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Battraw MA, Fitzgerald J, James MA, Bagley AM, Joiner WM, Schofield JS. Understanding the capacity of children with congenital unilateral below-elbow deficiency to actuate their affected muscles. Sci Rep 2024; 14:4563. [PMID: 38402326 PMCID: PMC10894282 DOI: 10.1038/s41598-024-54952-7] [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: 09/09/2023] [Accepted: 02/19/2024] [Indexed: 02/26/2024] Open
Abstract
In recent years, commercially available dexterous upper limb prostheses for children have begun to emerge. These devices derive control signals from surface electromyography (measure of affected muscle electrical activity, sEMG) to drive a variety of grasping motions. However, the ability for children with congenital upper limb deficiency to actuate their affected muscles to achieve naturalistic prosthetic control is not well understood, as compared to adults or children with acquired hand loss. To address this gap, we collected sEMG data from 9 congenital one-handed participants ages 8-20 years as they envisioned and attempted to perform 10 different movements with their missing hands. Seven sEMG electrodes were adhered circumferentially around the participant's affected and unaffected limbs and participants mirrored the attempted missing hand motions with their intact side. To analyze the collected sEMG data, we used time and frequency domain analyses. We found that for the majority of participants, attempted hand movements produced detectable and consistent muscle activity, and the capacity to achieve this was not dissimilar across the affected and unaffected sides. These data suggest that children with congenital hand absence retain a degree of control over their affected muscles, which has important implications for translating and refining advanced prosthetic control technologies for children.
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Affiliation(s)
- Marcus A Battraw
- Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, USA
| | - Justin Fitzgerald
- Department of Biomedical Engineering, University of California, Davis, Davis, CA, USA
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, USA
- Clinical and Translational Science Center, University of California, Davis Health, Sacramento, CA, USA
| | - Michelle A James
- Shriners Children's - Northern California, Sacramento, CA, USA
- Department of Orthopaedic Surgery, University of California, Davis Health, Sacramento, CA, USA
| | - Anita M Bagley
- Shriners Children's - Northern California, Sacramento, CA, USA
- Department of Orthopaedic Surgery, University of California, Davis Health, Sacramento, CA, USA
| | - Wilsaan M Joiner
- Department of Neurobiology, Physiology and Behavior, University of California, Davis, Davis, CA, USA
- Department of Neurology, University of California, Davis Health, Sacramento, CA, USA
| | - Jonathon S Schofield
- Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, USA.
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2
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Yeo DJ, Pollack C, Conrad BN, Price GR. Functional and representational differences between bilateral inferior temporal numeral areas. Cortex 2024; 171:113-135. [PMID: 37992508 DOI: 10.1016/j.cortex.2023.08.018] [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: 01/27/2022] [Revised: 12/15/2022] [Accepted: 08/09/2023] [Indexed: 11/24/2023]
Abstract
The processing of numerals as visual objects is supported by an "Inferior Temporal Numeral Area" (ITNA) in the bilateral inferior temporal gyri (ITG). Extant findings suggest some degree of hemispheric asymmetry in how the bilateral ITNAs process numerals. Pollack and Price (2019) reported such a hemispheric asymmetry by which a region in the left ITG was sensitive to digits during a visual search for a digit among letters, and a homologous region in the right ITG that showed greater digit sensitivity in individuals with higher calculation skills. However, the ITG regions were localized with separate analyses without directly contrasting their digit sensitivities and relation to calculation skills. So, the extent of and reasons for these functional asymmetries remain unclear. Here we probe whether the functional and representational properties of the ITNAs are asymmetric by applying both univariate and multivariate region-of-interest analyses to Pollack and Price's (2019) data. Contrary to the implications of the original findings, digit sensitivity did not differ between ITNAs, and digit sensitivity in both left and right ITNAs was associated with calculation skills. Representational similarity analyses revealed that the overall representational geometries of digits in the ITNAs were also correlated, albeit weakly, but the representational contents of the ITNAs were largely inconclusive. Nonetheless, we found a right lateralization in engagement in alphanumeric categorization, and that the right ITNA showed greater discriminability between digits and letters. Greater right lateralization of digit sensitivity and digit discriminability in the left ITNA were also related to higher calculation skills. Our findings thus suggest that the ITNAs may not be functionally identical and should be directly contrasted in future work. Our study also highlights the importance of within-individual comparisons for understanding hemispheric asymmetries, and analyses of individual differences and multivariate features to uncover effects that would otherwise be obscured by averages.
