A linear approach for sparse coding by a two-layer neural network

Many approaches to transform classification problems from non-linear to linear by feature transformation have been recently presented in the literature. These notably include sparse coding methods and deep neural networks. However, many of these approaches require the repeated application of a learning process upon the presentation of unseen data input vectors, or else involve the use of large numbers of parameters and hyper-parameters, which must be chosen through cross-validation, thus increasing running time dramatically. In this paper, we propose and experimentally investigate a new approach for the purpose of overcoming limitations of both kinds. The proposed approach makes use of a linear auto-associative network (called SCNN) with just one hidden layer. The combination of this architecture with a specific error function to be minimized enables one to learn a linear encoder computing a sparse code which turns out to be as similar as possible to the sparse coding that one obtains by re-training the neural network. Importantly, the linearity of SCNN and the choice of the error function allow one to achieve reduced running time in the learning phase. The proposed architecture is evaluated on the basis of two standard machine learning tasks. Its performances are compared with those of recently proposed non-linear auto-associative neural networks. The overall results suggest that linear encoders can be profitably used to obtain sparse data representations in the context of machine learning problems, provided that an appropriate error function is used during the learning phase

Optimal Social and Vaccination Control in the SVIR Epidemic Model

In this paper we introduce an approach to the management of infectious disease diffusion through the formulation of a controlled compartmental SVIR (Susceptible-Vaccinated-Infected-Recovered) model. We consider a cost functional encompassing three distinct yet interconnected dimensions: the social cost, the disease cost, and the vaccination cost. The proposed model addresses the pressing need for optimized strategies in disease containment, incorporating both social control measures and vaccination campaigns. Through the utilization of advanced control theory, we identify optimal control strategies that mitigate disease proliferation while considering the inherent trade-offs among social interventions and vaccination efforts. Finally, numerical implementation of the optimally controlled system through the Forward-Backward Sweep algorithm is presented.Comment: 22 pages, 8 figure

Neural Networks with Non-Uniform Embedding and Explicit Validation Phase to Assess Granger Causality

A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments performed will show that the method presented in this work can detect the correct dynamical information flows occurring in a system of time series. Additionally we adopt a non-uniform embedding framework according to which only the past states that actually help the prediction are entered into the model, improving the prediction and avoiding the risk of overfitting. This method also leads to a further improvement with respect to traditional Granger causality approaches when redundant variables (i.e. variables sharing the same information about the future of the system) are involved. Neural networks are also able to recognize dynamics in data sets completely different from the ones used during the training phase

Pain perception and migraine

Background: It is well-known that both inter-and intra-individual differences exist in the perception of pain; this is especially true in migraine, an elusive pain disorder of the head. Although electrophysiology and neuroimaging techniques have greatly contributed to a better understanding of the mechanisms involved in migraine during recent decades, the exact characteristics of pain threshold and pain intensity perception remain to be determined, and continue to be a matter of debate.Objective: The aim of this review is to provide a comprehensive overview of clinical, electrophysiological, and functional neuroimaging studies investigating changes during various phases of the so-called "migraine cycle" and in different migraine phenotypes, using pain threshold and pain intensity perception assessments.Methods: A systematic search for qualitative studies was conducted using search terms "migraine," "pain," "headache," "temporal summation," "quantitative sensory testing," and "threshold," alone and in combination (subject headings and keywords). The literature search was updated using the additional keywords "pain intensity," and "neuroimaging"to identify full-text papers written in English and published in peer-reviewed journals, using PubMed and Google Scholar databases. In addition, we manually searched the reference lists of all research articles and review articles.Conclusion: Consistent data indicate that pain threshold is lower during the ictal phase than during the interictal phase of migraine or healthy controls in response to pressure, cold and heat stimuli. There is evidence for preictal sub-allodynia, whereas interictal results are conflicting due to either reduced or no observed difference in pain threshold. On the other hand, despite methodological limitations, converging observations support the concept that migraine attacks may be characterized by an increased pain intensity perception, which normalizes between episodes. Nevertheless, future studies are required to longitudinally evaluate a large group of patients before and after pharmacological and non-pharmacological interventions to investigate phases of the migraine cycle, clinical parameters of disease severity and chronic medication usage

Acoustic characteristics of Italian Parkinsonian speech: a study on early-stage patients

Parkinson’s Disease (PD) is a neurological illness which also has effects on speech production, resulting in segmental and suprasegmental abnormalities. The aim of the current study is to test the validity of two acoustic parameters - %V and VtoV - for the detection of rhythmical variation in early-stage PD speech, in comparison to healthy speech. 40 Italian native speakers were enrolled in the research, 20 early-stage PD subjects and 20 neurologically healthy and matched controls, and a corpus of read speech was collected. The results of voice analysis confirmed an alteration of vocalic duration and %V in PD productions. In particular, %V could be a reliable cue for PD speech characterization, even at the very early onset of the disease

P019. Transcutaneous supraorbital neurostimulation in “de novo” patients with migraine without aura: the first Italian experience

Transcutaneous supraorbital neurostimulation (tSNS) has been recently found superior to sham stimulation for episodic migraine prevention in a randomized trial. We evaluated both the safety and efficacy of a brief period of tSNS in a group of patients with migraine without aura (MwoA)

Impulse Control Behaviors in Parkinson's Disease: Drugs or Disease? Contribution From Imaging Studies

