Subcortical Brain Structures Research Articles

Association of subcortical gray-matter volumes with life-course-persistent antisocial behavior in a population-representative longitudinal birth cohort.

Neuropsychological evidence supports the developmental taxonomy theory of antisocial behavior, suggesting that abnormal brain development distinguishes life-course-persistent from adolescence-limited antisocial behavior. Recent neuroimaging work confirmed that prospectively-measured life-course-persistent antisocial behavior is associated with differences in cortical brain structure. Whether this extends to subcortical brain structures remains uninvestigated. This study compared subcortical gray-matter volumes between 672 members of the Dunedin Study previously defined as exhibiting life-course-persistent, adolescence-limited or low-level antisocial behavior based on repeated assessments at ages 7-26 years. Gray-matter volumes of 10 subcortical structures were compared across groups. The life-course-persistent group had lower volumes of amygdala, brain stem, cerebellum, hippocampus, pallidum, thalamus, and ventral diencephalon compared to the low-antisocial group. Differences between life-course-persistent and adolescence-limited individuals were comparable in effect size to differences between life-course-persistent and low-antisocial individuals, but were not statistically significant due to less statistical power. Gray-matter volumes in adolescence-limited individuals were near the norm in this population-representative cohort and similar to volumes in low-antisocial individuals. Although this study could not establish causal links between brain volume and antisocial behavior, it constitutes new biological evidence that all people with antisocial behavior are not the same, supporting a need for greater developmental and diagnostic precision in clinical, forensic, and policy-based interventions.

Risk of lead exposure, subcortical brain structure, and cognition in a large cohort of 9- to 10-year-old children.

Lead, a toxic metal, affects cognitive development at the lowest measurable concentrations found in children, but little is known about its direct impact on brain development. Recently, we reported widespread decreases in cortical surface area and volume with increased risks of lead exposure, primarily in children of low-income families. We examined associations of neighborhood-level risk of lead exposure with cognitive test performance and subcortical brain volumes. We also examined whether subcortical structure mediated associations between lead risk and cognitive performance. Our analyses employed a cross-sectional analysis of baseline data from the observational Adolescent Brain Cognitive Development (ABCD) Study. The multi-center ABCD Study used school-based enrollment to recruit a demographically diverse cohort of almost 11,900 9- and 10-year-old children from an initial 22 study sites. The analyzed sample included data from 8,524 typically developing child participants and their parents or caregivers. The primary outcomes and measures were subcortical brain structure, cognitive performance using the National Institutes of Health Toolbox, and geocoded risk of lead exposure. Children who lived in neighborhoods with greater risks of environmental lead exposure exhibited smaller volumes of the mid-anterior (partial correlation coefficient [rp] = -0.040), central (rp = -0.038), and mid-posterior corpus callosum (rp = -0.035). Smaller volumes of these three callosal regions were associated with poorer performance on cognitive tests measuring language and processing speed. The association of lead exposure risk with cognitive performance was partially mediated through callosal volume, particularly the mid-posterior corpus callosum. In contrast, neighborhood-level indicators of disadvantage were not associated with smaller volumes of these brain structures. Environmental factors related to the risk of lead exposure may be associated with certain aspects of cognitive functioning via diminished subcortical brain structure, including the anterior splenium (i.e., mid-posterior corpus callosum).

Numerical Calculation of the Light Propagation in Tapered Optical Fibers for Optical Neural Interfaces.

As implantable optical systems recently enabled new approaches to study the brain with optical radiations, tapered optical fibers emerged as promising implantable waveguides to deliver and collect light from sub-cortical structures of the mouse brain. They rely on a specific feature of multimodal fiber optics: as the waveguide narrows, the number of guided modes decreases and the radiation can gradually couple with the environment. This happens along a taper segment whose length can be tailored to match with the depth of functional structures of the mouse brain, and can extend for a few millimeters. This anatomical requirement results in optical systems which have an active area that is very long compared to the wavelength of the light they guide and their behavior is typically estimated by ray tracing simulations, because finite element methods are too computationally demanding. Here we present a computational technique that exploits the beam-envelope method and the cylindrical symmetry of the fibers to provide an efficient and exact calculation of the electric field along the fibers, which may enable the design of neural interfaces optimized to meet different goals.

