Age of Insomnia Onset Correlates with a Reversal of Default Mode Network and Supplementary Motor Cortex Connectivity

Insomnia might occur as result of increased cognitive and physiological arousal caused by acute or long acting stressors and associated cognitive rumination. This might lead to alterations in brain connectivity patterns as those captured by functional connectivity fMRI analysis, leading to potential insight about primary insomnia (PI) pathophysiology as well as the impact of long-term exposure to sleep deprivation. We investigated changes of voxel-wise connectivity patterns in a sample of 17 drug-naïve PI patients and 17 age-gender matched healthy controls, as well as the relationship between brain connectivity and age of onset, illness duration, and severity. Results showed a significant increase in resting-state functional connectivity of the bilateral visual cortex in PI patients, associated with decreased connectivity between the visual cortex and bilateral temporal pole. Regression with clinical scores originally unveiled a pattern of increased local connectivity as measured by intrinsic connectivity contrast (ICC), specifically resembling the default mode network (DMN). Additionally, age of onset was found to be correlated with the connectivity of supplementary motor area (SMA), and the strength of DMN←→SMA connectivity was significantly correlated with both age of onset (R2 = 41%) and disease duration (R2 = 21%). Chronic sleep deprivation, but most importantly early insomnia onset, seems to have a significant disruptive effect over the physiological negative correlation between DMN and SMA, a well-known fMRI marker of attention performance in humans. This suggests the need for more in-depth investigations on the prevention and treatment of connectivity changes and associated cognitive and psychological deficits in PI patients

Peculiarities of Functional Connectivity—including Cross-Modal Patterns—in Professional Karate Athletes: Correlations with Cognitive and Motor Performances

Professional karate is a sport activity requiring both physical and psychological skills that have been associated with a better "global neural efficacy." By means of resting state functional magnetic resonance imaging (rs-fMRI), we investigated the neural correlates of cognitive and kinematic abilities in a group of 14 professional karateka and 14 heathy matched controls. All subjects underwent an extensive cognitive test battery for the identification of individual multidimensional cognitive profile and rs-fMRI scans investigating functional connectivity (FC). Moreover, kinematic performances in athletes were quantified by the Ergo-Mak, an integrated system developed for measuring motor reactivity, strength, and power of athletic gestures. Karateka performed significantly better than controls in the visual search task, an ability linked with increased positive correlations in FC between the right superior parietal lobe and bilateral occipital poles. Kinematic performances of athletic feats were sustained by increased positive correlations between subcortical (cerebellum and left thalamus) and cortical (inferior frontal cortex, superior parietal cortex, superior temporal cortex) regions. An unexpected FC increase between auditory and motor-related areas emerged in karateka, possibly reflecting a cross-modal coupling due to the continuous exposure to either internal or external auditory cues, positing this sensory channel as a possible target for novel training strategies. Results represent a further step in defining brain correlates of "neural efficiency" in these athletes, whose brain can be considered a model of continuous plastic train-related adaptation

Personalised, image-guided, noninvasive brain stimulation in gliomas : Rationale, challenges and opportunities

Malignant brain tumours are among the most aggressive human cancers, and despite intensive efforts made over the last decades, patients' survival has scarcely improved. Recently, high-grade gliomas (HGG) have been found to be electrically integrated with healthy brain tissue, a communication that facilitates tumour mitosis and invasion. This link to neuronal activity has provided new insights into HGG pathophysiology and opened prospects for therapeutic interventions based on electrical modulation of neural and synaptic activity in the proximity of tumour cells, which could potentially slow tumour growth. Noninvasive brain stimulation (NiBS), a group of techniques used in research and clinical settings to safely modulate brain activity and plasticity via electromagnetic or electrical stimulation, represents an appealing class of interventions to characterise and target the electrical properties of tumour-neuron interactions. Beyond neuronal activity, NiBS may also modulate function of a range of substrates and dynamics that locally interacts with HGG (e.g., vascular architecture, perfusion and blood-brain barrier permeability). Here we discuss emerging applications of NiBS in patients with brain tumours, covering potential mechanisms of action at both cellular, regional, network and whole-brain levels, also offering a conceptual roadmap for future research to prolong survival or promote wellbeing via personalised NiBS interventions

