Connectivity in the human brain dissociates entropy and complexity of auditory inputs ☆

Complex systems are described according to two central dimensions: (a) the randomness of their output, quantified via entropy; and (b) their complexity, which reflects the organization of a system's generators. Whereas some approaches hold that complexity can be reduced to uncertainty or entropy, an axiom of complexity science is that signals with very high or very low entropy are generated by relatively non-complex systems, while complex systems typically generate outputs with entropy peaking between these two extremes. In understanding their environment, individuals would benefit from coding for both input entropy and complexity; entropy indexes uncertainty and can inform probabilistic coding strategies, whereas complexity reflects a concise and abstract representation of the underlying environmental configuration, which can serve independent purposes, e.g., as a template for generalization and rapid comparisons between environments. Using functional neuroimaging, we demonstrate that, in response to passively processed auditory inputs, functional integration patterns in the human brain track both the entropy and complexity of the auditory signal. Connectivity between several brain regions scaled monotonically with input entropy, suggesting sensitivity to uncertainty, whereas connectivity between other regions tracked entropy in a convex manner consistent with sensitivity to input complexity. These findings suggest that the human brain simultaneously tracks the uncertainty of sensory data and effectively models their environmental generators. Introduction Theoretical and experimental work in the fields of psychology and complexity science has arrived at two separate approaches for describing how stimuli may be encoded and what constitutes a complex stimulus (see On the other hand, the second, more recent view (e.g., Crutchfield, 2012) holds that simplicity/complexity depends on how demanding it is to model the underlying system that generated a particular stimulus or signal via the interactions of its states. From this perspective, there is a convex, inverse U-shaped relation between disorder and complexity. This is because highly ordered and highly disordered signals are typically generated by succinct, easily describable systems, whereas more sophisticated, or complex, systems generally convey intermediate levels of entropy. 1 Note that in this latter approach, complexity does not capture how difficult it is to veridically encode or reproduce any specific stimulus or signal, but rather how computationally demanding it is to model the system or source generating that signal. As can be appreciated, the two views described above are independent, and graphs depicting 1 For instance, ABCDABCD can be thought of as generated by a system (e.g., a transition matrix) that transitions between four states deterministically (a simple explanation), while a random stimulus can be characterized by a system where all state transitions are equally likely (a similarly simple explanation). http://d

Bioaccumulation of dioxin-like substances and selected brominated flame retardant congeners in the fat and livers of black pigs farmed within the Nebrodi Regional Park of Sicily.

An observational study was designed to assess the bioaccumulation of polychlorodibenzodioxins (PCDD) and polychlorodibenzofurans (PCDF), dioxin-like polychlorobiphenyls (DL-PCB), and 13 selected polybromodiphenylethers (PBDE) in autochthonous pigs reared in the Nebrodi Park of Sicily (Italy). Perirenal fat and liver samples were drawn from animals representative of three different outdoor farming systems and from wild pigs and then analyzed for the chemicals mentioned previously. The highest concentrations of PCDD + PCDF and DL-PCB were detected in the fat (0.45 and 0.35 pg World Health Organization toxicity equivalents [WHO-TE] per g of fat base [FB], respectively) and livers (12.7 and 3.28 pg WHO-TE per g FB) of the wild group, whereas the free-ranging group showed the lowest levels (0.05 and 0.03 pg WHO-TE per g FB in fat and 0.78 and 0.27 pg WHO-TE per g FB in livers). The sum of PBDE congeners was highest in wild pigs (0.52 ng/g FB in fat and 5.64 ng/g FB in livers) and lowest in the farmed group (0.14 ng/g FB in fat and 0.28 ng/g FB in livers). The contamination levels in fat and livers of outdoor pigs had mean concentration values lower than those levels reported for intensively indoor-farmed animals. In wild pigs, bioaccumulation was associated with their free grazing in areas characterized by bush fires. The results of this study aid to emphasize the quality of the environment as a factor to guarantee food safety in typical processed pig meat products, specifically from outdoor and extensive Nebrodi farming systems

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition

The problem of the relationship between brain function, characterized by functional magnetic resonance imaging, and physiological fluctuations by means of cardiac / respiratory oscillations is one of the most debated topics in the last decade. In recent literature, a great number of studies are found that focus on both practical and conceptual aspects about this topic. In this work, we start with reviewing two distinct approaches in considering physiology - related sequences with respect to functional magnetic resonance imaging: one treating physiology - related fluctuations as generators of noise, the other considering them as carriers of cognitively relevant information. In chapter 2 – “Physiology – related effects in the BOLD signal at rest at 4T”, we consider physiological quantities as generators of noise, and discuss conceptual flaws researchers have to face when dealing with data de-noising procedures. We point out that it can be difficult to show that the procedure has achieved its stated aim, i.e. to remove only physiology - related components from the data. As a practical solution, we present a benchmark for assessing whether correction for physiological noise has achieved its stated aim, based on the principle of permutation testing. In chapter 3 – “Directional relationships between BOLD activity and autonomic nervous system fluctuations revealed by fast fMRI acquisition”, on the other hand, we will consider autonomic indicants derived from physiological time - series as meaningful components of the BOLD signal. There, we describe a FMRI experiment building on this, where the goal was to localize brain areas whose activity is directionally related to autonomic one, in a top - down modulation fashion. In chapter 4 we recap the conclusions we found from the two approaches and we summarize the general contributions of our findings. We point out that bringing together the distinct approaches we reviewed lead us to mainly two contributions. On one hand we thought back the validity of almost established procedures in FMRI resting - state pre-processing pipelines. On the other we were able to say something new about general relationship between BOLD and autonomic activity, resting state fluctuations and deactivation theory

Task-induced deactivation in diverse brain systems correlates with interindividual differences in distinct autonomic indices

Neuroimaging research has shown that different cognitive tasks induce relatively specific activation patterns, as well as less task-specific deactivation patterns. Here we examined whether individual differences in Autonomic Nervous System (ANS) activity during task performance correlate with the magnitude of task-induced deactivation. In an fMRI study, participants performed a continuous mental arithmetic task in a task/rest block design, while undergoing combined fMRI and heart/respiration rate acquisitions using photoplethysmograph and respiration belt. As expected, task performance increased heart-rate and reduced the RMSSD, a cardiac index related to vagal tone. Across participants, higher heart rate during task was linked to increased activation in fronto-parietal regions, as well as to stronger deactivation in ventromedial prefrontal regions. Respiration frequency during task was associated with similar patterns, but in different regions than those identified for heart-rate. Finally, in a large set of regions, almost exclusively limited to the Default Mode Network, lower RMSSD was associated with greater deactivation, and furthermore, the vast majority of these regions were task-deactivated at the group level. Together, our findings show that inter-individual differences in ANS activity are strongly linked to task-induced deactivation. Importantly, our findings suggest that deactivation is a multifaceted construct potentially linked to ANS control, because distinct ANS measures correlate with deactivation in different regions. We discuss the implications for current theories of cortical control of the ANS and for accounts of deactivation, with particular reference to studies documenting a ”failure to deactivate” in multiple clinical states