Adaptive template generation for amyloid PET using a deep learning approach

Accurate spatial normalization (SN) of amyloid positron emission tomography (PET) images for Alzheimer's disease assessment without coregistered anatomical magnetic resonance imaging (MRI) of the same individual is technically challenging. In this study, we applied deep neural networks to generate individually adaptive PET templates for robust and accurate SN of amyloid PET without using matched 3D MR images. Using 681 pairs of simultaneously acquired ¹¹C‐PIB PET and T1‐weighted 3D MRI scans of AD, MCI, and cognitively normal subjects, we trained and tested two deep neural networks [convolutional auto‐encoder (CAE) and generative adversarial network (GAN)] that produce adaptive best PET templates. More specifically, the networks were trained using 685,100 pieces of augmented data generated by rotating 527 randomly selected datasets and validated using 154 datasets. The input to the supervised neural networks was the 3D PET volume in native space and the label was the spatially normalized 3D PET image using the transformation parameters obtained from MRI‐based SN. The proposed deep learning approach significantly enhanced the quantitative accuracy of MRI‐less amyloid PET assessment by reducing the SN error observed when an average amyloid PET template is used. Given an input image, the trained deep neural networks rapidly provide individually adaptive 3D PET templates without any discontinuity between the slices (in 0.02 s). As the proposed method does not require 3D MRI for the SN of PET images, it has great potential for use in routine analysis of amyloid PET images in clinical practice and research.     

Atlas‐based segmentation of developing tissues in the human brain with quantitative validation in young fetuses

Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas‐based segmentation of individual tissue types in motion‐corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing gray and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57–22.86 weeks gestational age) demonstrates that this atlas‐based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects. Hum Brain Mapp, 2010. © 2010 Wiley‐Liss, Inc.     

Toward the automatic quantification of in utero brain development in 3D structural MRI: A review

Investigating the human brain in utero is important for researchers and clinicians seeking to understand early neurodevelopmental processes. With the advent of fast magnetic resonance imaging (MRI) techniques and the development of motion correction algorithms to obtain high‐quality 3D images of the fetal brain, it is now possible to gain more insight into the ongoing maturational processes in the brain. In this article, we present a review of the major building blocks of the pipeline toward performing quantitative analysis of in vivo MRI of the developing brain and its potential applications in clinical settings. The review focuses on T1‐ and T2‐weighted modalities, and covers state of the art methodologies involved in each step of the pipeline, in particular, 3D volume reconstruction, spatio‐temporal modeling of the developing brain, segmentation, quantification techniques, and clinical applications. Hum Brain Mapp 38:2772–2787, 2017. © 2017 Wiley Periodicals, Inc.     

Subtle in‐scanner motion biases automated measurement of brain anatomy from in vivo MRI

While the potential for small amounts of motion in functional magnetic resonance imaging (fMRI) scans to bias the results of functional neuroimaging studies is well appreciated, the impact of in‐scanner motion on morphological analysis of structural MRI is relatively under‐studied. Even among “good quality” structural scans, there may be systematic effects of motion on measures of brain morphometry. In the present study, the subjects' tendency to move during fMRI scans, acquired in the same scanning sessions as their structural scans, yielded a reliable, continuous estimate of in‐scanner motion. Using this approach within a sample of 127 children, adolescents, and young adults, significant relationships were found between this measure and estimates of cortical gray matter volume and mean curvature, as well as trend‐level relationships with cortical thickness. Specifically, cortical volume and thickness decreased with greater motion, and mean curvature increased. These effects of subtle motion were anatomically heterogeneous, were present across different automated imaging pipelines, showed convergent validity with effects of frank motion assessed in a separate sample of 274 scans, and could be demonstrated in both pediatric and adult populations. Thus, using different motion assays in two large non‐overlapping sets of structural MRI scans, convergent evidence showed that in‐scanner motion—even at levels which do not manifest in visible motion artifact—can lead to systematic and regionally specific biases in anatomical estimation. These findings have special relevance to structural neuroimaging in developmental and clinical datasets, and inform ongoing efforts to optimize neuroanatomical analysis of existing and future structural MRI datasets in non‐sedated humans. Hum Brain Mapp 37:2385–2397, 2016. © 2016 Wiley Periodicals, Inc .     

