Anatomical Brain Regions Research Articles

Bayesian Multiresolution Variable Selection for Ultra-High Dimensional Neuroimaging Data.

Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework. It recursively uses posterior samples for coarser-scale variable selection to guide the posterior inference on finer-scale variable selection, leading to very efficient Markov chain Monte Carlo (MCMC) algorithms. The proposed algorithms are computationally feasible for ultra-high dimensional data. Also, our model incorporates two levels of structural information into variable selection using Ising priors: the spatial dependence between voxels and the functional connectivity between anatomical brain regions. Applied to the resting state functional magnetic resonance imaging (R-fMRI) data in the ABIDE study, our methods identify voxel-level imaging biomarkers highly predictive of the ASD, which are biologically meaningful and interpretable. Extensive simulations also show that our methods achieve better performance in variable selection compared to existing methods.

Spatial variation in high-frequency oscillation rates and amplitudes in intracranial EEG.

To assess the variation in baseline and seizure onset zone interictal high-frequency oscillation (HFO) rates and amplitudes across different anatomic brain regions in a large cohort of patients. Seventy patients who had wide-bandwidth (5 kHz) intracranial EEG (iEEG) recordings during surgical evaluation for drug-resistant epilepsy between 2005 and 2014 who had high-resolution MRI and CT imaging were identified. Discrete HFOs were identified in 2-hour segments of high-quality interictal iEEG data with an automated detector. Electrode locations were determined by coregistering the patient's preoperative MRI with an X-ray CT scan acquired immediately after electrode implantation and correcting electrode locations for postimplant brain shift. The anatomic locations of electrodes were determined using the Desikan-Killiany brain atlas via FreeSurfer. HFO rates and mean amplitudes were measured in seizure onset zone (SOZ) and non-SOZ electrodes, as determined by the clinical iEEG seizure recordings. To promote reproducible research, imaging and iEEG data are made freely available (msel.mayo.edu). Baseline (non-SOZ) HFO rates and amplitudes vary significantly in different brain structures, and between homologous structures in left and right hemispheres. While HFO rates and amplitudes were significantly higher in SOZ than non-SOZ electrodes when analyzed regardless of contact location, SOZ and non-SOZ HFO rates and amplitudes were not separable in some lobes and structures (e.g., frontal and temporal neocortex). The anatomic variation in SOZ and non-SOZ HFO rates and amplitudes suggests the need to assess interictal HFO activity relative to anatomically accurate normative standards when using HFOs for presurgical planning.

Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence

The premature aging hypothesis of alcohol dependence proposes that the neurobiological and behavioural deficits in individuals with alcohol dependence are analogous to those of chronological aging. However, to date no systematic neurobiological evidence for this hypothesis has been provided. To test the hypothesis, 119 alcohol-dependent subjects and 97 age- and gender-matched healthy control subjects underwent structural MRI. Whole-brain grey matter volume maps were computed from structural MRI scans using voxel-based morphometry and parcelled into a comprehensive set of anatomical brain regions. Regional grey matter volume averages served as the basis for cross-regional similarity analyses and a brain age model. We found a striking correspondence between regional patterns of alcohol- and age-related grey matter loss across 110 brain regions. The brain age model revealed that the brain age of age-matched AD subjects was increased by up to 11.7 years. Interestingly, while no brain aging was detected in the youngest AD subjects (20–30 years), we found that alcohol-related brain aging systematically increased in the following age decades controlling for lifetime alcohol consumption and general health status. Together, these results provide strong evidence for an accelerated aging model of AD and indicate an elevated risk of alcohol-related brain aging in elderly individuals.

Detection of the LINE-1 retrotransposon RNA-binding protein ORF1p in different anatomical regions of the human brain

