Layer-dependent activity in human prefrontal cortex during working memory

Presented During:

Wednesday, June 12, 2019: 11:30 AM  - 11:42 AM 
Auditorium Parco Della Musica  
Room: Giusepppe Sinopoli Hall  

Poster No:

W734 

Submission Type:

Abstract Submission 

Authors:

Emily Finn1, Laurentius Huber2, David Jangraw2, Peter Bandettini3

Institutions:

1National Institute of Mental Health, Washington, DC, 2NIMH, Bethesda, MD, 3National Institute of Mental Health, Bethesda, MD

Introduction:

Working memory involves a series of processes: encoding a sensory stimulus, maintaining and/or manipulating its representation over a delay, and making a response. Working memory engages dorsolateral prefrontal cortex (dlPFC) in both humans[1-3] and non-human primates[4,5], and it is thought that these subprocesses preferentially localize to distinct cortical depths within this region[4,6,7]. Specifically, delay-period activity is thought to rely predominantly on recurrent excitation among pyramidal cells in superficial layers (i.e., layer III), while response-period activity is thought to occur primarily in deeper layers (i.e., layer V)[8-10]. While there is some limited electrophysiological evidence for this dissociation in non-human primates[11,12], to date, there is no direct evidence in humans, due to the difficulty of measuring depth-dependent activity non-invasively. Here, we develop and optimize state-of-the-art techniques in high-resolution fMRI to interrogate the layer specificity of neural activity during different periods of a working memory task in dlPFC.

Methods:

Task paradigm: Participants (n = 9 unique subjects scanned in 13 total sessions) saw a string of 5 letters followed by a cue telling them to either alphabetize the letters (manipulation condition) or remember them in their original order (maintenance condition). Following a delay period, on some trials participants responded to a probe letter by indicating its position within the string (go condition); on other trials they saw a dummy probe that did not require a response (no-go condition).

Imaging: All data were collected at 7T using two interleaved contrast types[13,14]: (1) blood-oxygen-level dependent (BOLD; more sensitive but less spatially specific) and (2) vascular space occupancy (VASO), a measure of cerebral blood-volume (CBV; less sensitive but more specific). Acquiring both contrasts near-simultaneously allows us to obtain a clean CBV-weighted signal that is more spatially specific than the BOLD signal alone. A dlPFC region of interest (ROI) was identified for each participant individually using an online functional localizer, and subsequent slab of high-resolution coverage was centered on this ROI. We used two acquisition protocols over the course of the study: an "axial readout" protocol (voxel size = 0.9 ✕ 0.9 ✕ 1.1 mm, TR = 2 s) that was mainly used for quantifying and comparing activity across layers (n = 8 sessions from 6 unique participants), and a "sagittal readout" protocol (voxel size = 0.76 ✕ 0.76 ✕ 0.99mm, TR = 2.5 s) that was mainly used to visualize layer-specific activity in individual participants (n = 5 participants).

Results:

We detected activity timecourses that followed the hypothesized patterns (Fig. 1a): namely, superficial layers were preferentially active during the delay, and specifically in trials requiring manipulation (rather than mere maintenance) of information held in working memory, while deeper layers were preferentially active during the response. Two-way analyses of variance (ANOVAs) for each layer indicated significant interactions between trial period and condition in the hypothesized directions (Fig. 1b).

In addition to group-level effects, we detected layer-specific activity in all five individual subjects scanned using the ultra-high-resolution sagittal readout protocol in both the BOLD and VASO contrasts (Fig. 2).
Supporting Image: Finn_OHBM2019_DLPFClayers_Fig1.png
Supporting Image: Finn_OHBM2019_DLPFClayers_Fig2.png
 

Conclusions:

Results show that in a highly evolved region of association cortex, layer functional specialization in humans follows a similar organizational principle to that of non-human primates. More generally, as the first demonstration of layer-fMRI in a higher-order (i.e., non-primary) brain region, results also demonstrate the potential to non-invasively map input and output during cognitive processing along cortical circuitry in humans, paving the way for future studies investigating information flow in both health and disease.

Imaging Methods:

BOLD fMRI
Non-BOLD fMRI

Learning and Memory:

Working Memory 1

Modeling and Analysis Methods:

Methods Development 2

Keywords:

Cognition
Cortical Layers
fMRI CONTRAST MECHANISMS
FUNCTIONAL MRI
HIGH FIELD MR

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Task-activation

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Are you Internal Review Board (IRB) certified? Please note: Failure to have IRB, if applicable will lead to automatic rejection of abstract.

Yes

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Functional MRI
Behavior

For human MRI, what field strength scanner do you use?

7T

Provide references using author date format

1. Courtney, S.M., et al., Transient and sustained activity in a distributed neural system for human working memory. Nature, 1997. 386(6625): p. 608.
2. Courtney, S.M., et al., An area specialized for spatial working memory in human frontal cortex. Science, 1998. 279(5355): p. 1347-1351.
3. D'esposito, M., et al., The neural basis of the central executive system of working memory. Nature, 1995. 378(6554): p. 279.
4. Goldman-Rakic, P., Cellular basis of working memory. Neuron, 1995. 14(3): p. 477-485.
5. Funahashi, S., C.J. Bruce, and P.S. Goldman-Rakic, Mnemonic coding of visual space in the monkey's dorsolateral prefrontal cortex. Journal of neurophysiology, 1989. 61(2): p. 331-349.
6. Sawaguchi, T., M. Matsumura, and K. Kubota, Depth distribution of neuronal activity related to a visual reaction time task in the monkey prefrontal cortex. Journal of neurophysiology, 1989. 61(2): p. 435-446.
7. Sawaguchi, T., M. Matsumura, and K. Kubota, Catecholaminergic effects on neuronal activity related to a delayed response task in monkey prefrontal cortex. Journal of Neurophysiology, 1990. 63(6): p. 1385-1400.
8. Arnsten, A.F., M.J. Wang, and C.D. Paspalas, Neuromodulation of thought: flexibilities and vulnerabilities in prefrontal cortical network synapses. Neuron, 2012. 76(1): p. 223-239.
9. Opris, I., et al., Neural activity in frontal cortical cell layers: evidence for columnar sensorimotor processing. Journal of cognitive neuroscience, 2011. 23(6): p. 1507-1521.
10. Wang, M., S. Vijayraghavan, and P.S. Goldman-Rakic, Selective D2 receptor actions on the functional circuitry of working memory. Science, 2004. 303(5659): p. 853-856.
11. Bastos, A.M., et al., Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory. Proceedings of the National Academy of Sciences, 2018: p. 201710323.
12. Markowitz, D.A., C.E. Curtis, and B. Pesaran, Multiple component networks support working memory in prefrontal cortex. Proceedings of the National Academy of Sciences, 2015. 112(35): p. 11084-11089.
13. Huber, L., et al., Slab‐selective, BOLD‐corrected VASO at 7 Tesla provides measures of cerebral blood volume reactivity with high signal‐to‐noise ratio. Magnetic resonance in medicine, 2014. 72(1): p. 137-148.
14. Huber, L., et al., High-Resolution CBV-fMRI Allows Mapping of Laminar Activity and Connectivity of Cortical Input and Output in Human M1. Neuron, 2017. 96(6): p. 1253-1263.e7.