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Paraskevopoulos E, Anagnostopoulou A, Chalas N, Karagianni M, Bamidis P. Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity. Neuroimage 2024; 291:120582. [PMID: 38521212 DOI: 10.1016/j.neuroimage.2024.120582] [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: 11/29/2023] [Revised: 03/12/2024] [Accepted: 03/20/2024] [Indexed: 03/25/2024] Open
Abstract
In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning.
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Affiliation(s)
| | - Alexandra Anagnostopoulou
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nikolas Chalas
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Germany
| | - Maria Karagianni
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Panagiotis Bamidis
- School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
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Han S, Helmchen F. Behavior-relevant top-down cross-modal predictions in mouse neocortex. Nat Neurosci 2024; 27:298-308. [PMID: 38177341 DOI: 10.1038/s41593-023-01534-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024]
Abstract
Animals adapt to a constantly changing world by predicting their environment and the consequences of their actions. The predictive coding hypothesis proposes that the brain generates predictions and continuously compares them with sensory inputs to guide behavior. However, how the brain reconciles conflicting top-down predictions and bottom-up sensory information remains unclear. To address this question, we simultaneously imaged neuronal populations in the mouse somatosensory barrel cortex and posterior parietal cortex during an auditory-cued texture discrimination task. In mice that had learned the task with fixed tone-texture matching, the presentation of mismatched pairing induced conflicts between tone-based texture predictions and actual texture inputs. When decisions were based on the predicted rather than the actual texture, top-down information flow was dominant and texture representations in both areas were modified, whereas dominant bottom-up information flow led to correct representations and behavioral choice. Our findings provide evidence for hierarchical predictive coding in the mouse neocortex.
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Affiliation(s)
- Shuting Han
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland.
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland.
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Bastos G, Holmes JT, Ross JM, Rader AM, Gallimore CG, Wargo JA, Peterka DS, Hamm JP. Top-down input modulates visual context processing through an interneuron-specific circuit. Cell Rep 2023; 42:113133. [PMID: 37708021 PMCID: PMC10591868 DOI: 10.1016/j.celrep.2023.113133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 07/17/2023] [Accepted: 08/30/2023] [Indexed: 09/16/2023] Open
Abstract
Visual stimuli that deviate from the current context elicit augmented responses in the primary visual cortex (V1). These heightened responses, known as "deviance detection," require local inhibition in the V1 and top-down input from the anterior cingulate area (ACa). Here, we investigated the mechanisms by which the ACa and V1 interact to support deviance detection. Local field potential recordings in mice during an oddball paradigm showed that ACa-V1 synchrony peaks in the theta/alpha band (≈10 Hz). Two-photon imaging in the V1 revealed that mainly pyramidal neurons exhibited deviance detection, while contextually redundant stimuli increased vasoactive intestinal peptide (VIP)-positive interneuron (VIP) activity and decreased somatostatin-positive interneuron (SST) activity. Optogenetic drive of ACa-V1 inputs at 10 Hz activated V1-VIPs but inhibited V1-SSTs, mirroring the dynamics present during the oddball paradigm. Chemogenetic inhibition of V1-VIPs disrupted Aca-V1 synchrony and deviance detection in the V1. These results outline temporal and interneuron-specific mechanisms of top-down modulation that support visual context processing.
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Affiliation(s)
- Georgia Bastos
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA; Center for Neuroinflammation and Cardiometabolic Diseases, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Jacob T Holmes
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Jordan M Ross
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA; Center for Behavioral Neuroscience, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Anna M Rader
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA; Center for Neuroinflammation and Cardiometabolic Diseases, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Connor G Gallimore
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Joseph A Wargo
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA
| | - Darcy S Peterka
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Jordan P Hamm
- Neuroscience Institute, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA; Center for Neuroinflammation and Cardiometabolic Diseases, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA; Center for Behavioral Neuroscience, Georgia State University, Petit Science Center, 100 Piedmont Ave, Atlanta, GA 30303, USA.
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