BackgroundRecent reports indicate that retrotransposons – a type of mobile DNA – can contribute to neuronal genetic diversity in mammals. Retrotransposons are genetic elements that mobilize via an RNA intermediate by a “copy-and-paste” mechanism termed retrotransposition. Long Interspersed Element-1 (LINE-1 or L1) is the only active autonomous retrotransposon in humans and its activity is responsible for ~ 30% of genomic mass. Historically, L1 retrotransposition was thought to be restricted to the germline; however, new data indicate L1 s are active in somatic tissue with certain regions of the brain being highly permissive. The functional implications of L1 insertional activity in the brain and how host cells regulate it are incomplete. While deep sequencing and qPCR analysis have shown that L1 copy number is much higher in certain parts of the human brain, direct in vivo studies regarding detection of L1-encoded proteins is lacking due to ineffective reagents.ResultsUsing a polyclonal antibody we generated against the RNA-binding (RRM) domain of L1 ORF1p, we observe widespread ORF1p expression in post-mortem human brain samples including the hippocampus which has known elevated rates of retrotransposition. In addition, we find that two brains from different individuals of different ages display very different expression of ORF1p, especially in the frontal cortex.ConclusionsWe hypothesize that discordance of ORF1p expression in parts of the brain reported to display elevated levels of retrotransposition may suggest the existence of factors mediating post-translational regulation of L1 activity in the human brain. Furthermore, this antibody reagent will be useful as a complementary means to confirm findings related to retrotransposon biology and activity in the brain and other tissues in vivo.

Brain parcellation based on information theory

Background and objectiveIn computational neuroimaging, brain parcellation methods subdivide the brain into individual regions that can be used to build a network to study its structure and function. Using anatomical or functional connectivity, hierarchical clustering methods aim to offer a meaningful parcellation of the brain at each level of granularity. However, some of these methods have been only applied to small regions and strongly depend on the similarity measure used to merge regions. The aim of this work is to present a robust whole-brain hierarchical parcellation that preserves the global structure of the network. MethodsBrain regions are modeled as a random walk on the connectome. From this model, a Markov process is derived, where the different nodes represent brain regions and in which the structure can be quantified. Functional or anatomical brain regions are clustered by using an agglomerative information bottleneck method that minimizes the overall loss of information of the structure by using mutual information as a similarity measure. ResultsThe method is tested with synthetic models, structural and functional human connectomes and is compared with the classic k-means. Results show that the parcellated networks preserve the main properties and are consistent across subjects. ConclusionThis work provides a new framework to study the human connectome using functional or anatomical connectivity at different levels.

Bayesian inference of high-dimensional, cluster-structured ordinary differential equation models with applications to brain connectivity studies

We build a new ordinary differential equation (ODE) model for the directional interaction, also called effective connectivity, among brain regions whose activities are measured by intracranial electrocorticography (ECoG) data. In contrast to existing ODE models that focus on effective connectivity among only a few large anatomic brain regions and that rely on strong prior belief of the existence and strength of the connectivity, the proposed high-dimensional ODE model, motivated by statistical considerations, can be used to explore connectivity among multiple small brain regions. The new model, called the modular and indicator-based dynamic directional model (MIDDM), features a cluster structure, which consists of modules of densely connected brain regions, and uses indicators to differentiate significant and void directional interactions among brain regions. We develop a unified Bayesian framework to quantify uncertainty in the assumed ODE model, identify clusters, select strongly connected brain regions, and make statistical comparison between brain networks across different experimental trials. The prior distributions in the Bayesian model for MIDDM parameters are carefully designed such that the ensuing joint posterior distributions for ODE state functions and the MIDDM parameters have well-defined and easy-to-simulate posterior conditional distributions. To further speed up the posterior simulation, we employ parallel computing schemes in Markov chain Monte Carlo steps. We show that the proposed Bayesian approach outperforms an existing optimization-based ODE estimation method. We apply the proposed method to an auditory electrocorticography dataset and evaluate brain auditory network changes across trials and different auditory stimuli.

Gross Anatomical Brain Region Approximation (GABRA): a new landmark‐based approach for estimating brain regions in dinosaurs and other archosaurs