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Affiliation(s)
- Darren J Yeo
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA; Division of Psychology, School of Social Sciences, Nanyang Technological University, Singapore
| | - Courtney Pollack
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Benjamin N Conrad
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA
| | - Gavin R Price
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, Nashville, TN, USA; Department of Psychology, University of Exeter, Exeter, United Kingdom.
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3
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Fitzgerald JJ, Battraw MA, James MA, Bagley AM, Schofield JS, Joiner WM. Moving a missing hand: children born with below elbow deficiency can enact hand grasp patterns with their residual muscles. J Neuroeng Rehabil 2024; 21:13. [PMID: 38263225 PMCID: PMC10804465 DOI: 10.1186/s12984-024-01306-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/12/2024] [Indexed: 01/25/2024] Open
Abstract
Children with a unilateral congenital below elbow deficiency (UCBED) have one typical upper limb and one that lacks a hand, ending below the elbow at the proximal/mid forearm. UCBED is an isolated condition, and affected children otherwise develop normal sensorimotor control. Unlike adults with upper limb absence, the majority of whom have an acquired loss, children with UCBED never developed a hand, so their residual muscles have never actuated an intact limb. Their ability to purposefully modulate affected muscle activity is often assumed to be limited, and this assumption has influenced prosthetic design and prescription practices for this population as many modern devices derive control signals from affected muscle activity. To better understand the motor capabilities of the affected muscles, we used ultrasound imaging to study 6 children with UCBED. We examined the extent to which subjects activate their affected muscles when performing mirrored movements with their typical and missing hands. We demonstrate that all subjects could intentionally and consistently enact at least five distinct muscle patterns when attempting different missing hand movements (e.g., power grasp) and found similar performance across affected and typically developed limbs. These results suggest that although participants had never actuated the missing hand they could distinctively and consistently activate the residual muscle patterns associated with actions on the unaffected side. These findings indicate that motor control still develops in the absence of the normal effector, and can serve as a guide for developing prostheses that leverage the full extent of these children's motor control capabilities.
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Affiliation(s)
- Justin J Fitzgerald
- Department of Biomedical Engineering, University of California, Davis, CA, USA
- Department of Neurobiology, Physiology and Behavior, University of California, 1 Shields Avenue, Davis, CA, 95616, USA
- Clinical and Translational Science Center, University of California Davis Health, Sacramento, CA, USA
| | - Marcus A Battraw
- Department of Mechanical and Aerospace Engineering, University of California, Davis, CA, USA
| | - Michelle A James
- Shriners Children's Northern California, Sacramento, CA, USA
- Department of Orthopaedic Surgery, University of California Davis Health, Sacramento, CA, USA
| | - Anita M Bagley
- Shriners Children's Northern California, Sacramento, CA, USA
- Department of Orthopaedic Surgery, University of California Davis Health, Sacramento, CA, USA
| | - Jonathon S Schofield
- Department of Mechanical and Aerospace Engineering, University of California, Davis, CA, USA
| | - Wilsaan M Joiner
- Department of Neurobiology, Physiology and Behavior, University of California, 1 Shields Avenue, Davis, CA, 95616, USA.
- Department of Neurology, University of California Davis Health, Sacramento, CA, USA.
