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Accumulating evidence have documented sex differences in brain anatomy from early childhood to late adulthood. However, whether sex difference of brain structure emerges in the neonatal brain and how sex modulates the development of cortical morphology during the perinatal stage remains unclear. Here, we utilized T2-weighted MRI from the Developing Human Connectome Project (dHCP) database, consisting of 41 male and 40 female neonates born between 35 and 43 postmenstrual weeks (PMW). Neonates of each sex were arranged in a continuous ascending order of age to capture the progressive changes in cortical thickness and curvature throughout the developmental continuum. The maturational covariance network (MCN) was defined as the coupled developmental fluctuations of morphology measures between cortical regions. We constructed MCNs based on the two features, respectively, to illustrate their developmental interdependencies, and then compared the network topology between sexes. Our results showed that cortical structural development exhibited a localized pattern in both males and females, with no significant sex differences in the developmental trajectory of cortical morphology, overall organization, nodal importance, and modular structure of the MCN. Furthermore, by merging male and female neonates into a unified cohort, we identified evident dependencies influences in structural development between different brain modules using the Granger causality analysis (GCA), emanating from high-order regions toward primary cortices. Our findings demonstrate that the maturational pattern of cortical morphology may not differ between sexes during the perinatal period, and provide evidence for the developmental causality among cortical structures in perinatal brains., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: Detection and prediction of the rate of brain volume loss with age is a significant unmet need in patients with primary progressive multiple sclerosis (PPMS). In this study we construct detailed brain volume maps for PPMS patients. These maps compare age-related changes in both cortical and sub-cortical regions with those in healthy individuals., Methods: We conducted retrospective analyses of brain volume using T1-weighted Magnetic Resonance Imaging (MRI) scans of a large cohort of PPMS patients and healthy subjects. The volume of brain parenchyma (BP), cortex, white matter (WM), deep gray matter, thalamus, and cerebellum were measured using the robust SynthSeg segmentation tool. Age- and gender-related regression curves were constructed based on data from healthy subjects, with the 95% prediction interval adopted as the normality threshold for each brain region., Results: We analyzed 495 MRI scans from 169 PPMS patients, aged 20-79 years, alongside 563 exams from healthy subjects aged 20-86. Compared to healthy subjects, a higher proportion of PPMS patients showed lower than expected brain volumes in all regions except the cerebellum. The most affected areas were BP, WM, and thalamus. Lower brain volumes correlated with longer disease duration for BP and WM, and higher disability for BP, WM, cortex, and thalamus., Conclusions: Constructing age- and gender-related brain volume maps enabled identifying PPMS patients at a higher risk of brain volume loss. Monitoring these high-risk patients may lead to better treatment decisions and improve patient outcomes., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Computer programming, the process of designing, writing, and testing executable computer code, is an essential skill in numerous fields. A description of the neural structures engaged and modified during programming skill acquisition could help improve training programs and provide clues to the neural substrates underlying the acquisition of related skills., Methods: Fourteen female university students without prior computer programing experience were examined by functional magnetic resonance imaging (fMRI) during the early and late stages of a 5-month 'Computer Processing' course. Brain regions involved in task performance and learning were identified by comparing responses to programming and control tasks during the early and late stages., Results: The accuracy of performing a programming task was significantly improved during the late stage. Various regions of the frontal, temporal, parietal, and occipital cortex as well as several subcortical structures (caudate nuclei and cerebellum) were activated during programming tasks. Brain activity in the right inferior frontal gyrus was greater during the late stage and significantly correlated with improved task performance. Although the left inferior frontal gyrus was also highly active during the programming task, there were no learning-induced changes in activity or a significant correlation between activity and improved task performances., Conclusion: Computer programming learning among novices induces functional neuroplasticity within the right inferior frontal gyrus but not the left inferior gyrus (Broca's area)., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The development of new molecular and neurobiological methods, computer-assisted quantification techniques and neurobiological investigation methods which can be applied to the human brain, all have evoked an increased demand for post-mortem tissue in research. Psychiatric disorders are considered to be of neurobiological origin. Thus far, however, the etiology and pathophysiology of schizophrenia, depression and dementias are not well understood at the cellular and molecular level. The following will outline the consensus of the working group for neuropsychiatric brain banking organized in the Brainnet Europe II, on ethical guidelines for brain banking, clinical diagnostic criteria, the minimal clinical data set of retrospectively analyzed cases as well as neuropathological standard investigations to perform stageing for neurodegenerative disorders in brain tissue. We will list regions of interest for assessments in psychiatric disorder, propose a dissection scheme and describe preservation and storage conditions of tissue. These guidelines may be of value for future implementations of additional neuropsychiatric brain banks world-wide.