Cortical and subcortical brain structure in generalized anxiety disorder: findings from 28 research sites in the ENIGMA-Anxiety Working Group

The goal of this study was to compare brain structure between individuals with generalized anxiety disorder (GAD) and healthy controls. Previous studies have generated inconsistent findings, possibly due to small sample sizes, or clinical/analytic heterogeneity. To address these concerns, we combined data from 28 research sites worldwide through the ENIGMA-Anxiety Working Group, using a single, pre-registered mega-analysis. Structural magnetic resonance imaging data from children and adults (5–90 years) were processed using FreeSurfer. The main analysis included the regional and vertex-wise cortical thickness, cortical surface area, and subcortical volume as dependent variables, and GAD, age, age-squared, sex, and their interactions as independent variables. Nuisance variables included IQ, years of education, medication use, comorbidities, and global brain measures. The main analysis (1020 individuals with GAD and 2999 healthy controls) included random slopes per site and random intercepts per scanner. A secondary analysis (1112 individuals with GAD and 3282 healthy controls) included fixed slopes and random intercepts per scanner with the same variables. The main analysis showed no effect of GAD on brain structure, nor interactions involving GAD, age, or sex. The secondary analysis showed increased volume in the right ventral diencephalon in male individuals with GAD compared to male healthy controls, whereas female individuals with GAD did not differ from female healthy controls. This mega-analysis combining worldwide data showed that differences in brain structure related to GAD are small, possibly reflecting heterogeneity or those structural alterations are not a major component of its pathophysiology.

Diversity of daily activities is associated with greater hippocampal volume.

Greater engagement in a range of daily activities is associated with better cognitive functioning (Lee et al., Lee et al., 2020). The hippocampus, a subcortical brain structure implicated in learning, memory, spatial navigation and other aspects of cognitive functioning, may be structurally sensitive to exposure to and engagement with novel experiences and environments. The present study tested whether greater activity diversity, defined as the range of common daily activities engaged in and the proportion of time spent in each, is associated with larger hippocampal volume. Greater diversity of activities, as measured using daily diaries across an 8-day period, was related to greater hippocampal volume averaged across the left and right hemispheres, even when adjusting for estimated intracranial volume, total activity time, sociodemographic factors, and self-reported physical health. These findings are broadly consistent with nonhuman animal studies, demonstrating a link between enriched environments and structural changes to the hippocampus. Future longitudinal and experimental work can elucidate causal and directional relationships between diversity of daily activities and hippocampal volume.

Automated claustrum segmentation in human brain MRI using deep learning

In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet‐like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi‐view DL‐based approach to segment the claustrum in T1‐weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as reference standard. Cross‐validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41 mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra‐rater reliability. The leave‐one‐scanner‐out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL‐based claustrum segmentation benefits from multi‐view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI‐based research of the human claustrum. The software and models of our method are made publicly available.

Brain volumetric MRI study in healthy adolescent and young person’s using automated method

Objective: Adolescence is a critical period for the maturation of neurobiological processes that underlie higher cognitive functions and social and emotional behaviour. However, there are limited studies that investigated brain volumes in healthy adolescents and young persons. The aim of this study was to compare the Grey Matter (GM), White Matter (WM) and some specific brain subcortical volumes such as hippocampus and amygdala between healthy adolescents and young groups by using VolBrain.
 Material and Methods: Magnetic resonance imaging brain scans were retrospectively obtained from 20 healthy adolescent and young subjects. The mean ages of the adolescent and young persons were 13±1 and 24±2, respectively. Brain parenchyma (BP), GM, WM and asymmetry features were calculated using VolBrain, and the GM and WM volumes of each subjects were compared with those of the both groups. The current study to examine whether regional gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), some brain subcortical structures volumes differed between healthy adolescent and young groups. Also, of the whole brain, hemispheres, and hippocampus, amigdala of adolescent and young subject volumes were measured with an automated method.
 Results: We have observed that the young group was found to have a 4 % less in volume of GM, when compared with adolescent groups.
 Conclusion: Our data indicate that quantitative structural Magnetic Resonance Imaging (MRI) data of the adolescent brain is important in understanding the age-related human morphological changes.