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4
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Schütt HH, Kipnis AD, Diedrichsen J, Kriegeskorte N. Statistical inference on representational geometries. eLife 2023; 12:e82566. [PMID: 37610302 PMCID: PMC10446828 DOI: 10.7554/elife.82566] [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: 08/09/2022] [Accepted: 08/07/2023] [Indexed: 08/24/2023] Open
Abstract
Neuroscience has recently made much progress, expanding the complexity of both neural activity measurements and brain-computational models. However, we lack robust methods for connecting theory and experiment by evaluating our new big models with our new big data. Here, we introduce new inference methods enabling researchers to evaluate and compare models based on the accuracy of their predictions of representational geometries: A good model should accurately predict the distances among the neural population representations (e.g. of a set of stimuli). Our inference methods combine novel 2-factor extensions of crossvalidation (to prevent overfitting to either subjects or conditions from inflating our estimates of model accuracy) and bootstrapping (to enable inferential model comparison with simultaneous generalization to both new subjects and new conditions). We validate the inference methods on data where the ground-truth model is known, by simulating data with deep neural networks and by resampling of calcium-imaging and functional MRI data. Results demonstrate that the methods are valid and conclusions generalize correctly. These data analysis methods are available in an open-source Python toolbox (rsatoolbox.readthedocs.io).
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Affiliation(s)
- Heiko H Schütt
- Zuckerman Institute, Columbia UniversityNew YorkUnited States
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5
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Palenciano AF, Senoussi M, Formica S, González-García C. Canonical template tracking: Measuring the activation state of specific neural representations. FRONTIERS IN NEUROIMAGING 2023; 1:974927. [PMID: 37555182 PMCID: PMC10406196 DOI: 10.3389/fnimg.2022.974927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/13/2022] [Indexed: 08/10/2023]
Abstract
Multivariate analyses of neural data have become increasingly influential in cognitive neuroscience since they allow to address questions about the representational signatures of neurocognitive phenomena. Here, we describe Canonical Template Tracking: a multivariate approach that employs independent localizer tasks to assess the activation state of specific representations during the execution of cognitive paradigms. We illustrate the benefits of this methodology in characterizing the particular content and format of task-induced representations, comparing it with standard (cross-)decoding and representational similarity analyses. Then, we discuss relevant design decisions for experiments using this analysis approach, focusing on the nature of the localizer tasks from which the canonical templates are derived. We further provide a step-by-step tutorial of this method, stressing the relevant analysis choices for functional magnetic resonance imaging and magneto/electroencephalography data. Importantly, we point out the potential pitfalls linked to canonical template tracking implementation and interpretation of the results, together with recommendations to mitigate them. To conclude, we provide some examples from previous literature that highlight the potential of this analysis to address relevant theoretical questions in cognitive neuroscience.
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Affiliation(s)
- Ana F. Palenciano
- Mind, Brain, and Behavior Research Center, University of Granada, Granada, Spain
| | - Mehdi Senoussi
- CLLE Lab, CNRS UMR 5263, University of Toulouse, Toulouse, France
- Department of Experimental Psychology, Ghent University, Ghent, Belgium
| | - Silvia Formica
- Department of Psychology, Berlin School of Mind and Brain, Humboldt Universität zu Berlin, Berlin, Germany
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6
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Simulation-based learning influences real-life attitudes. Cognition 2022; 227:105202. [PMID: 35714560 DOI: 10.1016/j.cognition.2022.105202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 06/02/2022] [Accepted: 06/06/2022] [Indexed: 11/20/2022]
Abstract
Humans can vividly simulate hypothetical experiences. This ability draws on our memories (e.g., of familiar people and locations) to construct imaginings that resemble real-life events (e.g., of meeting a person at a location). Here, we examine the hypothesis that we also learn from such simulated episodes much like from actual experiences. Specifically, we show that the mere simulation of meeting a familiar person (unconditioned stimulus; US) at a known location (conditioned stimulus; CS) changes how people value the location. We provide key evidence that this simulation-based learning strengthens pre-existing CS-US associations and that it leads to a transfer of valence from the US to the CS. The data thus highlight a mechanism by which we learn from simulated experiences.