Individual variability in cognition and behavior results from the differences in brain structure and function that have already emerged before birth. However, little is known about individual variability in brain functional architecture at local level in neonates which is of great significance to explore owing to largely undeveloped long-range functional connectivity and segregated functions in early brain development. To address this, resting-state fMRI data of 163 neonates ranged from 32 to 45 postconceptional weeks (PCW) were used in this study, and various functional features including functional parcellation similarity, local brain activity and local functional connectivity were used to characterize individual functional variability. We observed significantly higher local functional individual variability in superior parietal, sensorimotor, and visual cortex, and lower variability in the frontal, insula and cingulate cortex relative to other regions within each hemisphere. The mean local functional individual variability significantly increased with age, and the age effect was found larger in brain regions such as the occipital, temporal, prefrontal and parietal cortex. Our findings promote the understanding of brain plasticity and regional differential maturation in the early stage., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Computational morphometry of magnetic resonance images represents a powerful tool for studying macroscopic differences in human brains. In the present study (N participants = 829), we combined different techniques and measures of brain morphology to investigate one of the most compelling topics in neuroscience: sexual dimorphism in human brain structure. When accounting for overall larger male brains, results showed limited sex differences in gray matter volume (GMV) and surface area. On the other hand, we found larger differences in cortical thickness, favoring both males and females, arguably as a result of region-specific differences. We also observed higher values of fractal dimension, a measure of cortical complexity, for males versus females across the four lobes. In addition, we applied source-based morphometry, an alternative method for measuring GMV based on the independent component analysis. Analyses on independent components revealed higher GMV in fronto-parietal regions, thalamus and caudate nucleus for females, and in cerebellar- temporal cortices and putamen for males, a pattern that is largely consistent with previous findings., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Consumer wearables and health monitors, internet-based health and cognitive assessments, and at-home biosample (e.g., saliva and capillary blood) collection kits are increasingly used by public health researchers for large population-based studies without requiring intensive in-person visits. Alongside reduced participant time burden, remote and virtual data collection allows the participation of individuals who live long distances from hospital or university research centers, or who lack access to transportation. Unfortunately, studies that include magnetic resonance neuroimaging are challenging to perform remotely given the infrastructure requirements of MRI scanners, and, as a result, they often omit socially, economically, and educationally disadvantaged individuals. Lower field strength systems (< 100 mT) offer the potential to perform neuroimaging at a participant's home, enabling more accessible and equitable research. Here we report the first use of a low-field MRI "scan van" with an online assessment of paired-associate learning (PAL) to examine associations between brain morphometry and verbal memory performance. In a sample of 67 individuals, 18-93 years of age, imaged at or near their home, we show expected white and gray matter volume trends with age and find significant (p < 0.05 FWE) associations between PAL performance and hippocampus, amygdala, caudate, and thalamic volumes. High-quality data were acquired in 93% of individuals, and at-home scanning was preferred by all individuals with prior MRI at a hospital or research setting. Results demonstrate the feasibility of remote neuroimaging and cognitive data collection, with important implications for engaging traditionally under-represented communities in neuroimaging research., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Physiological functions require coordination of processes between diverse organs, tissues, and cells. This integrative view of science has reemerged complementary to the reductionist philosophy of studying individual cell types. An integrative approach has proven particularly powerful within the field of neuroscience where, intermingled among the most numerous neural cell types of the brain, are immune cells called microglia. Microglia act as a line of defense in the CNS by phagocytizing harmful pathogens and cellular debris and by releasing a variety of factors that mediate immune responses. However, microglia are also appreciated as critical mediators of neurophysiology making them a desired target to rectify neuropathological states. The goal of this review is to discuss microglia ontogenesis, referred to as microgliogenesis, a term that encompasses the events that drive the production, differentiation, migration, and maturation of microglia and opportunities to target microglia for brain repair., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Drug delivery to the brain is challenging to study due to the complexity of the barriers of the central nervous system (CNS). The present chapter describes and compares experimental methods such as microdialysis, two-photon laser scanning fluorescence microscopy and positron emission tomography (PET) that can be used for in vivo studies of drug transport across the blood-brain barrier (BBB). The selection of appropriate method is based on the research question, and the different methods will in most cases provide complementary information. Attention is also given to the fact that the BBB might undergo changes in integrity, protein expression and other morphological alterations as a result of disease. The use of animal models of human disease is therefore also discussed. Special emphasis is given to translational aspects of the different methods and readouts., (© 2020. Springer Nature Switzerland AG.)

Schizophrenia and autism spectrum disorder have long been associated with elevated levels of various small phenolic molecules (SPMs). In turn, the gut microbiota (GMB) has been implicated in the kinetics of many of these analytes. Unfortunately, research into the possible relevance of GMB-mediated SPMs to neuropsychiatry continues to be limited by heterogeneous study design, numerous sources of variance and technical challenges. Some SPMs have multiple structural isomers and most have conjugates. Without specialized approaches, SPMs can be incorrectly assigned or inaccurately quantified. In addition, SPM levels can be affected by dietary polyphenol or protein consumption and by various medications and diseases. Nonetheless, heterotypical excretion of various SPMs in association with schizophrenia or autism continues to be reported in independent samples. Recent studies in human cerebrospinal fluid demonstrate the presence of many SPMs A large number of these are bioactive in experimental models. Whether such mechanisms are relevant to the human brain in health or disease is not known. Systematic metabolomic and microbiome studies of well-characterized populations, an appreciation of multiple confounds, and implementation of standardized approaches across platforms and sites are needed to delineate the potential utility of the phenolic interactome in neuropsychiatry.

Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

With increasing age, functional connectomes become dissimilar across normal individuals, reflecting heterogenous aging effects on functional connectivity (FC). We investigated the distribution of these effects across the connectome and their relationship with age-related differences in dopamine (DA) D1 receptor availability and gray matter density (GMD). With this aim, we determined aging effects on mean and interindividual variance of FC using fMRI in 30 younger and 30 older healthy subjects and mapped the contribution of each connection to the patterns of age-related similarity loss. Aging effects on mean FC accounted mainly for the dissimilarity between connectomes of younger and older adults, and were related, across brain regions, to aging effects on DA D1 receptor availability. Aging effects on the variance of FC indicated a global increase in variance with advancing age, explained connectome dissimilarity among older subjects and were related to aging effects on variance of GMD. The relationship between aging and the similarity of connectomes can thus be partly explained by age differences in DA modulation and gray matter structure.

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

Network representation has been an incredibly useful concept for understanding the behavior of complex systems in social sciences, biology, neuroscience, and beyond. Network science is mathematically founded on graph theory, where nodal importance is gauged using measures of centrality. Notably, recent work suggests that the topological centrality of a node should not be over-interpreted as its dynamical or causal importance in the network. Hence, identifying the influential nodes in dynamic causal models (DCM) remains an open question. This paper introduces causal centrality for DCM, a dynamics-sensitive and causally-founded centrality measure based on the notion of intervention in graphical models. Operationally, this measure simplifies to an identifiable expression using Bayesian model reduction. As a proof of concept, the average DCM of the extended default mode network (eDMN) was computed in 74 healthy subjects. Next, causal centralities of different regions were computed for this causal graph, and compared against several graph-theoretical centralities. The results showed that the subcortical structures of the eDMN were more causally central than the cortical regions, even though the graph-theoretical centralities unanimously favored the latter. Importantly, model comparison revealed that only the pattern of causal centrality was causally relevant. These results are consistent with the crucial role of the subcortical structures in the neuromodulatory systems of the brain, and highlight their contribution to the organization of large-scale networks. Potential applications of causal centrality-to study causal models of other neurotypical and pathological functional networks-are discussed, and some future lines of research are outlined., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Brain tumors are one of the most dangerous health problems for adults and children in many countries. Any failure in the diagnosis of brain tumors may lead to shortening of human life. Accurate and timely diagnosis of brain tumors provides appropriate treatment to increase the patient's chances of survival. Due to the different characteristics of tumors, one of the challenging problems is the classification of three types of brain tumors. With the advent of deep learning (DL) models, three classes of brain tumor classification have been addressed. However, the accuracy of these methods requires significant improvements in brain image classification. The main goal of this article is to design a new method for classifying the three types of brain tumors with extremely high accuracy. In this paper, we propose a novel deep stacked ensemble model called "BMRI-NET" that can detect brain tumors from MR images with high accuracy and recall. The stacked ensemble proposed in this article adapts three pre-trained models, namely DenseNe201, ResNet152V2, and InceptionResNetV2, to improve the generalization capability. We combine decisions from the three models using the stacking technique to obtain final results that are much more accurate than individual models for detecting brain tumors. The efficacy of the proposed model is evaluated on the Figshare brain MRI dataset of three types of brain tumors consisting of 3064 images. The experimental results clearly highlight the robustness of the proposed BMRI-NET model by achieving an overall classification of 98.69% and an average recall, F1-score and MCC of 98.33%, 98.40, and 97.95%, respectively. The results indicate that the proposed BMRI-NET model is superior to existing methods and can assist healthcare professionals in the diagnosis of brain tumors., (© 2023. International Association of Scientists in the Interdisciplinary Areas.)

