Brain Landmarks Research Articles

Fully automated planning for anatomical fetal brain MRI on 0.55T.

Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2 2.6 mm for the fetal eyes and 6.5 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.

High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter

Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure–function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy’s Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions.

Brain dysmorphologies associated with Alzheimer disease in Down syndrome

Down syndrome (DS) is a genetic disorder occurring in 1 out of every 700 to 1,000 live births caused by trisomy of chromosome 21. The overexpression of the Amyloid Precursor Protein (APP) gene located on this chromosome leads to an early onset and rapid accumulation of ß‐amyloid (Aß) in the brain that invariably results in Alzheimer Disease (AD) in people with DS. Therefore, AD, the most common form of dementia, is currently one of the most relevant medical issues in people with DS. In this study, we assessed brain morphological patterns to quantify dysmorphologies associated with AD in Down syndrome.Brain shape and size analyses were performed using Geometric Morphometrics in a sample of 243 adults, including 153 euploid healthy controls (EU) and 90 individuals with DS at different stages of dementia: asymptomatic DS with no signs of AD‐related cognitive impairment (N=58), individuals with DS and prodromal AD (N=21) and individuals diagnosed with AD dementia (N=11). A set of 38 3D brain landmarks representing the overall shape of the brain and internal brain structures (ventricles, corpus callosum, cerebellum and pons) were located on magnetic resonance images of the head.Results showed a significant separation between EU and DS populations based on both global and local brain shape and size analyses. Individuals with DS displayed a smaller cerebellum, a protruding posterior midbody in the corpus callosum, a frontally projected brain and expanded ventricles. Significant brain shape differences were detected in individuals diagnosed with AD, which mainly affected the shape of enlarged brain ventricles. Remarkably, individuals at advanced stages of the disease displayed more severe dysmorphologies as compared to those individuals at prodromal stages and individuals with no signs of dementia, confirming a phenotypic continuum associated with the severity of dementia that highlights the impact of this neurodegenerative disorder in brain morphology.

Brain morphology and underlying genetic correlates in schizophrenia

Schizophrenia (SZ) is a psychiatric disorder associated with brain and neurodevelopmental alterations that affects nearly 1% of the worldwide population. SZ is a complex disorder, polygenic and multifactorial, and despite large research efforts, its etiopathophysiology is still largely unknown. In this study, we assessed whether polymorphic variation in genes of the Sonic Hedgehog signaling pathway were associated with brain shape variation and contributed to differences between individuals diagnosed with SZ and healthy controls (HC). We genotyped three SNPs in SUFU, SHH and GLI3 genes (SUFU‐rs10786679, SHH‐rs10949808, GLI3‐rs3735361) in a sample of 308 subjects (113 patients with SZ and 195 HC), and quantified brain shape differences in a subsample of 100 subjects using the cartesian coordinates of brain landmarks recorded on head magnetic resonance scans.The genetic association analyses revealed that the T allele of the SHH‐rs10949808 was significantly associated with the risk for SZ, in both allelic (P=0.02586) and recessive (p=0.0312) models. Geometric Morphometric analyses showed that brain shape was significantly different between individuals diagnosed with SZ and HC. Discriminant functions correctly classified 72% of HC and 60% of patients based on brain morphology. Procrustes‐ANOVA analysis detected that diagnosis was a significant factor (P=0.001) and explained 5.11% of brain morphological variance; whereas any SNP presented significant effects in brain shape variance when analysed separately. However, when the three SNPs were combined into a single variable quantifying the number of risk variants (0 to 3) carried by each person, the analyses showed that patients with 3 risk variants presented a significantly different brain morphology as compared those carrying 2 (P=0.0297) or none risk variants (P=0.0487). These findings underscore the role of neurodevelopment‐related genes and Sonic Hedgehog pathway in SZ and the need to account for the polygenic nature of this disorder to further understand its etiology. Combining brain and genetic data, along with other morphological biomarkers, may open new lines of research in psychotic disorders.

