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Aguado-López B, Palenciano AF, Peñalver JMG, Díaz-Gutiérrez P, López-García D, Avancini C, Ciria LF, Ruz M. Proactive selective attention across competition contexts. Cortex 2024; 176:113-128. [PMID: 38772050 DOI: 10.1016/j.cortex.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/03/2024] [Accepted: 04/16/2024] [Indexed: 05/23/2024]
Abstract
Selective attention is a cognitive function that helps filter out unwanted information. Theories such as the biased competition model (Desimone & Duncan, 1995) explain how attentional templates bias processing towards targets in contexts where multiple stimuli compete for resources. However, it is unclear how the anticipation of different levels of competition influences the nature of attentional templates, in a proactive fashion. In this study, we used electroencephalography (EEG) to investigate how the anticipated demands of attentional selection (either high or low stimuli competition contexts) modulate target-specific preparatory brain activity and its relationship with task performance. To do so, participants performed a sex/gender judgment task in a cue-target paradigm where, depending on the block, target and distractor stimuli appeared simultaneously (high competition) or sequentially (low competition). Multivariate Pattern Analysis (MVPA) showed that, in both competition contexts, there was a preactivation of the target category to select, with a ramping-up profile at the end of the preparatory interval. However, cross-classification showed no generalization across competition conditions, suggesting different preparatory formats. Notably, time-frequency analyses showed differences between anticipated competition demands, with higher theta band power for high than low competition, which mediated the impact of subsequent stimuli competition on behavioral performance. Overall, our results show that, whereas preactivation of the internal templates associated with the category to select are engaged in advance in high and low competition contexts, their underlying neural patterns differ. In addition, these codes could not be associated with theta power, suggesting that they reflect different preparatory processes. The implications of these findings are crucial to increase our understanding of the nature of top-down processes across different contexts.
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Affiliation(s)
- Blanca Aguado-López
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Ana F Palenciano
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - José M G Peñalver
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Paloma Díaz-Gutiérrez
- Department of Management, Faculty of Business and Economics, University of Antwerp, 2000, Belgium
| | - David López-García
- Data Science & Computational Intelligence Institute, University of Granada, CP 18071, Spain
| | - Chiara Avancini
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - Luis F Ciria
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain
| | - María Ruz
- Mind, Brain and Behavior Research Center (CIMCYC), University of Granada, Granada 18071, Spain.
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Rafiei MH, Gauthier LV, Adeli H, Takabi D. Self-Supervised Learning for Electroencephalography. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:1457-1471. [PMID: 35867362 DOI: 10.1109/tnnls.2022.3190448] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques. However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories. EEG data collection and labeling are costly. Moreover, combining available datasets to achieve a large data volume is usually infeasible due to inconsistent experimental paradigms across trials. Self-supervised learning (SSL) solves these challenges because it enables learning from EEG records across trials with variable experimental paradigms, even when the trials explore different phenomena. It aggregates multiple EEG repositories to increase accuracy, reduce bias, and mitigate overfitting in machine learning training. In addition, SSL could be employed in situations where there is limited labeled training data, and manual labeling is costly. This article: 1) provides a brief introduction to SSL; 2) describes some SSL techniques employed in recent studies, including EEG; 3) proposes current and potential SSL techniques for future investigations in EEG studies; 4) discusses the cons and pros of different SSL techniques; and 5) proposes holistic implementation tips and potential future directions for EEG SSL practices.
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Marsicano G, Casartelli L, Federici A, Bertoni S, Vignali L, Molteni M, Facoetti A, Ronconi L. Prolonged neural encoding of visual information in autism. Autism Res 2024; 17:37-54. [PMID: 38009961 DOI: 10.1002/aur.3062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/30/2023] [Indexed: 11/29/2023]
Abstract
Autism spectrum disorder (ASD) is associated with a hyper-focused visual attentional style, impacting higher-order social and affective domains. The understanding of such peculiarity can benefit from the use of multivariate pattern analysis (MVPA) of high-resolution electroencephalography (EEG) data, which has proved to be a powerful technique to investigate the hidden neural dynamics orchestrating sensory and cognitive processes. Here, we recorded EEG in typically developing (TD) children and in children with ASD during a visuo-spatial attentional task where attention was exogenously captured by a small (zoom-in) or large (zoom-out) cue in the visual field before the appearance of a target at different eccentricities. MVPA was performed both in the cue-locked period, to reveal potential differences in the modulation of the attentional focus, and in the target-locked period, to reveal potential cascade effects on stimulus processing. Cue-locked MVPA revealed that while in the TD group the pattern of neural activity contained information about the cue mainly before the target appearance, the ASD group showed a temporally sustained and topographically diffuse significant decoding of the cue neural response even after the target onset, suggesting a delayed extinction of cue-related neural activity. Crucially, this delayed extinction positively correlated with behavioral measures of attentional hyperfocusing. Results of target-locked MVPA were coherent with a hyper-focused attentional profile, highlighting an earlier and stronger decoding of target neural responses in small cue trials in the ASD group. The present findings document a spatially and temporally overrepresented encoding of visual information in ASD, which can constitute one of the main reasons behind their peculiar cognitive style.
