Brain Template Research Articles

Construction of a novel Chinese normal brain database using 18F-FDG PET images and MIMneuro software, the initial application in epilepsy

Purpose: To create a standard Western Chinese normal functional brain database for quantitative analysis using 2-deoxy-2-[18F] fluoro-d-glucose (18F-FDG) positron emission tomography (PET) images and MIMneuro software.Methods: 78 healthy right-handed Chinese volunteers from Tangdu Hospital were scanned using 18F-FDG PET to evaluate brain metabolism between March and October 2016. All PET images were processed using MIMneuro software to create a normal database platform. The platform included anatomical optimization to facilitate spatial localization of abnormalities and a statistical comparison with normal cases utilizing the Z-scores, which represent the number of standard deviations from the mean of the normal controls in the database.Results: The novel Chinese brain metabolism database platform including 78 healthy volunteers (male: female 40:38; age 3–78 years, mean age, 45 years) was constructed based on the MIMneuro software, which increased the diagnostic confidence in the test patient by quantifying and emphasizing the abnormality. The BrainAlignTM deformation algorithm of MIMneuro matched the size, shape, and orientation of the patient's brain scan to a template brain for comparison against a database of normal controls. The quantitative analysis performed on a voxel and regional level was useful in assessing the areas of abnormalities.Conclusions: A novel Chinese 18F-FDG PET-based normal brain function database was created to highlight the local regions of abnormal metabolic activity through quantitative comparisons against the normal database. The Z-scores obtained by MIMneuro potentially aid in visualizing and quantifying the subtle lesions on 18FDG-PET scan images as observed in a patient diagnosed with epilepsy.

Automatic labeling of cortical sulci for the human fetal brain based on spatio-temporal information of gyrification

Accurate parcellation and labeling of primary cortical sulci in the human fetal brain is useful for regional analysis of brain development. However, human fetal brains show large spatio-temporal changes in brain size, cortical folding patterns, and relative position/size of cortical regions, making accurate automatic sulcal labeling challenging. Here, we introduce a novel sulcal labeling method for the fetal brain using spatio-temporal gyrification information from multiple fetal templates. First, spatial probability maps of primary sulci are generated on the templates from 23 to 33 gestational weeks and registered to an individual brain. Second, temporal weights, which determine the level of contribution to the labeling for each template, are defined by similarity of gyrification between the individual and the template brains. We combine the weighted sulcal probability maps from the multiple templates and adopt sulcal basin-wise approach to assign sulcal labels to each basin. Our labeling method was applied to 25 fetuses (22.9–29.6 gestational weeks), and the labeling accuracy was compared to manually assigned sulcal labels using the Dice coefficient. Moreover, our multi-template basin-wise approach was compared to a single-template approach, which does not consider the temporal dynamics of gyrification, and a fully-vertex-wise approach. The mean accuracy of our approach was 0.958 across subjects, significantly higher than the accuracies of the other approaches. This novel approach shows highly accurate sulcal labeling and provides a reliable means to examine characteristics of cortical regions in the fetal brain.

Iterative spatial fuzzy clustering for 3D brain magnetic resonance image supervoxel segmentation

BackgroundAlthough supervoxel segmentation methods have been employed for brain Magnetic Resonance Image (MRI) processing and analysis, due to the specific features of the brain, including complex-shaped internal structures and partial volume effect, their performance remains unsatisfactory. New methodsTo address these issues, this paper presents a novel iterative spatial fuzzy clustering (ISFC) algorithm to generate 3D supervoxels for brain MRI volume based on prior knowledge. This work makes use of the common topology among the human brains to obtain a set of seed templates from a population-based brain template MRI image. After selecting the number of supervoxels, the corresponding seed template is projected onto the considered individual brain for generating reliable seeds. Then, to deal with the influence of partial volume effect, an efficient iterative spatial fuzzy clustering algorithm is proposed to allocate voxels to the seeds and to generate the supervoxels for the overall brain MRI volume. ResultsThe performance of the proposed algorithm is evaluated on two widely used public brain MRI datasets and compared with three other up-to-date methods. ConclusionsThe proposed algorithm can be utilized for several brain MRI processing and analysis, including tissue segmentation, tumor detection and segmentation, functional parcellation and registration.