Background: Head circumference (HC) correlates with brain growth and development. However, radiologists typically use their visual impressions to evaluate head dimensions, which is a purely subjective assessment., Objective: The first objective of this paper was to analyze whether it is possible to obtain HC measurements by CT and MRI cross-sectional volumetric imaging using parameters similar to the tape measurement method, which is considered the gold standard. The second objective was to determine the most accurate method for characterizing head size: visual impression or imaging measurement., Materials and Methods: Children who underwent HC measurement by tape and with volumetric imaging exams were selected from the Paulo Niemeyer State Brain Institute, Brazil. Two radiologists classified the children's heads using their visual impressions and by direct measurement on imaging. We used anatomical parameters similar to the tape measurement method and appropriate head growth charts and compared the absolute values obtained., Results: The concordance between tape and imaging ranged 52.3-72.7% for visual impression (kappa 0.27-0.55), ≥ 95.5% for CT and ≥ 93.1% for MRI (kappa ≥ 0.92 and ≥ 0.88, respectively). Intraclass correlation coefficients ≥ 0.997 and ≥ 0.996, absolute technical error measurements of 0.20-0.31 cm and 0.24-0.29 cm, and relative technical error measurements of 0.49-0.73% and 0.58-0.70% were evidenced in the intra- and inter-rater evaluations, respectively. The means between the methods were not significantly different (P > 0.05), with high values of Pearson correlation coefficient (≥ 0.99) and Lin concordance correlation coefficient (≥ 0.99)., Conclusion: Head circumference values obtained by cross-sectional volumetric imaging are similar to those obtained using the tape measurement method (gold standard), with high repeatability and reproducibility. Head size characterization by visual impression is less accurate than head circumference measurement by imaging and plotting the values obtained on World Health Organization normative charts., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.

MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that call into question its accuracy in many applications. Therefore, there has been intense interest in defining and mitigating the intrinsic limitations of the method. Recent studies have reported that tractography is inherently limited in its ability to accurately reconstruct the connections of the brain, when based on voxel-averaged estimates of local fiber orientation alone. Several validation studies have confirmed that tractography techniques are plagued by both false-positive and false-negative connections. However, these validation studies which quantify sensitivity and specificity, particularly in animal models, have not utilized prior anatomical knowledge, as is done in the human literature, for virtual dissection of white matter pathways, instead assessing tractography implemented in a relatively unconstrained manner. Thus, they represent a worse-case scenario for bundle-segmentation techniques and may not be indicative of the anatomical accuracy in the process of bundle segmentation, where streamline filtering using inclusion and exclusion regions-of-interest is common. With this in mind, the aim of the current study is to investigate and quantify the upper bounds of accuracy using current tractography methods. Making use of the same dataset utilized in two seminal validation papers, we show that prior anatomical knowledge in the form of manually placed or template-driven constraints can significantly improve the anatomical accuracy of estimated brain connections. Thus, we show that it is possible to achieve a high sensitivity and high specificity simultaneously, and conclude that current tractography algorithms, in combination with anatomically driven constraints, can result in reconstructions which very accurately reflect the ground truth white matter connections.

Neuroanatomical tracing methods remain fundamental for elucidating the complexity of brain circuits. During the past decades, the technical arsenal at our disposal has been greatly enriched, with a steady supply of fresh arrivals. This paper provides a landscape view of classical and modern tools for tract-tracing purposes. Focus is placed on methods that have gone viral, i.e., became most widespread used and fully reliable. To keep an historical perspective, we start by reviewing one-dimensional, standalone transport-tracing tools; these including today's two most favorite anterograde neuroanatomical tracers such as Phaseolus vulgaris-leucoagglutinin and biotinylated dextran amine. Next, emphasis is placed on several classical tools widely used for retrograde neuroanatomical tracing purposes, where Fluoro-Gold in our opinion represents the best example. Furthermore, it is worth noting that multi-dimensional paradigms can be designed by combining different tracers or by applying a given tracer together with detecting one or more neurochemical substances, as illustrated here with several examples. Finally, it is without any doubt that we are currently witnessing the unstoppable and spectacular rise of modern molecular-genetic techniques based on the use of modified viruses as delivery vehicles for genetic material, therefore, pushing the tract-tracing field forward into a new era. In summary, here, we aim to provide neuroscientists with the advice and background required when facing a choice on which neuroanatomical tracer-or combination thereof-might be best suited for addressing a given experimental design.

In this paper, we review and discuss brain imaging studies which have used the source-based morphometry (SBM) approach over the past decade. SBM is a data-driven linear multivariate approach for decomposing structural brain imaging data into commonly covarying imaging components and subject-specific loading parameters. It is a well-established technique which has predominantly been used to study neuroanatomic differences between healthy controls and patients with neuropsychiatric diseases. We start by discussing the advantages of this technique over univariate analysis for imaging studies, followed by a discussion of results from recent studies which have successfully applied this methodology. We also present recent extensions of this framework including nonlinear SBM, biclustered independent component analysis (B-ICA) and conclude with the possible directions of work for future.

