Sleep disturbances, altered brain microstructure and chronic headache in youth

Chronic headache (persistent or recurrent headache for 3-months or longer) is highly prevalent among youth. While sleep disturbances have been associated with headache, their inter-relationship with brain connectivity remains unknown. This observational study examined whether self-report and actigraphy measures of sleep were associated with alterations to white matter tracts (i.e., uncinate fasciculus and cingulum) in youth with chronic headache versus healthy controls. Thirty youth aged 10-18 years with chronic headache and thirty controls underwent an MRI. Diffusion tensor images were obtained and mean fractional anisotropy values of the cingulum and uncinate were extracted. One-week prior to their MRI, youth wore an actigraph to obtain sleep duration, wake after sleep onset and sleep efficiency measures. Moreover, they completed questionnaires regarding their sleep quality and pain symptomatology. Linear regression was applied to examine the relationships between sleep (self-report and actigraphy), fractional anisotropy, and number of headache days per month. Self-report and actigraphy measures of sleep did not differ between patients and controls. However, poorer self-reported sleep quality was associated with lower fractional anisotropy values in the left uncinate (P = 0.05). Lower left uncinate fractional anisotropy was related to increased headache frequency (P = 0.002) in youth with chronic headache. Therefore, alterations to connectivity may be associated with the relationship between altered perceptions of sleep and headache chronicity.

Optimizing HR-pQCT workflow: a comparison of bias and precision error for quantitative bone analysis

Manual correction of automatically generated contours for high-resolution peripheral quantitative computed tomography can be time consuming and introduces precision error. However, bias related to the automated protocol is unknown. This study provides insight into error bias that is present when using uncorrected contours and inter-operator precision error based on operator training.

INTRODUCTION

High-resolution peripheral quantitative computed tomography workflow includes manually correcting contours generated by the manufacturer's automated protocol. There is interest in minimizing corrections to save time and reduce precision error; however, bias related to the automated protocol is unknown. This study quantifies error bias when contours are uncorrected and identifies the impact of operator training on bias and precision error.

METHODS

Forty-five radii and tibiae scans across a representative range of bone density were analyzed using the automated and manually corrected contours of three operators, with training ranging from beginner to expert, and compared with a "ground truth" to estimate bias. Inter-operator precision was measured across operators.

RESULTS

The tibia had greater error bias than the radius when contours were uncorrected, with compartmental bone mineral densities and cortical microarchitecture having greatest biases, which could have significant implications for interpretation of studies using this skeletal site. Bias and precision error were greatest when contours were corrected by the beginner operator; however, when this operator was removed, bias was no longer present and inter-operator precision was between 0.01 and 3.74% for all parameters except cortical porosity.

CONCLUSION

These findings establish the need for manual correction and provide guidance on operator training needed to maximize workflow efficiency.