Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

Ito, Teruaki; Murray, John D.

doi:10.1101/2021.11.29.470432

Cited by 8 publications

(12 citation statements)

References 69 publications

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“…2i) -systems that are functionally distinct/specialized from each other -we found a monotonic relationship between these systems and their gradient loading. This is consistent with a gradient of functional specialization, where sensory and motor regions are defined by distinct functions, while association regions integrate the two (Ito and Murray, 2021). Moreover, this sensory-to-motor SC gradient was significantly associated with the 2nd principal gradient of rNC (rank r=0.72, non-parametric p<0.001) (Fig.…”

Section: Cortical Scs Are Organized Along a Gradient Of Functional Sp...supporting

confidence: 82%

“…Portions of this section are paraphrased from the dataset's original publication's Methods section (King et al, 2019), and a prior study we used to investigate multi-task cortical representations (Ito and Murray, 2021).…”

Section: Multi-domain Task Battery Datasetmentioning

confidence: 99%

“…Portions of this section are paraphrased from a prior study using a similar preprocessing strategy (Ito and Murray, 2021).…”

Section: Fmri Preprocessingmentioning

confidence: 99%

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The impact of functional correlations on task information coding

Ito

Murray

2022

Preprint

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The brain is a complex system with dynamic network changes. Prior studies in theoretical neuroscience have demonstrated that state-dependent neural correlations can be understood from a neural coding framework. These so-called noise correlations - the trial-to-trial or moment-to-moment co-variability - can be interpreted only if the underlying signal correlation - the similarity of task selectivity between pairs of neural units - is known. While the impact of these correlations on task coding have been widely investigated in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here we investigate the relationship between large-scale noise correlations and their underlying signal correlations in a multi-task human fMRI dataset. We found that state-dependent noise correlation changes do not typically align in the same direction as their underlying signal correlation, suggesting that 1) trial-by-trial noise is typically reduced between similarly tuned regions, and 2) stimulus-driven activity does not linearly superimpose atop the network's underlying background activity. Crucially, we discovered that noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved the information coding of these brain regions. In contrast, noise correlation changes that were aligned with their signal correlation did not. These aligned noise correlations were primarily correlation increases, which have been commonly (yet incorrectly) assumed to increase information communication between brain regions in human neuroimaging studies. These findings illustrate that state-dependent noise correlations contribute to the information coding of functional brain networks, but interpretation of these correlation changes requires knowledge of the underlying signal correlations.

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Section: Cortical Scs Are Organized Along a Gradient Of Functional Sp...supporting

confidence: 82%

Section: Multi-domain Task Battery Datasetmentioning

confidence: 99%

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The impact of functional correlations on task information coding

Ito

Murray

2022

Preprint

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“…b) To match the number of layers in the ANN, we created three discretized systems based on the functional network partition -sensory, association, and motor -that followed the sensory-to-motor hierarchy (Fig. 8) [32,21] Supplementary Figure 7: A pairwise comparison of PS for all possible dichotomies in the Logic rule condition. a) We computed the PS for every pairwise dichotomy in the Logic rule domain, and computed the average PS across brain regions within each cortical system (Sensory, Association, and Motor cortex).…”

Section: A Supplementary Methodsmentioning

confidence: 99%

“…Analysis of human fMRI data revealed that content-specific abstraction was spatially heterogeneous across cortex. Recent neuroscience work has identified hierarchical gradients that organize along a sensory-to-motor output axis in both resting-state [32] and multi-task fMRI data [21]. We therefore sought to quantify PS across this sensory-to-motor hierarchy in fMRI data, and compare it to PS changes in the feedforward hierarchy (i.e., layer-depth) in ANNs.…”

Section: Convergent Hierarchy Of Abstract Representations In Humans A...mentioning

confidence: 99%

Compositional generalization through abstract representations in human and artificial neural networks

Ito¹,

Klinger²,

Schultz³

et al. 2022

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Humans have a remarkable ability to rapidly generalize to new tasks that is difficult to reproduce in artificial learning systems. Compositionality has been proposed as a key mechanism supporting generalization in humans, but evidence of its neural implementation and impact on behavior is still scarce. Here we study the computational properties associated with compositional generalization in both humans and artificial neural networks (ANNs) on a highly compositional task. First, we identified behavioral signatures of compositional generalization in humans, along with their neural correlates using whole-cortex functional magnetic resonance imaging (fMRI) data. Next, we designed pretraining paradigms aided by a procedure we term primitives pretraining to endow compositional task elements into ANNs. We found that ANNs with this prior knowledge had greater correspondence with human behavior and neural compositional signatures. Importantly, primitives pretraining induced abstract internal representations, excellent zero-shot generalization, and sample-efficient learning. Moreover, it gave rise to a hierarchy of abstract representations that matched human fMRI data, where sensory rule abstractions emerged in early sensory areas, and motor rule abstractions emerged in later motor areas. Our findings give empirical support to the role of compositional generalization in human behavior, implicate abstract representations as its neural implementation, and illustrate that these representations can be embedded into ANNs by designing simple and efficient pretraining procedures.

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Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest

Capouskova,

Zamora‐López,

Kringelbach

et al. 2023

Human Brain Mapping

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Adapting to a constantly changing environment requires the human brain to flexibly switch among many demanding cognitive tasks, processing both specialized and integrated information associated with the activity in functional networks over time. In this study, we investigated the nature of the temporal alternation between segregated and integrated states in the brain during rest and six cognitive tasks using functional MRI. We employed a deep autoencoder to explore the 2D latent space associated with the segregated and integrated states. Our results show that the integrated state occupies less space in the latent space manifold compared to the segregated states. Moreover, the integrated state is characterized by lower entropy of occupancy than the segregated state, suggesting that integration plays a consolidating role, while segregation may serve as cognitive expertness. Comparing rest and the tasks, we found that rest exhibits higher entropy of occupancy, indicating a more random wandering of the mind compared to the expected focus during task performance. Our study demonstrates that both transient, short‐lived integrated and segregated states are present during rest and task performance, flexibly switching between them, with integration serving as information compression and segregation related to information specialization.

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Multi-task representations in human cortex transform along a sensory-to-motor hierarchy

Cited by 8 publications

References 69 publications

The impact of functional correlations on task information coding

The impact of functional correlations on task information coding

Compositional generalization through abstract representations in human and artificial neural networks

Integration and segregation manifolds in the brain ensure cognitive flexibility during tasks and rest

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