Neurobiological origin of spurious brain morphological changes: A quantitative MRI study

The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R₁ (=1/T1), R₂*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.     

基于VTK软件的结直肠段三维数字可视化***☆

背景：将二维断层图像序列转变为具有直观立体效果的图像,展现人体器官的三维结构与形态,提供传统手段无法获得的解剖结构信息,是医学数据场可视化的基本任务。 目的：利用医学图像的三维可视化技术,建立肠道切片数据的三维可视化模型,为口服胶囊机器人在肠道中的空间位置跟踪研究、粪便嵌塞性肠梗阻情况下肠道承受水压冲洗能力的数值模拟研究提供肠道的几何空间结构参考依据。 方法：医学图像切片可视化的一般过程,包括图像获取、配准、三维面绘制重建,以及在Visual C++中利用VTK软件实现三维数字可视化。 结果与结论：利用VTK软件,实现了结直肠的三维数字可视化,可实现结直肠三维图像的旋转、缩放、平移,并将其集成到MFC图形用户界面中。实际得到的三维可视化模型可为口服胶囊机器人在肠道中的空间位置跟踪研究、粪便嵌塞性肠梗阻情况下肠道承受水压冲洗能力的数值模拟研究提供图像参考依据。     

Automated alignment of perioperative MRI scans: A technical note and application in pediatric epilepsy surgery

Conventional image registration utilizing brain voxel information may be erroneous in a neurosurgical setting due to pathology and surgery‐related anatomical distortions. We report a novel application of an automated image registration procedure based on skull segmentation for magnetic resonance imaging (MRI) scans acquired before, during and after surgery (i.e., perioperative). The procedure was implemented to assist analysis of intraoperative brain shift in 11 pediatric epilepsy surgery cases, each of whom had up to five consecutive perioperative MRI scans. The procedure consisted of the following steps: (1) Skull segmentation using tissue classification tools. (2) Estimation of rigid body transformation between image pairs using registration driven by the skull segmentation. (3) Composition of transformations to provide transformations between each scan and a common space. The procedure was validated using locations of three types of reference structural landmarks: the skull pin sites, the eye positions, and the scalp skin surface, detected using the peak intensity gradient. The mean target registration error (TRE) scores by skull pin sites and scalp skin rendering were around 1 mm and <1 mm, respectively. Validation by eye position demonstrated >1 mm TRE scores, suggesting it is not a reliable reference landmark in surgical scenarios. Comparable registration accuracy was achieved between opened and closed skull scan pairs and closed and closed skull scan pairs. Our procedure offers a reliable registration framework for processing intrasubject time series perioperative MRI data, with potential of improving intraoperative MRI‐based image guidance in neurosurgical practice. Hum Brain Mapp 37:3530–3543, 2016. © 2016 Wiley Periodicals, Inc.     

Effects of change in FreeSurfer version on classification accuracy of patients with Alzheimer's disease and mild cognitive impairment

