Brain Volume Estimation Research Articles

Deep Convolutional Neural Networks with Dilated Residual Connections for Total Brain Volume Estimation in Preterm Infants from 3D Ultrasound Images

Deep Convolutional Neural Networks with Dilated Residual Connections for Total Brain Volume Estimation in Preterm Infants from 3D Ultrasound Images

Participant followup rate can bias structural imaging measures in longitudinal studies

Longitudinal MRI analysis is essential to accurately describe neuroanatomical changes over time. Loss of participants to followup (dropout) in longitudinal studies is inevitable and can lead to great difficulty in interpretation of statistical results if dropout is correlated with a study outcome or exposure. Beyond this, technical aspects of longitudinal MRI analysis require specialised processing pipelines to improve reliability while avoiding bias towards individual timepoints. In this article we test whether there is an additional problem that must be considered in longitudinal imaging studies, namely whether dropout has an impact on the function of FreeSurfer, a popular software pipeline used to estimate important structural brain metrics.We find that the number of acquisitions available per individual can impact the estimation of cortical thickness and brain volume using the FreeSurfer longitudinal pipeline, and can induce group differences in brain metrics. The effect on trajectories of brain metrics is smaller than the effect on brain metrics.

Estimated Brain Volume at Term Age in Very Preterm Infants with Uncomplicated Grade II And III Intraventricular Haemorrhage

Background and aims: In very pre-term new-borns, cerebral lesions are usually complicated with neuro-developmental issues. But un-complicated intra-ventricular haemorrhage (IVH) has no reliable associations with poor outcomes. Objective: In the current work we aimed to assess the impact of un-complicated brain haemorrhage on assessed brain size at term-equivalent ages (TEA), through standard indices made from cranial ultrasound (cUS). Methods: This study included 87 very pre-term babies [< 32 weeks’ gestational age (GA)] up to and at TEA which were divided into two groups; group one included infants with uncomplicated grades II or III IVH, and group two included infants with normal cUS. A previously described standards based on linear measurements were used to calculate the estimated brain volumes at TEA and to compare between the two groups using independent groups t-test; p-value ≤ 0.05 was considered significant. Results: 19 very pre-term babies with un-complicated IVH and 68 with normal images have been studied. Bi-parietal diameter at TEA was shorter in the IVH new-borns (72 vs 87 mm, p-value < 0.05) while thalamo-occipital distance was longer in IVH infants (23.7/21.4 mm, p-value < 0.05)). But, there were non-significant changes at TEA for bi-frontal diameter (69.3 vs 70.1 mm), transverse cerebellar diameter (51.1 vs 49.9 mm), valued cranial size (384/414cc3), valued cerebral size (338/341cc3) and Levene’s ventricular index (1.38/1.23 cm). Conclusions: There is no difference in assessed cerebral size at TEA, built on measures performed at the bedside via cranial US among very pre-term new-borns with dependably ordinary images and those with un-complicated grade II and grade III IVH.

Ultrasonographic Estimation of Total Brain Volume: 3D Reliability and 2D Estimation. Enabling Routine Estimation During NICU Admission in the Preterm Infant.

Objectives: The aim of this study is to explore if manually segmented total brain volume (TBV) from 3D ultrasonography (US) is comparable to TBV estimated by magnetic resonance imaging (MRI). We then wanted to test 2D based TBV estimation obtained through three linear axes which would enable monitoring brain growth in the preterm infant during admission.Methods: We included very low birth weight preterm infants admitted to our neonatal intensive care unit (NICU) with normal neuroimaging findings. We measured biparietal diameter, anteroposterior axis, vertical axis from US and MRI and TBV from both MRI and 3D US. We calculated intra- and interobserver agreement within and between techniques using the intraclass correlation coefficient and Bland-Altman methodology. We then developed a multilevel prediction model of TBV based on linear measurements from both US and MRI, compared them and explored how they changed with increasing age. The multilevel prediction model for TBV from linear measures was tested for internal and external validity and we developed a reference table for ease of prediction of TBV.Results: We used measurements obtained from 426 US and 93 MRI scans from 118 patients. We found good intra- and interobserver agreement for all the measurements. US measurements were reliable when compared to MRI, including TBV which achieved excellent agreement with that of MRI [ICC of 0.98 (95% CI 0.96–0.99)]. TBV estimated through 2D measurements of biparietal diameter, anteroposterior axis, and vertical axis was comparable among both techniques. We estimated the population 95% confidence interval for the mean values of biparietal diameter, anteroposterior axis, vertical axis, and total brain volume by post-menstrual age. A TBV prediction table based on the three axes is proposed to enable easy implementation of TBV estimation in routine 2D US during admission in the NICU.Conclusions: US measurements of biparietal diameter, vertical axis, and anteroposterior axis are reliable. TBV segmented through 3D US is comparable to MRI estimated TBV. 2D US accurate estimation of TBV is possible through biparietal diameter, vertical, and anteroposterior axes.

