Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Lorenz, Romy; Violante, Inês R.; Monti, Ricardo Pio; Montana, Giovanni; Hampshire, Adam; Leech, Robert

doi:10.1038/s41467-018-03657-3

Cited by 64 publications

(50 citation statements)

References 61 publications

(70 reference statements)

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“…In this experiment, we compared neurofeedback to a condition where images from original context were 100% visible (we expected that this would be the strongest possible reinstatement cue); we observed a significantly larger relationship between our neural measure of context reinstatement and recall behavior in the neurofeedback condition than in the 100% visible control condition. Previous studies have demonstrated that providing real-time fMRI can reveal insights about cognition (e.g., Cortese et al, 2016;Lorenz et al, 2018). Here, we extend that finding to demonstrate neurofeedback can more tightly link fluctuations of internal mental context with memory retrieval.…”

Section: Discussionsupporting

confidence: 73%

“…For example, this technique has been used for training participants to improve their sustained attention performance (as in deBettencourt et al, 2015), training new associations (as in Amano et al, 2016; see also deBettencourt and Norman, 2016), and reducing established fearful associations (Koizumi et al, 2017). Recently, researchers have used real-time fMRI to link behavior and neural activity, e.g., to dissociate confidence from accuracy (Cortese et al, 2016), to link brain activity with experience in a focused attention task (Garrison et al, 2013), to optimize experimental design (Lorenz et al, 2016), and to characterize a multidimensional task space (Lorenz et al, 2018). Here, we used neurofeedback in a potentially complementary way, to amplify brain activity fluctuations and improve measurement sensitivity for a cognitive process (context reinstatement) that we think is important for memory.…”

Section: Discussionmentioning

confidence: 99%

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Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval

deBettencourt

Turk‐Browne

Norman

2018

Preprint

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Theories of mental context and memory posit that successful mental context reinstatement enables better retrieval of memories from the same context, at the expense of memories from other contexts.To test this hypothesis, we had participants study lists of words, interleaved with task-irrelevant images from one category (e.g., scenes). Following encoding, participants were cued to mentally reinstate the context associated with a particular list, by thinking about the images that had appeared between the words. We measured context reinstatement with fMRI, and related this to performance on a free recall test that followed immediately afterwards. To increase sensitivity, we used a closed-loop neurofeedback procedure, whereby higher levels of context reinstatement (measured neurally) elicited increased visibility of the images from the studied context onscreen. Our goal was to create a positive feedback loop that amplified small fluctuations in mental context reinstatement, making it easier to experimentally detect a relationship between context reinstatement and recall. As predicted, we found that higher levels of neural context reinstatement were associated with better recall of words from the reinstated context, and worse recall of words from a different context. In a second experiment, we assessed the role of neurofeedback in identifying this brain-behavior relationship by presenting context images again but manipulating whether their visibility depended on neural context reinstatement.When neurofeedback was removed, the relationship between context reinstatement and memory retrieval disappeared. Together, these findings demonstrate a clear effect of context reinstatement on memory recall and suggest that neurofeedback can be a useful tool for characterizing brain-behavior relationships.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval

deBettencourt

Turk‐Browne

Norman

2018

Preprint

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“…Similar activation patterns crossing many cognitive domains, roughly corresponding to our current MD findings, has been documented in a large body of previous work. At the same time, there have been many suggestions of functional differentiation between MD-like regions, albeit with little consensus emerging across studies (Champod and Petrides, 2010; Dosenbach et al, 2007; Hampshire et al, 2012; Lorenz et al, 2018; Yeo et al, 2015). Our fine-grained anatomical findings illustrate the challenges in interpreting studies that are based on traditional neuroimaging analyses.…”

Section: Discussionmentioning

confidence: 99%

“…(ii) What is the degree of functional differentiation within the MD network? There are many rival proposals and little agreement across studies (Champod and Petrides, 2010; Dosenbach et al, 2007; Hampshire et al, 2012; Lorenz et al, 2018; Yeo et al, 2015). (iii) What is the precise relationship to “canonical” resting-state fMRI (rfMRI) brain networks revealed by various ways of grouping regions based on the strength of their time-series correlations?…”

Section: Introductionmentioning

confidence: 99%

A Domain-general Cognitive Core defined in Multimodally Parcellated Human Cortex

Assem

Glasser

Essen

et al. 2019

Preprint

192

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AbstractNumerous brain imaging studies identified a domain-general or “multiple-demand” (MD) activation pattern accompanying many tasks and may play a core role in cognitive control. Though this finding is well established, the limited spatial localization provided by traditional imaging methods precluded a consensus regarding the precise anatomy, functional differentiation and connectivity of the MD system. To address these limitations, we used data from 449 subjects from the Human Connectome Project, with cortex of each individual parcellated using neurobiologically grounded multi-modal MRI features. The conjunction of three cognitive contrasts reveals a core of 10 widely distributed MD parcels per hemisphere that are most strongly activated and functionally interconnected, surrounded by a penumbra of 17 additional areas. Outside cerebral cortex, MD activation is most prominent in the caudate and cerebellum. Comparison with canonical resting state networks shows MD regions concentrated in the fronto-parietal network but also engaging three other networks. MD activations show modest relative task preferences accompanying strong co-recruitment. With distributed anatomical organization, mosaic functional preferences, and strong interconnectivity, we suggest MD regions are well positioned to integrate and assemble the diverse components of cognitive operations. Our precise delineation of MD regions provides a basis for refined analyses of their functions.

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“…In our current approach, we treated the source position coordinates as a continuous parameter and navigated to optimal positions within the usually smooth and thus easily optimiseable space in the volumes. A similar approach has recently also been developed (Lorenz et al, 2016) and applied (Lorenz et al, 2018) for closed-loop applications in fMRI.…”

Section: Syllable Level Processing In Superior Temporal Regionsmentioning

confidence: 99%

Phoneme-level processing in low-frequency cortical responses to speech explained by acoustic features

Daube

Ince

Groß

2018

Preprint

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When we listen to speech, we have to make sense of a waveform of sound pressure. Hierarchical models of speech perception assume that before giving rise to its final semantic meaning, the signal is transformed into unknown intermediate neuronal representations. Classically, studies of such intermediate representations are guided by linguistically defined concepts such as phonemes. Here we argue that in order to arrive at an unbiased understanding of the mechanisms of speech comprehension, the focus should instead lie on representations obtained directly from the stimulus. We illustrate our view with a strongly data-driven analysis of a dataset of 24 young, healthy humans who listened to a narrative of one hour duration while their magnetoencephalogram (MEG) was recorded. We find that two recent results, a performance gain of an encoding model based on acoustic and annotated linguistic features over a model based on acoustic features alone as well as the decoding of subgroups of phonemes from phoneme-locked responses, can be explained with an encoding model entirely based on acoustic features. These acoustic features capitalise on acoustic edges and outperform Gabor-filtered spectrograms, features with the potential to describe the spectrotemporal characteristics of individual phonemes. We conclude that models of brain responses based on linguistic features can serve as excellent benchmarks. However, we put forward that linguistic concepts are better used when interpreting models, not when building them. In doing so, we find that the results of our analyses favour syllables over phonemes as candidate intermediate speech representations visible with fast non-invasive neuroimaging.

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Dissociating frontoparietal brain networks with neuroadaptive Bayesian optimization

Cited by 64 publications

References 61 publications

Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval

Neurofeedback helps to reveal a relationship between context reinstatement and memory retrieval

A Domain-general Cognitive Core defined in Multimodally Parcellated Human Cortex

Phoneme-level processing in low-frequency cortical responses to speech explained by acoustic features

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