Studies have found non‐negligible differences in cortical thickness estimates across versions of software that are used for processing and quantifying MRI‐based cortical measurements, and issues have arisen regarding these differences, as obtained estimates could potentially affect the validity of the results. However, more critical for diagnostic classification than absolute thickness estimates across versions is the inter‐subject stability. We aimed to investigate the effect of change in software version on classification of older persons in groups of healthy, mild cognitive impairment and Alzheimer's Disease. Using MRI samples of 100 older normal controls, 100 with mild cognitive impairment and 100 Alzheimer's Disease patients obtained from the Alzheimer's Disease Neuroimaging Initiative database, we performed a standard reconstruction processing using the FreeSurfer image analysis suite versions 4.1.0, 4.5.0 and 5.1.0. Pair‐wise comparisons of cortical thickness between FreeSurfer versions revealed significant differences, ranging from 1.6% (4.1.0 vs. 4.5.0) to 5.8% (4.1.0 vs. 5.1.0) across the cortical mantle. However, change of version had very little effect on detectable differences in cortical thickness between diagnostic groups, and there were little differences in accuracy between versions when using entorhinal thickness for diagnostic classification. This lead us to conclude that differences in absolute thickness estimates across software versions in this case did not imply lacking validity, that classification results appeared reliable across software versions, and that classification results obtained in studies using different FreeSurfer versions can be reliably compared. Hum Brain Mapp 37:1831–1841, 2016. © 2016 Wiley Periodicals, Inc .     

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets

Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated identification tools. Clinical trials involving motion‐prone neonates typically pool data to obtain sufficient power, and automated quality control protocols are especially important to safeguard data quality. Current study tested an open source method to detect major artifacts among 2D neonatal MRI via supervised machine learning. A total of 1,020 two‐dimensional transverse T2‐weighted MRI images of preterm newborns were examined and classified as either QC Pass or QC Fail. Then 70 features across focus, texture, noise, and natural scene statistics categories were extracted from each image. Several different classifiers were trained and their performance was compared with subjective rating as the gold standard. We repeated the rating process again to examine the stability of the rating and classification. When tested via 10‐fold cross validation, the random undersampling and adaboost ensemble (RUSBoost) method achieved the best overall performance for QC Fail images with 85% positive predictive value along with 75% sensitivity. Similar classification performance was observed in the analyses of the repeated subjective rating. Current results served as a proof of concept for predicting images that fail quality control using no‐reference objective image features. We also highlighted the importance of evaluating results beyond mere accuracy as a performance measure for machine learning in imbalanced group settings due to larger proportion of QC Pass quality images.     

Lack of evidence of acute ischemic tissue change in transient global amnesia on single-shot echo-planar diffusion-weighted MRI.

BACKGROUND AND PURPOSE: There is uncertainty concerning the etiology of transient global amnesia (TGA). Previous CT and MRI studies have indicated that permanent structural abnormality is rare in TGA. Diffusion-weighted (DW) MRI is very sensitive to early ischemic parenchymal changes and has recently demonstrated embolic infarction in the posterior cerebral artery territory in 2 TGA patients. We report the findings of DW MRI in 8 patients in acute stages of TGA. METHODS: Conventional and echo-planar DW MRI was performed in 2 patients in the active phase and 6 patients in the recovery phase (1 to 8 hours after cessation of anterograde memory dysfunction) of spontaneously occurring TGA. RESULTS: None of the patients showed signs of hyperintensity on DW images or hypointensity on quantitative apparent diffusion coefficient (ADC) maps to suggest regional decreases of water mobility or acute T2 changes on transverse or coronal slices. CONCLUSIONS: We were unable to detect ADC or acute T2 changes with echo-planar DW MRI in patients with TGA, which suggests that mechanisms other than ischemic infarction may cause TGA. We did not identify spreading depression-associated changes of the ADC. Further refinement of MRI sequences may be necessary to detect subtle or transient signal change in brain parenchyma.     

MRI of the abnormal cervical spinal cord using 2D spoiled gradient echo multiecho sequence (MEDIC) with magnetization transfer saturation pulse. A T2* weighted feasibility study