Improved Gray Matter Atrophy Detection in Alzheimer Disease in Chinese Populations Using Chinese Brain Template.

This study aimed to test the hypothesis that the statistical Chinese brain template would be more effective to detect gray matter (GM) changes in patients with Alzheimer disease (AD) in Chinese populations. In total, 50 patients with AD and 50 sex-matched and age-matched healthy controls were included in this study. Chinese2020, a typical statistical Chinese brain template, and MNI152, a typical Caucasian template were used for spatial normalization respectively. The GM volume alterations in patients with AD were examined by using voxel-based morphometry with education level and total intracranial volume as nuisance variables. The GM proportions of the identified brain areas with group difference were compared. By using Chinese2020 and MNI152, significant GM atrophies in patients with AD were commonly detected in the bilateral medial temporal lobe, lateral temporal lobe, inferior/medial frontal cortex, as well as left thalamus. However, higher GM percentages of detected regions were acquired when Chinese2020 was used rather than MNI152. Furthermore, stronger statistical powers in the detected clusters were observed using Chinese2020 than MNI152. In addition, the laterality index analysis showed the bilateral atrophies with no hemispheric laterality in the para/hippocampus when using population-specific brain atlas (ie, Chinese2020). These findings indicated that applying the population-specific brain atlas to neuroimaging studies may achieve higher accuracy in activation detection. This may have implications to the imaging study of neurodegenerative diseases.

Clustering-based multi-view network fusion for estimating brain network atlases of healthy and disordered populations

BackgroundWhile several research methods were developed to estimate individual-based representations of brain connectional wiring (i.e., a connectome), traditionally captured using multimodal MRI data (e.g., functional and diffusion MRI), very limited works aimed to estimate brain network atlas for a population of connectomes. Estimating well-representative brain templates is a key step for group comparison studies. However, estimating a network atlas for a population of multi-source brain connectomes lying on different manifolds is absent. New methodTo fill this gap, we propose a cluster-based multi-view brain connectivity fusion framework to estimate a brain network atlas for a population of multi-view brain networks, where each view captures a specific facet of the brain construct. Specifically, given a population of subjects, each with multi-view networks, we first non-linearly fuse multi-view networks into a single fused network for each subject. Then, we cluster the fused networks to identify individuals sharing similar connectional traits in an unsupervised way, which are next averaged within each cluster to generate a representative network atlas. Finally, we construct the final multi-view network atlas by averaging the obtained templates of all clusters. ResultsWe evaluated our method on both healthy and disordered populations (with autism and dementia) and spotted differences between network atlases for healthy and autistic groups. Comparison with existing methods and conclusionsCompared to other baseline methods, our fusion strategy achieved the best results in terms of template centeredness and population representativeness.

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei

Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi).Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1-or T2-weighted acquisitions alone or a combination of T1-, T2-and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-the-art MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson's Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens.

Error analysis for nonconfocal ellipsoidal systems in the forward problem of electroencephalography

Electroencephalography (EEG) remains the utmost important technique recording brain activity. The present investigation examines the sensitivity of analytic algorithms employed in order to evaluate EEG data with respect to different brain template models. These algorithms are based on mathematical models built upon certain geometrical and/or physical assumptions regarding the head‐brain shape or the neuronal source. While the ellipsoidal head model provides a realistic approach regarding the interpretation of EEG signals in view of diverse geometries, its eccentricities influence strongly the results. The present study quantifies the deviation of the computed electric potentials when nonconfocal ellipsoids are used to model a homogeneous head conductor. To this end, a correspondence is proposed between points of the different ellipsoids, in view of the Gauss map. The investigation demonstrates that the introduction of nonconfocality imports a highly elevated error rate when EEG recordings are misinterpreted by arriving from different head‐brain models, including ellipsoidal vs spherical ones. Moreover, evidence is presented concerning the location of extrema regarding the errors in the upper brain hemisphere, which could lead to a more precise and accurate protocol regarding the placement of sensors. Although the present work refers to the forward EEG problem, its results may be used under other approaches as well. In particular, it provides the error in the solution of an elliptic boundary value problem with transmission conditions under small perturbations of the eccentricities of its ellipsoidal domain.

