White matter microstructural abnormalities in primary insomnia: A systematic review of diffusion tensor imaging studies

Moghaddam, Hossein Sanjari; Mohammadi, Esmaeil; Dolatshahi, Mahsa; Mohebi, Farnam; Ashrafi, Agaah; Khazaie, Habibolah; Aarabi, Mohammad Hadi

doi:10.1016/j.pnpbp.2020.110132

Cited by 22 publications

(20 citation statements)

References 57 publications

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“…Studies using diffusion tensor imaging (DTI) have suggested that primary insomnia was associated with WM tracts disruptions of several, such as superior longitudinal fasciculus (SLF) within the FPN network and corona radiate, internal capsule within the thalamocortical networks. 11 A previous study using DTI also found that participants vulnerable to SD-induced attentional impairments exhibited significantly lower integrity in several WM fiber tracts compared with resistant participants. 12 …”

Section: Introductionmentioning

confidence: 90%

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

Wang¹,

Peng²,

Li³

et al. 2022

NSS

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Background Large individual differences exist in sleep deprivation (SD) induced sustained attention deterioration. Several brain imaging studies have suggested that the activities within frontal-parietal network, cortico-thalamic connections, and inter-hemispheric connectivity might underlie the neural correlates of vulnerability/resistance to SD. However, those traditional approaches are based on average estimates of differences at the group level. Currently, a neuroimaging marker that can reliably predict this vulnerability at the individual level is lacking. Methods Efficient transfer of information relies on the integrity of white matter (WM) tracts in the human brain, we therefore applied machine learning approach to investigate whether the WM diffusion metrics can predict vulnerability to SD. Forty-nine participants completed the psychomotor vigilance task (PVT) both after resting wakefulness (RW) and after 24 h of sleep deprivation (SD). The number of PVT lapse (reaction time > 500 ms) was calculated for both RW condition and SD condition and participants were categorized as vulnerable (24 participants) or resistant (25 participants) to SD according to the change in the number of PVT lapses between the two conditions. Diffusion tensor imaging were acquired to extract four multitype WM features at a regional level: fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity. A linear support vector machine (LSVM) learning approach using leave-one-out cross-validation (LOOCV) was performed to assess the discriminative power of WM features in SD-vulnerable and SD-resistant participants. Results LSVM analysis achieved a correct classification rate of 83.67% (sensitivity: 87.50%; specificity: 80.00%; and area under the receiver operating characteristic curve: 0.85) for differentiating SD-vulnerable from SD-resistant participants. WM fiber tracts that contributed most to the classification model were primarily commissural pathways (superior longitudinal fasciculus), projection pathways (posterior corona radiata, anterior limb of internal capsule) and association pathways (body and genu of corpus callosum). Furthermore, we found a significantly negative correlation between changes in PVT lapses and the LSVM decision value. Conclusion These findings suggest that WM fibers connecting (1) regions within frontal-parietal attention network, (2) the thalamus to the prefrontal cortex, and (3) the left and right hemispheres contributed the most to classification accuracy.

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Section: Introductionmentioning

confidence: 90%

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

Wang¹,

Peng²,

Li³

et al. 2022

NSS

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“…The PSQI 23 and AIS 24 were used to evaluate sleep quality. The PSQI comprises 19 self-rated items and 7 subcomponents: (1) sleep quality (1 item), (2) sleep latency (2 items), (3) sleep duration (1 item), (4) sleep efficiency (3 items), (5) sleep disturbance (9 items), (6) sleep medication (1 item), and (7) daily dysfunction (2 items). Each subcomponent is scored on a 0-3 scale.…”

Section: Materials and Methods Subjectsmentioning

confidence: 99%

“…5 Therefore, exploring the underlying neural mechanisms may be helpful for understanding the pathological processes and selecting the appropriate intervention strategy for insomnia. 6,7 At the global brain level, sleep is regulated by coordinated wake and sleep brain networks, including wake-and sleep-promoting pathways. 8 Dysregulation within these networks may cause insomnia.…”

Section: Introductionmentioning

confidence: 99%

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Functional Connectivity Density in the Sensorimotor Area is Associated with Sleep Latency in Patients with Primary Insomnia

