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Jackson JB, Rich AN, Moerel D, Teichmann L, Duncan J, Woolgar A. Domain general frontoparietal regions show modality-dependent coding of auditory and visual rules. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583318. [PMID: 38903119 PMCID: PMC11188079 DOI: 10.1101/2024.03.04.583318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2024]
Abstract
A defining feature of human cognition is our ability to respond flexibly to what we see and hear, changing how we respond depending on our current goals. In fact, we can rapidly associate almost any input stimulus with any arbitrary behavioural response. This remarkable ability is thought to depend on a frontoparietal "multiple demand" circuit which is engaged by many types of cognitive demand and widely referred to as domain general. However, it is not clear how responses to multiple input modalities are structured within this system. Domain generality could be achieved by holding information in an abstract form that generalises over input modality, or in a modality-tagged form, which uses similar resources but produces unique codes to represent the information in each modality. We used a stimulus-response task, with conceptually identical rules in two sensory modalities (visual and auditory), to distinguish between these possibilities. Multivariate decoding of functional magnetic resonance imaging data showed that representations of visual and auditory rules recruited overlapping neural resources but were expressed in modality-tagged non-generalisable neural codes. Our data suggest that this frontoparietal system may draw on the same or similar resources to solve multiple tasks, but does not create modality-general representations of task rules, even when those rules are conceptually identical between domains.
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Affiliation(s)
- J. B. Jackson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - A. N. Rich
- Perception in Action Research Centre & School of Psychological Sciences, Macquarie University, Australia
| | - D. Moerel
- School of Psychology, University of Sydney, Sydney, NSW, Australia
| | - L. Teichmann
- Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - J. Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - A. Woolgar
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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Ashton K, Zinszer BD, Cichy RM, Nelson CA, Aslin RN, Bayet L. Time-resolved multivariate pattern analysis of infant EEG data: A practical tutorial. Dev Cogn Neurosci 2022; 54:101094. [PMID: 35248819 PMCID: PMC8897621 DOI: 10.1016/j.dcn.2022.101094] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Revised: 10/22/2021] [Accepted: 02/24/2022] [Indexed: 01/27/2023] Open
Abstract
Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA has recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. An example implementation of time-resolved MVPA based on linear SVM classification is described, with accompanying code in Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above-chance accuracy for classifying stimuli images. Extensions of the classification analysis are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.
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Affiliation(s)
- Kira Ashton
- Department of Neuroscience, American University, Washington, DC 20016, USA; Center for Neuroscience and Behavior, American University, Washington, DC 20016, USA.
| | | | - Radoslaw M Cichy
- Department of Education and Psychology, Freie Universität Berlin, 14195 Berlin, Germany
| | - Charles A Nelson
- Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA; Graduate School of Education, Harvard, Cambridge, MA 02138, USA
| | - Richard N Aslin
- Haskins Laboratories, 300 George Street, New Haven, CT 06511, USA; Psychological Sciences Department, University of Connecticut, Storrs, CT 06269, USA; Department of Psychology, Yale University, New Haven, CT 06511, USA; Yale Child Study Center, School of Medicine, New Haven, CT 06519, USA
| | - Laurie Bayet
- Department of Neuroscience, American University, Washington, DC 20016, USA; Center for Neuroscience and Behavior, American University, Washington, DC 20016, USA
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Scrivener CL. When Is Simultaneous Recording Necessary? A Guide for Researchers Considering Combined EEG-fMRI. Front Neurosci 2021; 15:636424. [PMID: 34267620 PMCID: PMC8276697 DOI: 10.3389/fnins.2021.636424] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 06/01/2021] [Indexed: 11/19/2022] Open
Abstract
Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide non-invasive measures of brain activity at varying spatial and temporal scales, offering different views on brain function for both clinical and experimental applications. Simultaneous recording of these measures attempts to maximize the respective strengths of each method, while compensating for their weaknesses. However, combined recording is not necessary to address all research questions of interest, and experiments may have greater statistical power to detect effects by maximizing the signal-to-noise ratio in separate recording sessions. While several existing papers discuss the reasons for or against combined recording, this article aims to synthesize these arguments into a flow chart of questions that researchers can consider when deciding whether to record EEG and fMRI separately or simultaneously. Given the potential advantages of simultaneous EEG-fMRI, the aim is to provide an initial overview of the most important concepts and to direct readers to relevant literature that will aid them in this decision.
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Affiliation(s)
- Catriona L. Scrivener
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
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Barnes L, Petit S, Badcock NA, Whyte CJ, Woolgar A. Word Detection in Individual Subjects Is Difficult to Probe With Fast Periodic Visual Stimulation. Front Neurosci 2021; 15:602798. [PMID: 33762904 PMCID: PMC7982886 DOI: 10.3389/fnins.2021.602798] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Accepted: 02/08/2021] [Indexed: 11/21/2022] Open
Abstract
Measuring cognition in single subjects presents unique challenges. On the other hand, individually sensitive measurements offer extraordinary opportunities, from informing theoretical models to enabling truly individualised clinical assessment. Here, we test the robustness of fast, periodic, and visual stimulation (FPVS), an emerging method proposed to elicit detectable responses to written words in the electroencephalogram (EEG) of individual subjects. The method is non-invasive, passive, and requires only a few minutes of testing, making it a potentially powerful tool to test comprehension in those who do not speak or who struggle with long testing procedures. In an initial study, Lochy et al. (2015) used FPVS to detect word processing in eight out of 10 fluent French readers. Here, we attempted to replicate their study in a new sample of 10 fluent English readers. Participants viewed rapid streams of pseudo-words with words embedded at regular intervals, while we recorded their EEG. Based on Lochy et al. (2015) we expected that words would elicit a steady-state response at the word-presentation frequency (2 Hz) over parieto-occipital electrode sites. However, across 40 datasets (10 participants, two conditions, and two regions of interest–ROIs), only four datasets met the criteria for a unique response to words. This corresponds to a 10% detection rate. We conclude that FPVS should be developed further before it can serve as an individually-sensitive measure of written word processing.
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Affiliation(s)
- Lydia Barnes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Selene Petit
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Nicholas A Badcock
- Department of Cognitive Science, Macquarie University, Sydney, NSW, Australia.,Macquarie Centre for Reading, Macquarie University, Sydney, NSW, Australia.,School of Psychological Science, University of Western Australia, Perth, WA, Australia
| | - Christopher J Whyte
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Alexandra Woolgar
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom.,Department of Cognitive Science, Macquarie University, Sydney, NSW, Australia
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