Measuring shared responses across subjects using intersubject correlation

Nastase, Samuel A.; Gazzola, Valeria; Hasson, Uri; Keysers, Christian

doi:10.1101/600114

Cited by 99 publications

(217 citation statements)

References 141 publications

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“…We computed spatial pattern inter-subject correlation (pISC) Nastase et al, 2019) in the Intact Story condition for each of 400 parcels from an independent whole-brain resting-state parcellation (Schaefer et al, 2018). Controls-vs-controls pISC was calculated in the following way.…”

Section: Parcellation Pattern Similarity Mapsmentioning

confidence: 99%

“…First, we established the regions where control participants exhibited similar brain activity patterns with each other while listening to the intact narrative by calculating pattern similarity in every parcel (Schaefer et al, 2018) across the brain. Previous fMRI studies of the temporal integration hierarchy have largely used temporal ISC (tISC; e.g., Hasson et al, 2008;Lerner et al, 2011;Simony et al, 2016); here we use spatial pattern ISC (pISC; e.g., Chen et al, 2017;Oedekoven et al, 2017;Baldassano et al, 2018;Nastase et al, 2019) which allows estimates of response reliability to be calculated at each TR. The two are closely related but not redundant ((Nastase et al, 2019); Supplementary Figure 4).…”

Section: A's Neural Responses To An Auditory Narrative Match the mentioning

confidence: 99%

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Temporal integration of narrative information in a hippocampal amnesic patient

Zuo¹,

Honey²,

Barense

et al. 2019

Preprint

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Default network regions appear to integrate information over time windows of 30 seconds or more during narrative listening. Does this long-timescale capability require the hippocampus? Amnesic behavior suggests that the hippocampus may not be needed for online processing when input is continuous and semantically rich: amnesics can participate in conversations and tell stories spanning minutes, and when tested immediately on recently heard prose their performance is relatively preserved. We hypothesized that default network regions can integrate the semantically coherent information of a narrative across long time windows, even in the absence of the hippocampus. To test this prediction, we measured BOLD activity in the brain of a hippocampal amnesic patient (D. A.) and healthy control participants while they listened to a seven-minute narrative. The narrative was played either in its intact form, or as a paragraphscrambled version, which has been previously shown to interfere with the long-range temporal dependencies in default network activity. In the intact story condition, D. A.'s moment-by-moment BOLD activity spatial patterns were similar to those of controls in low-level auditory cortex as well as in some high-level default network regions (including lateral and medial posterior parietal cortex). Moreover, as in controls, D. A.'s response patterns in medial and lateral posterior parietal cortex were disrupted when paragraphs of the story were presented in a shuffled order, suggesting that activity in these areas did depend on information from 30 seconds or more in the past. Together, these results suggest that some default network cortical areas can integrate information across long timescales, even in the absence of the hippocampus.People with hippocampal damage are profoundly impaired in recalling information after a distraction (Milner, 1966), or "as soon as their attention shifts to a new topic" (Milner, 2005). At the same time, these amnesic individuals are able to engage in conversation, retain near-normal immediate recall for prose passages (Baddeley and Wilson, 2002), and tell globally coherent stories (Keven et al., 2018;Kurczek and Duff, 2011;Rosenbaum et al., 2009). How is this possible? One potential explanation is that, in the absence of major topic changes or surprises that create a distraction, amnesic individuals are able to rely on default network cortical regions for retention of semantically-rich information across time. Default network areas are proposed to carry slowly-changing information during continuous natural input such as stories and conversation (Hasson et al., 2015). In healthy people, these areas are functionally coupled to the hippocampus and may work together to accumulate, maintain, and integrate information across events. However, it is an open question whether the long-timescale capability of default network regions depends critically on interactions with the hippocampus .In this study, we investigated whether default network areas can integrate information over tens of se...