Complementing canonical fMRI with functional Quantitative Susceptibility Mapping (fQSM) in modern neuroimaging research

Functional Quantitative Susceptibility Mapping (fQSM) allows for the quantitative measurement of time-varying magnetic susceptibility across cortical and subcortical brain structures with a potentially higher spatial specificity than conventional fMRI. While the usefulness of fQSM with General Linear Model and “On/Off” paradigms has been assessed, little is known about the potential applications and limitations of this technique in more sophisticated experimental paradigms and analyses, such as those currently used in modern neuroimaging.To thoroughly characterize fQSM activations, here we used 7T MRI, tonotopic mapping, as well as univariate (i.e., GLM and population Receptive Field) and multivariate (Representational Similarity Analysis; RSA) analyses.Although fQSM detected less tone-responsive voxels than fMRI, they were more consistently localized in gray matter. Also, the majority of active gray matter voxels exhibited negative fQSM response, signaling the expected oxyhemoglobin increase, whereas positive fQSM activations were mainly in white matter. Though fMRI- and fQSM-based tonotopic maps were overall comparable, the representation of frequency tunings in tone-sensitive regions was significantly more balanced for fQSM. Lastly, RSA revealed that frequency information from the auditory cortex could be successfully retrieved by using either methods.Overall, fQSM produces complementary results to conventional fMRI, as it captures small-scale variations in the activation pattern which inform multivariate measures. Although positive fQSM responses deserve further investigation, they do not impair the interpretation of contrasts of interest. The quantitative nature of fQSM, its spatial specificity and the possibility to simultaneously acquire canonical fMRI support the use of this technique for longitudinal and multicentric studies and pre-surgical mapping.

A meta-analysis of deep brain structural shape and asymmetry abnormalities in 2,833 individuals with schizophrenia compared with 3,929 healthy volunteers via the ENIGMA Consortium.

Schizophrenia is associated with widespread alterations in subcortical brain structure. While analytic methods have enabled more detailed morphometric characterization, findings are often equivocal. In this meta‐analysis, we employed the harmonized ENIGMA shape analysis protocols to collaboratively investigate subcortical brain structure shape differences between individuals with schizophrenia and healthy control participants. The study analyzed data from 2,833 individuals with schizophrenia and 3,929 healthy control participants contributed by 21 worldwide research groups participating in the ENIGMA Schizophrenia Working Group. Harmonized shape analysis protocols were applied to each site's data independently for bilateral hippocampus, amygdala, caudate, accumbens, putamen, pallidum, and thalamus obtained from T1‐weighted structural MRI scans. Mass univariate meta‐analyses revealed more‐concave‐than‐convex shape differences in the hippocampus, amygdala, accumbens, and thalamus in individuals with schizophrenia compared with control participants, more‐convex‐than‐concave shape differences in the putamen and pallidum, and both concave and convex shape differences in the caudate. Patterns of exaggerated asymmetry were observed across the hippocampus, amygdala, and thalamus in individuals with schizophrenia compared to control participants, while diminished asymmetry encompassed ventral striatum and ventral and dorsal thalamus. Our analyses also revealed that higher chlorpromazine dose equivalents and increased positive symptom levels were associated with patterns of contiguous convex shape differences across multiple subcortical structures. Findings from our shape meta‐analysis suggest that common neurobiological mechanisms may contribute to gray matter reduction across multiple subcortical regions, thus enhancing our understanding of the nature of network disorganization in schizophrenia.

Subcortical brain structure in children with developmental coordination disorder: A T1-weighted volumetric study

Developmental coordination disorder (DCD) is a neurodevelopmental disorder occurring in 5–6% of school-aged children. Converging evidence suggests that dysfunction within cortico-striatal and cortico-cerebellar networks may contribute to motor deficits in DCD, yet limited research has examined the brain morphology of these regions. Using T1-weighted magnetic resonance imaging the current study investigated cortical and subcortical volumes in 37 children with DCD, aged 8 to 12 years, and 48 controls of a similar age. Regional brain volumes of the thalamus, basal ganglia, cerebellum and primary motor and sensory cortices were extracted using the FreeSurfer recon-all pipeline and compared between groups. Reduced volumes within both the left and right pallidum (Left: F = 4.43, p = 0.039; Right: F = 5.24, p = 0.025) were observed in children with DCD; however, these results did not withstand correction for multiple comparisons. These findings provide preliminary evidence of altered subcortical brain structure in DCD. Future studies that examine the morphology of these subcortical regions are highly encouraged in order replicate these findings.

