Search

Background Carotid plaque ulceration has been associated with imaging characteristics of high risk of future events (plaque vulnerability) in patients with a significant degree of stenosis, while little is known about its role in patients with a lesser degree of stenosis and its association with markers of vascular inflammation. Purpose We evaluated the association between imaging characteristics of plaque vulnerability and vascular inflammation detected by different imaging tests. Methods One hundred and seven patients with unilateral asymptomatic carotid artery stenosis (40–60% detected by Doppler ultrasound (DUS)), enrolled in the ongoing Carotid Artery Multimodality Prognostic (CAMP) study were prospectively evaluated with computed tomography angiography (CTA), contrast-enhanced magnetic resonance imaging (CEMRA) and, in a subgroup of patients, with 18-F-fluorodeoxyglucose positron emission tomography (PET). Plaque ulceration was detected by CTA. Variables are expressed as median (1st–3rd quartiles) and number (percentages).The Student's t-test or Mann-Whitney and Chi square or Fisher's exact test were used to study associations among variables. Results An ulcerated plaque was present in 35% patients (n=33) of those undergoing CTA (n=94), while intraplaque hemorrhage and lipid-rich necrotic core were present in 9% and 15% of those undergoing CEMRA. Compared with patients with non-ulcerated plaques, those with an ulcerated plaque had a significant higher degree of stenosis at DUS, a higher target to background ratio (TBR) at 18F-FDG-PET and a higher high-sensitivity cardiac troponin (cTn) T. Conclusions Plaque ulceration is common and associated with higher degree of stenosis, higher indices of wall inflammation and higher serum markers of myocardial injury. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Regione Toscana

How communication between neurons gives rise to natural vision remains a matter of intense investigation. The mid-level visual areas along the ventral stream, as studies in primates have shown, are selective to a common class of natural images—textures—but a circuit-level understanding of this selectivity and its link to perception remain unclear. We addressed these questions in mice, first showing that they can perceptually discriminate between texture types and statistically simpler spectrally matched stimuli. Then, at the neural level, we found that the secondary visual area (LM), more than the primary one (V1), was selective for the higher-order statistics of textures, both at the mesoscopic and single-cell levels. At the circuit level, textures were encoded in neural activity subspaces whose relative distances correlated with the statistical complexity of the images and with the mice’s ability to discriminate between them. These dependencies were more significant in LM, in which the texture-related subspaces were smaller and closer to each other, enabling better stimulus decoding in this area. Together, our results demonstrate texture vision in mice, finding a linking framework between stimulus statistics, neural representations, and perceptual sensitivity—a distinct hallmark of efficient coding computations.

Genetic variants in YWHAZ contribute to psychiatric disorders such as autism spectrum disorder and schizophrenia, and have been related to an impaired neurodevelopment in humans and mice. Here, we have used zebrafish to investigate the mechanisms by which YWHAZ contributes to neurodevelopmental disorders. We observed that ywhaz expression was pan-neuronal during developmental stages and restricted to Purkinje cells in the adult cerebellum, cells that are described to be reduced in number and size in autistic patients. GCaMP6s albino zebrafish embryos were generated by the National Institute of Genetics (Japan) and obtained from Dr. Matt Parker from the University of Portsmouth, UK. The Tg(aldoca:gap43-Venus) line was obtained from Masahiko Hibi from the Bioscience and Biotechnology Center of Nagoya University, Japan. Tg(olig2:egfp)vu12 brains were obtained from the Center for Developmental Biology, UMR 5547 CNRS, Toulouse, France. Major financial support for this research was received by BC from the Spanish ‘Ministerio de Ciencia, Innovación y Universidades’ (RTI2018-100968-B-100, PID2021-1277760B-I100), the ‘Ministerio de Sanidad, Servicios Sociales e Igualdad/Plan Nacional Sobre Drogas’ (PNSD-2017I050 and PNSD-2020I042), ‘Generalitat de Catalunya/AGAUR’ (2017-SGR-738), ICREA Academia 2021, and the European Union H2020 Program [H2020/2014-2020] under grant agreements n° 667302 (CoCA) and Eat2beNICE (728018). E.A-G was supported by the Ministerio de Economía y Competitividad (Spanish Government) and the EU H2020 program (Eat2beNICE-728018). G.C., E.G. and P.L-A acknowledge financial support from the Spanish Ministerio de Economía y Competitividad (MINECO) through the “Severo Ochoa” program for Centres of Excellence in R&D CEX2019-000910-S), MINECO/FEDER Ramon y Cajal program (RYC-2015-17935); Laserlab-Europe EU-H2020 GA no. 871124, Fundació Privada Cellex, Fundación Mig-Puig and from the Generalitat de Catalunya through the CERCA program. F.A. acknowledges financial support from the Spanish ‘Ministerio de Ciencia, Innovación y Universidades’ (PID2019-107738RB-I00, MICINN/FEDER) and SGR (2017SGR1255).

