Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low‐dimensional space of brain dynamics

Gao, Siyuan; Mishne, Gal; Scheinost, Dustin

doi:10.1002/hbm.25561

Cited by 45 publications

(49 citation statements)

References 51 publications

(81 reference statements)

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“…In fact, increasing evidence suggests that key observable facets of brain processes operate in a low dimensional space ( 21 ). At the neuronal level, complex interactions between brain areas underlie emergent population-level properties that cannot be captured at the level of individual neurons; similar observations are becoming prevalent in neuroimaging ( 22 ). Notably, this substantial low dimensional structure is expected to account for the unmatched performance of the sole multivariate method tested here and in related recent work ( 14 ).…”

Section: Discussionmentioning

confidence: 71%

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Noble

Mejia

Zalesky

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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Inference in neuroimaging typically occurs at the level of focal brain areas or circuits. Yet, increasingly, well-powered studies paint a much richer picture of broad-scale effects distributed throughout the brain, suggesting that many focal reports may only reflect the tip of the iceberg of underlying effects. How focal versus broad-scale perspectives influence the inferences we make has not yet been comprehensively evaluated using real data. Here, we compare sensitivity and specificity across procedures representing multiple levels of inference using an empirical benchmarking procedure that resamples task-based connectomes from the Human Connectome Project dataset (∼1,000 subjects, 7 tasks, 3 resampling group sizes, 7 inferential procedures). Only broad-scale (network and whole brain) procedures obtained the traditional 80% statistical power level to detect an average effect, reflecting >20% more statistical power than focal (edge and cluster) procedures. Power also increased substantially for false discovery rate– compared with familywise error rate–controlling procedures. The downsides are fairly limited; the loss in specificity for broad-scale and FDR procedures was relatively modest compared to the gains in power. Furthermore, the broad-scale methods we introduce are simple, fast, and easy to use, providing a straightforward starting point for researchers. This also points to the promise of more sophisticated broad-scale methods for not only functional connectivity but also related fields, including task-based activation. Altogether, this work demonstrates that shifting the scale of inference and choosing FDR control are both immediately attainable and can help remedy the issues with statistical power plaguing typical studies in the field.

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Section: Discussionmentioning

confidence: 71%

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Noble

Mejia

Zalesky

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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“…We iteratively searched for the optimal number of states (K), within a range of 2-8. K was determined based on the model's consistency (Vidaurre et al, 2018) and clustering performance (Calinski and Harabasz, 1974;Gao et al, 2021). We first tested the model consistency across iterations, where the same HMM training and inference procedures were repeated 5 times using the same hyperparameters.…”

Section: Methodsmentioning

confidence: 99%

Cognitive and Neural State Dynamics of Narrative Comprehension

Song

Park

et al. 2021

J. Neurosci.

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Narrative comprehension involves a constant interplay of the accumulation of incoming events and their integration into a coherent structure. This study characterizes cognitive states during narrative comprehension and the network-level reconfiguration occurring dynamically in the functional brain. We presented movie clips of temporally scrambled sequences to human participants (male and female), eliciting fluctuations in the subjective feeling of comprehension. Comprehension occurred when processing events that were highly causally related to the previous events, suggesting that comprehension entails the integration of narratives into a causally coherent structure. The functional neuroimaging results demonstrated that the integrated and efficient brain state emerged during the moments of narrative integration with the increased level of activation and across-modular connections in the default mode network. Underlying brain states were synchronized across individuals when comprehending novel narratives, with increased occurrences of the default mode network state, integrated with sensory processing network, during narrative integration. A model based on time-resolved functional brain connectivity predicted changing cognitive states related to comprehension that are general across narratives. Together, these results support adaptive reconfiguration and interaction of the functional brain networks on causal integration of the narratives.

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“…Similarly, embedding the resting state data onto the task manifold extracted with the help of diffusion maps (Gao et al, 2021) found the resting state time-points concentrate in the task-fixation and transition subspaces complemented by a minority of time points in the cognitive subspaces of the manifold.…”

Section: Discussionmentioning

confidence: 99%

Symmetry breaking organizes the brain's resting state manifold

Fousek

Rabuffo

Gudibanda

et al. 2022

Preprint

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Spontaneously fluctuating brain activity patterns emerge at rest and relate to brain functional networks involved in task conditions. Despite detailed descriptions of the spatio-temporal brain patterns, our understanding of their generative mechanism is still incomplete. Using a combination of computational modeling and dynamical systems analysis we provide a complete mechanistic description in terms of the constituent entities and the productive relation of their causal activities leading to the formation of a resting state manifold via the network connectivity. We demonstrate that the symmetry breaking by the connectivity creates a characteristic flow on the manifold, which produces the major empirical data features including spontaneous high amplitude co-activations, neuronal cascades, spectral cortical gradients, multistability and characteristic functional connectivity dynamics. The understanding of the brain's resting state manifold is fundamental for the construction of task-specific flows and manifolds used in theories of brain function such as predictive coding.

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Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low‐dimensional space of brain dynamics

Cited by 45 publications

References 51 publications

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Improving power in functional magnetic resonance imaging by moving beyond cluster-level inference

Cognitive and Neural State Dynamics of Narrative Comprehension

Symmetry breaking organizes the brain's resting state manifold

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