A Novel tDCS Sham Approach Based on Model-Driven Controlled Shunting

Abstract Background Transcranial direct current stimulation (tDCS), a non-invasive brain stimulation technique able to transiently modulate brain activity, is surging as one of the most promising therapeutic solutions in many neurological and psychiatric disorders. However, profound limitations exist in current placebo (sham) protocols that limit single- and double-blinding, especially in non-naive subjects. Objective /hypothesis: To ensure better blinding and strengthen reliability of tDCS studies and trials, we tested a new optimization algorithm aimed at creating an "active" sham tDCS condition (ActiSham hereafter) capable of inducing the same scalp sensations perceived during real stimulation, while preventing currents from reaching the cortex and cause changes in brain excitability. Methods A novel model-based multielectrode technique —optimizing the location and currents of a set of small electrodes placed on the scalp— was used to control the relative amount of current delivered transcranially in real and placebo multichannel tDCS conditions. The presence, intensity and localization of scalp sensations during tDCS was evaluated by means of a specifically designed questionnaire administered to the participants. We compared blinding ratings by directly addressing subjects' ability to discriminate across conditions for both traditional (Bifocal-tDCS and -Sham, using sponge electrodes) and our novel multifocal approach (both real Multifocal-tDCS and ActiSham). Changes in corticospinal excitability were monitored based on Motor Evoked Potentials (MEPs) recorded via concurrent Transcranial Magnetic Stimulation (TMS) and electromyography (EMG). Results Subjects perceived Multifocal-tDCS and ActiSham similarly in terms of both scalp sensations and their localization on the scalp, whereas traditional Bifocal stimulation was rated as more painful and annoying compared to its Sham counterpart. Additionally, differences in scalp localization were reported for active/sham Bifocal-tDCS. As for MEPs amplitude, a main effect of stimulation was found when comparing Bifocal-Sham and ActiSham (F(1,13)= 6.67, p=.023), with higher MEPs amplitudes after the application of Bifocal-Sham. Conclusions Compared to traditional Bifocal-tDCS, ActiSham offers better participants' blinding by inducing very similar scalp sensations to those of real Multifocal tDCS both in terms of intensity and localization, while not affecting corticospinal excitability

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

EVALITA Evaluation of NLP and Speech Tools for Italian - December 17th, 2020

Welcome to EVALITA 2020! EVALITA is the evaluation campaign of Natural Language Processing and Speech Tools for Italian. EVALITA is an initiative of the Italian Association for Computational Linguistics (AILC, http://www.ai-lc.it) and it is endorsed by the Italian Association for Artificial Intelligence (AIxIA, http://www.aixia.it) and the Italian Association for Speech Sciences (AISV, http://www.aisv.it)

Le opere latine di Dante tra annotazione linguistica e web semantico

This paper describes the development of UDante, a linguistic resource that collects Dante Alighieri’s Latin works annotated linguistically at different levels. The texts are tokenized, splitted into sentences, lemmatized, tagged with parts of speech and enriched with a layer of syntactic annotation based on the Universal Dependencies style. Furthermore, the procedure for including UDante into the LiLa Knowledge Base is presented: this Knowledge Base collects and connects lexical and textual resources according to the Linked Data paradigm, making them interoperable. The potential of this connection and of the creation of interoperable resources for enhancing the studies on Dante’s texts are shown through examples of queries on the Knowledge Base: in particular, cases of lexical and syntactic analysis are described

EVALITA 2023: Overview of the 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian

EVALITA provides a shared framework for evaluating and comparing different Nautural Language Processing (NLP) and speech systems across various tasks suggested and organized by the Italian research community. These tasks represent scientific challenges and allow testing of methods, resources, and systems on shared benchmarks related to linguistic open issues and real-world applications, including considering multilingual and/or multi-modal perspectives. The EVALITA 2023 edition consisted of 13 different tasks grouped into four research areas: Affect, Authorship Analysis, Computational Ethics, and New Challenges in Long-standing Tasks. The participation saw 42 groups from 12 different countries, indicating an increasing international interest, partly due to the proposal of multilingual tasks. The final workshop showcases the results obtained and highlights the growing interest in using deep learning techniques based on Large Language Models as a new trend. Overall, EVALITA serves as a valuable platform for Italian and international researchers to explore NLP-related challenges, develop solutions, and foster discussions within the community

EVALITA 2023: Overview of the 8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian

EVALITA provides a shared framework for evaluating and comparing different Nautural Language Processing (NLP) and speech systems across various tasks suggested and organized by the Italian research community. These tasks represent scientific challenges and allow testing of methods, resources, and systems on shared benchmarks related to linguistic open issues and real-world applications, including considering multilingual and/or multi-modal perspectives. The EVALITA 2023 edition consisted of 13 different tasks grouped into four research areas: Affect, Authorship Analysis, Computational Ethics, and New Challenges in Long-standing Tasks. The participation saw 42 groups from 12 different countries, indicating an increasing international interest, partly due to the proposal of multilingual tasks. The final workshop showcases the results obtained and highlights the growing interest in using deep learning techniques based on Large Language Models as a new trend. Overall, EVALITA serves as a valuable platform for Italian and international researchers to explore NLP-related challenges, develop solutions, and foster discussions within the community