Mapping the Brain\u2019s electric fields with Magnetoelectric nanoparticles

BackgroundNeurodegenerative diseases are devastating diagnoses. Examining local electric fields in response to neural activity in real time could shed light on understanding the origins of these diseases. To date, there has not been found a way to directly map these fields without interfering with the electric circuitry of the brain. This theoretical study is focused on a nanotechnology concept to overcome the challenge of brain electric field mapping in real time. The paper shows that coupling the magnetoelectric effect of multiferroic nanoparticles, known as magnetoelectric nanoparticles (MENs), with the ultra-fast and high-sensitivity imaging capability of the recently emerged magnetic particle imaging (MPI) can enable wirelessly conducted electric-field mapping with specifications to meet the requirements for monitoring neural activity in real time.MethodsThe MPI signal is numerically simulated on a realistic human brain template obtained from BrainWeb, while brain segmentation was performed with BrainSuite software. The finite element mesh is generated with Computer Geometry Algorithm Library. The effect of MENs is modeled through local point magnetization changes according to the magnetoelectric effect.ResultsIt is shown that, unlike traditional magnetic nanoparticles, MENs, when coupled with MPI, provide information containing electric field’s spatial and temporal patterns due to local neural activity with signal sensitivities adequate for detection of minute changes at the sub-cellular level corresponding to early stage disease processes.ConclusionsLike no other nanoparticles known to date, MENs coupled with MPI can be used for mapping electric field activity of the brain at the sub-neuronal level in real time. The potential applications span from prevention and treatment of neurodegenerative diseases to paving the way to fundamental understanding and reverse engineering the brain.

Statistical Structural Analysis of Familial Spontaneous Epileptic Cats Using Voxel-Based Morphometry.

Voxel-based morphometry (VBM) based on high resolution three-dimensional data of magnetic resonance imaging has been developed as a statistical morphometric imaging analysis method to locate brain abnormalities in humans. Recently, VBM has been used for human patients with psychological or neurological disorders such as Alzheimer's disease, Parkinson's disease, and epilepsy. Traditional volumetry using region of interest (ROI) is performed manually and the observer needs detailed knowledge of the neuroanatomy having to trace objects of interest on many slices which can cause artificial errors. In contrast, VBM is an automatic technique that has less observer biases compared to the ROI method. In humans, VBM analysis is performed in patients with epilepsy to detect accurately structural abnormalities. Familial spontaneous epileptic cats (FSECs) have been developed as an animal model of mesial temporal lobe epilepsy. In FSECs, hippocampal asymmetry had been detected using three-dimensional magnetic resonance (MR) volumetry based on the ROI method. In this study, we produced a standard template of the feline brain and compared FSECs and healthy cats using standard VBM analysis. The feline standard template and tissue probability maps were created using 38 scans from 14 healthy cats. Subsequently, the gray matter was compared between FSECs (n = 25) and healthy controls (n = 12) as group analysis and between each FSEC and controls as individual analysis. The feline standard template and tissue probability maps could be created using the VBM tools for humans. There was no significant reduction of GM in the FSEC group compared to the control group. However, 5/25 (20%) FSECs showed significant decreases in the hippocampal and/or amygdaloid regions in individual analysis. Here, we established the feline standard templates of the brain that can be used to determine accurately abnormal zones. Furthermore, like MR volumetry, VBM identified morphometric changes in the hippocampus and/or amygdala in some FSECs.

