Biology subjects

Arcara, G.

Publications and source records attributed to Arcara, G..

14 recordsLinked to original sources

bioRxiv2026

Challenging the right-hemisphere assumption in post-stroke pragmatics: largely comparable impairment profiles across lesion sides

Traditional views assume that pragmatic deficits after stroke, which compromise the interpretation of communicative intentions and non-literal meanings, follow damage to the right hemisphere (RHD), with left-hemisphere damage (LHD) primarily linked to aphasia and structural language impairment. To examine hemispheric contributions to post-stroke pragmatic profiles, we assessed 99 stroke patients (40 LHD, including 14 with aphasia of minimal-to-moderate severity; 59 RHD) and 60 healthy controls with the Assessment of Pragmatic Abilities and Cognitive Substrates (APACS). While stroke patients overall performed worse than controls, LHD and RHD profiles were largely comparable across three converging analyses: (1) permutation tests revealed no hemispheric differences except on the two tasks requiring expressive components (Interview and Figurative Language 2), which in turn lowered the composites (APACS Production and Total); (2) equivalence testing established equivalence for most measures, with only these same tasks and composites remaining inconclusive; and (3) unsupervised clustering did not group patients by lesion side. Theory of Mind was robustly associated with pragmatic performance in both groups, whereas structural language abilities related specifically to LHD performance and general cognition only to RHD. Excluding aphasic LHD patients strengthened the evidence for comparable profiles, indicating that aphasic LHD patients largely drove the residual differences. In conclusion, primary pragmatic impairment, especially in the receptive domain, emerged comparably after LHD and RHD, with the only residual LHD disadvantage limited to tasks demanding open verbal output. These findings challenge the assumption of right-hemispheric specialization for pragmatics, stressing the need for pragmatic assessment in all post-stroke patients.

neuroscience↗

bioRxiv2026

Tuning the brain at 40 Hz: Synergistic effects of combined tACS and auditory steady-state response

Gamma oscillations are altered in several conditions such as schizophrenia, Alzheimers Disease, and Mild Cognitive Impairment. Both 40-Hz transcranial alternating current stimulation (tACS) and 40-Hz auditory steady-state stimulation can entrain gamma oscillations and improve cognitive outcomes, but their combined effects remain unclear. This study tested whether preconditioning gamma activity with tACS enhances the auditory steady-state response (ASSR) to 40-Hz auditory stimulation, reflecting a potential synergistic interaction. In a within-subject, sham-controlled design, EEG was recorded before and after 40-Hz tACS delivered over bilateral sensorimotor areas in 34 healthy participants. 40-Hz auditory stimulation was administered before and after tACS to evaluate potential changes in ASSR. Source-level gamma power was analyzed in temporal and sensorimotor regions using linear mixed-effects models. Compared to sham, real tACS increased ASSR-related gamma activity in the superior temporal gyrus, with no effects elsewhere or between hemispheres. These findings support the use of multimodal gamma entrainment to enhance neural oscillations. To conclude, combining 40-Hz tACS with auditory stimulation enhances gamma activity more than either intervention alone, supporting potential clinical applications for neuropsychiatric conditions with disrupted gamma oscillations.

neuroscience↗

bioRxiv2026

Convolutional Neural Networks and Neuroscience: A Tutorial Introduction for The Rest of Us

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

neuroscience↗

bioRxiv2026

Assessing whole-cortex excitability from electromagnetic brain signals

Cortical excitability, the propensity of neural circuits to respond to internal or external perturbations, is a fundamental property of brain functioning, shaped by local microcircuitry, large-scale networks, and neurochemical architecture. In humans, excitability is inferred from multiple indirect metrics derived from spontaneous and stimulus-driven electromagnetic activity, yet it remains unclear whether these measures reflect a common underlying construct or distinct physiological processes. Here, we systematically compared ten previously validated excitability metrics using whole-head magnetoencephalography (MEG) recorded at rest and during 40 Hz auditory stimulation in a large sample of healthy adults. The measures spanned stimulus-driven synchronization, spectral power, 1/f activity (hereafter defined as aperiodic), signal complexity, long-range temporal correlations, and phase-gamma synchronization. Hierarchical clustering revealed six separable excitability dimensions with limited redundancy, demonstrating that cortical excitability is inherently multidimensional. Aperiodic and alpha-band measures showed the strongest mutual coupling and the furthest spatial correspondence to stimulus-related responses, whereas temporal, complexity, and synchronization-based metrics were largely independent. The similarity between measures varied across cortical regions and functional networks, with maximal cluster separability in integrative and internally driven regions. These regions included orbitofrontal, temporal, anterior cingulate, and default mode areas, as well as visual and somatomotor networks. Finally, excitability dimensions showed distinct relationships with cortical morphology and neurotransmitter receptor density, implicating heterogeneous neurobiological substrates. Together, these findings provide a unified framework for interpreting excitability metrics and highlight the need for multimodal approaches when probing cortical excitability in health and disease.