OBJECTIVES: The aim of this study was to assess the potential of heavily T2* weighted 2D spoiled gradient echo multiecho sequence MEDIC (multi echo data image combination) with magnetization transfer saturation pulse (MTS) for detecting abnormality of the cervical spinal cord. METHODS: 11 patients, 5 women and 6 men aged from 14 to 79 years (mean age 51.18 years), with traumatic, hemolytic-hemorrhagic or neoplastic diseases of the cervical spinal cord were examined with MRI. In cases with suspected myelopathy, the feasibility of the 2D spoiled gradient echo multiecho sequence MEDIC with MTS was evaluated in comparison with the results of spin echo T1W, spin echo T2W, multi echo (TSE in our case) and spin-echo multi-echo technique with magnetization preparation (turbo inversion recovery--TIR--in our case) sequences. RESULTS: Distortion of the "H" sign was found in all but one case. Hemorrhage was best shown by MEDIC, massive edema was very well visible using MEDIC, TIR and TSE T2W, whereas mild edema was visible with MEDIC only. CONCLUSIONS: Our preliminary experience in 11 patients shows that MEDIC can be used for the diagnosis of cervical spinal cord pathology.     

Restoring statistical validity in group analyses of motion‐corrupted MRI data

Motion during the acquisition of magnetic resonance imaging (MRI) data degrades image quality, hindering our capacity to characterise disease in patient populations. Quality control procedures allow the exclusion of the most affected images from analysis. However, the criterion for exclusion is difficult to determine objectively and exclusion can lead to a suboptimal compromise between image quality and sample size. We provide an alternative, data‐driven solution that assigns weights to each image, computed from an index of image quality using restricted maximum likelihood. We illustrate this method through the analysis of quantitative MRI data. The proposed method restores the validity of statistical tests, and performs near optimally in all brain regions, despite local effects of head motion. This method is amenable to the analysis of a broad type of MRI data and can accommodate any measure of image quality.     

Application of a non-linear image registration algorithm to quantitative analysis of T2 relaxation time in transgenic mouse models of AD pathology

Transgenic mouse models have been essential for understanding the pathogenesis of Alzheimer's disease (AD) including those that model the deposition process of beta-amyloid (Abeta). Several laboratories have focused on research related to the non-invasive detection of early changes in brains of transgenic mouse models of Alzheimer's pathology. Most of this work has been performed using regional image analysis of individual mouse brains and pooling the results for statistical assessment. Here we report the implementation of a non-linear image registration algorithm to register anatomical and transverse relaxation time (T2) maps estimated from MR images of transgenic mice. The algorithm successfully registered mouse brain magnetic resonance imaging (MRI) volumes and T2 maps, allowing reliable estimates of T2 values for different regions of interest from the resultant combined images. This approach significantly reduced the data processing and analysis time, and improved the ability to statistically discriminate between groups. Additionally, 3D visualization of intra-regional distributions of T2 of the resultant registered images provided the ability to detect small changes between groups that otherwise would not be possible to detect.     

Quantitative Analysis of White Matter Hyperintensity: Comparison of Magnetic Resonance Imaging Image Analysis Software

ObjectiveWhite matter hyperintensity (WMH), defined as abnormal signals on magnetic resonance imaging (MRI), is an important clinical indicator of aging and dementia. Although MRI image analysis software can automatically detect WMH, the quantitative accuracy of periventricular hyperintensity (PVH) and deep white matter hyperintensity (DWMH) is unknown.Materials and MethodsThis study was a sub-analysis of MRI data from an ongoing hospital-based prospective cohort study (the Gimlet study). Between March 2016 and March 2017, we enrolled patients who visited our memory clinic and agreed to undergo medical assessments of cognitive function and fecal examination to study the gut microbiome. Participants with a history of stroke were excluded. WMH was independently quantitatively analyzed using two MRI imaging analysis software modalities: SNIPER and FUSION. Intraclass correlation coefficients and the mean difference in volume were calculated and compared between modalities.ResultsThe data of 87 patients (49 women, mean age 74.8 ± 7.9 years) were analyzed. Both total WMH and DWMH volumes obtained using FUSION were greater (p < 0.001), and PVH volume was smaller (p < 0.001) than those obtained using SNIPER. Intraclass correlation coefficients for the lesion measurements of WMH, PVH, and DWMH between the different software were 0.726 (p < 0.001), 0.673 (p < 0.001), and 0.048 (p = 0.231), respectively.ConclusionsThere were significant differences in the quantitative data of WMH between the two MRI imaging analysis software modalities. Thus, care should be taken for quantitative assessments of WMH.     