NEUROPSYCHIATRIC BURDEN IS RELATED TO INCREASED AMYLOID BUT NOT TAU DEPOSITION IN COGNITIVELY NORMAL OLDER ADULTS

Neuropsychiatric factors (NPF; e.g. depression, anxiety, apathy) are highly prevalent in older adults and are associated with an increased risk of Alzheimer's disease (AD) (Masters et al, 2015). Recent evidence also linked personality traits (e.g. neuroticism) with progression to AD (Terracciano et al, 2017). The mechanisms by which personality/NPF burden influence AD risk need to be better understood. In cognitively normal older adults at risk of AD, we investigated whether an index of personality/NPF burden was related to AD pathology (amyloid [Aβ] and tau deposition), brain function and cognition. Eighty-two participants (PREVENT-AD study, age=67±5) answered questionnaires assessing personality, perseverative thinking, depression, anxiety, stress and apathy. They underwent Aβ-PET ([18F]NAV4694), tau-PET ([18F]AV1451), resting-state fMRI and cognitive assessment (Repeatable Battery for Assessment of Neuropsychological Status). PET SUVRs were extracted bilaterally from all FreeSurfer Desikan neocortical regions. We used a multivariate approach (partial least squares correlation with permutation tests) to assess which combination of factors was related to Aβ and tau deposition. We then investigated whether this index of personality/NPF burden was related to 1) functional connectivity and 2) cognitive performance in individuals with and without significant AD pathology. Aβ (p=0.003), but not tau (p>0.05) was related to personality/NPF burden. More specifically, a combination of perseverative thinking, neuroticism and apathy was associated with higher Aβ accumulation, predominantly in the AD-signature regions (Fig.1). Higher scores on this personality/NPF component were additionally associated with higher connectivity within the dorsal attention and salience networks in Aβ+ subjects, and lower connectivity in Aβ- subjects (Aβ-status*connectivity: F1,69=5.7-5.2, p=.02-.03 respectively, Fig.2A). Furthermore, higher scores on this personality/NPF component were associated with worse immediate memory (F1,70=4.1, p=.05, Fig.2B), regardless of Aβ status. Relative importance of personality traits and neuropsychiatrie factors (NPF) in relation to regional Aβ accumulation. IA. Loading of each personality traits and NPF score from the component relating personality/NPF with Aβ burden. Traits and factors contributing the most to the model are perseverative thinking, neuroticism and apathy. Confidence intervals are derived from bootstrap resampling. IB. Cortical regions showing higher Aβ SUVR as personality/NPF burden increases. The importance of each region in the model was identified from bootstrap ratios and projected on a brain template. Personality/NPF relationship with brain function (A) and cognition (B) 2A. Functional networks showing a significant interaction of Aβ status by functional connectivity on the personality/NPF burden. 2B. Relationship between personality/NPF burden and immediate memory performance. Our multivariate approach revealed that Aβ burden was increased in cognitively normal individuals at risk of AD showing high levels of perseverative thinking, neuroticism and apathy, which were in turn related to altered brain connectivity and lower cognition. Overall, these results highlight the importance of personality/NPF burden on early AD pathophysiology.

NEUROPSYCHIATRIC BURDEN IS RELATED TO INCREASED AMYLOID BUT NOT TAU DEPOSITION IN LATE MIDDLE-AGED COGNITIVELY NORMAL INDIVIDUALS WITH A FAMILY HISTORY OF ALZHEIMER’S DISEASE

