Neural Substrates of Chronic Pain in the Thalamocortical Circuit

Chronic pain (CP), a pathological condition with a large repertory of signs and symptoms, has no recognizable neural functional common hallmark shared by its diverse expressions. The aim of the present research was to identify potential dynamic markers shared in CP models, by using simultaneous electrophysiological extracellular recordings from the rat ventrobasal thalamus and the primary somatosensory cortex. We have been able to extract a neural signature attributable solely to CP, independent from of the originating conditions. This study showed disrupted functional connectivity and increased redundancy in firing patterns in CP models versus controls, and interpreted these signs as a neural signature of CP. In a clinical perspective, we envisage CP as disconnection syndrome and hypothesize potential novel therapeutic appraisal

alternating dynamics of segregation and integration in human eeg functional networks during working memory task

Abstract Brain functional networks show high variability in short time windows but mechanisms governing these transient dynamics remain unknown. In this work, we studied the temporal evolution of functional brain networks involved in a working memory (WM) task while recording high-density electroencephalography (EEG) in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation faded after the prevailing the functional integration. Notably, wrong trials were associated with different or disrupted sequences of the segregation-integration profiles and measures of network centrality and modularity were able to identify crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a WM task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations

From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks

Background: Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model. Methods: To overcome these limitations, we propose a new procedure for computing global feature importance that involves aggregating local counterfactual explanations. This approach is specifically tailored to fMRI signals and is based on the hypothesis that instances close to the decision boundary and their counterfactuals mainly differ in the features identified as most important for the downstream classification task. We refer to this proposed feature importance measure as Boundary Crossing Solo Ratio (BoCSoR), since it quantifies the frequency with which a change in each feature in isolation leads to a change in classification outcome, i.e., the crossing of the model's decision boundary. Results and conclusions: Experimental results on synthetic data and real publicly available fMRI data from the Human Connect project show that the proposed BoCSoR measure is more robust to feature correlation and less computationally expensive than state-of-the-art methods. Additionally, it is equally effective in providing an explanation for the behavior of any AI model for brain signals. These properties are crucial for medical decision support systems, where many different features are often extracted from the same physiological measures and a gold standard is absent. Consequently, computing feature importance may become computationally expensive, and there may be a high probability of mutual correlation among features, leading to unreliable results from state-of-the-art XAI methods

Nonlinear machine learning pattern recognition and bacteria-metabolite multilayer network analysis of perturbed gastric microbiome

The stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of drugs such as proton pump inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in early phase and rely principally on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques with nonlinear ones to unveil hidden patterns that remain unseen by linear embedding. Then, we prove the advantages to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of bacterial network re-organizations which emerge from perturbations induced by a medical treatment (PPIs) or an infectious state (H. pylori). Finally, we show how to build bacteria-metabolite multilayer networks that can deepen our understanding of the metabolite pathways significantly associated to the perturbed microbial communities

Predicting Spike Occurrence and Neuronal Responsiveness from LFPs in Primary Somatosensory Cortex

Local Field Potentials (LFPs) integrate multiple neuronal events like synaptic inputs and intracellular potentials. LFP spatiotemporal features are particularly relevant in view of their applications both in research (e.g. for understanding brain rhythms, inter-areal neural communication and neronal coding) and in the clinics (e.g. for improving invasive Brain-Machine Interface devices). However the relation between LFPs and spikes is complex and not fully understood. As spikes represent the fundamental currency of neuronal communication this gap in knowledge strongly limits our comprehension of neuronal phenomena underlying LFPs. We investigated the LFP-spike relation during tactile stimulation in primary somatosensory (S-I) cortex in the rat. First we quantified how reliably LFPs and spikes code for a stimulus occurrence. Then we used the information obtained from our analyses to design a predictive model for spike occurrence based on LFP inputs. The model was endowed with a flexible meta-structure whose exact form, both in parameters and structure, was estimated by using a multi-objective optimization strategy. Our method provided a set of nonlinear simple equations that maximized the match between models and true neurons in terms of spike timings and Peri Stimulus Time Histograms. We found that both LFPs and spikes can code for stimulus occurrence with millisecond precision, showing, however, high variability. Spike patterns were predicted significantly above chance for 75% of the neurons analysed. Crucially, the level of prediction accuracy depended on the reliability in coding for the stimulus occurrence. The best predictions were obtained when both spikes and LFPs were highly responsive to the stimuli. Spike reliability is known to depend on neuron intrinsic properties (i.e. on channel noise) and on spontaneous local network fluctuations. Our results suggest that the latter, measured through the LFP response variability, play a dominant role

