Individual subject approaches to mapping sensory-biased and multiple-demand regions in human frontal cortex

doi:10.1016/j.cobeha.2021.05.002

Current Opinion in Behavioral Sciences

Volume 40, August 2021, Pages 169-177

https://doi.org/10.1016/j.cobeha.2021.05.002 Get rights and content

Highlights

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Deep imaging reveals multiple sensory-biased regions in lateral frontal cortex.
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Resting-state fMRI functional connectivity confirms sensory-biased organization.
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Probabilistic ROIs applied to large N dataset rs-fMRI reveals further structure.
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Connectivity fingerprinting predicts subject-specific organization from rs-fMRI.

Sensory modality, widely accepted as a key factor in the functional organization of posterior cortical areas, also shapes the organization of human frontal lobes. ‘Deep imaging,’ or the practice of collecting a sizable amount of data on individual subjects, offers significant advantages in revealing fine-scale aspects of functional organization of the human brain. Here, we review deep imaging approaches to mapping multiple sensory-biased and multiple-demand regions within human lateral frontal cortex. In addition, we discuss how deep imaging methods can be transferred to large public data sets to further extend functional mapping at the group level. We also review how ‘connectome fingerprinting’ approaches, combined with deep imaging, can be used to localize fine-grained functional organization in individual subjects using resting-state data. Finally, we summarize current ‘best practices’ for deep imaging.

Introduction

The accurate mapping of the functional architecture of the frontal lobes presents a number of scientific challenges. In the human brain, frontal cortex appears to consist of many small functional regions that exhibit substantial anatomical variation across individuals. Recent work has uncovered an increasingly fractionated architecture, with many functionally distinct regions lying within the larger regions defined by early fMRI studies [1,2]. Unlike primary sensory or motor areas, these regions typically exhibit modest levels of functional MRI activation for a variety of cognitive tasks. Because of the low activation levels, frontal lobe researchers traditionally average together the results from many subjects and spatially smooth data in hopes of observing statistically significant activation [3, 4, 5]. While that strategy provides advantages in terms of statistical power, it may come at the cost of obscuring fine-scale organization. In particular, small, functionally distinct regions could potentially be obscured and misinterpreted as large functionally homogenous regions. ‘Deep imaging,’ or the practice of collecting a sizable amount of data on individual subjects, offers significant advantages in revealing fine-scale aspects of functional organization [6^•,7^••,8, 9, 10, 11]. In this paper, we discuss deep imaging approaches to mapping sensory-biased and multiple-demand regions within human lateral frontal cortex.

Section snippets

Sensory-biased frontal lobe regions

Sensory modality preference plays a fundamental role in the functional organization of occipital, temporal, and parietal cortex [12, 13, 14, 15]; however, investigations of human frontal lobe have long reported no specificity for sensory modality [4,5,16, 17, 18, 19]. This amodal view of human frontal cortex contrasts with substantial non-human primate evidence for strong sensory modality influences in lateral frontal cortex [20,21^••,22]. Through the use of deep imaging fMRI methods and

Multiple-demand versus sensory-biased representations

Prior studies, including some that have emphasized within-subject analyses, have reported that lateral frontal cortex contains ‘multiple-demand’ regions that are recruited by a broad range of cognitive task-demands [4,25, 26, 27]. At first glance, the ‘sensory-biased’ account might be viewed as contradictory to the ‘multiple-demand’ account of lateral frontal cortical organization. In order to examine the relationship between sensory-biased regions and multiple-demand regions, we [11] scanned

Resting-state functional connectivity networks

Resting-state functional MRI (rs-fMRI) scans, in which participants are not given explicit cognitive tasks to perform (other than ‘keep your eyes open and let your mind wander’), are effective in revealing brain networks across groups of individuals [2,30, 31, 32], but also can reveal fine-scale individual differences in functional organization [6^•,7^••,33]. We advocate for routinely including rs-fMRI as part of deep imaging protocols. Here, we discuss 3 potential uses: network-level validation