Neuropsychiatric factors (NPF) are highly prevalent throughout the course of Alzheimer's disease (AD) (Masters et al, 2015). Recent evidence also linked neuroticism, a personality trait associated with negative emotional response, with progression to AD (Terracciano et al, 2017). We investigated whether NPF and/or personality traits were related to AD pathology, brain function and cognition in cognitively normal individuals with a family history of AD dementia. Eighty-two participants (PREVENT-AD study, age=67±5) underwent PET scanning. SUVRs values for Aβ ([18F]NAV4694) and tau ([18F]AV1451) were extracted from all FreeSurfer Desikan neocortical regions (grey cerebellum reference region). Resting-state functional connectivity was examined in 13 networks (Power et al, 2011). Cognitive performance was assessed using the RBANS that provides five composite scores: immediate and delayed memory, attention, language, and visuospatial functioning. All participants answered questionnaires assessing personality, perseverative thinking, depression, anxiety, stress and apathy. We used partial least squares correlation with permutation tests to assess which combinations of factors (derived as latent variables), if any, were related to Aβ and tau deposition. We also investigated whether this combination of NPF and personality traits was related to 1) functional connectivity and 2) cognitive performance in individuals with and without AD pathology. One latent variable related NPF and personality traits with Aβ accumulation (p=0.003). Fig.1A displays the weight of each NPF and personality trait to this relationship and Fig.1B, the brain regions presenting higher Aβ burden as a function of this latent variable. Higher scores on this variable were additionally associated with higher brain connectivity within the dorsal attention and salience networks in Aβ+ subjects, and lower connectivity in Aβ- subjects (both interactions p <0.05, Fig.2A). Furthermore, higher scores on this neuropsychiatric component were associated with worse immediate memory (p=0.05, Fig.2B), regardless of Aβ status. No latent variable related NPF/personality traits with tau accumulation. Partial least squares analysis showing the neuropsychiatrie latent variable related to Aβ burden 1A. Loading of each neuropsychiatri factor (NPF) and personality trait to the neuropsychiatri latent variable related with Aβ accumulation. Confidence intervals are derived from bootstrap resampling. 1B. Cortical regions showing higher Aβ SUVR as NPF/personality burden increases. The importance of each region in the model was identified from bootstrap ratios and projected on a brain template. Regression analysis showing the relationship between the neuropsychiatric latent variable with brain function and cognition 2A. Functional network showing an interaction of Aβ status by functional connectivity on neuropsychiatric burden (Salience network interaction F1,69=5.7, p =0.02; dorsal attentional network interaction F 1, 69=5.2, p =0.03). 2B. Association between neuropsychiatric burden and immediate memory performance (Main effect F1,70=4.1, p=0.05). Our multivariate approach revealed that NPF/personality traits, such as perseverative thinking, neuroticism and low agreeableness, were associated with Aβ accumulation, altered brain connectivity and lower cognition. Neuropsychiatric burden might be a consequence of AD pathology, or, because personality traits are relatively stable across lifespan, a risk factor.

Lesion locations associated with outcomes for post-stroke aphasia

Several studies identified age, gender, handedness, lesion size, stroke subtype, aphasia/stroke initial severity as prognostic factors for recovery from aphasia. However, prediction of recovery still remains difficult to establish initially for patients with initial severe aphasia. The aim of this study was to identify the lesion location associated with poor recovery. One hunderd aphasic patients were included within fourteen days after a first ischemic or haemorrhagic stroke of the left hemisphere and had analysable MRI sequences. The severity of aphasia was determined using the Aphasia Severity Rating Scale in the acute phase and six months post-stroke. The ASRS is a 6-point Likert scale from the lowest score 0 to 5. A severe aphasia initially was defined as an ASRS score < 3. Good recovery at M6 was defined as an ASRS = 4–5. In a first step, axial diffusion-weighted images, or T2*-weighted images for intra-cerebral haemorrhage, were imported. Lesions were drawn manually as regions of interest blinded to clinical data. Images were normalized to a standard brain template. The resulting normalized lesion maps were analysed in a VLSM approach, which applies the graded information at the behavioural level to voxel-wise binary lesion information. The resulting maps were corrected for multiple comparisons. Regarding all patients, a better outcome was significantly associated ( P < 0.01) with lesion excluding the following areas of the left hemisphere: Putamen, transverse temporal gyrus (Heschl gyrus), Insula, Rolandic Operculum. Regarding patients with severe aphasia initially, a poor recovery (i.e. ASRS at M6 < 4) was significantly associated ( P < 0.05) with lesion in the Putamen and transverse temporal gyrus (Heschl gyrus). The present study identified critical areas associated with poor outcomes for aphasia, particularly lesions of the superior temporal gyrus for patients with severe aphasia initially. These findings are in line with previous results in the literature.