MSGSE-Net: Multi-scale guided squeeze-and-excitation network for subcortical brain structure segmentation

Convolutional neural networks (CNNs) have been achieving remarkable results in medical image segmentation. However, for accurate segmentation of subcortical brain structure in MR images, it is still a challenge due to the ambiguous boundaries, the complex structures, and the various shapes, which limits their clinical application. In this paper, we focus on utilizing multi-scale image contexts and attention mechanisms to improve networks’ ability to learn discriminative feature representation for accurate segmentation and present a novel FCNN architecture called multis-scale guided squeeze-and-excitation network (MSGSE-Net). In particular, we first propose the multi-scale guided squeeze-and-excitation (MSGSE) attention module which can progressively and selectively aggregate discriminative features. In contrast to existing attention modules, the MSGSE module performs an adaptive recalibration that features at different locations of the feature map are recalibrated under the guidance of multi-scale contexts. Then multi-scale spatial attention supervision is adopted to enhance the intra-class homogeneity and inter-class distinction of the attention weights. Moreover, we propose a novel entropy-weighted Dice loss (EDL) to force the network to focus on the ambiguous voxels around the boundaries of subcortical structures. We evaluate the proposed method on two challenging benchmark datasets (the IBSR dataset and the MALC dataset). The experimental results show that our model consistently yields better segmentation performance than several state-of-the-art methods and improves the segmentation Dice score by 1.6% at most compared with baseline method U-Net. Our code is available at https://github.com/neulxlx/MSGSE-Net.

Left lateralization of neonatal caudate microstructure affects emerging language development at 24 months.

The complex interaction between brain and behaviour in language disorder is well established. Yet to date, the imaging literature in the language disorder field has continued to pursue heterogeneous and relatively small clinical cross‐sectional samples, with emphasis on cortical structures and volumetric analyses of subcortical brain structures. In our current work, we aimed to go beyond this state of knowledge to focus on the microstructural features of subcortical brain structures (specifically the caudate nucleus) in a large cohort of neonates and study its association with emerging language skills at 24 months. Variations in neonatal brain microstructure could be interpreted as a proxy for in utero brain development. As language development is highly dependent on cognitive function and home literacy environment, we also examined their effect on the caudate–language function relationship utilizing a conditional process model. Our findings suggest that emerging language development at 24 months is influenced by the degree of left lateralization of neonatal caudate microstructure, indexed by diffusion tensor imaging (DTI)‐derived fractional anisotropy (FA). FA is an indirect measure of neuronal and dendritic density within grey matter structures. We also found that the caudate–language function relationship is partially mediated by cognitive function. The conditional indirect effect of left caudate FA on language composite score through cognitive function was only statistically significant at low levels of home literacy score (−1 standard deviation [SD]). The authors proposed that this may be related to ‘compensatory’ development of cognitive skills in less favourable home literacy environments.

Brain-Specific Gene Expression and Quantitative Traits Association Analysis for Mild Cognitive Impairment.

Transcriptome–wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer’s disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early–stage AD. Single–Nucleotide Polymorphism (SNP) analysis results indicated that cis–expression Quantitative Trait Loci (cis–eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis–eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis–eQTL SNPs of correlated genes in the limbic region have effective predictive abilities.

Understanding Emotions: Origins and Roles of the Amygdala.

Emotions arise from activations of specialized neuronal populations in several parts of the cerebral cortex, notably the anterior cingulate, insula, ventromedial prefrontal, and subcortical structures, such as the amygdala, ventral striatum, putamen, caudate nucleus, and ventral tegmental area. Feelings are conscious, emotional experiences of these activations that contribute to neuronal networks mediating thoughts, language, and behavior, thus enhancing the ability to predict, learn, and reappraise stimuli and situations in the environment based on previous experiences. Contemporary theories of emotion converge around the key role of the amygdala as the central subcortical emotional brain structure that constantly evaluates and integrates a variety of sensory information from the surroundings and assigns them appropriate values of emotional dimensions, such as valence, intensity, and approachability. The amygdala participates in the regulation of autonomic and endocrine functions, decision-making and adaptations of instinctive and motivational behaviors to changes in the environment through implicit associative learning, changes in short- and long-term synaptic plasticity, and activation of the fight-or-flight response via efferent projections from its central nucleus to cortical and subcortical structures.