Multiple sclerosis (MS) is an immune-mediated neurodegenerative disease of the central nervous system (CNS). One of the most promising recent medications for MS is teriflunomide. Its primary mechanism of action is linked to effects on the peripheral immune system by inhibiting dihydroorotate dehydrogenase (DHODH)-catalyzed de novo pyrimidine synthesis and reducing the expansion of lymphocytes in the peripheral immune system. Some in vitro studies suggested, however, that it can also have a direct effect on the CNS compartment. This potential alternative mode of action depends on the drug's capacity to traverse the blood-brain barrier (BBB) and to exert an effect on the complex network of brain biochemical pathways. In this paper, we demonstrate the application of high-resolution/high-accuracy matrix-assisted laser desorption/ionization Fourier-transform ion cyclotron resonance mass spectrometry for molecular imaging of the mouse brain coronal sections from animals treated with teriflunomide. Specifically, in order to assess the effect of teriflunomide on the mouse CNS compartment, we investigated the feasibility of teriflunomide to traverse the BBB. Secondly, we systematically evaluated the spatial and semi-quantitative brain metabolic profiles of 24 different endogenous compounds after 4-day teriflunomide administration. Even though the drug was not detected in the examined cerebral sections (despite the high detection sensitivity of the developed method), in-depth study of the endogenous metabolic compartment revealed noticeable alterations as a result of teriflunomide administration compared to the control animals. The observed differences, particularly for purine and pyrimidine nucleotides as well as for glutathione and carbohydrate metabolism intermediates, shed some light on the potential impact of teriflunomide on the mouse brain metabolic networks. Graphical Abstract.

The identification and reconstruction of axonal pathways in the living brain or "ex-vivo" is promising a revolution in connectivity studies bridging the gap from animal to human neuroanatomy with extensions to brain structural-functional correlates. Unfortunately, the methods suffer from juvenile drawbacks. In this perspective paper we mention several computational and developmental principles, which might stimulate a new generation of algorithms and a discussion bridging the neuroimaging and neuroanatomy communities.

Robust spatial alignment of post mortem data and in vivo MRI acquisitions from different ages, especially from the early developmental stages, into standard spaces is still a bottleneck hampering easy comparison with the mainstream neuroimaging results. In this paper, we test a landmark-based spatial normalization strategy as a framework for the seamless integration of any macroscopic dataset in the context of the Human Brain Project (HBP). This strategy stems from an approach called DISCO embedding sulcal constraints in a registration framework used to initialize DARTEL, the widely used spatial normalization approach proposed in the SPM software. We show that this strategy is efficient with a heterogeneous dataset including challenging data as preterm newborns, infants, post mortem histological data and a synthetic atlas computed from averaging the ICBM database, as well as more commonly studied data acquired in vivo in adults. We then describe some perspectives for a research program aiming at improving folding pattern matching for atlas inference in the context of the future HBP's portal.

Investigations of brain complex integrative actions should consider beside neural networks, glial, extracellular molecular, and fluid channels networks. The present paper proposes that all these networks are assembled into the brain hyper-network that has as fundamental components, the tetra-partite synapses, formed by neural, glial, and extracellular molecular networks. Furthermore, peri-synaptic astrocytic processes by modulating the perviousness of extracellular fluid channels control the signals impinging on the tetra-partite synapses. It has also been surmised that global signalling via astrocytes networks and highly pervasive signals, such as electromagnetic fields (EMFs), allow the appropriate integration of the various networks especially at crucial nodes level, the tetra-partite synapses. As a matter of fact, it has been shown that astrocytes can form gap-junction-coupled syncytia allowing intercellular communication characterised by a rapid and possibly long-distance transfer of signals. As far as the EMFs are concerned, the concept of broadcasted neuroconnectomics (BNC) has been introduced to describe highly pervasive signals involved in resetting the information handling of brain networks at various miniaturisation levels. In other words, BNC creates, thanks to the EMFs, generated especially by neurons, different assemblages among the various networks forming the brain hyper-network. Thus, it is surmised that neuronal networks are the "core components" of the brain hyper-network that has as special "nodes" the multi-facet tetra-partite synapses. Furthermore, it is suggested that investigations on the functional plasticity of multi-partite synapses in response to BNC can be the background for a new understanding and perhaps a new modelling of brain morpho-functional organisation and integrative actions.

This paper presents an approach for label-free brain tumor tissue typing. For this application, our dual modality microspectroscopy system combines inelastic Raman scattering spectroscopy and Mie elastic light scattering spectroscopy. The system enables marker-free biomedical diagnostics and records both the chemical and morphologic changes of tissues on a cellular and subcellular level. The system setup is described and the suitability for measuring morphologic features is investigated. Graphical Abstract Bimodal approach for label-free brain tumor typing. Elastic and inelastic light scattering spectra are collected laterally resolved in one measurement setup. The spectra are investigated by multivariate data analysis for assigning the tissues to specific WHO grades according to their malignancy.

Purpose: The relationship between parameters of brain functional networks and clinical indexes is unclear so far in patients with diabetic retinopathy (DR). This paper is to investigate this., Methods: Twenty-one patients with different grades of DR and 21 age- and sex-matched healthy controls were enrolled from August 2012 to September 2014. The clinical indexes recorded included DR grade, duration of diabetes, HbA1c, diabetic foot screen, fasting plasma glucose, insulin, Homa-β, Homa-IR, insulin sensitive index (ISI), Mini-Mental State Examination (MMSE), and patient sex and age. Subjects were scanned using 3-T MR with blood-oxygen-level-dependent and 3D-FSPGR sequences. MR data was analyzed via preprocessing and functional network construction, and quantified indexes of network (clustering coefficient, characteristic path length, global efficiency, degree distribution, and small worldness) were evaluated. Statistics consisted of ANOVA and correlation., Results: There were significant differences between patients and controls among clustering coefficient, characteristic path length, degree distribution, and small worldness parameters (P < 0.05). MMSE scores negatively correlated with characteristic path length, and Hb1Ac negatively correlated with small worldness. MMSE, duration of diabetes, diabetic foot screen, fasting plasma glucose, insulin, Homa-β, Homa-IR, ISI, DR grade, and patient age, except from Hb1Ac, correlated with degree distribution in certain brain areas., Conclusion: Brain functional networks are altered, specifically in the areas of visual function and cognition, and these alterations may reflect the severity of visual weakness and cognitive decline in DR patients. Moreover, the brain networks may be affected both by long-standing and instant clinical factors.