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

Evaluation of the default-mode network by quantitative 15O-PET: comparative study between cerebral blood flow and oxygen consumption

ObjectiveResting-state functional MRI (rs-fMRI) has revealed the existence of a default-mode network (DMN) based on spontaneous oscillations of the blood oxygenation level-dependent (BOLD) signal. The BOLD signal reflects the deoxyhemoglobin concentration, which depends on the relationship between the regional cerebral blood flow (CBF) and the cerebral metabolic rate of oxygen (CMRO2). However, these two factors cannot be separated in BOLD rs-fMRI. In this study, we attempted to estimate the functional correlations in the DMN by means of quantitative 15O-labeled gases and water PET, and to compare the contribution of the CBF and CMRO2 to the DMN.MethodsNine healthy volunteers (5 men and 4 women; mean age, 47.0 ± 1.2 years) were studied by means of 15O-O2, 15O-CO gases and 15O-water PET. Quantitative CBF and CMRO2 images were generated by an autoradiographic method and transformed into MNI standardized brain template. Regions of interest were placed on normalized PET images according to the previous rs-fMRI study. For the functional correlation analysis, the intersubject Pearson’s correlation coefficients (r) were calculated for all pairs in the brain regions and correlation matrices were obtained for CBF and CMRO2, respectively. We defined r > 0.7 as a significant positive correlation and compared the correlation matrices of CBF and CMRO2.ResultsSignificant positive correlations (r > 0.7) were observed in 24 pairs of brain regions for the CBF and 22 pairs of brain regions for the CMRO2. Among them, 12 overlapping networks were observed between CBF and CMRO2. Correlation analysis of CBF led to the detection of more brain networks as compared to that of CMRO2, indicating that the CBF can capture the state of the spontaneous activity with a higher sensitivity.ConclusionsWe estimated the functional correlations in the DMN by means of quantitative PET using 15O-labeled gases and water. The correlation matrix derived from the CBF revealed a larger number of brain networks as compared to that derived from the CMRO2, indicating that contribution to the functional correlation in the DMN is higher in the blood flow more than the oxygen consumption.

White matter injury in term neonates with congenital heart diseases: Topology & comparison with preterm newborns

BackgroundNeonates with congenital heart disease (CHD) are at high risk of punctate white matter injury (WMI) and impaired brain development. We hypothesized that WMI in CHD neonates occurs in a characteristic distribution that shares topology with preterm WMI and that lower birth gestational age (GA) is associated with larger WMI volume. Objective(1) To quantitatively assess the volume and location of WMI in CHD neonates across three centres. (2) To compare the volume and spatial distribution of WMI between term CHD neonates and preterm neonates using lesion mapping. MethodsIn 216 term born CHD neonates from three prospective cohorts (mean birth GA: 39 weeks), WMI was identified in 86 neonates (UBC: 29; UCSF: 43; UCZ: 14) on pre- and/or post-operative T1 weighted MRI. WMI was manually segmented and volumes were calculated. A standard brain template was generated. Probabilistic WMI maps (total, pre- and post-operative) were developed in this common space. Using these maps, WMI in the term CHD neonates was compared with that in preterm neonates: 58 at early-in-life (mean postmenstrual age at scan 32.2 weeks); 41 at term-equivalent age (mean postmenstrual age at scan 40.1 weeks). ResultsThe total WMI volumes of CHD neonates across centres did not differ (p = 0.068): UBC (median = 84.6 mm3, IQR = 26–174.7 mm3); UCSF (median = 104 mm3, IQR = 44–243 mm3); UCZ (median = 121 mm3, IQR = 68–200.8 mm3). The spatial distribution of WMI in CHD neonates showed strong concordance across centres with predilection for anterior and posterior rather than central lesions. Predominance of anterior lesions was apparent on the post-operative WMI map relative to the pre-operative map. Lower GA at birth predicted an increasing volume of WMI across the full cohort (41.1 mm3 increase of WMI per week decrease in gestational age; 95% CI 11.5–70.8; p = 0.007), when accounting for centre and heart lesion. While WMI in term CHD and preterm neonates occurs most commonly in the intermediate zone/outer subventricular zone there is a paucity of central lesions in the CHD neonates relative to preterms. ConclusionsWMI in term neonates with CHD occurs in a characteristic topology. The spatial distribution of WMI in term neonates with CHD reflects the expected maturation of pre-oligodendrocytes such that the central regions are less vulnerable than in the preterm neonates.