Uncomplicated intraventricular hemorrhage is not associated with lower estimated cerebral volume at term age.

Cerebral lesions detected using cerebral ultrasound (cUS) in very preterm infants are associated with increased risk for neurodevelopmental problems. However, uncomplicated intraventricular hemorrhage (IVH) has no consistent association with poor outcome. In this study we evaluate the effect of uncomplicated IVH on estimated brain volume at term-equivalent age (TEA), using a model based on measurements made from cUS. We studied 2 groups of preterm infants (<32 weeks' gestational age (GA)) up to and at TEA: (1) infants with uncomplicated grades 2 or 3 IVH, (2) infants with consistently normal scans. Estimated cerebral volumes at TEA were calculated using a previously described model based on linear measurements and compared between the 2 groups using independent groups t-test or the Mann-Whitney test; p-value <0.05 was considered significant. We assessed 95 preterm infants (18 with uncomplicated IVH and 71 with normal scans). GA and birth weight were lower in infants with uncomplicated IVH (26.8/28.7weeks, p<0.001, 944/1082g, p<0.05, respectively); occipital-frontal circumference at TEA was smaller in the IVH infants (34.2 vs 35.3cm, p<0.05). However, no significant differences at TEA were found for estimated cranial volume (383/411cc3), estimated cerebral volume (337/341cc3), Levene ventricular index (13.5/12.2mm) or thalamo-occipital distance (21.5/20.3mm). Statistical adjustment for the lower GA in the IVH group confirmed the absence of a significant difference in the findings. In summary, we found that estimated cerebral volume at TEA, based on measurements made at the bedside using cranial US, is not different between very preterm infants with consistently normal scans and those with uncomplicated grades 2 and 3 IVH.

Amyloid (a), tau (t) and voxel‐based morphometry (n) correlates of visual memory performance

AbstractBackgroundThe Aggie Figures Learning Test (AFLT) is a memory test that was designed to be a visual analogue of the Rey Auditory Verbal Learning Test (RAVLT). Previous studies have found associations between tau and amyloid PET and brain volume and RAVLT scores. However, to date, no study has explored the associations between these markers and AFLT scores. We aimed at exploring such associations.MethodsStructural MRI, amyloid PET ([18F]‐NAV4694) and tau PET ([18F]‐MK6240) were acquired for 161 individuals. We conducted analyses on two sub‐samples. Demographic data is shown on Tables 1 and 2. MRI were segmented into probabilistic grey (GM) and white (WM) maps, non‐linearly registered to the ADNI template using Dartel and smoothed with an 8mm FWHM gaussian kernel. Voxel‐wise linear regression models were applied, using VoxelStats, with AFLT sub‐scores as dependent variables and either tau and amyloid binding or Voxel‐Based Morphometry as predictors. We corrected for sex, Apoe genotype, age and years of education.ResultsWe found negative associations between tau binding and AFLT total (trials 1‐5) and AFLT delayed recall (DR) scores in the MTL and temporo‐occipital cortices, as well as in some of their WM tracts. These associations were stronger for the total scores and, in both cases, in the right hemisphere. For the amyloid biomarker, associations with AFLT total and AFLT DR scores were negative in right ventromedial PFC, right posterior thalamus and basal ganglia. In this case, strongest associations are reported between amyloid burden and AFLT DR scores. Finally, we found positive associations between AFLT total and DR scores and VBM in the fusiform gyrus bilaterally.ConclusionsResults resemble previously reported findings on associations between RAVLT and tau, amyloid and brain volume estimates. Our findings are also in line with initial reporting of patients with right hemisphere damage having difficulties with AFLT tasks (Madjan, et al. 1996). In addition, relationships with tau tend to be more posterior, while relationships with amyloid were shown to be more anterior. Further analyses based on diagnostic categories are needed to explore how these associations change with pathology.