Human pluripotent stem cells (hPSCs) are a powerful tool for modelling human development. In recent years, hPSCs have become central in cell-based therapies for neurodegenerative diseases given their potential to replace affected neurons. However, directing hPSCs into specific neuronal types is complex and requires an accurate protocol that mimics endogenous neuronal development. Here we describe step-by-step a fast feeder-free neuronal differentiation protocol to direct hPSCs to mature forebrain neurons in 37 days in vitro (DIV). The protocol is based upon a combination of specific morphogens, trophic and growth factors, ions, neurotransmitters and extracellular matrix elements. A human-induced PSC line (Ctr-Q33) and a human embryonic stem cell line (GEN-Q18) were used to reinforce the potential of the protocol. Neuronal activity was analysed by single-cell calcium imaging. At 8 DIV, we obtained a homogeneous population of hPSC-derived neuroectodermal progenitors which self-arranged in bi-dimensional neural tube-like structures. At 16 DIV, we generated hPSC-derived neural progenitor cells (NPCs) with mostly a subpallial identity along with a subpopulation of pallial NPCs. Terminal in vitro neuronal differentiation was confirmed by the expression of microtubule associated protein 2b (Map 2b) by almost 100% of hPSC-derived neurons and the expression of specific-striatal neuronal markers including GABA, CTIP2 and DARPP-32. HPSC-derived neurons showed mature and functional phenotypes as they expressed synaptic markers, voltage-gated ion channels and neurotransmitter receptors. Neurons displayed diverse spontaneous activity patterns that were classified into three major groups, namely “high”, “intermediate” and “low” firing neurons. Finally, transplantation experiments showed that the NPCs survived and differentiated within mouse striatum for at least 3 months. NPCs integrated host environmental cues and differentiated into striatal medium-sized spiny neurons (MSNs), which successfully integrated into the endogenous circuitry without teratoma formation. Altogether, these findings demonstrate the potential of this robust human neuronal differentiation protocol, which will bring new opportunities for the study of human neurodevelopment and neurodegeneration, and will open new avenues in cell-based therapies, pharmacological studies and alternative in vitro toxicology. Electronic supplementary material The online version of this article (10.1007/s12035-020-01907-4) contains supplementary material, which is available to authorized users.

Choice information appears in multi-area brain networks mixed with sensory, motor, and cognitive variables. In the posterior cortex—traditionally implicated in decision computations—the presence, strength, and area specificity of choice signals are highly variable, limiting a cohesive understanding of their computational significance. Examining the mesoscale activity in the mouse posterior cortex during a visual task, we found that choice signals defined a decision variable in a low-dimensional embedding space with a prominent contribution along the ventral visual stream. Their subspace was near-orthogonal to concurrently represented sensory and motor-related activations, with modulations by task difficulty and by the animals’ attention state. A recurrent neural network trained with animals’ choices revealed an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. Our results demonstrated an independent, multi-area decision variable in the posterior cortex, controlled by task features and cognitive demands, possibly linked to contextual inference computations in dynamic animal–environment interactions.

We studied effective connectivity in rat cortical cultures with various degrees of spatial aggregation, ranging from homogeneous networks to highly aggregated ones. We considered small cultures 3 mm in diameter and that contained about $2,000$ neurons. Spatial inhomogeneity favored an increase of metric correlations and connectivity among neighboring neurons. Effective connectivity was determined from spontaneous activity recordings using calcium fluorescence imaging. We used generalized transfer entropy as tool to infer the effective connectivity. We carried out numerical simulations to build networks that mimicked the experimental ones and to test the reliability of the connectivity–inference algorithm. Effective connectivity traits were investigated during the development of the cultures over two weeks, and along the gradual blockade of excitatory connections through CNQX. We observed that the average effective connectivity rapidly increased during culture development. At day in vitro (DIV) 15 the average excitatory in–degree was measured as $\bar{k}^{\mathrm{in}}_E \simeq 50$ for homogeneous and semi aggregated networks, and $\bar{k}^{\mathrm{in}}_E \simeq 120$ for aggregated ones, and with 20 percent inhibition. Aggregated cultures exhibited assortative traits and a high resilience to chemical damage, while the other cultures were dissassortative or neutral, and less resilient. Our work illustrates the role of metric correlations in spatially embedded networks in shaping connectivity and activity traits in living neuronal networks.

Genetic risk variants in YWHAZ, encoding 14-3-3ζ, have been found to contribute to psychiatric disorders such as autism spectrum disorder and schizophrenia, and have been related to an impaired neurodevelopment in humans and mice. Here, we have used a zebrafish model to further understand the mechanisms by which YWHAZ contribute to neurodevelopmental disorders. We first observed pan-neuronal expression of ywhaz during developmental stages, suggesting an important role of this gene in neural development. During adulthood ywhaz expression was restricted to Purkinje cells in the cerebellum, a region that shows alterations in autistic patients. We then established a novel stable ywhaz knockout (KO) zebrafish line using CRISPR/Cas9 genome engineering. We performed whole-brain calcium imaging in wild-type (WT) and ywhaz KO larvae and found altered neural activity and functional connectivity in the hindbrain. Interestingly, adult ywhaz KO fish also display decreased levels of dopamine and serotonin in the hindbrain and freeze when exposed to novel stimuli, a phenotype that can be reversed with fluoxetine and quinpirole, drugs that target serotonin and dopamine neurotransmission. Together, these findings suggest an important role for ywhaz in establishing neuronal connectivity during developmental stages. ywhaz deficiency leads to impaired dopamine and serotonin neurotransmission that may underlie the altered behaviour observed during adulthood.