Studying brain evolution in extinct taxa can be challenging due to a lack of close correspondence between the morphology of the endocranial cavity and that of the underlying brain. Cranial endocasts may be faithful brain proxies in certain groups (mammals, birds) due to relatively complete filling of the cavity with neural tissue in life. However, the brain does not fill the endocranial cavity in many adult non‐avian archosaurs, making their endocasts less reliable indicators of brain size and shape. As such, previous studies of relative brain size and evolution in archosaurs relied on untested assumptions about brain‐endocast fidelity. We propose a new approach known as Gross Anatomical Brain Region Approximation (GABRA), which involves importing a digital endocast, derived from CT scanning and 3D visualization software, into modeling software (Maya). In Maya, brain regions underlying the endocast are modeled as 3D polygons. Discernable osteological correlates for soft‐tissue structures (e.g., neurovascular canals, dural sinuses, fossae formed by the brain itself) present on the endocast serve as anatomical landmarks that inform the location and size of the modeled general brain regions (e.g., cerebral hemispheres, cerebellum, optic lobes, olfactory bulbs). Together, the anatomical landmarks form a set of explicit criteria used to assess endocasts and model brain regions. GABRA criteria and resulting brain models were validated in extant diapsids (lizards, snakes, alligators, birds) via literature review, gross dissection, CT scanning of iodine‐stained specimens, and MRI studies. Therefore, GABRA models produced for extinct archosaurs are credible. Credible GABRA models for extinct dinosaurs provided volumetric estimates for general brain regions, which, when summed, offer a whole‐brain estimate. Such data from several dinosaurs have permitted analyses using modern comparative methods of relative brain‐size evolution (e.g., encephalization quotient), with results indicating that brain sizes for many dinosaurs were previously underestimated. Additionally, analyses of GABRA data show a mix of concerted and mosaic patterns of brain evolution, wherein the pituitary and olfactory bulbs emerge as evolving independently from the rest of the brain. In sum, GABRA provides insight into how brains evolved across dinosaur lineages. Future studies will examine within‐lineage changes, offering greater insight into how and why dinosaur brains evolved.Support or Funding InformationFunding for this project was provided by the follow sources: the National Science Foundation, Jurassic Foundation, Sigma Xi Grant‐in‐Aid of Research, Ohio Center for Ecology and Evolutionary Studies, Ohio University Student Enhancement Award, Ohio University Original Work Grant.

Quantitative Analysis of the Spatial Distribution of Metastatic Brain Lesions.

Brain metastases (BMs) are the most common intracranial malignancy and afflict ∼10%–20% of patients with cancer. BMs tend to present at the boundaries of gray and white matter because of the distribution of small vessels. In addition, metastases may not be randomly distributed across gross anatomical regions of the brain, but this has not previously been quantified. We retrospectively analyzed a series of 28 patients with recurrent BMs with a total of 150 lesions. Each lesion was manually defined based on T1 gadolinium-enhanced imaging. Standard brain atlases were used to identify the anatomical brain region affected by each BM and the frequency of metastases in each region was compared with the expected probability, which was assumed to be a random distribution based on the brain volume. After correction for multiple comparisons, the paracingulate gyrus was found to have a statistically significant increase (P = 4.731 × 10−9) in the rate of BMs relative to the random spatial distribution. A nonstochastic spatial distribution of metastases may be used to guide partial brain radiotherapy with risk-adapted dose delivery and reduce the risk of neurotoxicity due to overtreatment.

Reliability of cortical lesion detection on double inversion recovery MRI applying the MAGNIMS-Criteria in multiple sclerosis patients within a 16-months period.

PurposeIn patients with multiple sclerosis (MS), Double Inversion Recovery (DIR) magnetic resonance imaging (MRI) can be used to identify cortical lesions (CL). We sought to evaluate the reliability of CL detection on DIR longitudinally at multiple subsequent time-points applying the MAGNIMs scoring criteria for CLs.Methods26 MS patients received a 3T-MRI (Siemens, Skyra) with DIR at 12 time-points (TP) within a 16 months period. Scans were assessed in random order by two different raters. Both raters separately marked all CLs on each scan and total lesion numbers were obtained for each scan-TP and patient. After a retrospective re-evaluation, the number of consensus CLs (conL) was defined as the total number of CLs, which both raters finally agreed on. CLs volumes, relative signal intensities and CLs localizations were determined. Both ratings (conL vs. non-consensus scoring) were compared for further analysis.ResultsA total number of n = 334 CLs were identified by both raters in 26 MS patients with a first agreement of both raters on 160 out of 334 of the CLs found (κ = 0.48). After the retrospective re-evaluation, consensus agreement increased to 233 out of 334 CL (κ = 0.69). 93.8% of conL were visible in at least 2 consecutive TP. 74.7% of the conL were visible in all 12 consecutive TP. ConL had greater mean lesion volumes and higher mean signal intensities compared to lesions that were only detected by one of the raters (p<0.05). A higher number of CLs in the frontal, parietal, temporal and occipital lobe were identified by both raters than the number of those only identified by one of the raters (p<0.05).ConclusionsAfter a first assessment, slightly less than a half of the CL were considered as reliably detectable on longitudinal DIR images. A retrospective re-evaluation notably increased the consensus agreement. However, this finding is narrowed, considering the fact that retrospective evaluation steps might not be practicable in clinical routine. Lesions that were not reliably identifiable by both raters seem to be characterized by lower signal intensity and smaller size, or located in distinct anatomical brain regions.

FMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection

Functional magnetic resonance imaging (fMRI) is a useful technique for capturing deformities in brain activity patterns of several disorders. Schizophrenia is one such serious psychiatric disorder that, in absence of any standard diagnostic tests, is detected from behavioural symptoms observed externally. Thus, fMRI can be used for building an effective decision model for computer aided diagnosis of schizophrenia. However, fMRI data has huge dimension compared with the number of subjects; therefore it is essential to reduce the data dimension to avoid poor generalisation performance of the decision model. In the present work, we propose a three-phase dimension reduction that comprises of segmentation of voxels of 3-D spatial maps (independent component score-maps or β-maps) into anatomical brain regions; feature extraction from each region using a novel fuzzy kernel principal component analysis; and a novel hybrid (filter-cum-wrapper) feature selection for determining a reduced subset of discriminative features. These features are used as input to support vector machine classifier for learning a decision model. The method is carried out within leave-one-out cross-validation. Classification accuracy, sensitivity, and specificity are utilised to estimate the performance on two different balanced datasets D1 and D2 (respectively acquired on 1.5 T and 3 T scanners). Both the datasets contain fMRI data of age-matched healthy subjects and schizophrenia patients for auditory oddball task, obtained from FBIRN multisite dataset. The proposed method attains best classification accuracy of 95.6% and 96.0% for D1 and D2 respectively. The proposed method shows enhanced performance over the state-of-the-art methods. Further, the discriminative brain regions identified are in accordance with the findings in related literature and may be used as potential biomarkers.

Neuroanatomical correlates of stroke-associated infection and stroke-induced immunodepression

BackgroundInfections represent the most frequent medical complications in stroke patients. Their main determinants are dysphagia and a transient state of immunodepression. We analyzed whether distinct anatomical brain regions were associated with the occurrence of stroke-associated infections or immunodepression. Materials and methodsIn 106 patients with acute ischemic stroke, we evaluated the incidence of pneumonia, urinary tract infection, or other infections together with the characterization of biomarkers of immunodepression. Twenty control subjects served to provide reference values. Using voxel-based lesion-symptom mapping, the involvement of gray and white matter structures was correlated with clinical and laboratory findings in crude analyses and in volume adjusted models to rule out associations reflecting differences in the size of the infarction. ResultsStroke-associated infection occurred in 22 (21%) patients and prevailed in patients with larger infarcts. Volume adjusted voxel-based lesion-symptom mapping revealed the involvement of the superior and middle temporal gyri, the orbitofrontal cortex, the superior longitudinal fasciculus and the inferior fronto-occipital fasciculus amongst infected patients. These associations were similar for pneumonia but not for urinary tract infections. Lymphopenia was associated with lesions of the superior and middle temporal gyri. Laterality did not influence stroke-associated infections or the presence of immunodepressive traits after volume control. The greatest overlap in the neuroanatomical correlates occurred between pneumonia and dysphagia. ConclusionInfarct volume plays a relevant role in the occurrence of stroke-associated infections, but lesions in specific brain locations such as the superior and lateral temporal lobe and the orbitofrontal cortex are also associated with increased infectious risk, especially pneumonia.