neuroscience↗

bioRxiv2025

Education Shapes the Link Between EEG Aperiodic Components and Cognitive Aging

Healthy aging brings widespread shifts in aperiodic (non-oscillatory) electroencephalographic (EEG) components, which may underlie physiological changes in cognitive performance. Education, a known protective factor against age-related decline in cognitive performance, has been largely overlooked in studies linking aperiodic EEG components to cognition. This study addresses this gap, hypothesizing that education moderates the interplay between age, aperiodic components, and cognitive performance, as measured by Mini-Mental State Examination (MMSE) scores. We reanalyzed an open-source EEG dataset of 714 healthy individuals aged 18-91 years using Generalized Additive Mixed Models. Aperiodic exponent and offset both declined with age, but higher education levels mitigated these declines. Notably, exponent and offset interacted with age and education in predicting MMSE performance in the bilateral cingulate, left hippocampus, bilateral parietal, right occipital, and left temporal regions. Among older adults, the relationship between the aperiodic components and cognitive performance diverged by education: those with lower education showed worse cognitive outcomes with lower exponents and offsets, whereas higher-educated individuals after 60 years showed a reverse pattern, with lower exponents and offsets predicting better MMSE performance. Our findings suggest that the link between aperiodic components and cognitive aging is not straightforward but depends on moderating factors such as education. These results underscore the importance of accounting for individual differences, like educational background, when exploring age-related changes in EEG aperiodic components and cognition.

neuroscience↗

bioRxiv2024

The influence of the head model on magnetoencephalography-derived functional connectivity fingerprinting

Functional connectivity (FC)-based neural fingerprinting is an approach that promises to identify and/or differentiate subjects within a cohort on the basis of the patterns of statistical dependencies between time series recorded mostly if not always noninvasively, with electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). The message is that brain activity is what differentiates subjects, or what makes a neural fingerprint "unique". In EEG- and MEG-derived FC fingerprinting, the activity recorded at the sensors is usually projected back into cortical sources by means of an inverse model depending on head and brain shapes, sensor locations, and tissue conductivity, and further reduced in dimension to obtain time series of regional activity, used to compute FC. We think that the role of the head model in fingerprinting has been so far dismissed by means of suboptimal or incomplete tests. Here we employed a set of experiments aimed to decouple recorded activity and head model for each subject, and we found that the head model has a strong influence on both identification and differentiation.

neuroscience↗

bioRxiv2023

Functional displacement of cortical neuromagnetic somatosensory responses: enhancing embodiment in the rubber hand illusion

The integration of an artificial limb as part of ones body involves complex neuroplastic changes resulting from various sensory inputs to the brain. While sensory feedback is known to be crucial for embodiment, current evidence points merely to the attenuation of somatosensory processing, while the positive contributions of somatosensory areas to embodiment remain unknown. This study investigated the relationship between embodiment and adaptive neuroplasticity of early-latency somatosensory evoked fields (SEFs) in the primary somatosensory cortex (S1) following the Rubber Hand Illusion (RHI), known to induce short-term artificial limb embodiment. Nineteen healthy adults underwent neuromagnetic recordings during electrical stimulation of the little finger and thumb, before and after the RHI. We found a displacement of early SEF sources. In particular, we observed a correlation between the extent of rubber hand embodiment and specific changes to the m20 component (magnetic equivalent to the N20) in Brodmann Area 3b: a larger displacement and a greater reduction in m20 magnitude predicted the amount of embodiment, highlighting an important functional contribution of this first cortical input. Furthermore, we observed a posteriorly directed m35 displacement towards Area 1, known to be important for visual integration during touch perception (Rosenthal et al., 2023). Our finding that the larger displacement for the m35 did not correlate with the extent of embodiment implies a functional distinction between neuroplastic changes across these two components and areas in their contributions to successful artificial limb embodiment: (i) the earlier neuroplastic changes to m20 may shape the extent of artificial limb ownership, and (ii) the posteriorward shift of the m35 into Area 1 is suggestive of a mechanistic contribution to early visual-tactile integration that initially establishes the embodiment. Taken together, these findings suggest that multiple distinct changes occur during early-latency SEFs and their displacement in S1 last beyond the duration of the illusion and are important for the successful integration of an artificial limb within the body representation.

neuroscience↗

bioRxiv2023

Aperiodic component of EEG power spectrum and cognitive performance in aging: the role of education