Cortical folding pattern analysis is very important to understand brain organization and development. Since previous studies mostly focus on human brain cortex, the regularity and variability of cortical folding patterns across primate brains (macaques, chimpanzees and human) remain largely unknown. This paper presents a novel computational framework to identify common or unique gyral folding patterns in macaque, chimpanzee and human brains using magnetic resonance imaging (MRI) data. We quantitatively characterize gyral folding patterns via hinge numbers with cortical surfaces constructed from MRI data, and identify 6 common three-hinge gyral folds that exhibit consistent anatomical locations across these three species as well as 2 unique three hinges in macaque, 6 ones in chimpanzee and 14 ones in human. A novel morphology descriptor is then applied to classify three-hinge gyral folds, and the increasing complexity is identified among the species analyzed. This study may provide novel insights into the regularity and variability of the cerebral cortex from developmental perspective and may potentially facilitate novel neuroimage analyses such as cortical parcellation with correspondences across species in the future.

Distinguishing progressive mild cognitive impairment (pMCI) from stable mild cognitive impairment (sMCI) is critical for identification of patients who are at risk for Alzheimer's disease (AD), so that early treatment can be administered. In this paper, we propose a pMCI/sMCI classification framework that harnesses information available in longitudinal magnetic resonance imaging (MRI) data, which could be incomplete, to improve diagnostic accuracy. Volumetric features were first extracted from the baseline MRI scan and subsequent scans acquired after 6, 12, and 18 months. Dynamic features were then obtained using the 18th month scan as the reference and computing the ratios of feature differences for the earlier scans. Features that are linearly or non-linearly correlated with diagnostic labels are then selected using two elastic net sparse learning algorithms. Missing feature values due to the incomplete longitudinal data are imputed using a low-rank matrix completion method. Finally, based on the completed feature matrix, we build a multi-kernel support vector machine (mkSVM) to predict the diagnostic label of samples with unknown diagnostic statuses. Our evaluation indicates that a diagnosis accuracy as high as 78.2 % can be achieved when information from the longitudinal scans is used-6.6 % higher than the case using only the reference time point image. In other words, information provided by the longitudinal history of the disease improves diagnosis accuracy.

Accurate diagnosis of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, is very important for early treatment. Over the last decade, various machine learning methods have been proposed to predict disease status and clinical scores from brain images. It is worth noting that many features extracted from brain images are correlated significantly. In this case, feature selection combined with the additional correlation information among features can effectively improve classification/regression performance. Typically, the correlation information among features can be modeled by the connectivity of an undirected graph, where each node represents one feature and each edge indicates that the two involved features are correlated significantly. In this paper, we propose a new graph-guided multi-task learning method incorporating this undirected graph information to predict multiple response variables (i.e., class label and clinical scores) jointly. Specifically, based on the sparse undirected feature graph, we utilize a new latent group Lasso penalty to encourage the correlated features to be selected together. Furthermore, this new penalty also encourages the intrinsic correlated tasks to share a common feature subset. To validate our method, we have performed many numerical studies using simulated datasets and the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with the other methods, our proposed method has very promising performance.

Real-time feedback about dissected tissue during the neurosurgical procedure is strongly requested. A novel direct ionization mass spectrometric method for identifying pathological differences in tissues is proposed. The method is based on simultaneous extraction of tissue lipids and electrospray ionization which allows mass spectrometric data to be obtained directly from soft tissues. The advantage of this method is the stable flow of solvent, which leads to stable time-dependent spectra. The tissues included necrotized tissues and tumor tissues in different combinations. Capability for direct analysis of samples of dissected tissues during the neurosurgical procedure is demonstrated. Data validation is conducted by compound identification using precise masses from the MS profile, MS/MS, and isotopic distribution structure analysis. The method can be upgraded and applied for real-time identification of tissues during surgery. This paper describes the technique and its application perspective. For these purposes, other methods were compared with the investigated one and the results were shown to be reproducible. Differences in lipid profiles were observed even in tissues from one patient where distinctions between different samples could be poor. The paper presents a proof of concept for the method to be applied in neurosurgery particularly and in tissue analysis generically. The paper also contains preliminary results proving the possibility of observing differences in mass spectra of different tumors.

Background: To study early neurodevelopment in preterm infants, evaluation of brain maturation and injury is increasingly performed using diffusion tensor imaging, for which the reliability of underlying data is paramount., Objective: To review the literature to evaluate acquisition and processing methodology in diffusion tensor imaging studies of preterm infants., Materials and Methods: We searched the Embase, Medline, Web of Science and Cochrane databases for relevant papers published between 2003 and 2013. The following keywords were included in our search: prematurity, neuroimaging, brain, and diffusion tensor imaging., Results: We found 74 diffusion tensor imaging studies in preterm infants meeting our inclusion criteria. There was wide variation in acquisition and processing methodology, and we found incomplete reporting of these settings. Nineteen studies (26%) reported the use of neonatal hardware. Data quality assessment was not reported in 13 (18%) studies. Artefacts-correction and data-exclusion was not reported in 33 (45%) and 18 (24%) studies, respectively. Tensor estimation algorithms were reported in 56 (76%) studies but were often suboptimal., Conclusion: Diffusion tensor imaging acquisition and processing settings are incompletely described in current literature, vary considerably, and frequently do not meet the highest standards.

The aim of this paper is twofold: firstly, to explore the potential of simultaneously acquiring multimodal MR-PET-EEG data in a human 9.4 T scanner to provide a platform for metabolic brain imaging. Secondly, to demonstrate that the three modalities are complementary, with MRI providing excellent structural and functional imaging, PET providing quantitative molecular imaging, and EEG providing superior temporal resolution. A 9.4 T MRI scanner equipped with a PET insert and a commercially available EEG device was used to acquire in vivo proton-based images, spectra, and sodium- and oxygen-based images with MRI, EEG signals from a human subject in a static 9.4 T magnetic field, and demonstrate hybrid MR-PET capability in a rat model. High-resolution images of the in vivo human brain with an isotropic resolution of 0.5 mm and post-mortem brain images of the cerebellum with an isotropic resolution of 320 µm are presented. A (1)H spectrum was also acquired from 2 × 2 × 2 mm voxel in the brain allowing 12 metabolites to be identified. Imaging based on sodium and oxygen is demonstrated with isotropic resolutions of 2 and 5 mm, respectively. Auditory evoked potentials measured in a static field of 9.4 T are shown. Finally, hybrid MR-PET capability at 9.4 T in the human scanner is demonstrated in a rat model. Initial progress on the road to 9.4 T multimodal MR-PET-EEG is illustrated. Ultra-high resolution structural imaging, high-resolution images of the sodium distribution and proof-of-principle (17)O data are clearly demonstrated. Further, simultaneous MR-PET data are presented without artefacts and EEG data successfully corrected for the cardioballistic artefact at 9.4 T are presented.