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Section: Parcellation Pattern Similarity Mapsmentioning

confidence: 99%

Section: A's Neural Responses To An Auditory Narrative Match the mentioning

confidence: 99%

Temporal integration of narrative information in a hippocampal amnesic patient

Zuo¹,

Honey²,

Barense

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The second objective is to be flexible enough to accurately represent the organization of an individual brain. Simultaneously achieving these two objectives is challenging, in part due to inter-subject variability [1][2][3] , which is also associated with measures of cognitive performance [4] . In addition, it has also become apparent that brain functional connectivity substantially reorganizes dynamically [5] according to different cognitive states [6] .…”

Section: Introductionmentioning

confidence: 99%

Unraveling reproducible dynamic states of individual brain functional parcellation

Boukhdhir¹,

Mignotte

Bellec

2020

Preprint

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Data-driven parcellations are widely used for exploring the functional organization of the brain, and also for reducing the high dimensionality of fMRI data. Despite the flurry of methods proposed in the literature, functional brain parcellations are not highly reproducible at the level of individual subjects, even with very long acquisitions. Some brain areas are also more difficult to parcellate than others, with association heteromodal cortices being the most challenging. An important limitation of classical parcellations is that they are static, i.e. they neglect dynamic reconfigurations of brain networks. In this paper, we proposed a new method to identify dynamic states of parcellations, which we hypothesized would improve reproducibility over static parcellation approaches. For a series of seed voxels in the brain, we applied a cluster analysis to regroup short (3 minutes) time windows into "states" with highly similar seed parcel. We splitted individual time series of the Midnight Scan Club sample into two independent sets of 2.5 hours (test and retest). We found that average within-state parcels, called stability maps, were highly reproducible (over .9 test-retest spatial correlation in many instances) and subject specific (fingerprinting accuracy over 70% on average) between test and retest. Consistent with our hypothesis, seeds in heteromodal cortices (posterior and anterior cingulate) showed a richer repertoire of states than unimodal (visual) cortex. Taken together, our results indicate that static functional parcellations are incorrectly averaging well defined and distinct dynamic states of brain parcellations. This work calls to revisit previous methods based on static parcellations, which includes the majority of published network analyses of fMRI data. Our method may thus impact how researchers model the rich interactions between brain networks in health and disease.

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“…By evaluating dual-fMRI data in a model-free, hypothesis-free manner, whereby no a priori assumptions are made, these techniques are more appropriate for the nonlinear, unpredictable dynamic of naturalistic social exchange (Nastase, Gazzola, & Keysers, 2019). By evaluating dual-fMRI data in a model-free, hypothesis-free manner, whereby no a priori assumptions are made, these techniques are more appropriate for the nonlinear, unpredictable dynamic of naturalistic social exchange (Nastase, Gazzola, & Keysers, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven techniques have been developed to provide an alternative way of analyzing hyperscanning data, offering a means to address these outstanding questions. By evaluating dual-fMRI data in a model-free, hypothesis-free manner, whereby no a priori assumptions are made, these techniques are more appropriate for the nonlinear, unpredictable dynamic of naturalistic social exchange (Nastase, Gazzola, & Keysers, 2019). Recently, Bilek et al (2015Bilek et al ( , 2017 demonstrated how two such data-driven techniques can be combined to investigate neural coupling during social interaction.…”

mentioning

confidence: 99%

Getting into sync: Data‐driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

Špiláková

Shaw

Czekóová

et al. 2019

Human Brain Mapping

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In social interactions, each individual's brain drives an action that, in turn, elicits systematic neural responses in their partner that drive a reaction. Consequently, the brain responses of both interactants become temporally contingent upon one another through the actions they generate, and different interaction dynamics will be underpinned by distinct forms of between-brain coupling. In this study, we investigated this by "performing functional magnetic resonance imaging on two individuals simultaneously (dual-fMRI) while they competed or cooperated with one another in a turn-based or concurrent fashion." To assess whether distinct patterns of neural coupling were associated with these different interactions, we combined two data-driven, model-free analytical techniques: group-independent component analysis and inter-subject correlation. This revealed four distinct patterns of brain responses that were temporally aligned between interactants: one emerged during co-operative exchanges and encompassed brain regions involved in social cognitive processing, such as the temporo-parietal cortex. The other three were associated with competitive exchanges and comprised brain systems implicated in visuomotor processing and social decision-making, including the cerebellum and anterior cingulate cortex. Interestingly, neural coupling was significantly stronger in concurrent relative to turn-based exchanges. These results demonstrate the utility of data-driven approaches applied to "dual-fMRI" data in elucidating the interpersonal neural processes that give rise to the two-in-one dynamic characterizing social interaction.

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Measuring shared responses across subjects using intersubject correlation

Cited by 99 publications

References 141 publications

Temporal integration of narrative information in a hippocampal amnesic patient

Temporal integration of narrative information in a hippocampal amnesic patient

Unraveling reproducible dynamic states of individual brain functional parcellation

Getting into sync: Data‐driven analyses reveal patterns of neural coupling that distinguish among different social exchanges

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