The effects of the conditioned medium of fetal neural cell culture on the apoptosis in the rat brain after traumatic brain injury

A promising treatment method of the traumatic brain injury (TBI) may be stem cell therapy. However, the question of the nature and mechanisms of action of humoral factors produced by stem cells on apoptotic and reparative processes in the brain after trauma remains open. The purpose of the study was to research the effect of conditioned media of fetal neural cell cultures on the number of apoptotic cells in the cortex and subcortical structures of the rat brain after TBI. Materials and methods. TBI was modelled by dropping a metal cylinder on rat's head. Rat fetuses (E17-18) brain was used to obtain cultures of neural stem/progenitor cells. Conditioned media from cell cultures with high adhesive properties (HA-CM) and low adhesive properties (LA-CM) were used to treat the experimental TBI in rats. The presence of p53-positive cells in the cortex and subcortical structures was investigated by immunohistochemistry. Results. Immunohistochemical analysis of brain sections showed that on the 5th day after TBI in rats there was an increase in the number of p53-positive cells in both the cortex and subcortical structures of the brain. Injection of HA-CM and LA-CM to animals on the 2nd, 3rd, 4th days after TBI was found to reduce the number of p53-positive cells in the cortex, hippocampus and thalamus by approximately half compared to the TBI group. A significant difference in the inhibitory effect of two different conditioned media (HA-CM and LA-CM) on apoptosis in the brain of rats after TBI was not detected. Conclusions. The administration of conditioned media of rat fetal neural cell cultures caused a significant decrease in the number of p53-positive cells in both the cortex and subcortical structures on the 5th day after the brain injury.

MO354PRELIMINARY EVIDENCE OF RIVASTIGMINE EFFICACY IN CKD RELATED MILD COGNITIVE IMPAIRMENT

Abstract Background and Aims Mild Cognitive Impairment (MCI) is a common finding in chronic kidney disease (CKD) patients. Indeed, CKD represents a relevant risk factor for developing dementia and MCI. Cholinesterase inhibitors, such as rivastigmine, are among the few drugs approved for the treatment of dementia and MCI. Rivastigmine is also used to treat vascular dementia because it protects subcortical brain structures. Data are scanty regarding the use of rivastigmine in CKD patients with MCI and are much needed to guide the therapy for MCI in this cohort of patients. Method This retrospective case-control study compared the effects of rivastigmine on cognitive functions in MCI patients with CKD (stage III-IV; n= 20) and without CKD (n=21, control group), comparable for the extent of cognitive impairment (indexed by Montreal Cognitive Assessment, MoCA), age (range 18-65 years), gender, weight, and comorbidities. Patients under treatment with rivastigmine and with a baseline MoCA score available were included in the study. Exclusion criteria were ictus, psychiatric or other neurological conditions, heart failure, liver failure, severe obesity, anemia, electrolyte disorders, cancer, dialysis, and other severe comorbidities. Laboratory test data (glycemia, cholesterol, hemoglobin, proteinuria, creatinine) were used to characterize the two populations. MCI was defined as a MoCA score between 21-26. The cognitive screening was available at baseline (before treatment) and during a follow-up in a range of three-six months after the start of the treatment. CKD was defined by eGFR < 60 mL/min/1.73m2. Results The follow-up timing for cognitive screening was not statistically different between the two cohorts. The control group (MCI without CKD) showed a small, significant improvement in the MoCA score after treatment (baseline MoCA: 22.9±0.5, follow-up MoCA: 23.5±0.5, p=0.02, t-test for paired data). At variance, the MCI-CKD group showed a significant improvement in the MoCA score (baseline MoCA=23±0.4, follow-up MoCA=24.3±0.4, p<0.05). Accordingly, the extent of improvement of MoCA score after rivastigmine was inversely correlated to the eGFR (r = -0.23). Conclusion A significant improvement in MoCA score accompanied treatment with rivastigmine in the CKD group. More extensive population studies are needed to verify the greater efficacy of Acetylcholinesterase inhibitors in this population.