Recent evidence suggests that autophagy alterations are present in a variety of neurological disorders. These range from neurodegenerative diseases to acute neurological insults. Thus, despite a role of autophagy was investigated in a variety of neurological diseases, only recently these studies included epilepsy. This was fostered by the evidence that rapamycin, a powerful autophagy inducer, strongly modulates a variety of seizure models and epilepsies. These findings were originally interpreted as the results of the inhibition exerted by rapamycin on the molecular complex named "mammalian Target of Rapamycin" (mTOR). Recently, an increasing number of papers demonstrated that mTOR inhibition produces a strong activation of the autophagy machinery. In this way, it is now increasingly recognized that what was once defined as mTORpathy in epileptogenesis may be partially explained by abnormalities in the autophagy machinery. The present review features a brief introductory statement about the autophagy machinery and discusses the involvement of autophagy in seizures and epilepsies. An emphasis is posed on evidence addressing both pros and cons making it sometime puzzling and sometime evident, the role of autophagy in the epileptic brain.

Recently, there have been great interests for computer-aided diagnosis of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that considered simple low-level features such as gray matter tissue volumes from MRI, and mean signal intensities from PET, in this paper, we propose a deep learning-based latent feature representation with a stacked auto-encoder (SAE). We believe that there exist latent non-linear complicated patterns inherent in the low-level features such as relations among features. Combining the latent information with the original features helps build a robust model in AD/MCI classification, with high diagnostic accuracy. Furthermore, thanks to the unsupervised characteristic of the pre-training in deep learning, we can benefit from the target-unrelated samples to initialize parameters of SAE, thus finding optimal parameters in fine-tuning with the target-related samples, and further enhancing the classification performances across four binary classification problems: AD vs. healthy normal control (HC), MCI vs. HC, AD vs. MCI, and MCI converter (MCI-C) vs. MCI non-converter (MCI-NC). In our experiments on ADNI dataset, we validated the effectiveness of the proposed method, showing the accuracies of 98.8, 90.7, 83.7, and 83.3 % for AD/HC, MCI/HC, AD/MCI, and MCI-C/MCI-NC classification, respectively. We believe that deep learning can shed new light on the neuroimaging data analysis, and our work presented the applicability of this method to brain disease diagnosis.

Auditory event-related potentials (AERPs) are widely used in diverse fields of today's neuroscience, concerning auditory processing, speech perception, language acquisition, neurodevelopment, attention and cognition in normal aging, gender, developmental, neurologic and psychiatric disorders. However, its transposition to clinical practice has remained minimal. Mainly due to scarce literature on normative data across age, wide spectrum of results, variety of auditory stimuli used and to different neuropsychological meanings of AERPs components between authors. One of the most prominent AERP components studied in last decades was N1, which reflects auditory detection and discrimination. Subsequently, N2 indicates attention allocation and phonological analysis. The simultaneous analysis of N1 and N2 elicited by feasible novelty experimental paradigms, such as auditory oddball, seems an objective method to assess central auditory processing. The aim of this systematic review was to bring forward normative values for auditory oddball N1 and N2 components across age. EBSCO, PubMed, Web of Knowledge and Google Scholar were systematically searched for studies that elicited N1 and/or N2 by auditory oddball paradigm. A total of 2,764 papers were initially identified in the database, of which 19 resulted from hand search and additional references, between 1988 and 2013, last 25 years. A final total of 68 studies met the eligibility criteria with a total of 2,406 participants from control groups for N1 (age range 6.6-85 years; mean 34.42) and 1,507 for N2 (age range 9-85 years; mean 36.13). Polynomial regression analysis revealed that N1 latency decreases with aging at Fz and Cz, N1 amplitude at Cz decreases from childhood to adolescence and stabilizes after 30-40 years and at Fz the decrement finishes by 60 years and highly increases after this age. Regarding N2, latency did not covary with age but amplitude showed a significant decrement for both Cz and Fz. Results suggested reliable normative values for Cz and Fz electrode locations; however, changes in brain development and components topography over age should be considered in clinical practice.

Rationale: Pharmacological magnetic resonance imaging (phMRI) provides an approach to study effects of drug challenges on brain processes. Elucidating mechanisms of drug action helps us to better understand the workings of neurotransmitter systems, map brain function or facilitate drug development. phMRI is increasingly used in preclinical research employing rodent models; however, data interpretation and integration are complicated by the use of different experimental approaches between laboratories. In particular, the effects of different anaesthetic regimes upon neuronal and haemodynamic processes and baseline physiology could be problematic., Objectives: This paper investigates how differences in phMRI research methodologies are manifested and considers associated implications, placing particular emphasis on choice of anaesthetic regimes., Methods: A systematic review of rodent phMRI studies was conducted. Factors such as those describing anaesthetic regimes (e.g. agent, dosage) and parameters relating to physiological maintenance (e.g. ventilatory gases) and MRI method were recorded., Results: We identified 126 eligible studies and found that the volatile agents isoflurane (43.7 %) and halothane (33.3 %) were most commonly used for anaesthesia, but dosage and mixture of ventilatory gases varied substantially between laboratories. Relevant physiological parameters were usually recorded, although 32 % of studies did not provide cardiovascular measures., Conclusions: Anaesthesia and animal preparation can influence phMRI data profoundly. The variation of anaesthetic type, dosage regime and ventilatory gases makes consolidation of research findings (e.g. within a specific neurotransmitter system) difficult. Standardisation of a small(er) number of preclinical phMRI research methodologies and/or increased consideration of approaches that do not require anaesthesia is necessary to address these challenges.