Construction of population-specific Indian MRI brain template: Morphometric comparison with Chinese and Caucasian templates.

Spatial normalization of brain MR images is highly dependent on the choice of target brain template. Morphological differences caused by factors like genetic and environmental exposures, generates a necessity to construct population specific brain templates. Brain image analysis performed using brain templates from Caucasian population may not be appropriate for non-Caucasian population. In this study, our objective was to construct an Indian brain template from a large population (N = 157 subjects) and compare the morphometric parameters of this template with that of Chinese-56 and MNI-152 templates. In addition, using an independent MRI data of 15 Indian subjects, we also evaluated the potential registration accuracy differences using these three templates. Indian brain template was constructed using iterative routines as per established procedures. We compared our Indian template with standard MNI-152 template and Chinese template by measuring global brain features. We also examined accuracy of registration by aligning 15 new Indian brains to Indian, Chinese and MNI templates. Furthermore, we supported our measurement protocol with inter-rater and intra-rater reliability analysis. Our results showed that there were significant differences in global brain features of Indian template in comparison with Chinese and MNI brain templates. The results of registration accuracy analysis revealed that fewer deformations are required when Indian brains are registered to Indian template as compared to Chinese and MNI templates. This study concludes that population specific Indian template is likely to be more appropriate for structural and functional image analysis of Indian population.

Imaging neurodegeneration in Down syndrome: brain templates for amyloid burden and tissue segmentation

The focus of Alzheimer’s disease (AD) neuroimaging research has shifted towards an investigation of the earliest stages of AD pathogenesis, which manifests in every young adult with Down syndrome (DS; trisomy 21) resulting from a deterministic genetic predisposition to amyloid precursor protein overproduction. Due to morphological differences in brain structure in the DS population, special consideration must be given to processing pipelines and the use of normative atlases developed for the non-DS population. Further, the use of typical MRI to MRI template spatial normalization is less desirable in this cohort due to a greater presence of motion artefacts in MRI images. The diffuse nature of PiB uptake and comparatively lower spatial resolution of the PET image permits the purposing of this modality as a template for spatial normalization, which can substantially improve the robustness of this procedure in the cases of MRI images with motion. The aim of this work was to establish standardized methods for spatial normalization and tissue type segmentation using DS specific templates in order to perform voxel-wise analyses. A total of 72 adults with DS underwent [11C]PiB PET to assess brain amyloid burden and volumetric MRI imaging. A DS specific PiB template for spatial normalization and a set of DS specific prior probability templates were created with two-pass methods. With implementation of this DS specific PiB template, no participants were excluded due to poor spatial normalization, thus maximizing the sample size for PiB analyses in standardized space. In addition, difference images between prior probability templates created from the general population and the DS population reflected known morphological differences, particularly in the frontal cortex. In conclusion, DS specific templates that account for unique challenges improve spatial normalization and tissue type segmentation, and provide a framework for reliable voxel-wise analysis of AD biomarkers in this atypical population.