Connectome fingerprinting – connectivity predicts function

The localization of small functional cortical regions in individual subjects presents logistical and financial challenges. Although the deep imaging methods described above and in other articles in this special issue can identify small cortical regions effectively, deep imaging requires collection of a substantial amount of task-fMRI data per subject. This not only places a high price tag on functional localization, in terms of scanner time, but also the prolonged scan sessions required for

Best practices in deep imaging

Deep imaging has the potential to reveal fine-scale functional architecture that can be obscured by group-level analyses. There are notable trade-offs implicit in deep imaging approaches, since investing resources into collecting ample amounts of data per subject implies a potential cost in terms of the number of subjects and/or number of task contrasts studied. We consider four ‘best practices’ considerations for deep imaging fMRI experiments: Cognitive Task Design; Resting-State fMRI;

Conflict of interest statement

Nothing declared.

References and recommended reading

Papers of particular interest, published within the period of review, have been highlighted as:

• of special interest
•• of outstanding interest

CRediT authorship contribution statement

David C Somers: Conceptualization, Writing - original draft. Samantha W Michalka: Investigation, Writing - review & editing, Visualization. Sean M Tobyne: Investigation, Writing - review & editing, Visualization. Abigail L Noyce: Investigation, Writing - review & editing, Visualization.

Acknowledgements

This work was supported by National Science Foundation grant BCS-1829394 to D.C.S. as well as by National Institutes of Health grants R01-EY022229 to D.C.S., F31-MH101963 to S.W.M., F31-NS103306 to S.M.T., F32-EY026796 to A.L.N. The views expressed in this article do not necessarily represent the views of the NSF, NIH, or the United States Government.

References (46)

M.W. Cole et al.
Neuron
(2014)
J. Duncan et al.
Trends Neurosci
(2000)
R.M. Braga et al.
Neuron
(2017)
E.M. Gordon et al.
Curr Opin Behav Sci
(2021)
S.W. Michalka et al.
Neuron
(2015)
A.L. Noyce et al.
J Neurosci
(2017)
R.-S. Huang et al.
Proc Natl Acad Sci U S A
(2012)
J. Salmi et al.
Brain Struct Funct
(2007)
M.N. Tombu et al.
Proc Natl Acad Sci U S A
(2011)
S.W. Michalka et al.
Cereb Cortex
(2016)

C. Toro-Serey et al.

NeuroImage

(2020)

R.W. Lefco et al.

NeuroImage

(2020)

S.M. Tobyne et al.

Neuroimage

(2017)

S.M. Smith et al.

Neuroimage

(2013)

D.E. Osher et al.

Cereb Cortex

(2016)

D.E. Osher et al.

J Neurophysiol

(2019)

R.E. Passingham et al.

Nat Rev Neurosci

(2002)

M.F. Glasser et al.

Nature

(2016)

D. Badre et al.

J Cogn Neurosci

(2007)

J.A. Johnson et al.

Cereb Cortex

(2005)

E.M. Gordon et al.

Neuron

(2017)

T.O. Laumann et al.

Neuron

(2015)

L. Kong et al.

Cereb Cortex

(2012)

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View full text

Individual subject approaches to mapping sensory-biased and multiple-demand regions in human frontal cortex

Highlights

Introduction

Section snippets

Sensory-biased frontal lobe regions

Multiple-demand versus sensory-biased representations

Resting-state functional connectivity networks

Connectome fingerprinting – connectivity predicts function

Best practices in deep imaging

Conflict of interest statement

References and recommended reading

CRediT authorship contribution statement

Acknowledgements

Neuron

Trends Neurosci

Neuron

Curr Opin Behav Sci

Neuron

J Neurosci

Proc Natl Acad Sci U S A

Brain Struct Funct

Proc Natl Acad Sci U S A

Cereb Cortex

NeuroImage

NeuroImage

Neuroimage

Neuroimage

Cereb Cortex

J Neurophysiol

Nat Rev Neurosci

Nature

J Cogn Neurosci

Cereb Cortex

Neuron

Neuron

Cereb Cortex