Alterations of Subcortical Brain Structures in Paradoxical and Psychophysiological Insomnia Disorder

Insomnia disorder (ID) is a common illness associated with mood and cognitive impairments. Subtyping ID is an ongoing debate in sleep medicine, but the underlying mechanisms of each subtype is poorly understood. Growing evidence suggests that subcortical brain structures play the key roles in pathophysiology of ID and its subtypes. Here, we aimed to investigate structural alteration of subcortical regions in patients with two common ID subtypes i.e., paradoxical and psychophysiological insomnia. Fifty-five patients and 49 healthy controls were recruited for this study and T1-weighted images and subjective and objective sleep parameters (i.e., Pittsburgh Sleep Quality Index and polysomnography) were collected from participants. Subcortical structures including the hippocampus, amygdala, caudate, putamen, globus pallidus, nucleus accumbens, and thalamus were automatically segmented in FSL. Volume and shape (using surface vertices) of each structure were compared between the groups, controlled for covariates, and corrected for multiple comparisons. In addition, correlations of sleep parameters and surface vertices or volumes were calculated. The caudate's volume was smaller in patients than controls. Compared with controls, we found regional shrinkage in the caudate, nucleus accumbens, posterior putamen, hippocampus, thalamus, and amygdala in paradoxical insomnia and shrinkage in the amygdala, caudate, hippocampus, and putamen in psychophysiological insomnia. Interestingly, comparing two patients groups, shape alteration in the caudate, putamen, and nucleus accumbens in paradoxical insomnia and shrinkage in the thalamus, amygdala, and hippocampus in psychophysiological insomnia were observed. Both subjective and objective sleep parameters were associated with these regional shape alterations in patients. Our results support the differential role of subcortical brain structures in pathophysiology of paradoxical and psychophysiological insomnia.

Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation.

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.

Positivity of oligoclonal bands in the cerebrospinal fluid predisposed to metabolic changes and rearrangement of inhibitory/excitatory neurotransmitters in subcortical brain structures in multiple sclerosis

BackgroundThe latest diagnostic criteria for multiple sclerosis (MS) have revitalized the role of oligoclonal bands synthesis in the cerebrospinal fluid (CSF-OCB). This study identifies predictors of CSF-OCB-positivity among in vivo metabolic markers in the subcortical gray/white matter in MS patients after their first episode (CIS) and in patients with relapsing-remitting course (RRMS). MethodsThe study enrolled 13 CIS and 23 RRMS patients. Metabolism was evaluated using Mescher-Garwood-edited proton-magnetic resonance spectroscopy on a 3T MR scanner. In addition to N-acetyl-aspartate (tNAA), myoinositol (mIns), and choline- and creatine compounds (tCho, tCr) were also evaluated γ-aminobutyric acid (GABA) and glutamate-glutamine (Glx) ratios. ResultsCSF-OCB-positivity was found in 76.9% of CIS and 78.2% of RRMS patients. GABA and Glx ratios in putamen and corpus callosum strongly determined CSF-OCB-positive CIS patients. Other essential predictors of CSF-OCB-positive CIS were mIns and Glx ratios in the putamen, and tCho/tNAA in the corpus callosum. In RRMS, GABA ratios in the right thalamus and Glx ratios in the left hippocampus strongly predicted CSF-OCB-positive patients. tCho/tNAA and tNAA/tCr in the left hippocampus were also identified as essential predictors of CSF-OCB-positive RRMS patients. ConclusionThis is the first in vivo evidence of GABA-Glx rearrangement in CSF-OCB-positive patients since its early stages of MS.

Cortical and Subcortical Brain Volume Alterations Following Endurance Running at 38.6 km and 119.2 km in Male Athletes.

BackgroundAlthough several studies have shown that ultramarathon running causes severe physical and mental stress and harms organ systems, its effect on brain tissue remains unclear. The purpose of this study was to investigate the volumetric change of cortical and subcortical brain structures following 38.6-km and 119.8-km mountain races.Material/MethodsA total of 23 healthy male runners (age, 49.05±5.99 years) were classified as short-trail (ST; n=9) and ultra-trail (UT; n=14) endurance running. Pre- and post-test scanning of brain tissue was performed by using a 3-Tesla magnetic resonance imaging (MRI). Pre- and post-race differences in cortical and subcortical volumes in the ST and UT groups were separately determined by Wilcoxon signed-rank test.ResultsCortical gray matter (GM) and cerebral GM volume significantly increased after the race in both ST and UT groups, whereas the volume of the thalamus, caudate, pallidus, and hippocampus significantly increased only in the UT group. Cerebrospinal fluid (CSF) and white-matter (WM) volumes did not change after endurance running and remained unaltered in both groups.ConclusionsEndurance running has a site-specific acute effect on cortical and subcortical structures and may attenuate GM volume decrease in older adult male athletes. The increased volume of subcortical structures might be a response of physical exercise and additional physical stress experienced by ultramarathon runners.