Anatomical variability, multi-modal coordinate systems, and precision targeting in the marmoset brain

Localising accurate brain regions needs careful evaluation in each experimental species due to their individual variability. However, the function and connectivity of brain areas is commonly studied using a single-subject cranial landmark-based stereotactic atlas in animal neuroscience. Here, we address this issue in a small primate, the common marmoset, which is increasingly widely used in systems neuroscience. We developed a non-invasive multi-modal neuroimaging-based targeting pipeline, which accounts for intersubject anatomical variability in cranial and cortical landmarks in marmosets. This methodology allowed creation of multi-modal templates (MarmosetRIKEN20) including head CT and brain MR images, embedded in coordinate systems of anterior and posterior commissures (AC-PC) and CIFTI grayordinates. We found that the horizontal plane of the stereotactic coordinate was significantly rotated in pitch relative to the AC-PC coordinate system (10 degrees, frontal downwards), and had a significant bias and uncertainty due to positioning procedures. We also found that many common cranial and brain landmarks (e.g., bregma, intraparietal sulcus) vary in location across subjects and are substantial relative to average marmoset cortical area dimensions. Combining the neuroimaging-based targeting pipeline with robot-guided surgery enabled proof-of-concept targeting of deep brain structures with an accuracy of 0.2 mm. Altogether, our findings demonstrate substantial intersubject variability in marmoset brain and cranial landmarks, implying that subject-specific neuroimaging-based localization is needed for precision targeting in marmosets. The population-based templates and atlases in grayordinates, created for the first time in marmoset monkeys, should help bridging between macroscale and microscale analyses.

Morphological invariant of the midsagittal deep brain anatomy between humans and African great apes.

Efforts have been made to mathematically reconstruct the brain morphology from human fossil crania to clarify the evolutionary changes in the brain that are associated with the emergence of human cognitive ability. However, because conventional reconstruction methods are based solely on the endocranial shape, deep brain structures cannot be estimated with sufficient accuracy. Our study aims to investigate the possible morphological correspondence between the cranial and deep brain morphologies based on humans and African great apes, with the goal of a more precise reconstruction of fossil brains. Midsagittal endocranial and deep brain landmarks were obtained from magnetic resonance images of humans and three species of African great apes. The average midsagittal endocranial profile of all four species was calculated after Procrustes registration. The spatial deformation function from each of the endocranial profiles to the average endocranial profile was defined, and the brain landmarks enclosed in the endocranium were transformed using the deformation function to evaluate the interspecific variabilities of the positions of the brain landmarks on the average endocranial profile. The interspecific differences in the shape-normalized positions of the corpus callosum, anterior commissure, thalamus center, and brainstem were approximately within the range of 2% of the human cranial length, indicating that the interspecific variabilities of the positions of these deep brain structures were relatively small among the four species. Such an invariant relationship of the deep brain structure and the endocranium that encloses the brain can potentially be utilized to reconstruct the brains of fossil hominins.

Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas registration

Automated brain structures segmentation in positron emission tomography (PET) images has been widely investigated to help brain disease diagnosis and follow-up. To relieve the burden of a manual definition of volume of interest (VOI), automated atlas-based VOI definition algorithms were developed, but these algorithms mostly adopted a global optimization strategy which may not be particularly accurate for local small structures (especially the deep brain structures). This paper presents a PET/CT-based brain VOI segmentation algorithm combining anatomical atlas, local landmarks, and dual-modality information. The method incorporates local deep brain landmarks detected by the Deep Q-Network (DQN) to constrain the atlas registration process. Dual-modality PET/CT image information is also combined to improve the registration accuracy of the extracerebral contour. We compare our algorithm with the representative brain atlas registration methods based on 86 clinical PET/CT images. The proposed algorithm obtained accurate delineation of brain VOIs with an average Dice similarity score of 0.79, an average surface distance of 0.97 mm (sub-pixel level), and a volume recovery coefficient close to 1. The main advantage of our method is that it optimizes both global-scale brain matching and local-scale small structure alignment around the key landmarks, it is fully automated and produces high-quality parcellation of the brain structures from brain PET/CT images.

Detecting Anatomical Landmarks From Limited Medical Imaging Data Using Two-Stage Task-Oriented Deep Neural Networks.