Spreading processes on networks are ubiquitous in both human-made and natural systems. Understanding their behavior is of broad interest; from the control of epidemics to understanding brain dynamics. While in some cases there exists a clear separation of time scales between the propagation of a single spreading cascade and the initiation of the next -- such that spreading can be modelled as directed percolation or a branching process -- there are also processes for which this is not the case, such as zoonotic diseases or spiking cascades in neural networks. For a large class of relevant network topologies, we show here that in such a scenario the nature of the overall spreading fundamentally changes. This change manifests itself in a transition between different universality classes of critical spreading, which determines the onset and the properties of an avalanche turning epidemic or neural activity turning epileptic, for example. We present analytical results in the mean-field limit giving the critical line along which scale-free spreading behaviour can be observed. The two limits of this critical line correspond to the universality classes of directed and undirected percolation, respectively. Outside these two limits, this duality manifests itself in the appearance of critical exponents from the universality classes of both directed and undirected percolation. We find that the transition between these exponents is governed by a competition between merging and propagation of activity, and identify an appropriate scaling relationship for the transition point. Finally, we show that commonly used measures, such as the branching ratio and dynamic susceptibility, fail to establish criticality in the absence of time-scale separation calling for a reanalysis of criticality in the brain., 25 pages, 17 figures, 3 tables. Update with substantial re-write to introduction and discussion, minor tweaks throughout

The physics governing the formation of extreme coherent events, i.e., the systemwide emergence of an observable taking extraordinary values in a short time window, is a relevant yet elusive problem to a variety of disciplines ranging from climate science to neuroscience. Despite their inherent differences, systems exhibiting episodes of extreme coherence can be abstracted as a set of coupled nonlinear elements in a noisy and networked environment. Here, we propose a model describing the generation of extreme coherence by exploring theoretically and numerically the capacity of noise and network correlations to amplify a critical core of the system and trigger an extreme event. Although we principally center our study in modeling bursting phenomena in neuronal circuits, we extend our analysis to other systems such as algae blooms and infectious diseases. We show that extreme events originate in a relatively small core of the system and that different cores may coexist. We also show that the amplification mechanisms within a system are highly robust, so that the deletion of central nodes leads to other nodes taking leadership.

Within the classical eye-blink conditioning, Purkinje cells within the cerebellum are known to suppress their tonic firing rates for a well defined time period in response to the conditional stimulus after training. The temporal profile of the drop in tonic firing rate, i.e., the onset and the duration, depend upon the time interval between the onsets of the training conditional and unconditional stimulus. Direct stimulation of parallel fibers and climbing fiber by electrodes was found to be sufficient to reproduce the same characteristic drop in the firing rate of the Purkinje cell. In addition, the specific metabotropic glutamate-based receptor type 7 (mGluR7), which resides on the Purkinje cell synapses, was found responsible for the initiation of the response, suggesting an intrinsic mechanism within the Purkinje cell for the temporal learning. In an attempt to look for a mechanism for time-encoding memory formation within individual Purkinje cells, we propose a biochemical mechanism based on recent experimental findings. The proposed model tries to answer key aspects of the “Coding problem” of Neuroscience by focussing on the Purkinje cell’s ability to encode time intervals through training. According to the proposed mechanism, the time memory is encoded within the dynamics of a set of proteins — mGluR7, G-protein, G-protein coupled Inward Rectifier Potassium ion channel, Protein Kinase A and Protein Phosphatase 1 — which self-organize themselves into a protein complex. The intrinsic dynamics of these protein complexes can differ and thus can encode different time durations. Based on their amount and their collective dynamics within individual synapses, the Purkinje cell is able to suppress its own tonic firing rate for a specific time interval. The time memory is encoded within the effective rate constants of the biochemical reactions and altering these rates constants means storing a different time memory. The proposed mechanism is verified by a simplified mathematical model and corresponding dynamical simulations of the involved biomolecules, yielding testable experimental predictions.Author summaryHebbian plasticity is a widely accepted form of learning that can encode memories in our brain. Spike-timing dependent plasticity resulting in Long-term Potentiation or Depression of synapses has become the default mechanistic explanation behind memory formation within a neuronal population. However, recent experiments of conditional eyeblink response in Purkinje cells have challenged this point of view by showing that these mechanisms alone cannot account for time memory formation in the Purkinje cell. To explain the underlying mechanism behind this novel synaptic plasticity, we introduce a biochemical mechanism based on protein interactions occurring within a single synapse. These protein interactions and the associated effective rate constants are sufficient to encode time delays by auto-induced inhibition on a single excitatory synapse, suggesting that synapses are capable of storing more information than previously thought.

Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the animals’ attention state. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the animals’ choices and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

Made available in DSpace on 2020-12-12T01:58:39Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-02-19 Background: Pelvic floor muscles (PFM) and rectus abdominis muscles (RAM) of pregnant diabetic rats exhibit atrophy, co-localization of fast and slow fibers and an increased collagen type I/III ratio. However, the role of similar PFM or RAM hyperglycemic-related myopathy in women with gestational diabetes mellitus (GDM) remains poorly investigated. This study aims to assess the frequency of pelvic floor muscle disorders and pregnancy-specific urinary incontinence (PS-UI) 12 months after the Cesarean (C) section in women with GDM. Specifically, differences in PFM/RAM hyperglycemic myopathy will be evaluated. Methods: The Diamater is an ongoing cohort study of four groups of 59 pregnant women each from the Perinatal Diabetes Research Centre (PDRC), Botucatu Medical School (FMB)-UNESP (São Paulo State University), Brazil. Diagnosis of GDM and PS-UI will be made at 24-26 weeks, with a follow-up at 34-38 weeks of gestation. Inclusion in the study will occur at the time of C-section, and patients will be followed at 24-48 h, 6 weeks and 6 and 12 months postpartum. Study groups will be classified as (1) GDM plus PS-UI; (2) GDM without PS-UI; (3) Non-GDM plus PS-UI; and (4) Non-GDM without PS-UI. We will analyze relationships between GDM, PS-UI and hyperglycemic myopathy at 12 months after C-section. The mediator variables to be evaluated include digital palpation, vaginal squeeze pressure, 3D pelvic floor ultrasound, and 3D RAM ultrasound. RAM samples obtained during C-section will be analyzed for ex-vivo contractility, morphological, molecular and OMICS profiles to further characterize the hyperglycemic myopathy. Additional variables to be evaluated include maternal age, socioeconomic status, educational level, ethnicity, body mass index, weight gain during pregnancy, quality of glycemic control and insulin therapy. Discussion: To our knowledge, this will be the first study to provide data on the prevalence of PS-UI and RAM and PFM physical and biomolecular muscle profiles after C-section in mothers with GDM. The longitudinal design allows for the assessment of cause-effect relationships between GDM, PS-UI, and PFMs and RAMs myopathy. The findings may reveal previously undetermined consequences of GDM. Department of Gynecology and Obstetrics Botucatu Medical School (FMB) São Paulo State University (UNESP) Physics Department Institute of Biosciences Letters and Exact Sciences Multiuser Center for Biomolecular Innovation UNESP-São Paulo State University Physiotherapy Department Faculdades Integradas de Bauru FIB Universidade Do Sagrado Coração (USC), Jardim Brasil Department of Health Research Methods Evidence and Impact McMaster University Biostatistics Unit Father Sean o'Sullivan Research Centre St Joseph's Healthcare-Hamilton Department of Obstetrics and Gynecology Weill Cornell Medicine Institute of Tropical Medicine University of Sao Paulo Medical School School of Philosophy and Sciences Department of Physiotherapy and Occupational Therapy UNESP-São Paulo State University Department of Gynecology and Obstetrics Botucatu Medical School (FMB) São Paulo State University (UNESP) Physics Department Institute of Biosciences Letters and Exact Sciences Multiuser Center for Biomolecular Innovation UNESP-São Paulo State University School of Philosophy and Sciences Department of Physiotherapy and Occupational Therapy UNESP-São Paulo State University

Background Network alterations underlying neurodegenerative diseases often precede symptoms and functional deficits. Thus, their early identification is central for improved prognosis. In Huntington’s disease (HD), the cortico-striatal networks, involved in motor function processing, are the most compromised neural substrate. However, whether the network alterations are intrinsic of the striatum or the cortex is not fully understood. Results In order to identify early HD neural deficits, we characterized neuronal ensemble calcium activity and network topology of HD striatal and cortical cultures. We used large-scale calcium imaging combined with activity-based network inference analysis. We extracted collective activity events and inferred the topology of the neuronal network in cortical and striatal primary cultures from wild-type and R6/1 mouse model of HD. Striatal, but not cortical, HD networks displayed lower activity and a lessened ability to integrate information. GABAA receptor blockade in healthy and HD striatal cultures generated similar coordinated ensemble activity and network topology, highlighting that the excitatory component of striatal system is spared in HD. Conversely, NMDA receptor activation increased individual neuronal activity while coordinated activity became highly variable and undefined. Interestingly, by boosting NMDA activity, we rectified striatal HD network alterations. Conclusions Overall, our integrative approach highlights striatal defective network integration capacity as a major contributor of basal ganglia dysfunction in HD and suggests that increased excitatory drive may serve as a potential intervention. In addition, our work provides a valuable tool to evaluate in vitro network recovery after treatment intervention in basal ganglia disorders.

Additional file 5: Figure S5. Quantification of individual spontaneous activity in WT and HD striatal and cortical primary cultures. (A-E) Single neuron statistics. (A) Activity rate, i.e., average spikes per second of each neuron. (B) Inter-spike interval (ISI), i.e., average time between two consecutive spikes. (C) Bursting rate, i.e., frequency of single-cell bursts. (D) Average inter-burst interval (IBI), average time between consecutive individual bursts (STR WT n=38; STR HD n=37; CTX WT n= 19; CTX HD n=22). Each dot represents a single experiment, thick line the mean, thick shaded area the Standard Error of the Mean and thin shaded area the standard deviation. Statistical analysis was performed using Student t-test. * p< 0.05, ** p

Additional file 2: Figure S2. Scheme of the high-throughput calcium imaging recording and analyses in primary cultures. (A) Representative average image of a striatal primary culture at 15 DIV. (B) Calcium fluorescence traces from individual neurons highlighted in (A). Black vertical lines indicate reconstructed spikes. (C) Raster plot of spikes from ∼1000 neurons simultaneously recorded in the culture field of view. Each row represents an individual neuron. (D) Average fluorescence trace from all neurons (grey) with the detected network bursts highlighted in color. Scale bar 100 μM.