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Affiliation(s)
- Gianluca Marsicano
- Department of Psychology, University of Bologna, Bologna, Italy
- Centre for Studies and Research in Cognitive Neuroscience, University of Bologna, Cesena, Italy
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Luca Casartelli
- Child Psychopathology Department, Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E.MEDEA, Bosisio Parini, Italy
| | | | - Sara Bertoni
- Department of Human and Social Sciences, University of Bergamo, Bergamo, Italy
- Developmental and Cognitive Neuroscience Lab, Department of General Psychology, University of Padua, Padova, Italy
| | | | - Massimo Molteni
- Child Psychopathology Department, Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS E.MEDEA, Bosisio Parini, Italy
| | - Andrea Facoetti
- Developmental and Cognitive Neuroscience Lab, Department of General Psychology, University of Padua, Padova, Italy
| | - Luca Ronconi
- Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
- School of Psychology, Vita-Salute San Raffaele University, Milan, Italy
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Goelz C, Reuter EM, Fröhlich S, Rudisch J, Godde B, Vieluf S, Voelcker-Rehage C. Classification of age groups and task conditions provides additional evidence for differences in electrophysiological correlates of inhibitory control across the lifespan. Brain Inform 2023; 10:11. [PMID: 37154855 PMCID: PMC10167079 DOI: 10.1186/s40708-023-00190-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/01/2023] [Indexed: 05/10/2023] Open
Abstract
The aim of this study was to extend previous findings on selective attention over a lifetime using machine learning procedures. By decoding group membership and stimulus type, we aimed to study differences in the neural representation of inhibitory control across age groups at a single-trial level. We re-analyzed data from 211 subjects from six age groups between 8 and 83 years of age. Based on single-trial EEG recordings during a flanker task, we used support vector machines to predict the age group as well as to determine the presented stimulus type (i.e., congruent, or incongruent stimulus). The classification of group membership was highly above chance level (accuracy: 55%, chance level: 17%). Early EEG responses were found to play an important role, and a grouped pattern of classification performance emerged corresponding to age structure. There was a clear cluster of individuals after retirement, i.e., misclassifications mostly occurred within this cluster. The stimulus type could be classified above chance level in ~ 95% of subjects. We identified time windows relevant for classification performance that are discussed in the context of early visual attention and conflict processing. In children and older adults, a high variability and latency of these time windows were found. We were able to demonstrate differences in neuronal dynamics at the level of individual trials. Our analysis was sensitive to mapping gross changes, e.g., at retirement age, and to differentiating components of visual attention across age groups, adding value for the diagnosis of cognitive status across the lifespan. Overall, the results highlight the use of machine learning in the study of brain activity over a lifetime.
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Affiliation(s)
- Christian Goelz
- Institute of Sports Medicine, Paderborn University, Paderborn, Germany
| | - Eva-Maria Reuter
- Department of Sport and Health Sciences, Technical University of Munich, Munich, Germany
| | - Stephanie Fröhlich
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster, Wilhelm-Schickard-Str. 8, 48149, Münster, Germany
| | - Julian Rudisch
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster, Wilhelm-Schickard-Str. 8, 48149, Münster, Germany
| | - Ben Godde
- School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany
| | - Solveig Vieluf
- Institute of Sports Medicine, Paderborn University, Paderborn, Germany
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Claudia Voelcker-Rehage
- Department of Neuromotor Behavior and Exercise, Institute of Sport and Exercise Sciences, University of Münster, Wilhelm-Schickard-Str. 8, 48149, Münster, Germany.
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Top-down specific preparatory activations for selective attention and perceptual expectations. Neuroimage 2023; 271:119960. [PMID: 36854351 DOI: 10.1016/j.neuroimage.2023.119960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 03/01/2023] Open
Abstract
Proactive cognition brain models are mainstream nowadays. Within these, preparation is understood as an endogenous, top-down function that takes place prior to the actual perception of a stimulus and improves subsequent behavior. Neuroimaging has shown the existence of such preparatory activity separately in different cognitive domains, however no research to date has sought to uncover their potential similarities and differences. Two of these, often confounded in the literature, are Selective Attention (information relevance) and Perceptual Expectation (information probability). We used EEG to characterize the mechanisms that pre-activate specific contents in Attention and Expectation. In different blocks, participants were cued to the relevance or to the probability of target categories, faces vs. names, in a gender discrimination task. Multivariate Pattern (MVPA) and Representational Similarity Analyses (RSA) during the preparation window showed that both manipulations led to a significant, ramping-up prediction of the relevant or expected target category. However, classifiers trained with data from one condition did not generalize to the other, indicating the existence of unique anticipatory neural patterns. In addition, a Canonical Template Tracking procedure showed that there was stronger anticipatory perceptual reinstatement for relevance than for expectation blocks. Overall, the results indicate that preparation during attention and expectation acts through distinguishable neural mechanisms. These findings have important implications for current models of brain functioning, as they are a first step towards characterizing and dissociating the neural mechanisms involved in top-down anticipatory processing.
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