One of the major challenges in anatomical landmark detection, based on deep neural networks, is the limited availability of medical imaging data for network learning. To address this problem, we present a two-stage task-oriented deep learning method to detect large-scale anatomical landmarks simultaneously in real time, using limited training data. Specifically, our method consists of two deep convolutional neural networks (CNN), with each focusing on one specific task. Specifically, to alleviate the problem of limited training data, in the first stage, we propose a CNN based regression model using millions of image patches as input, aiming to learn inherent associations between local image patches and target anatomical landmarks. To further model the correlations among image patches, in the second stage, we develop another CNN model, which includes a) a fully convolutional network that shares the same architecture and network weights as the CNN used in the first stage and also b) several extra layers to jointly predict coordinates of multiple anatomical landmarks. Importantly, our method can jointly detect large-scale (e.g., thousands of) landmarks in real time. We have conducted various experiments for detecting 1200 brain landmarks from the 3D T1-weighted magnetic resonance images of 700 subjects, and also 7 prostate landmarks from the 3D computed tomography images of 73 subjects. The experimental results show the effectiveness of our method regarding both accuracy and efficiency in the anatomical landmark detection.

Easy methods to make the neuronavigated targeting of DLPFC accurate and routinely accessible for rTMS

Dorsolateral prefrontal cortex (DLPFC) is the main stimulation target for rTMS treatment of depression. DLPFC is located in the middle frontal gyrus and corresponds to the lateral part of Brodmann Areas 9 and 46. Current methods to locate the DLPFC are either based on head landmarks that are inaccurate, or based on MRI-neuronavigation. Neuronavigated-methods are based either on standardized stereotactic coordinates translated to the individual patient or on brain landmarks requiring neuroanatomical skills for their identification. We developed a script automating the inclusion of already validated targets into patients' MRI, and also a new method to target DLPFC based on neuroanatomical landmarks. The present study aims to assess this new approach. Four targets were compared on 40 hemispheres: three previously validated methods (2 using superimposition of standardized targets on patient MRI and 1 using neuroanatomical landmarks) and the new one presented here. Resulting targets were presented in the individual space and in stereotactic spaces (MNI and Talairach) with the main objective being to reach the middle frontal gyrus and BA9/46. Target dispersion and distances between targets were assessed. All targets were located in the middle frontal gyrus. Our proposed neuro-anatomical target was equivalent to or even better than the previously existing one if we consider the criteria of BA46 achievement and dispersion. The proposed neuroanatomical method and automation of the stereotactic method allow simple and reliable targeting of DLPFC for rTMS treatment.

Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures.

Visualization of whole brain activity during epileptic seizures is essential for both fundamental research into the disease mechanisms and the development of efficient treatment strategies. It has been previously discussed that pathological synchronization originating from cortical areas may reinforce backpropagating signaling from the thalamic neurons, leading to massive seizures through enhancement of high frequency neural activity in the thalamocortical loop. However, the study of deep brain neural activity is challenging with the existing functional neuroimaging methods due to lack of adequate spatiotemporal resolution or otherwise insufficient penetration into subcortical areas. To investigate the role of thalamocortical activity during epileptic seizures, we developed a new functional neuroimaging framework based on spatiotemporal correlation of volumetric optoacoustic hemodynamic responses with the concurrent electroencephalogram recordings and anatomical brain landmarks. The method is shown to be capable of accurate three-dimensional mapping of the onset, spread, and termination of the epileptiform events in a 4-aminopyridine acute model of focal epilepsy. Our study is the first to demonstrate entirely noninvasive real-time visualization of synchronized epileptic foci in the whole mouse brain, including the neocortex and subcortical structures, thus opening new vistas in systematic studies toward the understanding of brain signaling and the origins of neurological disorders.

Compensation through Functional Hyperconnectivity: A Longitudinal Connectome Assessment of Mild Traumatic Brain Injury.

Mild traumatic brain injury (mTBI) is a major public health concern. Functional MRI has reported alterations in several brain networks following mTBI. However, the connectome-scale brain network changes are still unknown. In this study, sixteen mTBI patients were prospectively recruited from an emergency department and followed up at 4–6 weeks after injury. Twenty-four healthy controls were also scanned twice with the same time interval. Three hundred fifty-eight brain landmarks that preserve structural and functional correspondence of brain networks across individuals were used to investigate longitudinal brain connectivity. Network-based statistic (NBS) analysis did not find significant difference in the group-by-time interaction and time effects. However, 258 functional pairs show group differences in which mTBI patients have higher functional connectivity. Meta-analysis showed that “Action” and “Cognition” are the most affected functional domains. Categorization of connectomic signatures using multiview group-wise cluster analysis identified two patterns of functional hyperconnectivity among mTBI patients: (I) between the posterior cingulate cortex and the association areas of the brain and (II) between the occipital and the frontal lobes of the brain. Our results demonstrate that brain concussion renders connectome-scale brain network connectivity changes, and the brain tends to be hyperactivated to compensate the pathophysiological disturbances.