Additional file 4: Figure S4. Network burst shape is preserved in HD striatal and cortical cultures. (A) Average duration of network bursts. (B) Burst amplitude measured in number of spikes per participating neurons in a burst (STR WT n=22; STR HD n=15; CTX WT n= 19; CTX HD n=22). Each dot represents a single experiment, thick line the mean, thick shaded area the Standard Error of the Mean and thin shaded area the standard deviation. Statistical analysis was performed using Student t-test.

Brownian ratchets are shown to feature a nontrivial vanishing-noise limit where the dynamics is reduced to a stochastic alternation between two deterministic circle maps (quasideterministic ratchets). Motivated by cooperative dynamics of molecular motors, here we solve exactly the problem of two interacting quasideterministic ratchets. We show that the dynamics can be described as a random walk on a graph that is specific to each set of parameters. We compute point by point the exact velocity-force $V(f)$ function as a summation over all paths in the specific graph for each $f$, revealing a complex structure that features self-similarity and nontrivial continuity properties. From a general perspective, we unveil that the alternation of two simple piecewise linear circle maps unfolds a very rich variety of dynamical complexity, in particular the phenomenon of piecewise chaos, where chaos emerges from the combination of nonchaotic maps. We show convergence of the finite-noise case to our exact solution.

Additional file 6: Figure S6. Quantification of network burst shape before and after bicuculline application. (A) Average duration of the network burst and (B) average number of spikes contained in burst (amplitude) (STR WT n=4; STR HD n=5). Each dot represents a single experiment, thick line the mean, thick shaded area the Standard Error of the Mean and thin shaded area the standard deviation. Statistical analysis was performed using mixed ANOVA and posterior Bonferroni’s post-hoc test. * p< 0.05, ** p

Additional file 1: Figure S1. Characterization of the striatal primary cultures. (A) Representative epifluorescence microscopy images showing nuclei stained with DAPI (blue), neurons stained with MAP2 (green), astrocytes stained with GFAP (red) and the merge of the three channels. (B) Quantification of the neurons and astrocytes as percentage of positive cells in the cultures. (C) Quantification of the number of MAP2 immunolabelled branches per neuron and (D) total tree length in WT and HD striatal cultures. Data are presented as mean ± SEM (STR WT n =6, STR HD n =6). Scale bar, 50μm.

Additional file 8: Figure S8. Individual neuronal activity increases after NMDA application in striatal cultures. (A-D) Quantification of the changes in the single cell averaged spiking activity features before and after addition of 10 μM NMDA + 10 μM glycine for WT and HD. (A) Spiking rate, (B) inter-spike interval (ISI), (C) Frequency of individual bursting activity (bursting rate) and (D) individual inter-burst interval. (STR WT n=11; STR HD n=9-10). Each dot represents a single experiment, thick line the mean, thick shaded area the Standard Error of the Mean and thin shaded area the standard deviation. Statistical analysis was performed using mixed ANOVA and posterior Bonferroni’s post-hoc test. * p< 0.05, ** p

Additional file 7: Figure S7. Individual neuronal activity increases after GABAA receptor blockade with bicuculline (BIC) in striatal cultures. (A-D) Quantification of the changes in the single cell averaged spiking activity features in basal conditions and after the addiction of BIC: (A) Spiking rate, (B) ISI, (C) average frequency of bursting activity of individual neurons and (D) inter-burst interval (IBI) (STR WT n = 11; STR HD n = 10). Each dot represents a single experiment, thick line the mean, thick shaded area the Standard Error of the Mean and thin shaded area the standard deviation. Statistical analysis was performed using mixed ANOVA and posterior Bonferroni’s post-hoc test. * p< 0.05, ** p

Additional file 3: Figure S3. Litter-grouped spontaneously active neurons and coordinated ensemble activity. The data is the same as Fig. 1 of the main text but, to evaluate intra- and inter-litter variability, the measurements are separated according to litters and for both WT and HD. The plotted data correspond to: (A) percentage of active neurons in the cultures (STR WT n =3, 4, 13, 14, 4; STR HD n =3, 3, 12, 12, 4; CTX WT n =2, 2, 6, 3, 3, 2; CTX HD n =3, 2, 6, 6, 2, 3) (B) percentage of active neurons that participate in spontaneous network burst (STR WT n =1, 0, 13, 7, 0; STR HD n =1, 1, 9, 5, 0; CTX WT n =3, 2, 6, 3, 3, 2; CTX HD n =3, 2, 6, 6, 1, 3), (C) average network inter-burst interval (IBI) (STR WT n =1, 0, 13, 8, 0; STR HD n =1, 0, 9, 5, 0; CTX WT n =3, 2, 6, 3, 2, 1; CTX HD n =3, 2, 6, 5, 2, 3), (D) global activity rate of the cultures (STR WT n =3, 4, 13, 14, 4; STR HD n =3, 3, 12, 12, 4; CTX WT n =3, 2, 5, 3, 3, 2; CTX HD n =3, 1, 6, 6, 1, 3) and (E) fraction of independent spikes (STR WT n =3, 4, 12, 13, 4; STR HD n =3, 3, 12, 14, 4; CTX WT n =3, 2, 6, 3, 1, 2; CTX HD n =3, 2, 6, 6, 2, 2). Each dot in the plot represents a single culture, and each column a different litter, thick line the mean, thick shaded area the standard error of the mean and thin shaded area the standard deviation.