Reduction of variance in measurements of average metabolite concentration in anatomically-defined brain regions

Multiple methods have been proposed for using Magnetic Resonance Spectroscopy Imaging (MRSI) to measure representative metabolite concentrations of anatomically-defined brain regions. Generally these methods require spectral analysis, quantitation of the signal, and reconciliation with anatomical brain regions. However, to simplify processing pipelines, it is practical to only include those corrections that significantly improve data quality. Of particular importance for cross-sectional studies is knowledge about how much each correction lowers the inter-subject variance of the measurement, thereby increasing statistical power. Here we use a data set of 72 subjects to calculate the reduction in inter-subject variance produced by several corrections that are commonly used to process MRSI data. Our results demonstrate that significant reductions of variance can be achieved by performing water scaling, accounting for tissue type, and integrating MRSI data over anatomical regions rather than simply assigning MRSI voxels with anatomical region labels.

Quantitative evaluation of simulated functional brain networks in graph theoretical analysis

There is increasing interest in the potential of whole-brain computational models to provide mechanistic insights into resting-state brain networks. It is therefore important to determine the degree to which computational models reproduce the topological features of empirical functional brain networks. We used empirical connectivity data derived from diffusion spectrum and resting-state functional magnetic resonance imaging data from healthy individuals. Empirical and simulated functional networks, constrained by structural connectivity, were defined based on 66 brain anatomical regions (nodes). Simulated functional data were generated using the Kuramoto model in which each anatomical region acts as a phase oscillator. Network topology was studied using graph theory in the empirical and simulated data. The difference (relative error) between graph theory measures derived from empirical and simulated data was then estimated. We found that simulated data can be used with confidence to model graph measures of global network organization at different dynamic states and highlight the sensitive dependence of the solutions obtained in simulated data on the specified connection densities. This study provides a method for the quantitative evaluation and external validation of graph theory metrics derived from simulated data that can be used to inform future study designs.

EP 113. Increased coherence to the midline in interictal EEG predicts generalized seizures in focal epilepsy

Background Presurgical evaluation of patients with focal epilepsy relies strongly on EEG. Usually, interictal EEG is analyzed for spikes and regional slowing, providing localizing information, and ictal EEG allows to localize electrical activity during a seizure, including seizure onset and secondary propagation. EEG coherence analyses allow to make inferences on the synchronization between different brain regions, but are rarely used in epilepsy. Here we evaluate the clinical value of interictal EEG coherence analyses to contribute clinically relevant information during the presurgical evaluation of epilepsy patients. Methods Nineteen patients with focal epilepsy were included in this study. To avoid effects from major structural lesions on the EEG, we included only patients with no structural lesion ( n = 10) or small lesions, less than 2 cm diameter ( n = 9). All patients had continuous video EEG monitoring for 5–12 days, where 40 channel EEG was recorded with a sampling rate of 512 Hz. We have selected 5 epochs of awake, artifact free, interictal EEG, with a duration of 4–10 min for each patient. EEG coherence was analyzed using Matlab and fieldtrip software. Coherence was calculated between all possible pairs of electrodes, for five different frequency bands. Coherence values between neighboring pairs of electrodes were averaged for a total of 17 anatomical brain regions (7 left, 7 right, 3 midline) and were analyzed for asymmetries between the left and right hemisphere. Coherence data was correlated with clinical information from the video EEG monitoring. Results Regional coherence changes were seen in 17 of 19 patients (89%). Two different patterns were observed: In 10 patients (53%) we saw an increased coherence between the epileptogenic region and the midline regions, and 7 of these 10 showed a reduced coherence within the hemisphere ipsilateral to the epileptogenic zone. In the other 7 patients (37%) there was increased coherence within the epileptogenic hemisphere and 5 of these 7 showed decreased coherence between the epileptogenic region and the midline regions. Nine of the 10 patients with increased coherence between the epileptogenic region and the midline regions had clinical signs of fast propagation, such as predominant bilateral seizure pattern in EEG, or frequent and fast clinical evolution to generalized tonic clonic seizures. On the other hand, none of the patients with decreased coherence to the midline had contralateral spikes, bilateral seizure onset in ictal EEG or frequent generalized seizures. Conclusions Coherence analysis of interictal EEG seems to be able to distinguish two patient groups. Those with an increased coherence between the epileptigenic zone and midline regions also had frequent clinical signs of a more widespread pathology, including a higher risk for fast propagation and generalization. Patients with increased coherence exclusively within the epileptigenic hemisphere, on the other hand, also had more clearly lateralized clinical signs and less frequent generalized seizure, potentially making them better candidates for resective surgery.

Metabolic drift in the aging brain.