Violently shaking a baby leads to clinical presentations ranging from seizures to cardiopulmonary arrest. The main injuries sustained are retinal hemorrhages, subdural hemorrhages, and sometimes fractures and spine injury. It is important to have a global view of the injuries sustained by the infant to correctly discuss the biomechanical aspects of abusive head trauma. Recent works based on finite element models have shown that whiplash-shaking alone is enough to generate vitreo-retinal traction leading to retinal hemorrhage and to cause the rupture of bridging veins leading to subdural hemorrhage. We will review the main papers dealing with the mechanisms of shaken baby syndrome and present the most relevant hypothesis concerning the biomechanical aspects of injuries related to shaken baby syndrome.

Early-onset dementia (EOD) is characterized by functionally impairing deterioration in memory, language, personality or visuospatial skills emerging under the age of 65. Cerebral functioning can be assessed by visual electroencephalography (EEG) interpretation. The aim of this systematic review is to evaluate the diagnostic utility of visual EEG in EOD focusing on Alzheimer's disease (AD), vascular dementia (VAD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). Medline, Embase, Scopus, Web of Knowledge, and Google Scholar were systematically searched for studies where EEGs were included in the diagnostic evaluation of patients with dementia under the age of 65. Each paper was quality assessed and the results grouped according to dementia cause with a narrative summary. 4,157 papers were screened, 12 studies met the eligibility criteria with a total of 965 patients. An abnormal EEG was common to all causes of EOD. EEG abnormalities are more severe in early-onset AD patients. EEG severity grade is independent of disease duration. Slow wave activity is common to all dementias, but is most prominent in DLB. Frontal intermittent rhythmic delta activity could be considered as supportive for the diagnosis of DLB as can a Grand Total EEG score of over 9.5. EEG is usually normal in FTD. Focal changes can be seen in advanced VAD. Studies employed small patient groups, varying diagnostic criteria, and only a minority of patient diagnoses was pathologically confirmed. EEG may be useful as an adjunct in the diagnosis of DLB and AD. Further prospective well-powered studies are required to investigate diagnostic utility more robustly.

Evolution of the brain has been an inherently interesting problem for centuries. Recent studies have indicated that neuroimaging is a powerful technique for studying brain evolution. In particular, a variety of reports have demonstrated that consistent white matter fiber connection patterns derived from diffusion tensor imaging (DTI) tractography reveal common brain architecture and are predictive of brain functions. In this paper, based on our recently discovered 358 dense individualized and common connectivity-based cortical landmarks (DICCCOL) defined by consistent fiber connection patterns in DTI datasets of human brains, we derived 65 DICCCOLs that are common in macaque monkey, chimpanzee and human brains and 175 DICCCOLs that exhibit significant discrepancies amongst these three primate species. Qualitative and quantitative evaluations not only demonstrated the consistencies of anatomical locations and structural fiber connection patterns of these 65 common DICCCOLs across three primates, suggesting an evolutionarily preserved common brain architecture but also revealed regional patterns of evolutionarily induced complexity and variability of those 175 discrepant DICCCOLs across the three species.

Focal lesions and brain atrophy are the most extensively studied aspects of multiple sclerosis (MS), but the image acquisition and analysis techniques used can be further improved, especially those for studying within-patient changes of lesion load and atrophy longitudinally. Improved accuracy and sensitivity will reduce the numbers of patients required to detect a given treatment effect in a trial, and ultimately, will allow reliable characterization of individual patients for personalized treatment. Based on open issues in the field of MS research, and the current state of the art in magnetic resonance image analysis methods for assessing brain lesion load and atrophy, this paper makes recommendations to improve these measures for longitudinal studies of MS. Briefly, they are (1) images should be acquired using 3D pulse sequences, with near-isotropic spatial resolution and multiple image contrasts to allow more comprehensive analyses of lesion load and atrophy, across timepoints. Image artifacts need special attention given their effects on image analysis results. (2) Automated image segmentation methods integrating the assessment of lesion load and atrophy are desirable. (3) A standard dataset with benchmark results should be set up to facilitate development, calibration, and objective evaluation of image analysis methods for MS.

Introduction: Neurodevelopmental outcome in prematures who suffer from a neonatal brain injury depends on the lesion itself, and on how the lesion interferes with the still developing functional anatomy., Methods: Most of the neuronal migration is completed by midgestation. The second part of the gestation corresponds to the development of the connectivity and sulcation, of the maturation of the oligodendrocytic lineage and of the microglia, and of the vascular bed in the parenchyma beyond the germinal matrix., Results: In this paper, the main processes of the developmental anatomy of the premature brain are reviewed, and are correlated with the findings in a prospective series of 105 premature infants born between 24 and 32 weeks of gestation, and serially examined with MR imaging at birth, at term-equivalent age, at 2 years, and at 4 years. Special emphasis was placed (1) on the intraventricular hemorrhage because of the resulting destruction of the germinal matrix and its impact on the late cellular production, (2) on the periventricular venous hemorrhagic infarction because of the selective destruction of the intermediate zone which is associated, and (3) on the apparently perivenous punctate lesions of the white matter because they involve the intermediate zone also, because they have no convincing explanation yet, and because the microglia seems to be associated with their pathogenesis., Conclusion: These deep venous injuries appear to preserve the subplate zone, which is likely to be a significant element to consider in the perspective of the neurodevelopmental outcome.

Introduction: Depression is a major issue worldwide and is seen as a significant health problem. Stigma and patient denial, clinical experience, time limitations, and reliability of psychometrics are barriers to the clinical diagnoses of depression. Thus, the establishment of an automated system that could detect such abnormalities would assist medical experts in their decision-making process. This paper reviews existing methods for the automated detection of depression from brain structural magnetic resonance images (sMRI)., Methods: Relevant sources were identified from various databases and online sites using a combination of keywords and terms including depression, major depressive disorder, detection, classification, and MRI databases. Reference lists of chosen articles were further reviewed for associated publications., Results: The paper introduces a generic structure for representing and describing the methods developed for the detection of depression from sMRI of the brain. It consists of a number of components including acquisition and preprocessing, feature extraction, feature selection, and classification., Conclusion: Automated sMRI-based detection methods have the potential to provide an objective measure of depression, hence improving the confidence level in the diagnosis and prognosis of depression.

