing HS and LS during closed-eye hypnosis. Our results suggest that LZC is better capable of discriminating subjects with different hypnotic susceptibility, as compared to standard power analysis

Ld-EEG Effective Brain Connectivity in Patients With Cheyne-Stokes Respiration

The characterization of brain cortical activity in heart-failure patients affected by Cheyne-Stokes Respiration might provide relevant information about the mechanism underlying this pathology. Central autonomic network is gaining increasing attention for its role in the regulation of breathing and cardiac functions. In this scenario, evaluating changes in cortical connectivity associated with Cheyne-Stokes Respiration may be of interest in the study of specific brain-activity related to such disease. Nonetheless, the inter subject variability, the temporal dynamics of Central-Apnea/Hyperpnea cycles and the limitations of clinical setups lead to different methodological challenges. To this aim, we present a framework for the assessment of cortico-cortical interactions from Electroencephalographic signals acquired using low-density caps and block-design paradigms, arising from endogenous triggers. The framework combines ICA-decomposition, unsupervised clustering, MVAR modelling and a permutation-bootstrap strategy for evaluating significant connectivity differences between conditions. A common network, lateralized towards the left hemisphere, was depicted across 8 patients exhibiting Cheyne-Stokes Respiration patterns during acquisitions. Significant differences in connectivity at the group level were observed based on patients' ventilatory condition. Interactions were significantly higher during hyperpnea periods with respect to central apneas and occurred mainly in the delta band. Opposite-sign differences were observed for higher frequencies (i.e. beta, low-gamma)