Preserved white matter microstructure in young patients with anorexia nervosa?

A massive but reversible reduction of cortical thickness and subcortical gray matter (GM) volumes in Anorexia Nervosa (AN) has been recently reported. However, the literature on alterations in white matter (WM) volume and microstructure changes in both acutely underweight AN (acAN) and after recovery (recAN) is sparse and results are inconclusive. Here, T1‐weighted and diffusion‐weighted MRI data in a sizable sample of young and medication‐free acAN (n = 35), recAN (n = 32), and age‐matched female healthy controls (HC, n = 62) were obtained. For analysis, a well‐validated global probabilistic tractography reconstruction algorithm including rigorous motion correction implemented in FreeSurfer: TRACULA (TRActs Constrained by UnderLying Anatomy) were used. Additionally, a clustering algorithm and a multivariate pattern classification technique to WM metrics to predict group membership were applied. No group differences in either WM volume or WM microstructure were detected with standard analysis procedures either in acAN or recAN relative to HC after controlling for the number of performed statistical tests. These findings were not affected by age, IQ, or psychiatric symptoms. While cluster analysis was unsuccessful at discriminating between groups, multivariate pattern classification showed some ability to separate acAN from HC (but not recAN from HC). However, these results were not compatible with a straightforward hypothesis of impaired WM microstructure. The current findings suggest that WM integrity is largely preserved in non‐chronic AN. This finding stands in contrast to findings in GM, but may help to explain the relatively intact cognitive performance of young patients with AN and provide the basis for the fast recovery of GM structures. Hum Brain Mapp 37:4069–4083, 2016. © 2016 Wiley Periodicals, Inc.     

Subthalamic nucleus volumes are highly consistent but decrease age‐dependently—a combined magnetic resonance imaging and stereology approach in humans

The subthalamic nucleus (STN) is a main target structure of deep brain stimulation (DBS) in idiopathic Parkinson's disease. Nevertheless, there is an ongoing discussion regarding human STN volumes and neuron count, which could potentially have an impact on STN‐DBS. Moreover, a suspected functional subdivision forms the basis of the tripartite hypothesis, which has not yet been morphologically substantiated. In this study, it was aimed to investigate the human STN by means of combined magnetic resonance imaging (MRI) and stereology. STN volumes were obtained from 14 individuals (ranging from 65 to 96 years, 25 hemispheres) in 3 T MRI and in luxol‐stained histology slices. Neuron number and cell densities were investigated stereologically over the entire STN and in pre‐defined subregions in anti‐human neuronal protein HuC/D‐stained slices. STN volumes measured with MRI were smaller than in stereology but appeared to be highly consistent, measuring on average 99 ± 6 mm³ (MRI) and 132 ± 20 mm³ (stereology). The neuron count was 431,088 ± 72,172. Both STN volumes and cell count decreased age‐dependently. Neuron density was different for the dorsal, medial and ventral subregion with significantly higher values ventrally than dorsally. Small variations in STN volumes in both MRI and stereology contradict previous findings of large variations in STN size. Age‐dependent decreases in STN volumes and neuron numbers might influence the efficacy of STN‐DBS in a geriatric population. Though the study is limited in sample size, site‐dependent differences for the STN subregions form a morphological basis for the tripartite theory. Hum Brain Mapp 38:909–922, 2017. © 2016 Wiley Periodicals, Inc.     