Stereoscopic three-dimensional visualization applied to multimodal brain images: clinical applications and a functional connectivity atlas.

Effective visualization is central to the exploration and comprehension of brain imaging data. While MRI data are acquired in three-dimensional space, the methods for visualizing such data have rarely taken advantage of three-dimensional stereoscopic technologies. We present here results of stereoscopic visualization of clinical data, as well as an atlas of whole-brain functional connectivity. In comparison with traditional 3D rendering techniques, we demonstrate the utility of stereoscopic visualizations to provide an intuitive description of the exact location and the relative sizes of various brain landmarks, structures and lesions. In the case of resting state fMRI, stereoscopic 3D visualization facilitated comprehension of the anatomical position of complex large-scale functional connectivity patterns. Overall, stereoscopic visualization improves the intuitive visual comprehension of image contents, and brings increased dimensionality to visualization of traditional MRI data, as well as patterns of functional connectivity.

Waxholm Space atlas of the Sprague Dawley rat brain

Three-dimensional digital brain atlases represent an important new generation of neuroinformatics tools for understanding complex brain anatomy, assigning location to experimental data, and planning of experiments. We have acquired a microscopic resolution isotropic MRI and DTI atlasing template for the Sprague Dawley rat brain with 39μm isotropic voxels for the MRI volume and 78μm isotropic voxels for the DTI. Building on this template, we have delineated 76 major anatomical structures in the brain. Delineation criteria are provided for each structure. We have applied a spatial reference system based on internal brain landmarks according to the Waxholm Space standard, previously developed for the mouse brain, and furthermore connected this spatial reference system to the widely used stereotaxic coordinate system by identifying cranial sutures and related stereotaxic landmarks in the template using contrast given by the active staining technique applied to the tissue. With the release of the present atlasing template and anatomical delineations, we provide a new tool for spatial orientationanalysis of neuroanatomical location, and planning and guidance of experimental procedures in the rat brain. The use of Waxholm Space and related infrastructures will connect the atlas to interoperable resources and services for multi-level data integration and analysis across reference spaces.

Landmarking the Brain for Geometric Morphometric Analysis: An Error Study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm–5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm–3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.

16 landmarks of the mouse brain have been validated as fiducials for registration to WHS