Aging is associated with changes in the oscillatory -periodic-brain activity in the alpha band (8-12 Hz), as measured with resting-state EEG (rsEEG); it is characterized by a significantly lower alpha frequency and power. Aging influences the aperiodic component of the power spectrum: at a higher age the slope flattens, which is related with lower cognitive efficiency. It is not known whether education, a cognitive reserve proxy recognized for its modulatory role on cognition, influences such relationship. N=179 healthy participants of the LEMON dataset (Babayan et al., 2019) were grouped based on age and education: young adults with high education and older adults with high and low education. Eyes-closed rsEEG power spectrum was parametrized at the occipital level. Lower IAPF, exponent, and offset in older adults were shown, compared to younger adults. Visual attention and working memory were differently predicted by the aperiodic component across education: in older adults with high education, higher exponent predicted slower processing speed and less working memory capacity, with an opposite trend in those with lower education. Further investigation is needed; the study shows the potential modulatory role of education in the relationship between the aperiodic component of the EEG power spectrum and aging cognition.

neuroscience↗

bioRxiv2023

Education differentiates cognitive performance and resting state fMRI connectivity in healthy aging

ObjectivesIn healthy aging, the way people differently cope with cognitive and neural decline is influenced by the exposure to cognitively enriching life-experiences. Education is one of them, so that in general the higher the education the better the expected cognitive performance in aging. At the neural level, it is not clear yet whether education can differentiate resting state functional connectivity profiles and their cognitive underpinnings. Thus, with this study, we aimed to investigate whether the variable education allowed for a finer description of age-related differences in cognition and resting state FC. MethodsWe analyzed in 197 healthy individuals (137 young adults aged 20-35, and 60 older adults aged 55-80 from the publicly available LEMON database), a pool of cognitive and neural variables, derived from magnetic resonance imaging, in relation to education. Firstly, we assessed age-related differences, by comparing young and older adults. Then, we investigated the possible role of education in outlining such differences, by splitting the group of older adults based on their education. ResultsIn terms of cognitive performance, older adults with higher education and young adults were comparable in language and executive functions. Interestingly, they had a wider vocabulary compared to young adults and older adults with lower education. Concerning functional connectivity, the results showed significant age- and education-related differences within three networks: the Visual-Medial, the Dorsal Attentional and the Default Mode network (DMN). For the DMN, we also found a relationship with memory performance, which strengthen the evidence that this network has a specific role in linking cognitive maintenance and FC at rest in healthy aging. DiscussionOur study revealed that education contributes to differentiate cognitive and neural profiles in healthy older adults. Also, the DMN could be a key network in this context, as it may reflect some compensatory mechanisms relative to memory capacities in older adults with higher education.

neuroscience↗

bioRxiv2022

Validating MEG source imaging of resting state oscillatory patterns with an intracranial EEG atlas

BackgroundMagnetoencephalography (MEG) is a widely used non-invasive tool to estimate brain activity with high temporal resolution. However, due to the ill-posed nature of the MEG source imaging (MSI) problem, the ability of MSI to identify accurately underlying brain sources along the cortical surface is still uncertain and requires validation. MethodWe validated the ability of MSI to estimate the background resting state activity of 45 healthy participants by comparing it to the intracranial EEG (IEEG) atlas (https://mni-open-ieegatlas.research.mcgill.ca/). First, we applied wavelet-based Maximum Entropy on the Mean (wMEM) as an MSI technique. Next, we converted MEG source maps into intracranial space, by applying a forward model to the MEG reconstructed source maps and estimated virtual IEEG (VIEEG) potentials on each IEEG channel location and quantitatively compared those with actual IEEG signals from the atlas for 38 regions of interest in the canonical frequency bands. ResultsThe MEG spectra were more accurately estimated in the lateral regions compared to the medial regions. The regions with higher amplitude in the VIEEG than in the IEEG were more accurately recovered. In the deep regions, MEG estimated amplitudes were largely underestimated and the spectra were poorly recovered. Moreover, the MEG largely overestimated oscillatory peaks in the alpha band, especially in the anterior and deep regions. This is possibly due to higher phase synchronization of alpha oscillations over extended regions, exceeding the spatial sensitivity of IEEG but detected by MEG. Importantly, we found that MEG estimated spectra were more comparable to spectra from the IEEG atlas after the aperiodic components were removed. ConclusionThis study identifies brain regions and frequencies for which MEG source analysis is likely to be reliable, a promising step towards resolving the uncertainty in recovering intracerebral activity from non-invasive MEG studies. HighlightsO_LIValidation of MEG source imaging with intracranial EEE atlas C_LIO_LIAssessment of resting state human brain oscillations from healthy brain C_LIO_LIAdapted source imaging method, wMEM, to localize resting state oscillations C_LIO_LIIdentified brain regions with oscillations accurately estimated by MEG C_LIO_LIMEG estimated spectra dominated by oscillations in the alpha band C_LI

neuroscience↗

bioRxiv2022

Task matters: individual MEG signatures from naturalistic and neurophysiological brain states