Impulse control behaviors (ICB) are recognized as non-motor complications of dopaminergic medications in patients with Parkinson's disease (PD). Compelling evidence suggests that ICB are not merely due to the PD-related pathology itself. Several risk factors have been identified, either demographic, clinical, genetic or neuropsychological. Neuroimaging studies have yielded controversial results regarding ICB correlates in PD and still it is not clear whether they can be triggered by the PD biology or the dopaminergic treatment stimulation. We provided an overview of the imaging studies that offered the most relevant insights into the debate about the role of drugs and disease in ICB pathophysiology. Understanding neural correlates and potential predisposing factors of these severe neuropsychiatric symptoms will be crucial to guide clinical practice and to foster preventive strategies

Speech Rhythm Variation in Early-Stage Parkinson's Disease: A Study on Different Speaking Tasks

Patients with Parkinson's disease (PD) usually reveal speech disorders and, among other symptoms, the alteration of speech rhythm. The purpose of this study is twofold: (1) to test the validity of two acoustic parameters—%V, vowel percentage and VtoV, the mean interval between two consecutive vowel onset points—for the identification of rhythm variation in early-stage PD speech and (2) to analyze the effect of PD on speech rhythm in two different speaking tasks: reading passage and monolog. A group of 20 patients with early-stage PD was involved in this study and compared with 20 age- and sex-matched healthy controls (HCs). The results of the acoustic analysis confirmed that %V is a useful cue for early-stage PD speech characterization, having significantly higher values in the production of patients with PD than the values in HC speech. A simple speaking task, such as the reading task, was found to be more effective than spontaneous speech in the detection of rhythmic variations

Non-motor impairments affect walking kinematics in Parkinson disease patients: A cross-sectional study

Background: In patients with Parkinson disease (PD), severe postural and gait impairments are rarely observed in early stage of disease and non-motor symptoms (NMS) are often overlooked. Objective: This observational study aimed to characterize the impact of non-motor impairments on walking kinematics in early stages PD patients, and to assess the differences of gait parameters and NMS between PD patients with and without mild cognitive impairment (MCI). Methods: Twenty-six patients with Modified Hoehn and Yahr Scale score≤2 were evaluated for NMS using Kings Parkinson's Pain Scale, Parkinson Fatigue Severity scale, Parkinson Anxiety Scale, Beck Depression Inventory and Epworth Sleepiness Scale, kinematic parameters through an inertial sensor and cognitive performance by a comprehensive neuropsychological battery. Results: Fatigue had a moderate negative correlation with step cadence, and a moderate to strong positive correlation with gait duration, Timed Up and Go (TUG) and TUG Dual Task (p < 0.01). Pain showed positive moderate correlation with gait duration (p < 0.01). Twelve patients resulted affected by MCI and reported significantly worse scores in gait duration, pain and fatigue (p < 0.05). According to cognitive z scores, PD-MCI group showed a moderate negative correlation between visuospatial abilities and fatigue (p < 0.05). Conclusions: NMS significantly affect walking kinematics whereas a limited role of cognitive status on motor performance occur in the early PD stages

Cognitive dysfunctions and psychological symptoms in migraine without aura: a cross-sectional study

BACKGROUND: The occurrence of cognitive dysfunctions and psychological symptoms, as well as their mutual relationships, in migraine patients are still debated. The aim of the study was to characterize the cognitive profile and psychological symptoms (i.e. depression, anxiety and apathy) in drug-naïve migraine without aura (MwoA) patients. METHODS: Seventy-two consecutive MwoA patients, referred to the Italian University Headache Clinic and 72 healthy subjects (HCs) were enrolled. Patients, during an attack-free period, and HCs completed Montreal Cognitive Assessment (MoCA), Beck Depression Inventory-II (BDI-II), Self-version of Apathy Evaluation Scale (AES-S) and State and Trait Anxiety Inventory (STAI-Y-1 and 2). Clinical parameters of disease severity (i.e. disease duration, migraine attacks per month, mean pain intensity during migraine attacks, migraine disability and impact on daily life) were recorded. RESULTS: Although performance of MwoA patients on MoCA was above Italian cut-off threshold (<15.5) suggesting presence of cognitive impairment, MwoA patients achieved significantly lower scores than HCs on total MoCA scale (22.3 ± 2.7 versus 25.4 ± 2.3) and on its attention (4.9 ± 1.1 versus 5.6 ± 0.7), memory (1.8 ± 1.4 versus 3.1 ± 1.3), visuospatial (3.2 ± 0.9 versus 3.6 ± 0.6) and executive subscales (2.6 ± 1.1 versus 3.1 ± 0.8). In addition, we observed significant correlations between MoCA executive domain subscore and the attack-related disability score (MIDAS). As for behavioral profile, the percentage of depressive symptoms (4.2 %), high state and trait anxiety (13.9 and 9.7 %, respectively), and apathy (11.1 %) in MwoA patients were similar to that of HCs. No significant associations of behavioural symptoms with cognitive performance and clinical parameters were found. CONCLUSIONS: Drug-naïve MwoA patients are characterized by subtle cognitive dysfunctions and low percentage of behavioural symptoms. The results support the importance of searching for subclinical cognitive disturbances in patients with MwoA, who deserve to be followed-up to verify whether they develop clinically relevant disorders over time