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7
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Meyer AK, Benoit RG. Suppression weakens unwanted memories via a sustained reduction of neural reactivation. eLife 2022; 11:71309. [PMID: 35352679 PMCID: PMC8967383 DOI: 10.7554/elife.71309] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 03/07/2022] [Indexed: 01/09/2023] Open
Abstract
Aversive events sometimes turn into intrusive memories. However, prior evidence indicates that such memories can be controlled via a mechanism of retrieval suppression. Here, we test the hypothesis that suppression exerts a sustained influence on memories by deteriorating their neural representations. This deterioration, in turn, would hinder their subsequent reactivation and thus impoverish the vividness with which they can be recalled. In an fMRI study, participants repeatedly suppressed memories of aversive scenes. As predicted, this process rendered the memories less vivid. Using a pattern classifier, we observed that suppression diminished the neural reactivation of scene information both globally across the brain and locally in the parahippocampal cortices. Moreover, the decline in vividness was associated with reduced reinstatement of unique memory representations in right parahippocampal cortex. These results support the hypothesis that suppression weakens memories by causing a sustained reduction in the potential to reactivate their neural representations.
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Affiliation(s)
- Ann-Kristin Meyer
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Roland G Benoit
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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8
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Karimi-Rouzbahani H, Woolgar A, Henson R, Nili H. Caveats and Nuances of Model-Based and Model-Free Representational Connectivity Analysis. Front Neurosci 2022; 16:755988. [PMID: 35360178 PMCID: PMC8960982 DOI: 10.3389/fnins.2022.755988] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 02/02/2022] [Indexed: 11/30/2022] Open
Abstract
Brain connectivity analyses have conventionally relied on statistical relationship between one-dimensional summaries of activation in different brain areas. However, summarizing activation patterns within each area to a single dimension ignores the potential statistical dependencies between their multi-dimensional activity patterns. Representational Connectivity Analyses (RCA) is a method that quantifies the relationship between multi-dimensional patterns of activity without reducing the dimensionality of the data. We consider two variants of RCA. In model-free RCA, the goal is to quantify the shared information for two brain regions. In model-based RCA, one tests whether two regions have shared information about a specific aspect of the stimuli/task, as defined by a model. However, this is a new approach and the potential caveats of model-free and model-based RCA are still understudied. We first explain how model-based RCA detects connectivity through the lens of models, and then present three scenarios where model-based and model-free RCA give discrepant results. These conflicting results complicate the interpretation of functional connectivity. We highlight the challenges in three scenarios: complex intermediate models, common patterns across regions, and transformation of representational structure across brain regions. The article is accompanied by scripts (https://osf.io/3nxfa/) that reproduce the results. In each case, we suggest potential ways to mitigate the difficulties caused by inconsistent results. The results of this study shed light on some understudied aspects of RCA, and allow researchers to use the method more effectively.
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Affiliation(s)
- Hamid Karimi-Rouzbahani
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Computing, Macquarie University, Sydney, NSW, Australia
| | - Alexandra Woolgar
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Richard Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Hamed Nili
- Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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9
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Pakravan M, Abbaszadeh M, Ghazizadeh A. Coordinated multivoxel coding beyond univariate effects is not likely to be observable in fMRI data. Neuroimage 2021; 247:118825. [PMID: 34942362 DOI: 10.1016/j.neuroimage.2021.118825] [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: 06/14/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 10/19/2022] Open
Abstract
Simultaneous recording of activity across brain regions can contain additional information compared to regional recordings done in isolation. In particular, multivariate pattern analysis (MVPA) across voxels has been interpreted as evidence for distributed coding of cognitive or sensorimotor processes beyond what can be gleaned from a collection of univariate effects (UVE) using functional magnetic resonance imaging (fMRI). Here, we argue that regardless of patterns revealed, conventional MVPA is merely a decoding tool with increased sensitivity arising from considering a large number of 'weak classifiers' (i.e., single voxels) in higher dimensions. We propose instead that 'real' multivoxel coding should result in changes in higher-order statistics across voxels between conditions such as second-order multivariate effects (sMVE). Surprisingly, analysis of conditions with robust multivariate effects (MVE) revealed by MVPA failed to show significant sMVE in two species (humans and macaques). Further analysis showed that while both MVE and sMVE can be readily observed in the spiking activity of neuronal populations, the slow and nonlinear hemodynamic coupling and low spatial resolution of fMRI activations make the observation of higher-order statistics between voxels highly unlikely. These results reveal inherent limitations of fMRI signals for studying coordinated coding across voxels. Together, these findings suggest that care should be taken in interpreting significant MVPA results as representing anything beyond a collection of univariate effects.
