Neuroimage. Reports最新文献

Introduction

This study investigated whether internalizing and externalizing behavioral problems in children were associated with fractional anisotropy of white matter tracts connecting other brain regions to the frontal lobes. We contrasted patterns of association between children born at term (FT) and very preterm (PT: gestational age at birth =< 32 weeks).

Methods

Parents completed the Child Behavior Checklist/6–18 questionnaire to quantify behavioral problems when their children were age 8 years (N = 36 FT and 37 PT). Diffusion magnetic resonance scans were collected at the same age and analyzed using probabilistic tractography. Multiple linear regressions investigated the strength of association between age-adjusted T-scores of internalizing and externalizing problems and mean fractional anisotropy (mean-FA) of right and left uncinate, arcuate, anterior thalamic radiations, and dorsal cingulate bundle, controlling for birth group and sex.

Results

Models predicting internalizing T-scores found significant group-by-tract interactions for left and right arcuate and right uncinate. Internalizing scores were negatively associated with mean-FA of left and right arcuate only in FT children (p_{left AF} = 0.01, p_{right AF} = 0.01). Models predicting externalizing T-scores found significant group-by-tract interactions for the left arcuate and right uncinate. Externalizing scores were negatively associated with mean-FA of right uncinate in FT (p_{right UF} = 0.01) and positively associated in PT children (p_{right UF preterm} = 0.01). Other models were not significant.

Conclusions

In children with a full range of scores on behavioral problems from normal to significantly elevated, internalizing and externalizing behavioral problems were negatively associated with mean-FA of white matter tracts connecting to frontal lobes in FT children; externalizing behavioral problems were positively associated with mean-FA of the right uncinate in PT children. The different associations by birth group suggest that the neurobiology of behavioral problems differs in the two birth groups.

Background

Deficient sleep is implicated in nicotine dependence as well as depressive and anxiety disorders. The hypothalamus regulates the sleep-wake cycle and supports motivated behavior, and hypothalamic dysfunction may underpin comorbid nicotine dependence, depression and anxiety. We aimed to investigate whether and how the resting state functional connectivities (rsFCs) of the hypothalamus relate to cigarette smoking, deficient sleep, depression and anxiety.

Methods

We used the data of 64 smokers and 198 age- and sex-matched adults who never smoked, curated from the Human Connectome Project. Deficient sleep and psychiatric problems were each assessed with Pittsburgh Sleep Quality Index (PSQI) and Achenbach Adult Self-Report. We processed the imaging data with published routines and evaluated the results at a corrected threshold, all with age, sex, and the severity of alcohol use as covariates.

Results

Smokers vs. never smokers showed poorer sleep quality and greater severity of depression and anxiety. In smokers only, the total PSQI score, indicating more sleep deficits, was positively associated with hypothalamic rsFCs with the right inferior frontal/insula/superior temporal and postcentral (rPoCG) gyri. Stronger hypothalamus-rPoCG rsFCs were also associated with greater severity of depression and anxiety in smokers but not never smokers. Additionally, in smokers, the PSQI score completely mediated the relationships of hypothalamus-rPoCG rsFCs with depression and anxiety severity.

Conclusions

These findings associate hypothalamic circuit dysfunction to sleep deficiency and severity of depression and anxiety symptoms in adults who smoke. Future studies may investigate the roles of the hypothalamic circuit in motivated behaviors to better characterize the inter-related neural markers of smoking, deficient sleep, depression and anxiety.

Introduction

Although cerebral edema is common following traumatic brain injury (TBI), its formation and progression are poorly understood. This is especially true for the mild TBI population, who rarely undergo magnetic resonance imaging (MRI) studies, which can pick up subtle structural details not visualized on computed tomography, in the first few days after injury. This study aimed to visually classify and quantitatively measure edema progression in relation to traumatic microbleeds (TMBs) in a cohort of primarily mild TBI patients up to 30 days after injury. Researchers hypothesized that hypointense lesions on Apparent Diffusion Coefficient (ADC) detected acutely after injury would evolve into hyperintense Fluid Attenuated Inversion Recover (FLAIR) lesions.

Methods

This study analyzed the progression of cerebral edema after acute injury using multimodal MRI to classify TMBs as potential edema-related biomarkers. ADC and FLAIR MRI were utilized for edema classification at three different timepoints: ≤48 h, ∼1 week, and 30 days after injury. Hypointense lesions on ADC (ADC+) suggested the presence of cytotoxic edema while hyperintense lesions on FLAIR (FLAIR+) suggested vasogenic edema. Signal intensity Ratio (SIR) calculations were made using ADC and FLAIR to quantitatively confirm edema progression.

