Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Nastase, Samuel A.; Liu, Yunfei; Hillman, Hanna; Norman, Kenneth A.; Hasson, Uri

doi:10.1101/741975

Cited by 8 publications

(9 citation statements)

References 85 publications

(91 reference statements)

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“…Overall, these findings suggest that neural coupling between teachers and students can be used as an index of learning. While the precise biological processes that give rise to neural alignment has not yet been elucidated, we suggest that neural alignment reflects shared representation of semantic knowledge (Vodrahalli et al, 2018;Cetron et al, 2019;Nastase et al, 2019b).…”

Section: Discussionmentioning

confidence: 84%

Teacher-student neural coupling during teaching and learning

Nguyen

Chang

Micciche

et al. 2020

Preprint

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Human communication is remarkably versatile, enabling teachers to share highly abstracted and novel information with their students. What neural processes enable such transfer of information across brains during naturalistic teaching and learning? Here, we show that during lectures, wherein information transmission is unidirectional and flows from the teacher to the student, the student's brain mirrors the teacher's brain and that this neural coupling is correlated with learning outcomes. A teacher was scanned in fMRI giving an oral lecture with slides on a scientific topic followed by a review lecture. Students were then scanned watching either the intact lecture and review (N = 20) or a temporally scrambled version of the lecture (N = 20).Using intersubject correlation (ISC), we observed widespread teacher-student neural coupling spanning sensory cortex and language regions along the superior temporal sulcus as well as higher-level regions including posterior medial cortex (PMC), superior parietal lobule (SPL), and dorsolateral and dorsomedial prefrontal cortex. Teacher-student alignment in higher-level areas was not observed when learning was disrupted by temporally scrambling the lecture. Moreover, teacher-student coupling in PMC was significantly correlated with learning outcomes: the more closely the student's brain mirrored the teacher's brain, the more the student improved between behavioral pre-learning and post-learning assessments. Together, these results suggest that the alignment of neural responses between teacher and students may underlie effective communication of complex information across brains in classroom settings. 3 Significance statementHow is technical, non-narrative information communicated from one brain to another during teaching and learning? In this fMRI study, we show that the DMN activity of teachers and students are coupled during naturalistic teaching. This teacher-student neural coupling emerges only during intact learning and is correlated with learning outcomes. Together, these findings suggest that teacher-student neural alignment underlies effective communication during teaching.

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Section: Discussionmentioning

confidence: 84%

Teacher-student neural coupling during teaching and learning

Nguyen

Chang

Micciche

et al. 2020

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“…In a developmental sample, this could be a significant issue, and using better parcellations that are defined within the study sample (Shen et al, 2013), or that are condition-specific (Salehi et al, 2019) may reveal that individually distinct patterns in FC are robustly present during childhood if small differences in topography are better accounted for. Another potential avenue to fully optimize this approach would be to use hyperalignment (Conroy et al, 2013;Guntupalli et al, 2018;Haxby et al, 2011;Nastase et al, 2019) in a developmental sample, though the data duration (upwards of 20 minutes) required for this approach-even with movie-watchingremains a challenge.…”

Section: Fingerprinting In Developmental Samplesmentioning

confidence: 99%

Stability and similarity of the pediatric connectome as developmental measures

Vanderwal

Eilbott²,

Kelly

et al. 2019

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Patterns of functional connectivity are unique at the individual level, enabling test-retest matching algorithms to identify a subject from among a group using only their functional connectome. Recent findings show that accuracies of these algorithms in children increase with age. Relatedly, the persistence of functional connectivity (FC) patterns across tasks and rest also increases with age. This study investigated the hypothesis that within-subject stability and between-subject similarity of the whole-brain pediatric connectome are developmentally relevant outcomes. Using data from 210 help-seeking children and adolescents, ages 6-21 years (Healthy Brain Network Biobank), we computed whole-brain FC matrices for each participant during two different movies (MovieDM and MovieTP) and two runs of taskfree rest (all from a single scan session) and fed these matrices to a test-retest matching algorithm. We replicated the finding that matching accuracies for children are low (18-44%) relative to previous work in adults and found that cross-state and cross-movie accuracies for children were the lowest. Next, we calculated three measures of connectome stability for each subject: cross-rest (Rest1-Rest2), cross-state (Rest1-MovieDM), and cross-movie (MovieDM-MovieTP), and three measures of within-state betweensubject connectome similarity for Rest1, MovieDM, and MovieTP. We show that all six measures of stability and similarity are correlated, but that these measures are not related to age. A principal component analysis of these measures yielded two components that were used to test for brain-behavior correlations with IQ, general psychopathology, and a social skills measure (n=119). The first component was significantly correlated with the social skills measure (r=-0.27, p=0.003) and overall psychiatric scores (r=-0.239, p=0.009). Post hoc correlations showed that cross-rest stability was correlated with both social skills (p=0.0007) and general psychopathology (p=0.001). Connectome similarity during MovieDM was also correlated with social skills (p=0.0026). These findings suggest that the stability and similarity of the whole-brain connectome relate to overall brain development, and in particular, to those regions that support social cognition. We infer that the development of the functional connectome simultaneously achieves patterns of FC that are distinct at the individual subject level, that are shared across individuals, and that are persistent across states and across runs-features which presumably combine to optimize neural processing during development. Future longitudinal work could reveal the developmental trajectories of stability and similarity of the connectome.

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“…In the last several years, there have been a number of large-scale neuroimaging datasets that have been made publicly available for re-use (Aliko et al, 2020; Gordon et al, 2017; Nastase et al, 2020; Taylor et al, 2017; Van Essen et al, 2013). Several distinguishing aspects of the present work set NSD apart from past datasets.…”

Section: Discussionmentioning

confidence: 99%

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Allen

St-Yves

et al. 2021

Preprint

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Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

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Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space

Cited by 8 publications

References 85 publications

Teacher-student neural coupling during teaching and learning

Teacher-student neural coupling during teaching and learning

Stability and similarity of the pediatric connectome as developmental measures

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

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