The HECT-domain ubiquitin ligase Huwe1 controls neural differentiation and proliferation by destabilizing the N-Myc oncoprotein

Development of the nervous system requires that timely withdrawal from the cell cycle be coupled with initiation of differentiation. Ubiquitin-mediated degradation of the N-Myc oncoprotein in neural stem/progenitor cells is thought to trigger the arrest of proliferation and begin differentiation. Here we report that the HECT-domain ubiquitin ligase Huwe1 ubiquitinates the N-Myc oncoprotein through Lys 48-mediated linkages and targets it for destruction by the proteasome. This process is physiologically implemented by embryonic stem (ES) cells differentiating along the neuronal lineage and in the mouse brain during development. Genetic and RNA interference-mediated inactivation of the Huwe1 gene impedes N-Myc degradation, prevents exit from the cell cycle by opposing the expression of Cdk inhibitors and blocks differentiation through persistent inhibition of early and late markers of neuronal differentiation. Silencing of N-myc in cells lacking Huwe1 restores neural differentiation of ES cells and rescues cell-cycle exit and differentiation of the mouse cortex, demonstrating that Huwe1 restrains proliferation and enables neuronal differentiation by mediating the degradation of N-Myc. These findings indicate that Huwe1 links destruction of N-Myc to the quiescent state that complements differentiation in the neural tissue

7. Le reazioni dei neurofisiologi

1. Prime perplessità “neurofisiologiche” All’indomani della pubblicazione dei risultati sperimentali del gruppo di Haynes, tra le voci dissonanti nei confronti delle (presunte) certezze conseguite in quella ricerca non hanno tardato a farsi sentire quelle dei neurofisiologi, molti dei quali sono ben lontani da posizioni deterministiche. Nel panorama italiano, chi ha mostrato circospezione per non dire scetticismo sulle risultanze del gruppo tedesco è stato Filippo Tempia dell’Università di To..

10. Storia e contesto

1. Il “peso” della storia pregressa Una strategia adottata da Bode et al. nel 2014 per prendere le distanze dal cognitivismo consisteva nel ribadire l’impossibilità di correlare i risultati conseguiti negli esperimenti cruciali sulle “determinanti inconsce” a un modello cognitivistico di decision-making. L’idea fondamentale alla base di questa presunzione era che il comportamento decisionale è libero, spontaneo e casuale, e che ogni decisione può venir presa in maniera indipendente da qualsia..

3. La decodifica delle intenzioni

1. Decifrare gli stati mentali. La percezione visiva Nel proseguire la ricerca avviata da Libet, in una prima fase delle loro indagini gli scienziati avevano privilegiato lo studio sui correlati neurali delle intenzioni che si concretavano in un’azione. Presto però le loro mire si fecero più ardite. Considerando che la vita mentale è fatta (tra le altre cose) di eventi che possono essere rinviati, rimandati e dilazionati nel tempo, non di rado accade che si anticipino o ritardardino atti seco..

Epilogo

In più luoghi delle proprie pubblicazioni complesse e di non sempre facile lettura, Friston ha riconosciuto che la sua teoria vanta una «lunga storia» (Friston et al., 2006, p. 85) e costituisce una riformulazione in chiave moderna della dottrina helmholtziana della percezione visiva relativamente al problema dell’inferenza e dell’apprendimento percettivi. Le ricerche condotte sul sistema visivo hanno consentito di delineare l’organizzazione dell’architettura gerarchica del cervello, dove le ..