The relation between accelerometer‐derived physical activity and brain structure: Findings from the Rhineland Study

AbstractBackgroundPhysical activity is associated with a reduced risk of dementia. Although physical activity induces potent neuroprotective effects in animals, human studies assessing its effects on brain structure are sparse and inconclusive. The discrepancies in human studies may have been due to application of subjective physical activity measures, low spatial‐resolution imaging or small sample sizes. Therefore, we aimed to assess whether objectively quantified physical activity levels are associated with detailed measures of brain structure in a large cohort study.MethodOur analyses were based on data of the first 4,000 participants (56.6% female, mean age: 55.2 years (range 30 – 95 years)) of the Rhineland Study, a population‐based prospective cohort study in Bonn, Germany. Free‐living physical activity over seven days was continuously measured using the ActivPAL™ accelerometer. Average daily physical activity measures included metabolic equivalent (MET)‐hours, total step count, as well as minutes spent stepping or performing light and moderate‐to‐vigorous physical activities. Grey matter (GM), white matter (WM) and total brain volume (TBV) estimates were obtained from 3T MRI scans using FreeSurfer. Multivariable regression models were used to examine the association between physical activity and brain structure, with physical activity levels as independent and brain structure measures as dependent variables, while adjusting for age, sex, intracranial volume, education and smoking status.ResultDaily MET‐hours were associated with all volumetric measures of brain structure (effect estimates [in cm3/hour] for TBV: ß=2.36, 95% CI: 1.05 – 3.67; GM: ß=0.82, 95% CI: 0.15 – 1.50; WM: ß=0.97, 95% CI: 0.08 – 1.85). Importantly, we found a significant interaction between the effects of MET‐hours and age on TBV: in participants below median age the effect of MET‐hours on TBV was 0.28 cm3/hour (95% CI: ‐1.40 – 1.97), while in those above median age the effect was 2.01 cm3/hour (95% CI: 0.08–3.94). Overall, we obtained comparable findings for other physical activity measures as well (data not shown).ConclusionPhysical activity was consistently associated with grey matter, white matter and total brain volumes in a large cohort of individuals from the general population. These findings suggest that promoting physical activity could be critical in the prevention of age‐associated brain atrophy.

Lesion Induced Error on Automated Measures of Brain Volume: Data From a Pediatric Traumatic Brain Injury Cohort.

Structural segmentation of T1-weighted (T1w) MRI has shown morphometric differences, both compared to controls and longitudinally, following a traumatic brain injury (TBI). While many patients with TBI present with abnormalities on structural MRI images, most neuroimaging software packages have not been systematically evaluated for accuracy in the presence of these pathology-related MRI abnormalities. The current study aimed to assess whether acute MRI lesions (MRI acquired 7–71 days post-injury) cause error in the estimates of brain volume produced by the semi-automated segmentation tool, Freesurfer. More specifically, to investigate whether this error was global, the presence of lesion-induced error in the contralesional hemisphere, where no abnormal signal was present, was measured. A dataset of 176 simulated lesion cases was generated using actual lesions from 16 pediatric TBI (pTBI) cases recruited from the emergency department and 11 typically-developing controls. Simulated lesion cases were compared to the “ground truth” of the non-lesion control-case T1w images. Using linear mixed-effects models, results showed that hemispheric measures of cortex volume were significantly lower in the contralesional-hemisphere compared to the ground truth. Interestingly, however, cortex volume (and cerebral white matter volume) were not significantly different in the lesioned hemisphere. However, percent volume difference (PVD) between the simulated lesion and ground truth showed that the magnitude of difference of cortex volume in the contralesional-hemisphere (mean PVD = 0.37%) was significantly smaller than that in the lesioned hemisphere (mean PVD = 0.47%), suggesting a small, but systematic lesion-induced error. Lesion characteristics that could explain variance in the PVD for each hemisphere were investigated. Taken together, these results suggest that the lesion-induced error caused by simulated lesions was not focal, but globally distributed. Previous post-processing approaches to adjust for lesions in structural analyses address the focal region where the lesion was located however, our results suggest that focal correction approaches are insufficient for the global error in morphometric measures of the injured brain.

Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL

Background and purposeThere are instances in which an estimate of the brain volume should be obtained from MRI in clinical practice. Our objective is to calculate cross-sectional robustness of a convolutional neural network (CNN) based software (Entelai Pic) for brain volume estimation and compare it to traditional software such as FreeSurfer, CAT12 and FSL in healthy controls (HC). Materials and MethodsSixteen HC were scanned four times, two different days on two different MRI scanners (1.5 T and 3 T). Volumetric T1-weighted images were acquired and post-processed with FreeSurfer v6.0.0, Entelai Pic v2, CAT12 v12.5 and FSL v5.0.9. Whole-brain, grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) volumes were calculated. Correlation and agreement between methods was assessed using intraclass correlation coefficient (ICC) and Bland Altman plots. Robustness was assessed using the coefficient of variation (CV). ResultsWhole-brain volume estimation had better correlation between FreeSurfer and Entelai Pic (ICC (95% CI) 0.96 (0.94−0.97)) than FreeSurfer and CAT12 (0.92 (0.88−0.96)) and FSL (0.87 (0.79−0.91)). WM, GM and CSF showed a similar trend. Compared to FreeSurfer, Entelai Pic provided similarly robust segmentations of brain volumes both on same-scanner (mean CV 1.07, range 0.20–3.13% vs. mean CV 1.05, range 0.21–3.20%, p = 0.86) and on different-scanner variables (mean CV 3.84, range 2.49–5.91% vs. mean CV 3.84, range 2.62–5.13%, p = 0.96). Mean post-processing times were 480, 5, 40 and 5 min for FreeSurfer, Entelai Pic, CAT12 and FSL respectively. ConclusionBased on robustness and processing times, our CNN-based model is suitable for cross-sectional volumetry on clinical practice.

4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network

OBJECTIVES/GOALS: We seek to develop an automated deep learning-based method for segmentation and volumetric quantification of the fetal brain on T2-weighted fetal MRIs. We will evaluate the performance of the algorithm by comparing it to gold standard manual segmentations. The method will be used to create a normative sample of brain volumes across gestational ages. METHODS/STUDY POPULATION: We will adapt a U-Net convolutional neural network architecture for fetal brain MRIs using 3D volumes. After re-sampling 2D fetal brain acquisitions to 3mm3 3D volumes using linear interpolation, the network will be trained to perform automated brain segmentation on 40 randomly-sampled, normal fetal brain MRI scans of singleton pregnancies. Training will be performed in 3 acquisition planes (axial, coronal, sagittal). Performance will be evaluated on 10 test MRIs (in 3 acquisition planes, 30 total test samples) using Dice scores, compared to radiologists’ manual segmentations. The algorithm’s performance on measuring total brain volume will also be evaluated. RESULTS/ANTICIPATED RESULTS: Based on the success of prior U-net architectures for volumetric segmentation tasks in medical imaging (e.g. Duong et al., 2019), we anticipate that the convolutional neural network will accurately provide segmentations and associated volumetry of fetal brains in fractions of a second. We anticipate median Dice scores greater than 0.8 across our test sample. Once validated, the method will retrospectively generate a normative database of over 1500 fetal brain volumes across gestational ages (18 weeks to 30 weeks) collected at our institution. DISCUSSION/SIGNIFICANCE OF IMPACT: Quantitative estimates of brain volume, and deviations from normative data, would be a major advancement in objective clinical assessments of fetal MRI. Such data can currently only be obtained through laborious manual segmentations; automated deep learning methods have the potential to reduce the time and cost of this process.

Multi-atlas based neonatal brain extraction using atlas library clustering and local label fusion