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Affiliation(s)
- Mansooreh Pakravan
- Electrical and Computer Engineering Department, Tarbiat Modares University, Tehran, Iran.
| | - Mojtaba Abbaszadeh
- Bio-intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran
| | - Ali Ghazizadeh
- Bio-intelligence Research Unit, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences, IPM, Tehran, Iran.
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10
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Spatially Adjacent Regions in Posterior Cingulate Cortex Represent Familiar Faces at Different Levels of Complexity. J Neurosci 2021; 41:9807-9826. [PMID: 34670848 PMCID: PMC8612644 DOI: 10.1523/jneurosci.1580-20.2021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 08/25/2021] [Accepted: 09/26/2021] [Indexed: 11/21/2022] Open
Abstract
Extensive research has shown that perceptual information of faces is processed in a network of hierarchically-organized areas within ventral temporal cortex. For familiar and famous faces, perceptual processing of faces is normally accompanied by extraction of semantic knowledge about the social status of persons. Semantic processing of familiar faces could entail progressive stages of information abstraction. However, the cortical mechanisms supporting multistage processing of familiar faces have not been characterized. Here, using an event-related fMRI experiment, familiar faces from four celebrity groups (actors, singers, politicians, and football players) and unfamiliar faces were presented to the human subjects (both males and females) while they were engaged in a face categorization task. We systematically explored the cortical representations for faces, familiar faces, subcategories of familiar faces, and familiar face identities using whole-brain univariate analysis, searchlight-based multivariate pattern analysis (MVPA), and functional connectivity analysis. Convergent evidence from all these analyses revealed a set of overlapping regions within posterior cingulate cortex (PCC) that contained decodable fMRI responses for representing different levels of semantic knowledge about familiar faces. Our results suggest a multistage pathway in PCC for processing semantic information of faces, analogous to the multistage pathway in ventral temporal cortex for processing perceptual information of faces.SIGNIFICANCE STATEMENT Recognizing familiar faces is an important component of social communications. Previous research has shown that a distributed network of brain areas is involved in processing the semantic information of familiar faces. However, it is not clear how different levels of semantic information are represented in the brain. Here, we evaluated the multivariate response patterns across the entire cortex to discover the areas that contain information for familiar faces, subcategories of familiar faces, and identities of familiar faces. The searchlight maps revealed that different levels of semantic information are represented in topographically adjacent areas within posterior cingulate cortex (PCC). The results suggest that semantic processing of faces is mediated through progressive stages of information abstraction in PCC.
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11
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Ritchie JB, Lee Masson H, Bracci S, Op de Beeck HP. The unreliable influence of multivariate noise normalization on the reliability of neural dissimilarity. Neuroimage 2021; 245:118686. [PMID: 34728244 DOI: 10.1016/j.neuroimage.2021.118686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/21/2021] [Accepted: 10/26/2021] [Indexed: 10/19/2022] Open
Abstract
Representational similarity analysis (RSA) is a key element in the multivariate pattern analysis toolkit. The central construct of the method is the representational dissimilarity matrix (RDM), which can be generated for datasets from different modalities (neuroimaging, behavior, and computational models) and directly correlated in order to evaluate their second-order similarity. Given the inherent noisiness of neuroimaging signals it is important to evaluate the reliability of neuroimaging RDMs in order to determine whether these comparisons are meaningful. Recently, multivariate noise normalization (NNM) has been proposed as a widely applicable method for boosting signal estimates for RSA, regardless of choice of dissimilarity metrics, based on evidence that the analysis improves the within-subject reliability of RDMs (Guggenmos et al. 2018; Walther et al. 2016). We revisited this issue with three fMRI datasets and evaluated the impact of NNM on within- and between-subject reliability and RSA effect sizes using multiple dissimilarity metrics. We also assessed its impact across regions of interest from the same dataset, its interaction with spatial smoothing, and compared it to GLMdenoise, which has also been proposed as a method that improves signal estimates for RSA (Charest et al. 2018). We found that across these tests the impact of NNM was highly variable, as also seems to be the case for other analysis choices. Overall, we suggest being conservative before adding steps and complexities to the (pre)processing pipeline for RSA.