Recent experiments have shown that the spontaneous activity of developing dissociated neuronal cultures can be described as a process of highly inhomogeneous nucleation and front propagation due to the localization of noise activity, i.e., noise focusing. However, the basic understanding of the mechanisms of noise build-up leading to the nucleation remains an open fundamental problem. Here we present a minimal dynamical model called stochastic quorum percolation that can account for the observed phenomena, while providing a robust theoretical framework. The model reproduces the first- and second-order phase transitions of bursting dynamics and neuronal avalanches, respectively, and captures the profound effect metric correlations in the network topology can have on the dynamics. The application of our results to other systems such as in the propagation of infectious diseases and of rumors is discussed.

To predict patients who would benefit from adaptive radiotherapy (ART) and re-planning intervention based on machine learning from anatomical and dosimetric variations in a retrospective dataset.90 patients (pts) treated for head-neck cancer (HN) formed a multicenter data-set. 41 HN pts (45.6%) were considered for learning; 49 pts (54.4%) were used to test the tool. A homemade machine-learning classifier was developed to analyze volume and dose variations of parotid glands (PG). Using deformable image registration (DIR) and GPU, patients' conditions were analyzed automatically. Support Vector Machines (SVM) was used for time-series evaluation. "Inadequate" class identified patients that might benefit from replanning. Double-blind evaluation by two radiation oncologists (ROs) was carried out to validate day/week selected for re-planning by the classifier.The cohort was affected by PG mean reduction of 23.7±8.8%. During the first 3weeks, 86.7% cases show PG deformation aligned with predefined tolerance, thus not requiring re-planning. From 4th week, an increased number of pts would potentially benefit from re-planning: a mean of 58% of cases, with an inter-center variability of 8.3%, showed "inadequate" conditions. 11% of cases showed "bias" due to DIR and script failure; 6% showed "warning" output due to potential positioning issues. Comparing re-planning suggested by tool with recommended by ROs, the 4th week seems the most favorable time in 70% cases.SVM and decision-making tool was applied to overcome ART challenges. Pts would benefit from ART and ideal time for re-planning intervention was identified in this retrospective analysis.

Neuronal avalanches have become an ubiquitous tool to describe the activity of large neuronal assemblies. The emergence of scale-free statistics with well-defined exponents has led to the belief that the brain might operate near a critical point. Yet not much is known in terms of how the different exponents arise or how robust they are. Using calcium imaging recordings of dissociated neuronal cultures we show that the exponents are not universal, and that significantly different exponents arise with different culture preparations, leading to the existence of different universality classes. Naturally developing cultures show avalanche statistics consistent with those of a mean-field branching process, however, cultures grown in the presence of folic acid metabolites appear to be in a distinct universality class with significantly different critical exponents. Given the increased synaptic density and number of feedback loops in folate reared cultures, our results suggest that network topology plays a leading role in shaping the avalanche dynamics. We also show that for both types of cultures pronounced correlations exist in the sizes of neuronal avalanches indicating size clustering, being much stronger in folate reared cultures.

Summary Induced pluripotent stem cell (iPSC) technology has been successfully used to recapitulate phenotypic traits of several human diseases in vitro. Patient-specific iPSC-based disease models are also expected to reveal early functional phenotypes, although this remains to be proved. Here, we generated iPSC lines from two patients with Sanfilippo type C syndrome, a lysosomal storage disorder with inheritable progressive neurodegeneration. Mature neurons obtained from patient-specific iPSC lines recapitulated the main known phenotypes of the disease, not present in genetically corrected patient-specific iPSC-derived cultures. Moreover, neuronal networks organized in vitro from mature patient-derived neurons showed early defects in neuronal activity, network-wide degradation, and altered effective connectivity. Our findings establish the importance of iPSC-based technology to identify early functional phenotypes, which can in turn shed light on the pathological mechanisms occurring in Sanfilippo syndrome. This technology also has the potential to provide valuable readouts to screen compounds, which can prevent the onset of neurodegeneration., Graphical Abstract, Highlights • Fibroblasts from two Sanfilippo C patients were reprogrammed to obtain iPSCs • iPSCs were successfully differentiated to neural cells that mimic the disease • Networks of patients’ neurons show altered activity and connectivity • Early functional phenotypes are prevented in gene-corrected patients’ neurons, iPSC technology has been used to model dozens of diseases. In this article, Raya, Grinberg, and colleagues take a step forward and show that this technology can also be used to model pre-symptomatic stages of human diseases. In particular, they used it to generate neurons from Sanfilippo C patients and found functional alterations in how these neurons interacted with one another, before the neurons themselves had any obvious alteration.