Ji¹,

Dai²,

Guo³

et al. 2022

NDT

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Objective: It is unclear whether the patterns of functional connectivity density (FCD) differ between patients with primary insomnia (PI) and healthy subjects. In the present study, we investigated the features of FCD in patients with PI using resting-state functional MRI (rsfMRI). Methods: rsfMRI datasets of 32 patients with PI and 34 healthy controls (HCs) were obtained using a 3-Tesla scanner. FCD analysis was performed to compare voxels with abnormal whole-brain functional connectivity with other voxels among patients with PI and HCs. Abnormal brain regions were then used as seed points for FC analysis. Results: Compared with HCs, patients with PI exhibited significantly decreased FCD in the left medial frontal gyrus and increased FCD in the left supplementary motor area (SMA). With the left medial frontal gyrus as the seed point, patients with PI showed decreased FC between the left medial frontal gyrus and the left fusiform gyrus compared with HCs. With the left SMA as the seed point, patients with PI exhibited increased FC between the left SMA and the right anterior cingulate gyrus. Correlation analysis revealed that the increased FCD values in the left SMA were positively correlated with sleep latency in patients with PI. Conclusion: Default-mode network and SMA dysfunctions may be related to the pathophysiology of PI.

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“…Diffusion MRI (dMRI) is a widely used tool that characterizes the microstructural organization of biological tissues based on the diffusion behaviors of water molecules 17–19 . Due to the anisotropic diffusion of water molecules in fiber microstructures, dMRI can be used to reconstruct the fiber tracts 20 (so‐called tractography) in both brain white matter 21 and muscular tissues, such as skeletal muscle, 22 myocardium, 23 tongue, 24 and myometrium 25 . The myometrium that is adhesive to the outer rim of the placenta is comprised of three layers—the sub‐endometrium circular layer of myometrium that wraps the placenta in the coronal plane, the outer longitudinal layer that wraps the placenta in the sagittal plane, and the middle intertwined layer that comprises the major part of the myometrium 26 .…”

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confidence: 99%

Diffusion MRI Based Myometrium Tractography for Detection of Placenta Accreta Spectrum Disorder

Yan

Liao

et al. 2021

Magnetic Resonance Imaging

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Background Prenatal diagnosis of placenta accreta spectrum (PAS) disorders is difficult. Magnetic resonance imaging (MRI) has been shown to be a useful supplementary method to ultrasound. Purpose To investigate diffusion MRI (dMRI) based tractography as a tool for detecting PAS disorders, and to evaluate its performance compared with anatomical MRI. Study Type Prospective. Population Forty‐seven pregnant women in the third trimester with risk factors for PAS. Field Strength/Sequence Using fast imaging employing steady‐state acquisition and high‐angular resolution dMRI at 1.5 Tesla. Assessment Diagnosis of PAS was performed by three radiologists based on the dMRI‐based feature of myometrial fiber discontinuity and on commonly used anatomical features including presence of dark band, discontinuous myometrium and bladder wall interruption. We evaluated the sensitivity, specificity, accuracy, and area‐under‐the‐curve (AUC) of the individual features and established an integrated model with random forest analysis. Statistical Tests Maternal age and gestational age at scan were compared between PAS and control group using a t‐test, and childbearing history was compared using a chi‐squared test. The random forest model was employed to combine the anatomical and dMRI features with 5‐fold cross‐validation, and the weight of each feature was normalized to evaluate its importance in predicting PAS. Results Based on surgical pathology reports, 16 out of 47 patients had confirmed PAS. The anatomical feature of dark bands and tractography marker achieved the highest AUC of 0.842 for predicting PAS, and the integrated anatomical and tractography features further improved the AUC of 0.880 with an accuracy of 87.2%. The tractography feature contributed most (30.1%) to the integrated model. Data Conclusion Myometrial tractography demonstrated superior performance in detecting PAS. Moreover, the combination of dMRI‐based tractography and anatomical MRI could potentially improve the diagnosis of PAS disorders in clinical practice. Level of Evidence 2 Technical Efficacy Stage 2

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White matter microstructural abnormalities in primary insomnia: A systematic review of diffusion tensor imaging studies

Cited by 22 publications

References 57 publications

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

Predicting Attentional Vulnerability to Sleep Deprivation: A Multivariate Pattern Analysis of DTI Data

Functional Connectivity Density in the Sensorimotor Area is Associated with Sleep Latency in Patients with Primary Insomnia

Diffusion MRI Based Myometrium Tractography for Detection of Placenta Accreta Spectrum Disorder

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