Brain extraction is one of the most important preprocessing steps in cerebral magnetic resonance (MR) image analysis. Brain extraction from neonatal MR images is particularly challenging due to significant differences in head size and shape between neonates and rapid changes in neonatal brain structure in the weeks and months after birth. In this work, a multi-atlas-based neonatal brain extraction method using atlas library clustering and local label fusion (NOBELL) is presented. In NOBELL, an affinity propagation (AP) approach is first applied to cluster images of an atlas library into clusters represented by exemplars, which are used to select best matching clusters for target images. A local weighted voting strategy based on Jacobian determinant ranking is then employed to extract brain from target images using training images in best matching clusters. The performance of NOBELL was evaluated on T2- and T1-weighted scans of 40 neonates aged between 37 and 44 weeks. NOBELL outperformed two popular brain extraction tools, FSL’s Brain Extraction Tool (BET) and BrainSuite’s Brain Surface Extractor (BSE), and achieved higher accuracy with brain masks very close to manually extracted ones. NOBELL showed an average Jaccard coefficient of 0.974 (0.942) on T2 (T1)-weighted images in comparison with 0.908 (0.602) and 0.845 (0.762) achieved by BSE, and BET, respectively. NOBELL allows for accurate and efficient brain extraction, a crucial step in brain MRI applications such as accurate brain tissue segmentation and volume estimation as well as accurate cortical surface delineation in neonates.

A Method to Estimate Brain Volume from Head CT Images and Application to Detect Brain Atrophy in Alzheimer Disease.

Total brain volume and total intracranial volume are important measures for assessing whole-brain atrophy in Alzheimer disease, dementia, and other neurodegenerative diseases. Unlike MR imaging, which has a number of well-validated fully-automated methods, only a handful of methods segment CT images. Available methods either use enhanced CT, do not estimate both volumes, or require formal validation. Reliable computation of total brain volume and total intracranial volume from CT is needed because head CTs are more widely used than head MRIs in the clinical setting. We present an automated head CT segmentation method (CTseg) to estimate total brain volume and total intracranial volume. CTseg adapts a widely used brain MR imaging segmentation method from the Statistical Parametric Mapping toolbox using a CT-based template for initial registration. CTseg was tested and validated using head CT images from a clinical archive. CTseg showed excellent agreement with 20 manually segmented head CTs. The intraclass correlation was 0.97 (P < .001) for total intracranial volume and 0.94 (P < .001) for total brain volume. When CTseg was applied to a cross-sectional Alzheimer disease dataset (58 with Alzheimer disease patients and 58 matched controls), CTseg detected a loss in percentage total brain volume (as a percentage of total intracranial volume) with age (P < .001) as well as a group difference between patients with Alzheimer disease and controls (P < .01). We observed similar results when total brain volume was modeled with total intracranial volume as a confounding variable. In current clinical practice, brain atrophy is assessed by inaccurate and subjective "eyeballing" of CT images. Manual segmentation of head CT images is prohibitively arduous and time-consuming. CTseg can potentially help clinicians to automatically measure total brain volume and detect and track atrophy in neurodegenerative diseases. In addition, CTseg can be applied to large clinical archives for a variety of research studies.

Combined Use of Clinical, Lesion Topography, and Thalamic Volume Helps Differentiating Multiple Sclerosis from its Mimickers

Early diagnosis of multiple sclerosis (MS) is important. Non-specific white matter changes usually confound earlier diagnosis. Cross-sectional, global and regional studies have shown that brain volume estimates are of diagnostic and prognostic relevance to the management of the disease; however, it did not reach an individual patient level of precision. We aimed to use both conventional (subjective lesion count) and advanced (brain volume and thalamic volume) magnetic resonance imaging (MRI) measures to investigate the ability of these measures to differentiate MS from other mimickers. Seventy MS patients and seventy-two age- and sex-matched healthy controls were recruited for this study. A third group of patients diagnosed with MS mimickers who presented with symptoms that might be attributed to MS, and had multiple white matter lesions was also included. Whole brain volume and thalamic volume were evaluated for all groups using FSL software pipelines (SIENAX and FIRST respectively). Z scores were calculated for brain and thalamic volume from healthy controls group and two standard deviations below the mean were considered the cut-off value for comparison. Brain and thalamic volumes differed significantly between the study groups. Brain volume correlated significantly with thalamic volume in healthy controls (p<0.00, r=0.59) and MS group (p<0.00, r=0.76), but not in the MS mimickers group (p=0.26, r=0.27). A significantly low brain volume was observed in 7 MS patients (10%) and a significantly low thalamic volume was observed in 15 MS patients (21%). Only one subject from MS mimickers group showed a significant low brain volume (5%); otherwise no significant thalamic atrophy was observed in this group. Thalamic and brain atrophy occur early in MS. Non-specific white matter changes do not contribute significantly to brain volume changes. Hemispheric volume changes may occur in MS mimickers but not particularly thalamic atrophy. Cross-sectional volumetric evaluation may add some valuable information in distinguishing MS patients from common radiological mimickers of the disease.

