Automated Sulcal Depth Measurement on Cortical Surface Reflecting Geometrical Properties of Sulci

Sulcal depth that is one of the quantitative measures of cerebral cortex has been widely used as an important marker for brain morphological studies. Several studies have employed Euclidean (EUD) or geodesic (GED) algorithms to measure sulcal depth, which have limitations that ignore sulcal geometry in highly convoluted regions and result in under or overestimated depth. In this study, we proposed an automated measurement for sulcal depth on cortical surface reflecting geometrical properties of sulci, which named the adaptive distance transform (ADT). We first defined the volume region of cerebrospinal fluid between the 3D convex hull and the cortical surface, and constructed local coordinates for that restricted region. Dijkstra’s algorithm was then used to compute the shortest paths from the convex hull to the vertices of the cortical surface based on the local coordinates, which may be the most proper approach for defining sulcal depth. We applied our algorithm to both a clinical dataset including patients with mild Alzheimer’s disease (AD) and 25 normal controls and a simulated dataset whose shape was similar to a single sulcus. The mean sulcal depth in the mild AD group was significantly lower than controls (p = 0.007, normal [mean±SD]: 7.29±0.23 mm, AD: 7.11±0.29) and the area under the receiver operating characteristic curve was relatively high, showing the value of 0.818. Results from clinical dataset that were consistent with former studies using EUD or GED demonstrated that ADT was sensitive to cortical atrophy. The robustness against inter-individual variability of ADT was highlighted through simulation dataset. ADT showed a low and constant normalized difference between the depth of the simulated data and the calculated depth, whereas EUD and GED had high and variable differences. We suggest that ADT is more robust than EUD or GED and might be a useful alternative algorithm for measuring sulcal depth

Influence of Herbal Complexes Containing Licorice on Potassium Levels: A Retrospective Study

To observe the influence of these complexes on potassium levels in a clinical setting, we investigated the influence of herbal complexes containing licorice on potassium levels. We retrospectively examined the medical records of patients treated with herbal complexes containing licorice from January 1, 2010, to December 31, 2010. We recorded the changes in the levels of potassium, creatinine, and blood urea nitrogen and examined the differences between before and after herbal complexes intake using a paired t-test. In addition, we investigated the prevalence of hypokalemia among these patients and reviewed such patients. We identified 360 patients who did not show significant changes in the levels of potassium and creatinine (P=0.815, 0.289). We observed hypokalemia in 6 patients. However, in 5 patients, the hypokalemia did not appear to be related to the licorice. Thus, we could suggest that herbal complexes containing licorice do not significantly influence the potassium levels in routine clinical herbal therapies. However, we propose that follow-up examination for potassium levels is required to prevent any unpredictable side effects of administration of licorice in routine herbal medicine care

Reliable Identification of Deep Sulcal Pits: The Effects of Scan Session, Scanner, and Surface Extraction Tool

Sulcal pit analysis has been providing novel insights into brain function and development. The purpose of this study was to evaluate the reliability of sulcal pit extraction with respect to the effects of scan session, scanner, and surface extraction tool. Five subjects were scanned 4 times at 3 MRI centers and other 5 subjects were scanned 3 times at 2 MRI centers, including 1 test-retest session. Sulcal pits were extracted on the white matter surfaces reconstructed with both Montreal Neurological Institute and Freesurfer pipelines. We estimated similarity of the presence of sulcal pits having a maximum value of 1 and their spatial difference within the same subject. The tests showed high similarity of the sulcal pit presence and low spatial difference. The similarity was more than 0.90 and the spatial difference was less than 1.7 mm in most cases according to different scan sessions or scanners, and more than 0.85 and about 2.0 mm across surface extraction tools. The reliability of sulcal pit extraction was more affected by the image processing-related factors than the scan session or scanner factors. Moreover, the similarity of sulcal pit distribution appeared to be largely influenced by the presence or absence of the sulcal pits on the shallow and small folds. We suggest that our sulcal pit extraction from MRI is highly reliable and could be useful for clinical applications as an imaging biomarker

Quantification of sulcal emergence timing and its variability in early fetal life: Hemispheric asymmetry and sex difference

Human fetal brains show regionally different temporal patterns of sulcal emergence following a regular timeline, which may be associated with spatiotemporal patterns of gene expression among cortical regions. This study aims to quantify the timing of sulcal emergence and its temporal variability across typically developing fetuses by fitting a logistic curve to presence or absence of sulcus. We found that the sulcal emergence started from the central to the temporo-parieto-occipital lobes and frontal lobe, and the temporal variability of emergence in most of the sulci was similar between 1 and 2 weeks. Small variability (\u3c 1 week) was found in the left central and postcentral sulci and larger variability (\u3e2 weeks) was shown in the bilateral occipitotemporal and left superior temporal sulci. The temporal variability showed a positive correlation with the emergence timing that may be associated with differential contributions between genetic and environmental factors. Our statistical analysis revealed that the right superior temporal sulcus emerged earlier than the left. Female fetuses showed a trend of earlier sulcal emergence in the right superior temporal sulcus, lower temporal variability in the right intraparietal sulcus, and higher variability in the right precentral sulcus compared to male fetuses. Our quantitative and statistical approach quantified the temporal patterns of sulcal emergence in detail that can be a reference for assessing the normality of developing fetal gyrification

