Processing of visual hapaxes in picture naming task: An event-related potential study.

Object recognition and visual categorization are typically swift and seemingly effortless tasks that involve numerous underlying processes. In our investigation, we utilized a picture naming task to explore the processing of rarely encountered objects (visual hapaxes) in comparison to common objects. Our aim was to determine the stage at which these rare objects are classified as unnamable. Contrary to our expectations and in contrast to some prior research on event-related potentials (ERPs) with novel and atypical objects, no differences between conditions were observed in the late time windows corresponding to the P300 or N400 components. However, distinctive patterns between hapaxes and common objects surfaced in three early time windows, corresponding to the posterior N1 and P2 waves, as well as a widespread N2 wave. According to the ERP data, the differentiation between hapaxes and common objects occurs within the first 380 ms of the processing line, involving only limited and indirect top-down influence.

The impact of primary headaches on disability outcomes: a literature review and meta-analysis to inform future iterations of the Global Burden of Disease study.

BACKGROUND: The burden and disability associated with headaches are conceptualized and measured differently at patients' and populations' levels. At the patients' level, through patient-reported outcome measures (PROMs); at population level, through disability weights (DW) and years lived with a disability (YLDs) developed by the Global Burden of Disease Study (GBD). DW are 0-1 coefficients that address health loss and have been defined through lay descriptions. With this literature review, we aimed to provide a comprehensive analysis of disability in headache disorders, and to present a coefficient referring to patients' disability which might inform future GBD definitions of DW for headache disorders. METHODS: We searched SCOPUS and PubMed for papers published between 2015 and 2023 addressing disability in headache disorders. The selected manuscript included a reference to headache frequency and at least one PROM. A meta-analytic approach was carried out to address relevant differences for the most commonly used PROMs (by headache type, tertiles of medication intake, tertiles of females' percentage in the sample, and age). We developed a 0-1 coefficient based on the MIDAS, on the HIT-6, and on MIDAS + HIT-6 which was intended to promote future DW iterations by the GBD consortium. RESULTS: A total of 366 studies, 596 sub-samples, and more than 133,000 single patients were available, mostly referred to cases with migraine. Almost all PROMs showed the ability to differentiate disability severity across conditions and tertiles of medication intake. The indexes we developed can be used to inform future iterations of DW, in particular considering their ability to differentiate across age and tertiles of medication intake. CONCLUSIONS: Our review provides reference values for the most commonly used PROMS and a data-driven coefficient whose main added value is its ability to differentiate across tertiles of age and medication intake which underlie on one side the increased burden due to aging (it is likely connected to the increased impact of common comorbidities), and by the other side the increased burden due to medication consumption, which can be considered as a proxy for headache severity. Both elements should be considered when describing disability of headache disorders at population levels.

Machine learning approach for Migraine Aura Complexity Score prediction based on magnetic resonance imaging data.

BACKGROUND: Previous studies have developed the Migraine Aura Complexity Score (MACS) system. MACS shows great potential in studying the complexity of migraine with aura (MwA) pathophysiology especially when implemented in neuroimaging studies. The use of sophisticated machine learning (ML) algorithms, together with deep profiling of MwA, could bring new knowledge in this field. We aimed to test several ML algorithms to study the potential of structural cortical features for predicting the MACS and therefore gain a better insight into MwA pathophysiology. METHODS: The data set used in this research consists of 340 MRI features collected from 40 MwA patients. Average MACS score was obtained for each subject. Feature selection for ML models was performed using several approaches, including a correlation test and a wrapper feature selection methodology. Regression was performed with the Support Vector Machine (SVM), Linear Regression, and Radial Basis Function network. RESULTS: SVM achieved a 0.89 coefficient of determination score with a wrapper feature selection. The results suggest a set of cortical features, located mostly in the parietal and temporal lobes, that show changes in MwA patients depending on aura complexity. CONCLUSIONS: The SVM algorithm demonstrated the best potential in average MACS prediction when using a wrapper feature selection methodology. The proposed method achieved promising results in determining MwA complexity, which can provide a basis for future MwA studies and the development of MwA diagnosis and treatment.