Deep learning network with Euclidean similarity factor for Brain MR Tumor segmentation and volume estimation

The major goal of this paper is to isolate tumor region from nontumor regions and the estimation of tumor volume. Accurate segmentation is not an easy task due to the varying size, shape and location of the tumor. After segmentation, volume estimation is necessary in order to accurately estimate the tumor volume. By exactly estimating the volume of abnormal tissue, physicians can do excellent prognosis, clinical planning and dosage estimation. This paper describes a new Euclidean Similarity factor (ESF) based active contour model with deep learning for segmenting the tumor region into complete, core and enhanced tumor portions. Initially, the ESF considers the spatial distances and intensity differences of the region automatically to detect the tumor region. It preserves the image details but removes the noisy details. Then, the 3D Convolutional Neural Network (3D CNN) segments the tumor by automatically extracting spatiotemporal features. Finally, the extended shoelace method estimates the volume of the tumor accurately for [Formula: see text]-sided polygons. The simulation result achieves a high accuracy of 92% and Jaccard index of 0.912 and computes the tumor volume with effective performance than existing approaches.

Brain Volumes of very Low Birth Weight Infants Measured by Two-dimensional Cranial Ultrasonography: A Prospective Cohort Study.

Cranial ultrasonography is the main neuroimaging technique for very low birth weight infants. Low brain volume is associated with poor neurologic outcome. This study aimed to calculate brain volumes of preterm infants with two-dimensional measurements of cranial ultrasonography. Intracranial height, anteroposterior diameter, bi-parietal diamater, ventricular height, thalamo-occipital distance and ventricular index were measured with routine cranial ultrasonographic scanning. Brain considered a spheric, ellipsoid model and estimated brain volume (EBV) was calculated by subtracting two lateral ventricular volumes from the total brain volume. One hundred and twenty-one preterm infants under a birth weight of 1500 g and 32 weeks of gestational age were included in this study. The mean gestational age of study population was 27.7 weeks, and mean birthweight was 1057 grams. Twenty-two of 121 infants had dilated ventricle, in this group, EBV was lower than normal group (202 ± 58 cm3 vs 250 ± 53 cm3, respectively, p<0.01). Advanced resuscitation, bronchopulmonary dysplasia and late-onset sepsis were found to be independent risk factors for low brain volume in our data. Estimated brain volume can be calculated by two-dimensional measurements with cranial ultrasonography.

Active Contour Model for Brain MR Tumor Segmentation and Volume Estimation

Brain MR tumor segmentation and estimation of volume is a critical task in medical applications. Brain tumors are analyzed by the common test method known as magnetic resonance imaging (MRI) which provides a detail image of brain. The proposed work involves detection of tumor in brain using deep learning based active contour model. Segmentation is the main objective of the proposed work for achieving detailed information about the tumor and accurate volume estimation to detect the size of the tumor. The Euclidean similarity factor (ESF) is used for considering the spatial distances and intensity differences of the region there by preserving all the fine details of the image. 3D convolutional neural network (3DCNN) is used for extracting the features and segmentation to identify the tumor location in the brain. Finally, shoelace method is used to estimate the volume of the tumor, and it provides treatment planning, surgical methods, estimation of dose, etc. The simulation results in this suggested approach could attain effective performance as compared with the existing approaches.

Structural Brain Correlates of Loneliness among Older Adults

Ample evidence indicates that loneliness in old age is associated with poor bodily and mental health. However, little is known about structural cerebral correlates of loneliness in healthy older adults. We examined such correlates in a magnetic resonance imaging (MRI) subsample of 319 older adults aged 61 to 82 years drawn from the Berlin Aging Study II. Using voxel-based morphometry (VBM) and structural equation modeling (SEM), latent hierarchical regression analyses were performed to examine associations of (i) loneliness, (ii) a range of covariates, and (iii) loneliness by covariate interactions with latent brain volume estimates of brain structures known to be involved in processing, expressing, and regulating emotions. Results from whole-brain VBM analyses showed that individuals with higher loneliness scores tended to have smaller gray matter volumes in three clusters comprising (i) the left amygdala/anterior hippocampus, (ii) the left posterior parahippocampus and (iii) the left cerebellum. Significant associations and interactions between loneliness and latent factors for the amygdala and the hippocampus were confirmed with a region-of-interest (ROI)-based approach. These findings suggest that individual differences in loneliness among older adults are correlated with individual differences in the volumes of brain regions that are central to cognitive processing and emotional regulation, also after correcting for confounders such as social network size. We discuss possible mechanisms underlying these associations and their implications.