The primary photochemical event in vision, isomerization of the 11-cis chromophore in rhodopsin to the all-trans form, is one of the fastest natural photochemical processes known, taking less than a millionth of a millionth of a second. The molecular details of reactions of such rapidity are a stiff challenge to experimenters, but Polli et al. now report the characterization of the reaction using ultrafast optical spectroscopy with sub-20-femtosecond time resolution and spectral coverage from the visible to the near infrared. The data confirm that rhodopsin's extreme reactivity results from a molecular funnel mechanism that involves a 'conical intersection' between the potential energy surfaces of the starting and product molecules. Chemical reactions are usually described in terms of the movement of nuclei between the potential energy surfaces of ground and excited electronic states. Crossings known as conical intersections permit efficient transitions between the surfaces. It is shown here that ultrafast optical spectroscopy, with sub-20-fs time resolution and spectral coverage from the visible to the near-infrared, can map the isomerization of rhodopsin with sufficient resolution to shown that a conical intersection is important in this crucial event in vision. Ever since the conversion of the 11-cis retinal chromophore to its all-trans form in rhodopsin was identified as the primary photochemical event in vision1, experimentalists and theoreticians have tried to unravel the molecular details of this process. The high quantum yield of 0.65 (ref. 2), the production of the primary ground-state rhodopsin photoproduct within a mere 200 fs (refs 3–7), and the storage of considerable energy in the first stable bathorhodopsin intermediate8 all suggest an unusually fast and efficient photoactivated one-way reaction9. Rhodopsin's unique reactivity is generally attributed to a conical intersection between the potential energy surfaces of the ground and excited electronic states10,11 enabling the efficient and ultrafast conversion of photon energy into chemical energy12,13,14,15,16. But obtaining direct experimental evidence for the involvement of a conical intersection is challenging: the energy gap between the electronic states of the reacting molecule changes significantly over an ultrashort timescale, which calls for observational methods that combine high temporal resolution with a broad spectral observation window. Here we show that ultrafast optical spectroscopy with sub-20-fs time resolution and spectral coverage from the visible to the near-infrared allows us to follow the dynamics leading to the conical intersection in rhodopsin isomerization. We track coherent wave-packet motion from the photoexcited Franck–Condon region to the photoproduct by monitoring the loss of reactant emission and the subsequent appearance of photoproduct absorption, and find excellent agreement between the experimental observations and molecular dynamics calculations that involve a true electronic state crossing. Taken together, these findings constitute the most compelling evidence to date for the existence and importance of conical intersections in visual photochemistry.

Smart homes play a strategic role for improving life quality of people, enabling to monitor people at home with numerous intelligent devices. Sensors can be installed to provide a continuous assistance without limiting the resident&rsquo, s daily routine, giving her/him greater comfort, well-being and safety. This paper is based on the development of domestic technological solutions to improve the life quality of citizens and monitor the users and the domestic environment, based on features extracted from the collected data. The proposed smart sensing architecture is based on an integrated sensor network to monitor the user and the environment to derive information about the user&rsquo, s behavior and her/his health status. The proposed platform includes biomedical, wearable, and unobtrusive sensors for monitoring user&rsquo, s physiological parameters and home automation sensors to obtain information about her/his environment. The sensor network stores the heterogeneous data both locally and remotely in Cloud, where machine learning algorithms and data mining strategies are used for user behavior identification, classification of user health conditions, classification of the smart home profile, and data analytics to implement services for the community. The proposed solution has been experimentally tested in a pilot study based on the development of both sensors and services for elderly users at home.

Huntington's disease (HD) is a fatal neurodegenerative disease with motor, cognitive and psychiatric impairment. Dysfunctions in HD models have been related to reduced levels of striatal brain-derived neurotrophic factor (BDNF) and imbalance between its receptors TrkB and p75(NTR). Thus, molecules with activity on the BDNF/TrkB/p75 system can have therapeutic potential. 7,8-Dihydroxyflavone (7,8-DHF) was described as a TrkB agonist in several models of neuro-degenerative diseases, however, its TrkB activation profile needs further investigation due to its pleiotropic properties and divergence from BDNF effect. To investigate this, we used in vitro and in vivo models of HD to dissect TrkB activation upon 7,8-DHF treatment. 7,8-DHF treatment in primary cultures showed phosphorylation of TrkBY816 but not TrkBY515 with activation of the PLCγ1 pathway leading to morphological and functional improvements. Chronic administration of 7,8-DHF delayed motor deficits in R6/1 mice and reversed deficits on the Novel Object Recognition Test (NORT) at 17 weeks. Morphological and biochemical analyses revealed improved striatal levels of enkephalin, and prevention of striatal volume loss. We found a TrkBY816 but not TrkBY515 phosphorylation recovery in striatum concordant with in vitro results. Additionally, 7,8-DHF normalized striatal levels of induced and neuronal nitric oxide synthase (iNOS and nNOS, respectively) and ameliorated the imbalance of p75/TrkB. Our results provide new insights into the mechanism of action of 7,8-DHF suggesting that its effect through the TrkB receptor in striatum is via selective phosphorylation of its Y816 residue and activation of PLCγ1 pathway, but pleiotropic effects of the drug also contribute to its therapeutic potential.