F123. Ultra-high-density scalp EEG outperforms localized invasive ECoG grids in inferring depth of seizure foci

Introduction During pre-surgical evaluation for medically intractable epilepsy, intracranial electrocorticography (ECoG) is often used to locate seizure foci. However, ECoG grids usually have small spatial coverage because large coverage requires large craniotomies. This may pose difficulties in inferring the depth of the focus, which is important because foci can lie deep inside the brain, e.g. in insular, mesial or basal regions, or within a sulcus. Our team recently demonstrated theoretically [Grover & Venkatesh, Proc. IEEE, ‘17] and experimentally[Robinson et al., Scientific Reports ‘17] that ultra-high-density (UHD) scalp EEG (with sub-centimeter electrode spacing) can recover high spatial resolution information of brain activity. Here, we ask whether UHD-EEG with full scalp coverage can outperform spatially localized ECoG grids in inferring the depth of seizure foci. Methods In our simulation study, ECoG and UHD-EEG electrodes were placed on a template-head MRI. Brainstorm and OpenMEEG were used to generate EEG and ECoG forward models using the Boundary Element Method for the ICBM-152 Brain template with 15,765 dipoles. For 20-electrode ECoG simulations, 8 different locations were considered (over frontal, fronto-parietal, posterior-temporal and occipital regions on left and right hemispheres), with sources at different locations in each case. For 40-electrode ECoG simulations, two adjacent 20-electrode ECoG grids were considered, again for varying source locations. A single radially oriented dipole was manually activated at different depths from each of the 8 locations on the cortex, beneath the 20-electrode ECoG grid. For EEG, 128, 256 and 1378 electrodes (the last being a theoretical UHD EEG system) with whole scalp coverage were considered. Noiseless ECoG and EEG recordings were simulated, and source localization was performed to estimate the source location using the L2-regularized MNE algorithm. Results of source localization were then compared using two standard metrics: the distance of the center of the reconstruction from the true source (bias) and the diffusive extent of the reconstruction (width of Point Spread Function, PSF). Results The reconstruction-bias using UHD-EEG is consistently about 2 cm less than any ECoG reconstruction. PSF widths of deeper sources are observed to be slightly larger for ultra-high-density EEG than other modalities. Nevertheless, visual inference of depth is more accurate using reconstructions of ultra-high-density EEG. Conclusion UHD-EEG can complement other modalities used during pre-surgical evaluation for epilepsy, before and after intracranial electrode implantation, particularly for inferring the presence of a deep seizure focus. ECoG recordings strongly bias reconstructions towards the cortical surface. Future work will similarly compare stereo-EEG and UHD-EEG, for inferring depth of seizure foci, and evaluate how UHD-EEG can inform stereo-EEG electrode placement.

A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain.

Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.

Cortico-Amygdala-Striatal Activation by Modafinil/Flecainide Combination.

BackgroundModafinil, a nonamphetaminic wake-promoting compound, is prescribed as first line therapy in narcolepsy, an invalidating disorder characterized by excessive daytime sleepiness and cataplexy. Although its mode of action remains incompletely known, recent studies indicated that modafinil modulates astroglial connexin-based gap junctional communication as administration of a low dose of flecainide, an astroglial connexin inhibitor, enhanced the wake-promoting and procognitive activity of modafinil in rodents and healthy volunteers. The aim of this study is to investigate changes in glucose cerebral metabolism in rodents, induced by the combination of modafinil+flecainide low dose (called THN102).MethodsThe impact of THN102 on brain glucose metabolism was noninvasively investigated using 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography imaging in Sprague-Dawley male rats. Animals were injected with vehicle, flecainide, modafinil, or THN102 and further injected with 18F-2-fluoro-2-deoxy-D-glucose followed by 60-minute Positron Emission Tomography acquisition. 18F-2-fluoro-2-deoxy-D-glucose Positron Emission Tomography images were coregistered to a rat brain template and normalized from the total brain Positron Emission Tomography signal. Voxel-to-voxel analysis was performed using SPM8 software. Comparison of brain glucose metabolism between groups was then performed.ResultsTHN102 significantly increased regional brain glucose metabolism as it resulted in large clusters of 18F-2-fluoro-2-deoxy-D-glucose uptake localized in the cortex, striatum, and amygdala compared with control or drugs administered alone. These regions, highly involved in the regulation of sleep-wake cycle, emotions, and cognitive functions were hence quantitatively modulated by THN102.ConclusionData presented here provide the first evidence of a regional brain activation induced by THN102, currently being tested in a phase II clinical trial in narcoleptic patients.