Event Abstract Back to Event 16 landmarks of the mouse brain have been validated as fiducials for registration to WHS Marina Sergejeva1*, Rembrandt Bakker2, André Gaudnek1, Yuko Okamura-Oho3, Jyl Boline4 and Andreas Hess1 1 Institute of Pharmacology, FAU Erlangen, Germany 2 Radboud University Nijmegen, Netherlands 3 Brain Research Network and RIKEN Advanced Science Institute, Japan 4 Informed Minds, United States A standard space for describing coordinate-based knowledge about the rodent brain is urgently needed. The INCF Digital Atlasing Program has yielded an open access 3D atlas reference system for the mouse brain - Waxholm Space (WHS) and a supporting Digital Atlasing Infrastructure (DAI) – for sharing of multimodal data (genomic, proteomic, imaging) from research groups around the world. We need convenient methods that permit researchers to register their own data to WHS. Existing automatic registration processes are rather complex. In addition, we propose an easier and faster approach: registration based on well recognizable brain landmarks (LMs) or fiducials. Here we present a set of LMs validated as fiducials, as they were reliably identified • by different individuals (anatomy specialists and novice) • in different MR imaging modalities (T1, T2, T2*) • in various specimens • by different cutting directions • by different image resolutions. On coronal MR datasets (T1, T2, T2*, 256x256x128, 80 μm) from an adult C57BL/6J male we defined an initial set of LMs recognizable in all 3 modalities and rendered descriptions how to find them. 15 guessers identified these LMs according to the descriptions on datasets from two C57BL/6J males, visualized in ImageJ as coronal slices. The probability of finding them, mean values for x, y and z coordinates and deviations from the mean were calculated for every LM. Finally, we excluded LMs with a deviation of more than 1,5 voxels in the x and y directions in both animals and ended up with 16 potential fiducials. Their average deviations were: 1,0 (x), 0,6 (y) and 1,5 (z), the probability of finding was > 95%. Further, we located these 16 LMs on the canonical WHS datasets, and presented them in the web-based atlasing tool Scalable Brain Atlas (http://scalablebrainatlas.incf.org/WHS10). WHS images have a four times higher resolution and a different inclination than ours, but despite the differences all LMs were well identifiable according to our definitions. This supports their validity as fiducials. We also evaluated “the classical” LMs, Bregma and Lambda derived from the skull 3D µCT datasets coregistered to brain MRI datasets of five mice. We found that positions of these LMs with respect to brain anatomy vary considerably between the mice. The largest distance between Bregma z-positions was 1,2 mm, and between Lambda z-positions - 1,68 mm. Thus, we cannot accept these two LMs as fiducials. Keywords: digital atlasing, Waxholm Space, neuroinformatics, rodent brain, Open access Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Sergejeva M, Bakker R, Gaudnek A, Okamura-Oho Y, Boline J and Hess A (2014). 16 landmarks of the mouse brain have been validated as fiducials for registration to WHS. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00028 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Marina Sergejeva, Institute of Pharmacology, FAU Erlangen, Erlangen, Germany, sergeeva@pharmakologie.uni-erlangen.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Marina Sergejeva Rembrandt Bakker André Gaudnek Yuko Okamura-Oho Jyl Boline Andreas Hess Google Marina Sergejeva Rembrandt Bakker André Gaudnek Yuko Okamura-Oho Jyl Boline Andreas Hess Google Scholar Marina Sergejeva Rembrandt Bakker André Gaudnek Yuko Okamura-Oho Jyl Boline Andreas Hess PubMed Marina Sergejeva Rembrandt Bakker André Gaudnek Yuko Okamura-Oho Jyl Boline Andreas Hess Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Group-wise optimization of common brain landmarks with joint structural and functional regulations.

An unrelenting human quest regarding the brain science is: what is the intrinsic relationship between the brain's structural and functional architectures, which partly defines what we are and who we are. Recent studies suggest that each brain's cytoarchitectonic region has a unique set of extrinsic inputs and outputs, named as "connectional fingerprint", which largely determines the functions that each brain area performs. However, their explicit connections are largely unknown. For example, in what extent they are inclined to be coherent with each other and otherwise they will intend to show more heterogeneity? In this work, based on a widely used brain structural atlas which represents the most consistent structural connectome across different populations, we proposed a novel group-wise optimization framework to computationally model the functional homogeneity behind them. The optimization procedure is conducted under the joint structural and functional regulations and therefore the achieved common brain landmarks reflect the consistency of brain structure and function simultaneously. The Human Connectome Project (HCP) Q1 dataset, which includes 68 subjects with high quality imaging data, was used as test bed and the results imply that there exists extraordinary accordance between brain structural and functional architectures.

Postnatal brain and skull growth in an Apert syndrome mouse model.

Craniofacial and neural tissues develop in concert throughout prenatal and postnatal growth. FGFR-related craniosynostosis syndromes, such as Apert syndrome (AS), are associated with specific phenotypes involving both the skull and the brain. We analyzed the effects of the FGFR P253R mutation for AS using the Fgfr2(+/P253R) Apert syndrome mouse to evaluate the effects of this mutation on these two tissues over the course of development from day of birth (P0) to postnatal day 2 (P2). Three-dimensional magnetic resonance microscopy and computed tomography images were acquired from Fgfr2(+/P253R) mice and unaffected littermates at P0 (N = 28) and P2 (N = 20).Three-dimensional coordinate data for 23 skull and 15 brain landmarks were statistically compared between groups. Results demonstrate that the Fgfr2(+/P253R) mice show reduced growth in the facial skeleton and the cerebrum, while the height and width of the neurocranium and caudal regions of the brain show increased growth relative to unaffected littermates. This localized correspondence of differential growth patterns in skull and brain point to their continued interaction through development and suggest that both tissues display divergent postnatal growth patterns relative to unaffected littermates. However, the change in the skull-brain relationship from P0 to P2 implies that each tissue affected by the mutation retains a degree of independence, rather than one tissue directing the development of the other.