Results

Our results indicated the presence of ADC+ lesions ≤48 h and ∼1 week were associated with FLAIR + lesions at ∼1 week and 30 days, respectively, suggesting some progression of cytotoxic edema to vasogenic edema over time. Ten out of 15 FLAIR + lesions at 30 days (67%) were ADC+ ≤48 h. However, ADC + lesions ≤48 h were not associated with FLAIR + lesions at 30 days; 10 out of 25 (40%) ADC + lesions ≤48 h were FLAIR + at 30 days, which could indicate that some lesions resolved or were not visualized due to associated atrophy or tissue necrosis. Quantitative analysis confirmed the visual progression of some TMB lesions from ADC + to FLAIR+. FLAIR SIRs at ∼1 week were significantly higher when lesions were ADC+ ≤48 h (1.22 [1.08–1.32] vs 1.03 [0.97–1.11], p = 0.002).

Conclusion

Awareness of how cerebral edema can evolve in proximity to TMBs acutely after injury may facilitate identification and monitoring of patients with traumatic cerebrovascular injury and assist in development of novel therapeutic strategies.

Background

Blood pressure variability is increasingly linked with cerebrovascular disease and Alzheimer's disease, independent of mean blood pressure levels. Elevated blood pressure variability is also associated with attenuated cerebrovascular reactivity, which may have implications for functional hyperemia underpinning brain network connectivity. It remains unclear whether blood pressure variability is related to functional network connectivity. We examined relationships between beat-to-beat blood pressure variability and functional connectivity in brain networks vulnerable to aging and Alzheimer's disease.

Methods

53 community-dwelling older adults (mean [SD] age = 69.9 [7.5] years, 62.3% female) without history of dementia or clinical stroke underwent continuous blood pressure monitoring and resting state fMRI scan. Blood pressure variability was calculated as variability independent of mean. Functional connectivity was determined by resting state fMRI for several brain networks: default, salience, dorsal attention, fronto-parietal, and language. Multiple linear regression examined relationships between short-term blood pressure variability and functional network connectivity.

Results

Elevated short-term blood pressure variability was associated with lower functional connectivity in the default network (systolic: standardized ß = −0.30 [95% CI -0.59, −0.01], p = .04). There were no significant associations between blood pressure variability and connectivity in other functional networks or between mean blood pressure and functional connectivity in any network.

Discussion

Older adults with elevated short-term blood pressure variability exhibit lower resting state functional connectivity in the default network. Findings support the role of blood pressure variability in neurovascular dysfunction and Alzheimer's disease. Blood pressure variability may represent an understudied early vascular risk factor for neurovascular dysfunction relevant to Alzheimer's disease, with potential therapeutic implications.

Clinical magnetic resonance images (MRIs) lack a standard intensity scale due to differences in scanner hardware and the pulse sequences used to acquire the images. When MRIs are used for quantification, as in the evaluation of white matter lesions (WMLs) in multiple sclerosis, this lack of intensity standardization becomes a critical problem affecting both the staging and tracking of the disease and its treatment. This paper presents a study of harmonization on WML segmentation consistency, which is evaluated using an object detection classification scheme that incorporates manual delineations from both the original and harmonized MRIs. A cohort of ten people scanned on two different imaging platforms was studied. An expert rater, blinded to the image source, manually delineated WMLs on images from both scanners before and after harmonization. It was found that there is closer agreement in both global and per-lesion WML volume and spatial distribution after harmonization, demonstrating the importance of image harmonization prior to the creation of manual delineations. These results could lead to better truth models in both the development and evaluation of automated lesion segmentation algorithms.

Pain is a frequent and disabling non-motor symptom of Parkinson’s Disease (PD). Yet, no treatment to date can efficiently reduce this pain. This article investigates the brain functional connectivity of PD patients with central pain and the effects of levodopa and oxycodone on this connectivity.

Thirty-eight PD patients received either levodopa, oxycodone, or a placebo during an eight-week period. Pain intensity was evaluated using the Visual Analogue Scale and resting-state functional connectivity was measured before and after treatments. PD patients were also separated into two groups: responders and non-responders.