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Affiliation(s)
- J Brendan Ritchie
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Flemish Brabant, Belgium.
| | - Haemy Lee Masson
- Department of Cognitive Science, Johns Hopkins University, Baltimore, USA
| | - Stefania Bracci
- Centre for Mind/Brain Sciences, University of Trento, Rovereto, Italy
| | - Hans P Op de Beeck
- Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000 Leuven, Flemish Brabant, Belgium
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12
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Shahbazi M, Shirali A, Aghajan H, Nili H. Using distance on the Riemannian manifold to compare representations in brain and in models. Neuroimage 2021; 239:118271. [PMID: 34157410 DOI: 10.1016/j.neuroimage.2021.118271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 06/10/2021] [Accepted: 06/14/2021] [Indexed: 11/28/2022] Open
Abstract
Representational similarity analysis (RSA) summarizes activity patterns for a set of experimental conditions into a matrix composed of pairwise comparisons between activity patterns. Two examples of such matrices are the condition-by-condition inner product and correlation matrix. These representational matrices reside on the manifold of positive semidefinite matrices, called the Riemannian manifold. We hypothesize that representational similarities would be more accurately quantified by considering the underlying manifold of the representational matrices. Thus, we introduce the distance on the Riemannian manifold as a metric for comparing representations. Analyzing simulated and real fMRI data and considering a wide range of metrics, we show that the Riemannian distance is least susceptible to sampling bias, results in larger intra-subject reliability, and affords searchlight mapping with high sensitivity and specificity. Furthermore, we show that the Riemannian distance can be used for measuring multi-dimensional connectivity. This measure captures both univariate and multivariate connectivity and is also more sensitive to nonlinear regional interactions compared to the state-of-the-art measures. Applying our proposed metric to neural network representations of natural images, we demonstrate that it also possesses outstanding performance in quantifying similarity in models. Taken together, our results lend credence to the proposition that RSA should consider the manifold of the representational matrices to summarize response patterns in the brain and in models.
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Affiliation(s)
- Mahdiyar Shahbazi
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Ali Shirali
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamid Aghajan
- Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, United Kingdom.
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13
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Nili H, Walther A, Alink A, Kriegeskorte N. Correction: Inferring exemplar discriminability in brain representations. PLoS One 2021; 16:e0250474. [PMID: 33872341 PMCID: PMC8055031 DOI: 10.1371/journal.pone.0250474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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14
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Bainbridge WA, Hall EH, Baker CI. Distinct Representational Structure and Localization for Visual Encoding and Recall during Visual Imagery. Cereb Cortex 2020; 31:1898-1913. [PMID: 33285563 DOI: 10.1093/cercor/bhaa329] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Revised: 10/12/2020] [Accepted: 10/12/2020] [Indexed: 01/03/2023] Open
Abstract
During memory recall and visual imagery, reinstatement is thought to occur as an echoing of the neural patterns during encoding. However, the precise information in these recall traces is relatively unknown, with previous work primarily investigating either broad distinctions or specific images, rarely bridging these levels of information. Using ultra-high-field (7T) functional magnetic resonance imaging with an item-based visual recall task, we conducted an in-depth comparison of encoding and recall along a spectrum of granularity, from coarse (scenes, objects) to mid (e.g., natural, manmade scenes) to fine (e.g., living room, cupcake) levels. In the scanner, participants viewed a trial-unique item, and after a distractor task, visually imagined the initial item. During encoding, we observed decodable information at all levels of granularity in category-selective visual cortex. In contrast, information during recall was primarily at the coarse level with fine-level information in some areas; there was no evidence of mid-level information. A closer look revealed segregation between voxels showing the strongest effects during encoding and those during recall, and peaks of encoding-recall similarity extended anterior to category-selective cortex. Collectively, these results suggest visual recall is not merely a reactivation of encoding patterns, displaying a different representational structure and localization from encoding, despite some overlap.