Topological correction of brain surface meshes using spherical harmonics

Surface reconstruction methods allow advanced analysis of structural and functional brain data beyond what can be achieved using volumetric images alone. Automated generation of cortical surface meshes from 3D brain MRI often leads to topological defects and geometrical artifacts that must be corrected to permit subsequent analysis. Here, we propose a novel method to repair topological defects using a surface reconstruction that relies on spherical harmonics. First, during reparameterization of the surface using a tiled platonic solid, the original MRI intensity values are used as a basis to select either a “fill” or “cut” operation for each topological defect. We modify the spherical map of the uncorrected brain surface mesh, such that certain triangles are favored while searching for the bounding triangle during reparameterization. Then, a low‐pass filtered alternative reconstruction based on spherical harmonics is patched into the reconstructed surface in areas that previously contained defects. Self‐intersections are repaired using a local smoothing algorithm that limits the number of affected points to less than 0.1% of the total, and as a last step, all modified points are adjusted based on the T1 intensity. We found that the corrected reconstructions have reduced distance error metrics compared with a “gold standard” surface created by averaging 12 scans of the same brain. Ninety‐three percent of the topological defects in a set of 10 scans of control subjects were accurately corrected. The entire process takes 6–8 min of computation time. Further improvements are discussed, especially regarding the use of the T1‐weighted image to make corrections. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.     

MR‐PET head motion correction based on co‐registration of multicontrast MR images

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance‐positron emission tomography (MR‐PET) makes it possible to estimate head motion information from high‐resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR‐guided MAF, based on the co‐registration of multicontrast MR images. The performance of the MR‐guided MAF method was evaluated using MR‐PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18‐F]FDG). Compared with conventional methods, MR‐guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR‐PET scanners. The fully automated motion estimation method has been implemented as a publicly available web‐service.     

Motion‐related artifacts in structural brain images revealed with independent estimates of in‐scanner head motion

Motion‐contaminated T1‐weighted (T1w) magnetic resonance imaging (MRI) results in misestimates of brain structure. Because conventional T1w scans are not collected with direct measures of head motion, a practical alternative is needed to identify potential motion‐induced bias in measures of brain anatomy. Head movements during functional MRI (fMRI) scanning of 266 healthy adults (20–89 years) were analyzed to reveal stable features of in‐scanner head motion. The magnitude of head motion increased with age and exhibited within‐participant stability across different fMRI scans. fMRI head motion was then related to measurements of both quality control (QC) and brain anatomy derived from a T1w structural image from the same scan session. A procedure was adopted to “flag” individuals exhibiting excessive head movement during fMRI or poor T1w quality rating. The flagging procedure reliably reduced the influence of head motion on estimates of gray matter thickness across the cortical surface. Moreover, T1w images from flagged participants exhibited reduced estimates of gray matter thickness and volume in comparison to age‐ and gender‐matched samples, resulting in inflated effect sizes in the relationships between regional anatomical measures and age. Gray matter thickness differences were noted in numerous regions previously reported to undergo prominent atrophy with age. Recommendations are provided for mitigating this potential confound, and highlight how the procedure may lead to more accurate measurement and comparison of anatomical features. Hum Brain Mapp 38:472–492, 2017. © 2016 Wiley Periodicals, Inc.     

Targeted gene delivery to telencephalic inhibitory neurons by directional in utero electroporation

Telencephalic inhibitory neurons originate in the ganglionic eminences and migrate to the cerebral cortex following a tangential trajectory, before they differentiate and integrate within the local circuitry. Current studies of interneuron development and function benefit from the use of knock-out and transgenic mice, whereas none take advantage of the versatility of in utero electroporation. Here, we show how in utero electroporation can be directed to the ganglionic eminences to specifically target gene expression to interneurons. Electroporation of GFP-encoding plasmids into the ganglionic eminences results in selective labeling of migrating interneurons during development. In the adult brain of electroporated animals, a wide variety of cortical, hippocampal and olfactory bulb interneurons are labeled. We also show that GFP-expressing interneurons can be visualized in living slices of adult cerebral cortex, where they display normal electrophysiological properties. Photostimulation studies using acute slices show that cortical GFP+ interneurons receive normal, layer-specific synaptic input, indicating that these neurons integrate within the local cortical circuitry. Ganglionic eminence-directed in utero electroporation is therefore an effective, rapid, and versatile method of selectively transfecting telencephalic interneurons, optimal for both developmental studies and adult functional studies.