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

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

doi:10.1016/j.neuroimage.2020.116865

Cited by 32 publications

(19 citation statements)

References 91 publications

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“…The results show that individual differences in fine-grained patterns of functional connectivity are a markedly better predictor of general intelligence than are coarse-grained patterns, indicating that differences in cortical architecture that underlie inter-individual variation in the efficiency of information processing are more evident at the same spatial scale as topographic patterns that encode information. Discovering the dominant role of fine-grained connectivity patterns in the neural basis of intelligence required a method that resolves individual differences in these idiosyncratic patterns, a method that was not available prior to hyperalignment (Feilong et al, 2018; Guntupalli et al, 2018, 2016; Haxby et al, 2020, 2011; Nastase etal., 2020).…”

Section: Discussionmentioning

confidence: 99%

The neural basis of intelligence in fine-grained cortical topographies

Feilong

Guntupalli²,

Haxby

2020

Preprint

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Neural basis of intelligence in fine-grained topographies Feilong 2 SummaryIntelligent thought is the product of efficient neural information processing. Previous work on the neural basis of intelligence has focused on coarse-grained features of brain anatomy and function, even though information is encoded in fine-grained, topographically-organized population responses. We hypothesized that individual differences in fine-grained cortical architecture would provide stronger predictions of general intelligence. Using hyperalignment, we were able to model these fine-grained architectural differences by resolving idiosyncratic interindividual variation of finegrained topographies of functional connectivity. We found that predictions of general intelligence based on fine-grained patterns of connectivity were markedly stronger than predictions based on coarse-grained patterns, especially in the cortical systems associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods couldn't resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

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Section: Discussionmentioning

confidence: 99%

The neural basis of intelligence in fine-grained cortical topographies

Feilong

Guntupalli²,

Haxby

2020

Preprint

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“…H2A and RHA both require that participants share the same time-locked stimulus with the same number of time points, so they cannot be applied to resting-state data or data sets that implement different stimuli. Because CHA aligns functional connectivity profiles rather than time series data, it alone can be used with datasets that don’t have time-locked stimuli ( Guntupalli et al 2018 ; Nastase et al, 2020 ). Although we derive the RHA and CHA estimates from the same movie stimulus in the current application of H2A, the CHA component of the algorithm could also be applied to subjects with both movie and resting-state scans.…”

Section: Discussionmentioning

confidence: 99%

Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

et al. 2021

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Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals’ brain data into a common model space while maintaining the geometric relationships between distinct patterns of activity or connectivity. The dimensions of this common model capture functional profiles that are shared across individuals such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Hyperalignment can use either response-based or connectivity-based input data to derive transformations that project individuals’ neural data from anatomical space into the common model space. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we developed a new hyperalignment algorithm, hybrid hyperalignment, that derives transformations based on both response-based and connectivity-based information. We used three different movie-viewing fMRI datasets to test the performance of our new algorithm. Hybrid hyperalignment derives a single common model space that aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment. These results suggest that a single common information space can encode both shared cortical response and functional connectivity profiles across individuals.

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“…The SRM procedure thus has the effect of highlighting (shared) music-related variance (see Figure 3 for diagram of analysis pipeline). The "optimal" number of shared features will tend to vary across both stimuli and brain areas (e.g., as determined by cross-validation); here, for the sake of simplicity, we fixed the number of shared features at 30 as a reasonable middle ground based on values used in prior work (Chen et al, 2015;Chen et al, 2017;Nastase et al, 2020). We applied an SRM with parameters estimated from run 1 to transform the run 2 data, and applied an SRM with parameters estimated from run 2 to transform the run 1 data.…”

Section: Shared Response Modelmentioning

confidence: 99%

High-order areas and auditory cortex both represent the high-level event structure of music

Williams

Margulis

Nastase

et al. 2021

Preprint

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Recent fMRI studies of event segmentation have found that default mode regions represent high-level event structure during movie watching. In these regions, neural patterns are relatively stable during events and shift at event boundaries. Music, like narratives, contains hierarchical event structure (e.g., sections are composed of phrases). Here, we tested the hypothesis that brain activity patterns in default mode regions reflect the high-level event structure of music. We used fMRI to record brain activity from 25 participants (male and female) as they listened to a continuous playlist of 16 musical excerpts, and additionally collected annotations for these excerpts by asking a separate group of participants to mark when meaningful changes occurred in each one. We then identified temporal boundaries between stable patterns of brain activity using a hidden Markov model and compared the location of the model boundaries to the location of the human annotations. We identified multiple brain regions with significant matches to the observer-identified boundaries, including auditory cortex, mPFC, parietal cortex, and angular gyrus. From these results, we conclude that both higher-order and sensory areas contain information relating to the high-level event structure of music. Moreover, the higher-order areas in this study overlap with areas found in previous studies of event perception in movies and audio narratives, including regions in the default mode network.Significance StatementListening to music requires the brain to track dynamics at multiple hierarchical timescales. In our study, we had fMRI participants listen to real-world music (classical and jazz pieces) and then used an unsupervised learning algorithm (a hidden Markov model) to model the high-level event structure of music within participants’ brain data. This approach revealed that default mode brain regions involved in representing the high-level event structure of narratives are also involved in representing the high-level event structure of music. These findings provide converging support for the hypothesis that these regions play a domain-general role in processing stimuli with long-timescale dependencies.

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

Cited by 32 publications

References 91 publications

The neural basis of intelligence in fine-grained cortical topographies

The neural basis of intelligence in fine-grained cortical topographies

Hybrid hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

High-order areas and auditory cortex both represent the high-level event structure of music

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