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Affiliation(s)
- Wilma A Bainbridge
- Department of Psychology, University of Chicago, Chicago, IL 60637, USA.,Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20814, USA
| | - Elizabeth H Hall
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20814, USA.,Department of Psychology, University of California Davis, Davis, CA 95616, USA.,Center for Mind and Brain, University of California Davis, Davis, CA 95618, USA
| | - Chris I Baker
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20814, USA
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15
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Park SA, Miller DS, Nili H, Ranganath C, Boorman ED. Map Making: Constructing, Combining, and Inferring on Abstract Cognitive Maps. Neuron 2020; 107:1226-1238.e8. [PMID: 32702288 PMCID: PMC7529977 DOI: 10.1016/j.neuron.2020.06.030] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2019] [Revised: 05/29/2020] [Accepted: 06/24/2020] [Indexed: 10/23/2022]
Abstract
Cognitive maps enable efficient inferences from limited experience that can guide novel decisions. We tested whether the hippocampus (HC), entorhinal cortex (EC), and ventromedial prefrontal cortex (vmPFC)/medial orbitofrontal cortex (mOFC) organize abstract and discrete relational information into a cognitive map to guide novel inferences. Subjects learned the status of people in two unseen 2D social hierarchies, with each dimension learned on a separate day. Although one dimension was behaviorally relevant, multivariate activity patterns in HC, EC, and vmPFC/mOFC were linearly related to the Euclidean distance between people in the mentally reconstructed 2D space. Hubs created unique comparisons between the hierarchies, enabling inferences between novel pairs. We found that both behavior and neural activity in EC and vmPFC/mOFC reflected the Euclidean distance to the retrieved hub, which was reinstated in HC. These findings reveal how abstract and discrete relational structures are represented, are combined, and enable novel inferences in the human brain.
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Affiliation(s)
- Seongmin A Park
- Center for Mind and Brain, University of California, Davis, Davis, CA, USA; Center for Neuroscience, University of California, Davis, Davis, CA, USA.
| | - Douglas S Miller
- Center for Mind and Brain, University of California, Davis, Davis, CA, USA; Center for Neuroscience, University of California, Davis, Davis, CA, USA
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Charan Ranganath
- Center for Neuroscience, University of California, Davis, Davis, CA, USA; Department of Psychology, University of California, Davis, Davis, CA, USA
| | - Erie D Boorman
- Center for Mind and Brain, University of California, Davis, Davis, CA, USA; Department of Psychology, University of California, Davis, Davis, CA, USA.
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Bang D, Ershadmanesh S, Nili H, Fleming SM. Private-public mappings in human prefrontal cortex. eLife 2020; 9:e56477. [PMID: 32701449 PMCID: PMC7377905 DOI: 10.7554/elife.56477] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 07/01/2020] [Indexed: 12/29/2022] Open
Abstract
A core feature of human cognition is an ability to separate private states of mind - what we think or believe - from public actions - what we say or do. This ability is central to successful social interaction - with different social contexts often requiring different mappings between private states and public actions in order to minimise conflict and facilitate communication. Here we investigated how the human brain supports private-public mappings, using an interactive task which required subjects to adapt how they communicated their confidence about a perceptual decision to the social context. Univariate and multivariate analysis of fMRI data revealed that a private-public distinction is reflected in a medial-lateral division of prefrontal cortex - with lateral frontal pole (FPl) supporting the context-dependent mapping from a private sense of confidence to a public report. The concept of private-public mappings provides a promising framework for understanding flexible social behaviour.
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Affiliation(s)
- Dan Bang
- Wellcome Centre for Human Neuroimaging, UCLLondonUnited Kingdom
- Department of Experimental Psychology, University of OxfordOxfordUnited Kingdom
| | - Sara Ershadmanesh
- School of Cognitive Sciences, Institute for Research in Fundamental SciencesTehranIslamic Republic of Iran
| | - Hamed Nili
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional Magnetic Resonance Imaging of the Brain, University of OxfordOxfordUnited Kingdom
| | - Stephen M Fleming
- Wellcome Centre for Human Neuroimaging, UCLLondonUnited Kingdom
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, UCLLondonUnited Kingdom
- Department of Experimental Psychology, UCLLondonUnited Kingdom
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