At baseline, the intensity of pain was correlated with the connectivity between the anterior insula and the posterior cingulate cortex and between the nucleus accumbens, the brainstem, and the hippocampus. Levodopa and oxycodone had no specific effects on functional connectivity. Responders had a decrease in connectivity between the anterior insula and the posterior cingulate cortex, while non-responders showed an increase in connectivity.

The correlation between pain intensity and specific brain connectivity may represent a “hyper-awareness” of pain and a distortion of learning and memory systems in PD patients with central pain, leading to a state of chronic pain. The placebo effect could explain the changes in connectivity that are associated with a potential reduction in pain awareness.

Introduction

During the subacute phase of ischemic stroke, MR diffusion-weighted imaging (DWI) is used to assess the extent of tissue injury. Segmentation of DWI infarct is challenging due to disease variability, but Deep Learning (DL) provides a solution, outperforming existing methods on small datasets. However, a lack of clinically meaningful performance evaluation hinders clinical translation. Here we develop a DL DWI segmentation tool and provide clinical performance review.

Methods

Subjects in this retrospective study presented with stroke symptoms and later underwent DWI imaging. DL architectures U-Net and DenseNet were used to develop a DWI segmentation tool. The Dice Similarly Coefficient (DSC) was used to select the best- and worst-performing model. Clinical experts reviewed these models on the clinical test set, agreeing with the model if no 'significant’ error was present. The average agreement with the model and interrater agreement was also derived.

Results

In total, 573 participants with an ischemic stroke were included. The DenseNet delivered the best model (DSC = 0.831 ± 0.064) with a mean inference time of 0.07 s. Clinicians compared this with the worst model (U-Net, DSC = 0.759 ± 0.122), agreeing with the DenseNet predictions more than the U-Net (83.8 % vs. 79.3 %). Clinicians also agreed with each other more over performance interpretation when evaluating the DenseNet over the U-Net (87.9 % vs. 72.7 %).

Conclusion

Our DWI segmentation tool achieved high performance with clinical review providing meaningful performance evaluation. Model development will continue towards prospective deployment before which clinical review will be repeated. This work will benefit physicians in assessing patient prognosis.

Background

The detection and quantification of changes in white matter lesions in the brain is important to monitor treatment effects in patients with multiple sclerosis (MS). Existing automatic tools predominantly require FLAIR images as input which are not always available, or only focus on new/enlarging activity. Therefore, we developed and validated a semi-automated method to quantify lesion volume changes based on 2D proton-density (PD)-weighted images and image subtraction. This semi-automated method provides insight in both “positive” activity (defined as new and enlarging lesions) and “negative” activity (disappearing and shrinking lesions).

Methods

Yearly MRI scans of patients with early MS from the REFLEX/REFLEXION studies were used. The maximum follow-up period was 5 years. Two PD-weighted images were normalized, registered to a common halfway-space, intensity-matched, and subsequently subtracted. Within manual lesion masks, lesion changes were quantified using a subtraction intensity threshold and total lesion volume change (TLVC) was calculated. Reproducibility was measured by assessing transitivity, specifically, we calculated the intraclass correlation coefficient for the absolute agreement (ICC_trans) and the difference (Δ_trans) between the direct one-step and indirect multi-step measurements of TLVC between two visits. Accuracy was assessed by calculating both the intraclass correlation coefficient for absolute agreement (ICC_acc) and the difference (Δ_acc) between the one-step semi-automated TLVC and manually measured lesion volume change (numerical difference) between two visits. Spearman's correlations (r_s) were used to assess the relation of global and central atrophy, manually measured T2 lesion volume, and lesion volume change with the method's performance as reflected by the difference measures |Δ_trans| and Δ_acc. An alpha of 0.05 was used as the cut-off for significance.

Results

Reproducibility was excellent, with ICC_trans values ranging from 0.90 to 0.96. Accuracy was good overall, with ICC_acc values ranging from 0.67 to 0.86. The standard deviation of Δ_trans ranged from 0.25 to 0.86 mL. The mean of Δ_acc ranged from 0.11 to 0.37 mL and was significantly different from zero. Both global and central atrophy significantly correlated with lower reproducibility (correlation of |Δ_trans| with global atrophy, r_s = −0.19 to −0.28, and correlation of |Δ_trans| with central atrophy, r_s = 0.22 to 0.34). There was generally no significant correlation between global/central atrophy and accuracy. Higher lesion volume was significantly correlated with lower reproducibility (r_s = 0.62). Higher lesion volume change was significantly correlated with lower reproducibility (r_s = 0.22) and lower acc