Elsevier

NeuroImage

Volume 106, 1 February 2015, Pages 123-133
NeuroImage

Microstructure of frontoparietal connections predicts individual resistance to sleep deprivation

https://doi.org/10.1016/j.neuroimage.2014.11.035Get rights and content

Highlights

  • There are large individual differences in the vulnerability to sleep deprivation (SD).

  • Fronto-parietal activation during working memory is related with the resistance to SD.

  • We found fronto-parietal fMRI activated clusters during Sternberg working memory task.

  • We used probabilistic tractography to reconstruct the interconnected tracts.

  • The integrity of interconnected tracts was also associated with resistance to SD.

Abstract

Sleep deprivation (SD) can degrade cognitive functioning, but growing evidence suggests that there are large individual differences in the vulnerability to this effect. Some evidence suggests that baseline differences in the responsiveness of a fronto-parietal attention system that is activated during working memory (WM) tasks may be associated with the ability to sustain vigilance during sleep deprivation. However, the neurocircuitry underlying this network remains virtually unexplored. In this study, we employed diffusion tensor imaging (DTI) to investigate the association between the microstructure of the axonal pathway connecting the frontal and parietal regions—i.e., the superior longitudinal fasciculus (SLF)—and individual resistance to SD. Thirty healthy participants (15 males) aged 20–43 years underwent functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) at rested wakefulness prior to a 28-hour period of SD. Task-related fronto-parietal fMRI activation clusters during a Sternberg WM Task were localized and used as seed regions for probabilistic fiber tractography. DTI metrics, including fractional anisotropy, mean diffusivity, axial and radial diffusivity were measured in the SLF. The psychomotor vigilance test (PVT) was used to evaluate resistance to SD. We found that activation in the left inferior parietal lobule (IPL) and dorsolateral prefrontal cortex (DLPFC) positively correlated with resistance. Higher fractional anisotropy of the left SLF comprising the primary axons connecting IPL and DLPFC was also associated with better resistance. These findings suggest that individual differences in resistance to SD are associated with the functional responsiveness of a fronto-parietal attention system and the microstructural properties of the axonal interconnections.

Introduction

Sleep deprivation (SD), even for one night, can lead to impairments in cognitive function and performance (Killgore, 2010). It has been found that individual differences in resistance to cognitive performance impairment following SD are consistent, trait-like, and stable over time (Rupp et al., 2012, Van Dongen et al., 2004). Numerous neuroimaging studies suggest that task-related activation in frontal and parietal cortices is particularly susceptible to the effects of SD and related to alterations in cognitive performance during SD (Chee and Choo, 2004, Choo et al., 2005). Moreover, several studies have reported that the extent of fronto-parietal activation in response to a working memory (WM) task under normal well-rested conditions can predict the magnitude of activation change and performance decline after SD (Caldwell et al., 2005, Chee et al., 2006, Lythe et al., 2012, Mu et al., 2005). However, the structure–function relationship underlying this fronto-parietal network in SD is not well understood. Most studies regarding the effects of SD on cognitive functioning have focused primarily on changes in functional brain activation, but much less is known about the microstructural properties of white matter fiber tracts underlying the fronto-parietal brain regions typically affected by sleep loss. Of particular interest is the association between individual resistance to SD and the microstructure of the superior longitudinal fasciculus (SLF), a primary and direct tract supporting bidirectional information transfer between the frontal and parietal cortices (Schmahmann and Pandya, 2006).

Diffusion tensor imaging (DTI) enables an in vivo characterization of microstructural properties of white matter based on water molecular diffusion (Basser et al., 1994, Hagmann et al., 2006). Water tends to diffuse preferentially in a direction parallel to the orientation of axons (Basser, 1995, Beaulieu, 2002). This phenomenon is called diffusion anisotropy and is represented by a diffusion tensor model. The tensor has three eigenvalues that represent the magnitude of diffusion in three axis directions (Hagmann et al., 2006). The most commonly used parameter in DTI analysis, fractional anisotropy (FA), is calculated from the three eigenvalues to indicate the degree of anisotropy of the diffusion tensor. FA is a scalar value that ranges from 0 (low) to 1 (high) and reflects axon caliber, degree of myelination, and axon density within a voxel (Beaulieu, 2002). Higher FA values represent better microarchitecture of the white matter tracts. The average of the three eigenvalues is called mean diffusivity and is considered an estimation of membrane density (Schmithorst and Yuan, 2010). The largest eigenvalue, indicating diffusion along the direction of the axons, is termed axial diffusivity (AD), which may reflect aspects of axon morphology and pathology including axon diameter, loss, or damage (Budde et al., 2007, Song et al., 2003). The average of the other two eigenvalues provides a measure of radial diffusivity (RD), which is considered to reflect the degree of myelination (Nair et al., 2005, Song et al., 2002).

