What the success of brain imaging implies about the neural code

Guest, Olivia; Love, Bradley C.

doi:10.7554/elife.21397

Cited by 50 publications

(29 citation statements)

References 83 publications

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“…In one sense this is not surprising, as the states of the task are represented by images of objects, and visual objects (especially faces) are represented in the anterior temporal lobe (33). Still, the fact that the anterior temporal lobe shows representations consistent with planning mechanisms suggests a more active role in planning beyond feedforward sensory processing as commonly understood (34). Figure 1 and Figure S1B.…”

Section: A)mentioning

confidence: 81%

Foundations of human problem solving

Zarr

Brown

2019

Preprint

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Despite great strides in both machine learning and neuroscience, we do not know how the human brain solves problems in the general sense. We approach this question by drawing on the framework of engineering control theory. We demonstrate a computational neural model with only localist learning laws that is able to find solutions to arbitrary problems. Using a combination of computational neural modeling, human fMRI, and representational similarity analysis, we show here that the roles of a number of brain regions can be reinterpreted as interacting mechanisms of a control theoretic system. The results suggest a new set of functional perspectives on the orbitofrontal cortex, hippocampus, basal ganglia, anterior temporal lobe, lateral prefrontal cortex, and visual cortex, as well as a new path toward artificial general intelligence.

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Section: A)mentioning

confidence: 81%

Foundations of human problem solving

Zarr

Brown

2019

Preprint

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“…It is possible that poor integration of prefrontal and striatal signals in the AI predisposes teens towards risky behavior by virtue of regional differences in the neural code—the exact patterns and firing rates of neurons that give rise to behaviors, emotions, cognitions, and psychomotor activity. Strides have been made in using fMRI activity to map out a higher order neural code for advanced mental processes (Guest & Love, ), but much remains unknown. Given what is not known, it is still plausible that neural activity encoding emotional salience, motivation, and reward in the striatum needs translating in the AI in order to interface with a separate code used to encode top‐down, prefrontal regulation.…”

Section: Discussionmentioning

confidence: 99%

Family conflict is associated with longitudinal changes in insular‐striatal functional connectivity during adolescent risk taking under maternal influence

Moreira

Telzer

2017

Developmental Science

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Maternal presence has marked effects on adolescent neurocognition during risk taking, influencing teenagers to make safer decisions. However, it is currently unknown whether maternal buffering changes over the course of adolescence itself, and whether its effects are robust to individual differences family relationship quality. In the current longitudinal study, 23 adolescents completed a risk-taking task under maternal presence during an fMRI scan before and after the transition to high school. Behavioral results reveal that adolescent risk taking increased in the presence of mother across a one year period. At the neural level, we found adolescents reporting higher family conflict showed longitudinal increases in functional coupling between the anterior insula (AI) and ventral striatum (VS) when making safe decisions in the presence of their mother, which was associated with increased real-world risk taking. These findings show that individual differences in family relationship quality undermine effective development of AI-VS connectivity resulting in increased risk taking.

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“…The reappraisal task was modeled with five task regressors, each convolved with the canonical HRF (double gamma). A regressor for the instructional cue ('Instruction'), one for each task condition ('Far', 'Look- We used univariate activation estimates from the voxels within each sphere to calculate Gini coefficients, a simple but powerful way to quantify spatial variability (Guest & Love, 2017;Leech et al, 2014;Pyatt, 1976). The Gini coefficient was originally developed to study income inequality within specified geographic locations (e.g., cities, countries; Pyatt, 1976).…”

Section: Methodsmentioning

confidence: 99%

Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

Moreira¹,

McLaughlin²,

Silvers³

2019

Developmental Psychology

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Variability is a fundamental feature of human brain activity that is particularly pronounced during development. However, developmental neuroimaging research has only recently begun to move beyond characterizing brain function across development exclusively in terms of magnitude of neural activation to incorporating estimates of variability. No prior neuroimaging study has done so in the domain of emotion regulation. We investigated how age and affective experiences influence spatial and temporal variability in neural activity during emotion regulation. In the current study, 70 typically developing youth aged 8-17 years completed a cognitive reappraisal task of emotion regulation while undergoing functional magnetic resonance imaging. Estimates of spatial and temporal variability during emotion regulation were calculated across a network of brain regions, defined a priori, and were then related to age and affective experiences. Results showed that increasing age was associated with reduced spatial and temporal variability in a set of frontoparietal regions (e.g., dorsomedial prefrontal cortex, superior parietal lobule) known to be involved in effortful emotion regulation. In addition, youth who reported less negative affect during emotion regulation had less spatial variability in the ventrolateral prefrontal cortex. We interpret age-related reductions in spatial and temporal variability as evidence of neural specialization. These results imply that the development of emotion regulation is undergirded by a process of neural specialization and open up a host of possibilities for incorporating neural variability into the study of emotion regulation development.

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What the success of brain imaging implies about the neural code

Cited by 50 publications

References 83 publications

Foundations of human problem solving

Foundations of human problem solving

Family conflict is associated with longitudinal changes in insular‐striatal functional connectivity during adolescent risk taking under maternal influence

Spatial and temporal cortical variability track with age and affective experience during emotion regulation in youth.

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