REM behavior disorder (RBD) is a parasomnia characterized by REM sleep without atonia, leading to abnormal and potentially injurious behavior during REM sleep. It is considered one of the most specific predictors of neurodegenerative disorders, such as Parkinson's disease. In this paper, we provide an overview of animal models contributing to our current understanding of REM-associated atonia, and, as a consequence, the pathophysiology of RBD. The generator of REM-associated atonia is located in glutamatergic neurons of the pontine sublaterodorsal nucleus (SLD), as shown in cats, rats and mice. These findings are supported by clinical cases of patients with lesions of the homologous structure in humans. Glutamatergic SLD neurons, presumably in conjunction with others, project to (a) the ventromedial medulla, where they either directly target inhibitory interneurons to alpha motor neurons or are relayed, and (b) the spinal cord directly. At the spinal level, alpha motor neurons are inhibited by GABAergic and glycinergic interneurons. Our current understanding is that lesions of the glutamatergic SLD are the key factor for REM sleep behavior disorder. However, open questions remain, e.g. other features of RBD (such as the typically aggressive dream content) or the frequent progression from idiopathic RBD to neurodegenerative disorders, to name only a few. In order to elucidate these questions, a constant interaction between basic and clinical researchers is required, which might, ultimately, create an early therapeutic window for neurodegenerative disorders.

In the medical diagnosis and treatment planning, radiologists and surgeons rely heavily on the slices produced by medical imaging devices. Unfortunately, these image scanners could only present the 3-D human anatomical structure in 2-D. Traditionally, this requires medical professional concerned to study and analyze the 2-D images based on their expert experience. This is tedious, time consuming and prone to error; expecially when certain features are occluding the desired region of interest. Reconstruction procedures was earlier proposed to handle such situation. However, 3-D reconstruction system requires high performance computation and longer processing time. Integrating efficient reconstruction system into clinical procedures involves high resulting cost. Previously, brain's blood vessels reconstruction with MRA was achieved using SurLens Visualization System. However, adapting such system to other image modalities, applicable to the entire human anatomical structures, would be a meaningful contribution towards achieving a resourceful system for medical diagnosis and disease therapy. This paper attempts to adapt SurLens to possible visualisation of abnormalities in human anatomical structures using CT and MR images. The study was evaluated with brain MR images from the department of Surgery, University of North Carolina, United States and CT abdominal pelvic, from the Swedish National Infrastructure for Computing. The MR images contain around 109 datasets each of T1-FLASH, T2-Weighted, DTI and T1-MPRAGE. Significantly, visualization of human anatomical structure was achieved without prior segmentation. SurLens was adapted to visualize and display abnormalities, such as an indication of walderstrom's macroglobulinemia, stroke and penetrating brain injury in the human brain using Magentic Resonance (MR) images. Moreover, possible abnormalities in abdominal pelvic was also visualized using Computed Tomography (CT) slices. The study shows SurLens' functionality as a 3-D Multimodal Visualization System.

This paper discusses the application of a reagentless, selective microbiosensor as a useful alternative tool for monitoring D-serine in neural samples. The main components of the 125-μm-diameter disk biosensor were D-amino acid oxidase for D-serine sensitivity (linear region slope, 61 ± 7 μA cm(-2) mM(-1); limit of detection, 20 nM), and poly-phenylenediamine for rejection of electroactive interference. The response time of the biosensor was of the order of 1 s, ideal for 'real-time' monitoring, and detection of systemically administered D-serine in brain extracellular fluid is demonstrated. Exploitation of this probe might resolve queries involving regulation of D-serine in excitotoxicity, and modulation of N-methyl-D-aspartate receptor function by D-serine and glycine in the central nervous system.

Among dopamine receptors, the expression and function of the D3 receptor subtype is not well understood. The receptor has the highest affinity for dopamine and many drugs that target dopamine receptors.In this paper, we examined, at the single cell level, the characteristics of D3 receptor-expressing cells isolated from different brain regions of male and female mice that were either 35 or 70 days old. The brain regions included nucleus accumbens, Islands of Calleja, olfactory tubercle,retrosplenial cortex, dorsal subiculum, mammillary body,amygdala and septum. The expression analysis was done in the drd3-enhanced green fluorescent protein transgenic mice that report the endogenous expression of D3 receptor mRNA. Using single cell reverse transcriptase PCR, we determined if the D3 receptor-expressing fluorescent cells in these mice were neurons or glia and if they were glutamatergic, GABAergic or catecholaminergic. Next, we determined if the fluorescent cells co-expressed the four other dopamine receptor subtypes, adenylate cyclase V(ACV) isoform, and three different isoforms of G protein coupled inward rectifier potassium (GIRK) channels. The results suggest that D3 receptor is expressed in neurons,with region-specific expression in glutamatergic and GABAergic neurons. The D3 receptor primarily coexpressed with D1 and D2 dopamine receptors with regional, sex and age-dependent differences in the coexpression pattern. The percentage of cells co-expressing D3 receptor and ACV or GIRK channels varied significantly by brain region, sex and age. The molecular characterization of D3 receptor-expressing cells in mouse brain reported here will facilitate the characterization of D(3) receptor function in physiology and pathophysiology.

CT and MRI scans are widely used in medical diagnosis procedures, but they only produce 2-D images. However, the human anatomical structure, the abnormalities, tumors, tissues and organs are in 3-D. 2-D images from these devices are difficult to interpret because they only show cross-sectional views of the human structure. Consequently, such circumstances require doctors to use their expert experiences in the interpretation of the possible location, size or shape of the abnormalities, even for large datasets of enormous amount of slices. Previously, the concept of reconstructing 2-D images to 3-D was introduced. However, such reconstruction model requires high performance computation, may either be time-consuming or costly. Furthermore, detecting the internal features of human anatomical structure, such as the imaging of the blood vessels, is still an open topic in the computer-aided diagnosis of disorders and pathologies. This paper proposes a volume visualization framework using Compute Unified Device Architecture (CUDA), augmenting the widely proven ray casting technique in terms of superior qualities of images but with slow speed. Considering the rapid development of technology in the medical community, our framework is implemented on Microsoft.NET environment for easy interoperability with other emerging revolutionary tools. The framework was evaluated with brain datasets from the department of Surgery, University of North Carolina, United States, containing around 109 MRA datasets. Uniquely, at a reasonably cheaper cost, our framework achieves immediate reconstruction and obvious mappings of the internal features of human brain, reliable enough for instantaneous locations of possible blockages in the brain blood vessels.