To our knowledge, only one study has used DTI to investigate the relationship between white matter integrity and cognitive vulnerability to SD (Rocklage et al., 2009). Higher FA values were found in SD-resilient compared to SD-vulnerable groups in multiple white matter regions, including the corpus callosum, forceps major, posterior limb of the internal capsule, retrolenticular portion of the internal capsule, superior corona radiata, posterior corona radiata, SLF, posterior thalamic radiation, and corticospinal tract (CST), but these were not linked to functional responses in that study. No DTI studies have yet explored the specific relationship between resistance to SD and microstructural architecture of white matter fiber tracts, and none have linked structural indices with known functional regions, such as those involved in sustained attention or working memory, to identify potential structure–function networks that may predict cognitive resistance to sleep loss. In this study, we aimed to determine if the microstructural properties of the SLF, which connects primary attention and vigilance regions, might be associated with SD resistance. At rested baseline, we measured the DTI metrics of this tract of interest (TOI) using the Johns Hopkins University (JHU) white matter atlas and also employed probabilistic fiber tractography to reconstruct the specific white matter tracts connecting fronto-parietal activated regions identified with WM task-related functional MRI (fMRI). Participants were then sleep deprived for one night and their cognitive vigilance was monitored hourly throughout the night using the gold standard psychomotor vigilance test (PVT) to determine individual resistance to SD (Dinges and Powell, 1985). For each participant, a simple resistance score was calculated by determining the mean percent decline in PVT performance speed during the overnight sleep deprivation period compared to baseline. After identifying functionally defined frontal–parietal attention regions, we correlated the DTI metrics of the SLF with individual resistance scores. We hypothesized that individuals with higher FA values of the SLF tracts connecting functionally activated fronto-parietal attention regions would demonstrate greater resistance to SD.

Section snippets

Participants

Thirty-four right-handed, healthy, native English-speaking adults (mean age 25.4 ± 5.8 years, range 20–43; 17 males, 16 females) were recruited from the greater Boston area and underwent neuroimaging. Exclusion criteria included any history of self-reported medical, neurological, psychiatric, or sleep disorders. Data from four participants were excluded due to poor image quality. The final analyzed group consisted of 30 subjects (mean age 25.8 ± 6.0 years, range 20–43; 15 males, 15 females). All

Behavioral assessment

Demographic and behavioral features of our sample are summarized in Table 1. For the PVT, individual PRSC from the baseline cognitive performance ranged from 68.1% to 100% (mean ± SD: 88.2% ± 8.3%) and the mean frequency of attentional lapses at each session ranged from 1.0 to 18.9 (mean ± SD: 8.6 ± 4.7).

In the SWMT, longer RT was found in trials with greater memory load (see Table 1). One-way ANOVA revealed a significant effect of memory load (F = 14.94, p < 0.001). In addition, it was found that PRSC was

Discussion

The present study extends the investigation of neural correlates of resistance to SD from brain activation to metrics of microstructural architecture of the axonal fiber tracts. We used fMRI to examine the relationship of resistance to SD with functional brain activation during a working memory task. We also employed DTI and probabilistic tractography to examine the association between white matter microarchitecture and cognitive resistance to SD. We found that both functional activation of

Conclusion

This study employed a multimodal imaging approach to investigate the effect of white matter microstructure and functional cortical activation on the ability to resist the degrading effects of SD on psychomotor vigilance performance. In accordance with prior work, fronto-parietal activation at rested wakefulness during a WM task was predictive of reduced individual cognitive decline following SD. We provide novel information regarding the primary fiber bundle comprising the SLF, which connects

Funding

This research was supported by a DARPA Young Faculty Award to W.D.S.K. (D12AP00241).

Conflicts of interests

None declared.

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