The role of cortical structural variance in deep learning-based prediction of fetal brain age

BackgroundDeep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.MethodsWe examined the association between the predicted brain age difference (PAD: predicted brain age–chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.ResultsOur results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.ConclusionThese results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model

Fetal cortical plate segmentation using fully convolutional networks with multiple plane aggregation

Fetal magnetic resonance imaging (MRI) has the potential to advance our understanding of human brain development by providing quantitative information of cortical plate (CP) developmen

Optimal method for fetal brain age prediction using multiplanar slices from structural magnetic resonance imaging

The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979

Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia

We proposed pattern classification based on principal components of cortical thickness between schizophrenic patients and healthy controls, which was trained using a leave-one-out cross-validation. The cortical thickness was measured by calculating the Euclidean distance between linked vertices on the inner and outer cortical surfaces. Principal component analysis was applied to each lobe for practical computational issues and stability of principal components. And, discriminative patterns derived at every vertex in the original feature space with respect to support vector machine were analyzed with definitive findings of brain abnormalities in schizophrenia for establishing practical confidence. It was simulated with 50 randomly selected validation set for the generalization and the average accuracy of classification was reported. This study showed that some principal components might be more useful than others for classification, but not necessarily matching the ordering of the variance amounts they explained. In particular, 40-70 principal components rearranged by a simple two-sample t-test which ranked the effectiveness of features were used for the best mean accuracy of simulated classification (frontal: (left(%)|right(%))=91.07|88.80, parietal: 91.40|91.53, temporal: 93.60|91.47, occipital: 88.80|91.60). And, discriminative power appeared more spatially diffused bilaterally in the several regions, especially precentral, postcentral, superior frontal and temporal, cingulate and parahippocampal gyri. Since our results of discriminative patterns derived from classifier were consistent with a previous morphological analysis of schizophrenia, it can be said that the cortical thickness is a reliable feature for pattern classification and the potential benefits of such diagnostic tools are enhanced by our finding

Inhibitory effect of 4-O-methylhonokiol on lipopolysaccharide-induced neuroinflammation, amyloidogenesis and memory impairment via inhibition of nuclear factor-kappaB in vitro and in vivo models

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation is important in the pathogenesis and progression of Alzheimer disease (AD). Previously, we demonstrated that lipopolysaccharide (LPS)-induced neuroinflammation caused memory impairments. In the present study, we investigated the possible preventive effects of 4-<it>O</it>-methylhonokiol, a constituent of <it>Magnolia officinalis</it>, on memory deficiency caused by LPS, along with the underlying mechanisms.</p> <p>Methods</p> <p>We investigated whether 4-<it>O</it>-methylhonokiol (0.5 and 1 mg/kg in 0.05% ethanol) prevents memory dysfunction and amyloidogenesis on AD model mice by intraperitoneal LPS (250 μg/kg daily 7 times) injection. In addition, LPS-treated cultured astrocytes and microglial BV-2 cells were investigated for anti-neuroinflammatory and anti-amyloidogenic effect of 4-<it>O</it>-methylhonkiol (0.5, 1 and 2 μM).</p> <p>Results</p> <p>Oral administration of 4-<it>O</it>-methylhonokiol ameliorated LPS-induced memory impairment in a dose-dependent manner. In addition, 4-<it>O</it>-methylhonokiol prevented the LPS-induced expression of inflammatory proteins; inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) as well as activation of astrocytes (expression of glial fibrillary acidic protein; GFAP) in the brain. In <it>in vitro </it>study, we also found that 4-<it>O</it>-methylhonokiol suppressed the expression of iNOS and COX-2 as well as the production of reactive oxygen species, nitric oxide, prostaglandin E₂, tumor necrosis factor-α, and interleukin-1β in the LPS-stimulated cultured astrocytes. 4-<it>O</it>-methylhonokiol also inhibited transcriptional and DNA binding activity of NF-κB via inhibition of IκB degradation as well as p50 and p65 translocation into nucleus of the brain and cultured astrocytes. Consistent with the inhibitory effect on neuroinflammation, 4-<it>O</it>-methylhonokiol inhibited LPS-induced Aβ_1-42generation, β- and γ-secretase activities, and expression of amyloid precursor protein (APP), BACE1 and C99 as well as activation of astrocytes and neuronal cell death in the brain, in cultured astrocytes and in microglial BV-2 cells.</p> <p>Conclusion</p> <p>These results suggest that 4-<it>O</it>-methylhonokiol inhibits LPS-induced amyloidogenesis via anti-inflammatory mechanisms. Thus, 4-<it>O</it>-methylhonokiol can be a useful agent against neuroinflammation-associated development or the progression of AD.</p