Brain function is highly dependent upon controlled energy metabolism whose loss heralds cognitive impairments. This is particularly notable in the aged individuals and in age-related neurodegenerative diseases. However, how metabolic homeostasis is disrupted in the aging brain is still poorly understood. Here we performed global, metabolomic and proteomic analyses across different anatomical regions of mouse brain at different stages of its adult lifespan. Interestingly, while severe proteomic imbalance was absent, global-untargeted metabolomics revealed an energy metabolic drift or significant imbalance in core metabolite levels in aged mouse brains. Metabolic imbalance was characterized by compromised cellular energy status (NAD decline, increased AMP/ATP, purine/pyrimidine accumulation) and significantly altered oxidative phosphorylation and nucleotide biosynthesis and degradation. The central energy metabolic drift suggests a failure of the cellular machinery to restore metabostasis (metabolite homeostasis) in the aged brain and therefore an inability to respond properly to external stimuli, likely driving the alterations in signaling activity and thus in neuronal function and communication.

Increased levels of brain serotonin correlated with MMP-9 activity and IL-4 levels resulted in severe experimental autoimmune encephalomyelitis (EAE) in obese mice

The aim of this study was to investigate the role of monoamine neurotransmitters on the severity of experimental autoimmune encephalomyelitis (EAE) in obese mice. EAE was induced in mice with normal diets (ND-EAE) and obese mice with high-fat diets (HFD-EAE) through the immune response to myelin oligodendrocyte glycoprotein (MOG) (35–55). The levels of dopamine (DA), serotonin (5-HT) and their metabolites in different anatomical brain regions were measured by high-performance liquid chromatography. The plasma and tissue NADPH oxidase and matrix metalloproteinases (MMP)-9 activities were analyzed by fluorescence spectrophotometry. The cumulative disease index and disease peaks were significantly higher in HFD-EAE compared with those in ND-EAE. Significantly higher 5-HT levels and lower 5-HT turnovers 5-hydroxyindole acetic acid ((5-HIAA)/5-HT) were found in the brains of HFD-EAE mice compared with those found in the HFD-CON and ND-EAE mice brains. Moreover, increased DA levels were observed in the caudate nucleus of the HFD-EAE mice compared with the control and ND-EAE mice. The NADPH oxidase and MMP-9 activities in the plasma and tissues were significantly higher in both the ND-EAE and HFD-EAE groups than in their respective controls. The cytokine levels in the plasma, tissues, and cultured splenocytes were found to be significantly altered in EAE mice compared with control mice. Moreover, HFD-EAE mice exhibited significantly higher MMP-9 activity and lower IL-4 levels than ND-EAE mice and were significantly correlated with brain 5-HT levels. In conclusion, the increased 5-HT levels in the brain significantly correlated with MMP-9 activity and IL-4 levels play an important role in the exacerbation of disease severity in HFD-EAE mice.

Epigenomic annotation of gene regulatory alterations during evolution of the primate brain.

Although genome sequencing has identified numerous noncoding alterations between primate species, which of those are regulatory and potentially relevant to the evolution of the human brain is unclear. Here we annotated cis-regulatory elements (CREs) in the human, rhesus macaque and chimpanzee genomes using chromatin immunoprecipitation followed by sequencing (ChIP-seq) in different anatomical regions of the adult brain. We found high similarity in the genomic positioning of rhesus macaque and human CREs, suggesting that the majority of these elements were already present in a common ancestor 25 million years ago. Most of the observed regulatory changes between humans and rhesus macaques occurred before the ancestral separation of humans and chimpanzees, leaving a modest set of regulatory elements with predicted human specificity. Our data refine previous predictions and hypotheses on the consequences of genomic changes between primate species and allow the identification of regulatory alterations relevant to the evolution of the brain.

Regional Values of Diffusional Kurtosis Estimates in the Healthy Brain during Normal Aging.