Fumaric and maleic amides are the photoactive units of an important and widely investigated class of photocontrollable rotaxanes as they trigger ring shuttling via a cis−trans photoisomerization. Here, ultrafast decay and photoinduced isomerization in isolated fumaramide and solvated nitrogen-substituted fumaramides (that are employed as threads in those rotaxanes) have been investigated by means of CASPT2//CASSCF computational and time-resolved spectroscopic techniques, respectively. A complex multistate network of competitive deactivation channels, involving both internal conversion and intersystem crossing (ISC) processes, has been detected and characterized that accounts for the picosecond decay and photochemical/photophysical properties observed in the singlet as well as triplet (photosensitized) photochemistry of fumaramides threads. Interestingly, singlet photochemistry appears to follow a non-Kasha rule model, where nonequilibrium dynamical factors control the outcome of the photochemical process: accessible high energy portions of extended crossing seams turn out to drive the deactivation process and ground-state recovery. Concurrently, extended singlet/triplet degenerate regions of twisted molecular structures with significant spin−orbit-coupling values account for ultrafast (picosecond time scale) ISC processes that lead to higher photoisomerization efficiencies. This model discloses the principles behind the intrinsic photochemical reactivity of fumaramide and its control.

Neuronal networks can spontaneously exhibit periodic bursts of collective activity. High-resolution calcium imaging and computer modelling of in vitro cultures now reveal that this behaviour is a consequence of noise focusing—an implosive concentration of spontaneous activity due to the interplay between network topology and intrinsic neuronal dynamics.

We introduce a novel random field Ising model, grounded on experimental observations, to assess the importance of metric correlations in cortical circuits in vitro. Metric correlations arise from both the finite axonal length and the heterogeneity in the spatial arrangement of neurons. The experiments consider the response of neuronal cultures to an external electric stimulation for a gradually weaker connectivity strength between neurons, and in cultures with different spatial configurations. The model can be analytically solved in the metric-free, mean-field scenario. The presence of metric correlations precipitates a strong deviation from the mean field. Null models of the same networks that preserve the distribution of connections recover the mean field. Our results show that metric-inherited correlations in spatial networks dominate the connectivity blueprint, mask the actual distribution of connections, and may emerge as the asset that shapes network dynamics.

This book illustrates the thrust of the scientific community to use machine learning concepts for tackling a complex problem: given time series of neuronal spontaneous activity, which is the underlying connectivity between the neurons in the network? The contributing authors also develop tools for the advancement of neuroscience through machine learning techniques, with a focus on the major open problems in neuroscience. While the techniques have been developed for a specific application, they address the more general problem of network reconstruction from observational time series, a problem of interest in a wide variety of domains, including econometrics, epidemiology, and climatology, to cite only a few. The book is designed for the mathematics, physics and computer science communities that carry out research in neuroscience problems. The content is also suitable for the machine learning community because it exemplifies how to approach the same problem from different perspectives.

We introduce a coarse-grained stochastic model for the spontaneous activity of neuronal cultures to explain the phenomenon of noise focusing, which entails localization of the noise activity in excitable networks with metric correlations. The system is modeled as a continuum excitable medium with a state-dependent spatial coupling that accounts for the dynamics of synaptic connections. The most salient feature is the emergence at the mesoscale of a vector field $\mathbf{V}(\mathbf{r})$, which acts as an advective carrier of the noise. This entails an explicit symmetry breaking of isotropy and homogeneity that stems from the amplification of the quenched fluctuations of the network by the activity avalanches, concomitant with the excitable dynamics. We discuss the microscopic interpretation of $\mathbf{V}(\mathbf{r})$ and propose an explicit construction of it. The coarse-grained model shows excellent agreement with simulations at the network level. The generic nature of the observed phenomena is discussed.

Purpose To investigate the accuracy and robustness of commercial algorithms for Deformable Image Registration (DIR) to propagate regions of interest (ROIs), against image quality typical of Cone Beam CT (CBCT) and Megavoltage Cone-beam CT (MVCT) images, using computational phantoms based on real head and neck (HN) patient images. Methods Eleven institutions joined the study with five commercial DIR solutions. Three real patient data-sets from different on board imaging devices (OBI by Varian, XVI by Elekta, and MVCT by Tomotherapy) were used with different image quality. Two specific Deformation Vector Fields (DVF) with realistic level of deformations, were generated with a dedicated software and then applied to each data-set. The accuracy of the algorithms was assessed by comparing the DIR-mapped ROIs from each center with those of reference, using the Dice Similarity Coefficient (DSC) and Mean Distance to Conformity (MDC) metrics. Prognostic factors of DIR performances were carried out. Results Based on more than 700 DIR-mapped ROIs the 4-way ANOVA analysis states that DIR algorithm, image quality and organ type are significant predictors of DIR performances. A slightly dependence from applied DVF is detected (p = 0.035) with a post hoc test. Putting together all CI data of all institutions for the first DVF, the mean CI was 0.88 ± 0.05 (1 SD), and the mean MDC is 0.38 ± 0.96 (1 SD); for the second DVF, the mean CI was 0.86 ± 0.11 (1 SD), and the mean MDC is 0.43 ± 1.08 (1 SD). The thyroid, located near the FOV border, was the underperformer organ with large errors (unacceptable). All other organs were mapped with an acceptable accuracy (at voxel size level). One imaging system performs statistically better than the others, and one algorithm produced unsatisfactory results respect all the others. Conclusions This work studies the impact of CBCT and MVCT image quality on DIR performance in a ground truth provided scenario. Clinical issues like Adaptive Radiation Therapy (ART) need accurate and robust DIR software. The reported result highlight that in HN district the image quality and the implemented algorithm are strictly related to DIR performances.