A Simple and Fast Method of 3D Registration and Statistical Landmark Localization for Sparse Multi-Modal/Time-Series Neuroimages Based on Cortex Ellipse Fitting

Existing methods of neuroimage registration typically require high quality scans and are time-consuming. We propose a simple and fast method which allows intra-patient multi-modal and time-series neuroimage registration as well as landmark identification (including commissures and superior/inferior brain landmarks) for sparse data. The method is based on elliptical approximation of the brain cortical surface in the vicinity of the midsagittal plane (MSP). Scan registration is performed by a 3D affine transformation based on parameters of the cortex elliptical fit and by aligning the MSPs. The landmarks are computed using a statistical localization method based on analysis of 53 structural scans without detectable pathology. The method is illustrated for multi-modal registration, analysis of hemorrhagic stroke time series, and ischemic stroke follow ups, as well as for localization of hardly visible or not discernible landmarks in sparse neuroimages. The method also enables a statistical localization of landmarks in sparse morphological/non-morphological images, where landmark points may be invisible.

The Developmental Trajectory of Brain-Scalp Distance from Birth through Childhood: Implications for Functional Neuroimaging

Measurements of human brain function in children are of increasing interest in cognitive neuroscience. Many techniques for brain mapping used in children, including functional near-infrared spectroscopy (fNIRS), electroencephalography (EEG), magnetoencephalography (MEG) and transcranial magnetic stimulation (TMS), use probes placed on or near the scalp. The distance between the scalp and the brain is a key variable for these techniques because optical, electrical and magnetic signals are attenuated by distance. However, little is known about how scalp-brain distance differs between different cortical regions in children or how it changes with development. We investigated scalp-brain distance in 71 children, from newborn to age 12 years, using structural T1-weighted MRI scans of the whole head. Three-dimensional reconstructions were created from the scalp surface to allow for accurate calculation of brain-scalp distance. Nine brain landmarks in different cortical regions were manually selected in each subject based on the published fNIRS literature. Significant effects were found for age, cortical region and hemisphere. Brain-scalp distances were lowest in young children, and increased with age to up to double the newborn distance. There were also dramatic differences between brain regions, with up to 50% differences between landmarks. In frontal and temporal regions, scalp-brain distances were significantly greater in the right hemisphere than in the left hemisphere. The largest contributors to developmental changes in brain-scalp distance were increases in the corticospinal fluid (CSF) and inner table of the cranium. These results have important implications for functional imaging studies of children: age and brain-region related differences in fNIRS signals could be due to the confounding factor of brain-scalp distance and not true differences in brain activity.

Accuracy of Magnetic Resonance Imaging–Directed Frame-Based Stereotaxis

Accurate placement of a probe to the deep regions of the brain is an important part of neurosurgery. In the modern era, magnetic resonance image (MRI)-based target planning with frame-based stereotaxis is the most common technique. To quantify the inaccuracy in MRI-guided frame-based stereotaxis and to assess the relative contributions of frame movements and MRI distortion. The MRI-directed implantable guide-tube technique was used to place carbothane stylettes before implantation of the deep brain stimulation electrodes. The coordinates of target, dural entry point, and other brain landmarks were compared between preoperative and intraoperative MRIs to determine the inaccuracy. The mean 3-dimensional inaccuracy of the stylette at the target was 1.8 mm (95% confidence interval [CI], 1.5-2.1. In deep brain stimulation surgery, the accuracy in the x and y (axial) planes is important; the mean axial inaccuracy was 1.4 mm (95% CI, 1.1-1.8). The maximal mean deviation of the head frame compared with brain over 24.1 ± 1.8 hours was 0.9 mm (95% CI, 0.5-1.1). The mean 3-dimensional inaccuracy of the dural entry point of the stylette was 1.8 mm (95% CI, 1.5-2.1), which is identical to that of the target. Stylette positions did deviate from the plan, albeit by 1.4 mm in the axial plane and 1.8 mm in 3-dimensional space. There was no difference between the accuracies at the dura and the target approximately 70 mm deep in the brain, suggesting potential feasibility for accurate planning along the whole trajectory.