The discovery that human brain connectivity data can be used as a "fingerprint" to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have identified the possibility to extract these brain signatures from the temporal rich dynamics of resting-state magnetoencephalography (MEG) recordings. However, to what extent MEG signatures constitute a marker of human identifiability when engaged in task-related behavior remains an open question. Here, using MEG data from naturalistic and neurophysiological tasks, we show that identification improves in tasks relative to resting-state, providing compelling evidence for a task dependent axis of MEG signatures. Notably, improvements in identifiability were more prominent in strictly controlled tasks. Lastly, the brain regions contributing most towards individual identification were also modified when engaged in task activities. We hope that this investigation advances our understanding of the driving factors behind brain identification from MEG signals.

neuroscience↗

bioRxiv2021

When inefficient speech-motor control affects speech comprehension: atypical electrophysiological correlates of language prediction in stuttering

It is well attested that people predict forthcoming information during language comprehension. The literature presents different proposals on how this ability could be implemented. Here, we tested the hypothesis according to which language production mechanisms have a role in such predictive processing. To this aim, we studied two electroencephalographic correlates of predictability during speech comprehension - pretarget alpha-beta (8-30 Hz) power decrease and the post-target N400 event-related potential (ERP) effect, - in a population with impaired speech-motor control, i.e., adults who stutter (AWS), compared to typically fluent adults (TFA). Participants listened to sentences that could either constrain towards a target word or not, allowing or not to make predictions. We analyzed time-frequency modulations in a silent interval preceding the target and ERPs at the presentation of the target. Results showed that, compared to TFA, AWS display: i) a widespread and bilateral reduced power decrease in posterior temporal and parietal regions, and a power increase in anterior regions, especially in the left hemisphere (high vs. low constraining) and ii) a reduced N400 effect (non-predictable vs. predictable). The results suggest a reduced efficiency in generating predictions in AWS with respect to TFA. Additionally, the magnitude of the N400 effect in AWS is correlated with alpha power change in the right pre-motor and supplementary motor cortex, a key node in the dysfunctional network in stuttering. Overall, the results support the idea that processes and neural structures prominently devoted to speech planning and execution support prediction during language comprehension. Significance StatementThe study contributes to the developing enterprise of investigating language production and comprehension not as separate systems, but as sets of processes which may be partly shared. We showed that a population with impaired speech-motor control, i.e., adults who stutter, are characterized by atypical electrophysiological patterns associated with prediction in speech comprehension. The results highlight that an underlying atypical function of neural structures supporting speech production also affects processes deployed during auditory comprehension. The implications are twofold: on the theoretical side, the study supports the need for a more integrated view of language comprehension and production as human capabilities, while on the applied and clinical side, these results might open new venues for efficient treatments of developmental stuttering.

neuroscience↗

bioRxiv2020

Commonalities in alpha and beta neural desynchronizations during prediction in language comprehension and production

The present study investigates whether predictions during language comprehension are generated by engaging the production system. We recorded EEG from participants performing both a comprehension and a production task in two separate blocks. Participants listened to high and low constraint incomplete sentences and were asked either to name a picture to complete it (production) or to simply listen to the final word (comprehension). We found that in a silent gap before the final stimulus, predictable stimuli elicited alpha (8-10 Hz) and beta (13-30 Hz) desynchronization in both tasks. Source estimation highlighted not only the involvement of the left-lateralized language network, but also of temporo-parietal areas in the right hemisphere. Furthermore, correlations between the desynchronizations in comprehension and production showed spatiotemporal commonalities in language-relevant areas in the left hemisphere, especially in the temporal, lateral inferior and dorsal frontal, and inferior parietal corteces. As proposed by prediction-by-production models, our results show that comprehenders engage the production system while predicting upcoming words.

neuroscience↗

bioRxiv2018

Italian norms and naming latencies for 357 high quality color images

In the domain of cognitive studies on the lexico-semantic representational system, one of the most important means of ensuring well-suited experimental designs is using ecological stimulus sets accompanied by normative data on the most relevant variables affecting the processing of their items. In the context of image sets, color photographs are particularly suited for this aim as they reduce the difficulty of visual decoding processes that may emerge with traditional image sets of line drawings, especially in clinical populations. We provide Italian norms for a set of 357 high quality image-items belonging to 23 semantic subcategories. Data from several variables affecting image processing: age of acquisition, familiarity, lexical frequency, manipulability, name agreement, typicality and visual complexity; were collected from a sample of 255 Italian-speaking participants. Lexical frequency data were derived from the CoLFIS corpus. Furthermore, we collected data with on image naming latencies aimed at exploring how much of the variance in these latencies could be explained by the above mentioned critical variables. Multiple regression analyses on the naming latencies show classical psycholinguistic phenomena, such as the effects of age of acquisition and name agreement. In addition, manipulability is also a significant predictor. The described Italian normative data and naming latencies are available for download as supplementary material.

animal behavior and cognition↗