To provide estimates of the diffusional kurtosis in different anatomical regions of a healthy brain and to assess age dependency of diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI) derived parametric values in these regions. Eighty healthy volunteers underwent DKI of the brain with 3.0 T magnetic resonance imaging. The DKI was obtained by using three b values of 0, 1000, 2000 s/mm2, and with 50 diffusion directions. The regions of interest-based measurements were calculated to obtain several DKI estimates of 21 different locations of brain, and then, the age dependency for DKI- and DTI-derived parameters in these regions were assessed by using linear and nonlinear regressions. The mean kurtosis varied from 0.73 ± 0.01 (head of caudate nucleus) to 1.07 ± 0.08 (splenium of corpus callosum (CC)). The radial kurtosis varied from 0.84 ± 0.06 (head of caudate nucleus) to 1.05 ± 0.07 (splenium of CC), and axial kurtosis from 0.41 ± 0.02 (genu of CC) to 0.78 ± 0.02 (pallidum). DTI-derived parametric values also varied across the region. Age dependence was found for DKI-derived parameters in almost all measured regions except for corona radiata and centrum semiovale. On the contrary, DTI failed to show age dependency in many regions including gray matter structure. In conclusion, the knowledge of range of diffusion kurtosis parameters in each anatomical region in different age group is important before its clinical application to diagnose the pathology.

Differential Anatomical Expression of Ganglioside GM1 Species Containing d18:1 or d20:1 Sphingosine Detected by MALDI Imaging Mass Spectrometry in Mature Rat Brain.

GM1 ganglioside plays a role in essential neuronal processes, including differentiation, survival, and signaling. Yet, little is known about GM1 species with different sphingosine bases, such as the most abundant species containing 18 carbon atoms in the sphingosine chain (GM1d18:1), and the less abundant containing 20 carbon atoms (GM1d20:1). While absent in the early fetal brain, GM1d20:1 continues to increase throughout pre- and postnatal development and into old age, raising questions about the functional relevance of the GM1d18:1 to GM1d20:1 ratio. Matrix-assisted laser desorption/ionization imaging mass spectrometry is a novel technology that allows differentiation between these two GM1 species and quantification of their expression within an anatomical context. Using this technology, we find GM1d18:1/d20:1 expression ratios are highly specific to defined anatomical brain regions in adult rats. Thus, the ratio was significantly different among different thalamic nuclei and between the corpus callosum and internal capsule. Differential GM1d18:1/GM1d20:1 ratios measured in hippocampal subregions in rat brain complement previous studies conducted in mice. Across layers of the sensory cortex, opposing expression gradients were found for GM1d18:1 and GM1d20:1. Superficial layers demonstrated lower GM1d18:1 and higher GM1d20:1 signal than other layers, while in deep layers GM1d18:1 expression was relatively high and GM1d20:1 expression low. By far the highest GM1d18:1/d20:1 ratio was found in the amygdala. Differential expression of GM1 with d18:1- or d20:1-sphingosine bases in the adult rat brain suggests tight regulation of expression and points toward a distinct functional relevance for each of these GM1 species in neuronal processes.

Shared Pathways Among Autism Candidate Genes Determined by Co-expression Network Analysis of the Developing Human Brain Transcriptome

Autism spectrum disorder (ASD) is a neurodevelopmental syndrome known to have a significant but complex genetic etiology. Hundreds of diverse genes have been implicated in ASD; yet understanding how many genes, each with disparate function, can all be linked to a single clinical phenotype remains unclear. We hypothesized that understanding functional relationships between autism candidate genes during normal human brain development may provide convergent mechanistic insight into the genetic heterogeneity of ASD. We analyzed the co-expression relationships of 455 genes previously implicated in autism using the BrainSpan human transcriptome database, across 16 anatomical brain regions spanning prenatal life through adulthood. We discovered modules of ASD candidate genes with biologically relevant temporal co-expression dynamics, which were enriched for functional ontologies related to synaptogenesis, apoptosis, and GABA-ergic neurons. Furthermore, we also constructed co-expression networks from the entire transcriptome and found that ASD candidate genes were enriched in modules related to mitochondrial function, protein translation, and ubiquitination. Hub genes central to these ASD-enriched modules were further identified, and their functions supported these ontological findings. Overall, our multi-dimensional co-expression analysis of ASD candidate genes in the normal developing human brain suggests the heterogeneous set of ASD candidates share transcriptional networks related to synapse formation and elimination, protein turnover, and mitochondrial function.Electronic supplementary materialThe online version of this article (doi:10.1007/s12031-015-0641-3) contains supplementary material, which is available to authorized users.