The Neural Architecture of General Knowledge

Cognitive performance varies widely between individuals and is highly influenced by structural and functional properties of the brain. In the past, neuroscientific research was principally concerned with fluid intelligence, while neglecting its equally important counterpart crystallized intelligence. Crystallized intelligence is defined as the depth and breadth of knowledge and skills that are valued by one's culture. The accumulation of crystallized intelligence is guided by information storage capacities and is likely to be reflected in an individual's level of general knowledge. In spite of the significant role general knowledge plays for everyday life, its neural foundation largely remains unknown. In a large sample of 324 healthy individuals, we used standard magnetic resonance imaging along with functional magnetic resonance imaging and diffusion tensor imaging to examine different estimates of brain volume and brain network connectivity and assessed their predictive power with regard to both general knowledge and fluid intelligence. Our results demonstrate that an individual's level of general knowledge is associated with structural brain network connectivity beyond any confounding effects exerted by age or sex. Moreover, we found fluid intelligence to be best predicted by cortex volume in male subjects and functional network connectivity in female subjects. Combined, these findings potentially indicate different neural architectures for information storage and information processing. © 2019 European Association of Personality Psychology

Changes in behavioral and neuronal parameters by alcohol, cigarette, or their combined use in rats.

Few studies have explored the effects of the combined use of alcohol and cigarette in humans, despite its prevalence. Here we evaluated the effect of isolated and combined use on behaviors and neuronal parameters in rats. Male adult rats were divided into alcohol (AL, 2 g/kg, by oral gavage), cigarette smoke (TB, six cigarettes, by inhalation), combined use (ALTB), or control (CT, water by oral gavage and environmental air) groups, treated twice a day (09.00 and 14.00 h). After 4 weeks, the rats were tested in the open field for behavioral analysis and euthanized for brain volume estimation and counting of neurons in the hippocampus. All treatments increased locomotion, and this behavior was higher in the ALTB than TB group. Latency to exit from the central area was lower in the ALTB than in the AL or CT groups. Rearing behavior increased in TB and decreased in AL and ALTB rats. Combined ALTB rats significantly increased their grooming behavior. Only the AL group showed decreased neuron counts and increased brain volume. Our results show that the isolated and combined uses of alcohol and cigarette smoke have diverse effects on behavioral and neuronal parameters in rats after long-term treatment.

DeepVolume: Brain Structure and Spatial Connection-Aware Network for Brain MRI Super-Resolution.

Thin-section magnetic resonance imaging (MRI) can provide higher resolution anatomical structures and more precise clinical information than thick-section images. However, thin-section MRI is not always available due to the imaging cost issue. In multicenter retrospective studies, a large number of data are often in thick-section manner with different section thickness. The lack of thin-section data and the difference in section thickness bring considerable difficulties in the study based on the image big data. In this article, we introduce DeepVolume, a two-step deep learning architecture to address the challenge of accurate thin-section MR image reconstruction. The first stage is the brain structure-aware network, in which the thick-section MR images in axial and sagittal planes are fused by a multitask 3-D U-net with prior knowledge of brain volume segmentation, which encourages the reconstruction result to have correct brain structure. The second stage is the spatial connection-aware network, in which the preliminary reconstruction results are adjusted slice-by-slice by a recurrent convolutional network embedding convolutional long short-term memory (LSTM) block, which enhances the precision of the reconstruction by utilizing the previously unassessed sagittal information. We used 305 paired brain MRI samples with thickness of 1.0 mm and 6.5 mm in this article. Extensive experiments illustrate that DeepVolume can produce the state-of-the-art reconstruction results by embedding more anatomical knowledge. Furthermore, considering DeepVolume as an intermediate step, the practical and clinical value of our method is validated by applying the brain volume estimation and voxel-based morphometry. The results show that DeepVolume can provide much more reliable brain volume estimation in the normalized space based on the thick-section MR images compared with the traditional solutions.