Jason P. Lerch

Maze training in mice induces MRI-detectable brain shape changes specific to the type of learning.

Multiple recent human imaging studies have suggested that the structure of the brain can change with learning. To investigate the mechanism behind such structural plasticity, we sought to determine whether maze learning in mice induces brain shape changes that are detectable by MRI and whether such changes are specific to the type of learning. Here we trained inbred mice for 5 days on one of three different versions of the Morris water maze and, using high-resolution MRI, revealed specific growth in the hippocampus of mice trained on a spatial variant of the maze, whereas mice trained on the cued version were found to have growth in the striatum. The structure-specific growth found furthermore correlated with GAP-43 staining, a marker of neuronal process remodelling, but not with neurogenesis nor neuron or astrocyte numbers or sizes. Our findings provide evidence that brain morphology changes rapidly at a scale detectable by MRI and furthermore demonstrate that specific brain regions grow or shrink in response to the changing environmental demands. The data presented herein have implications for both human imaging as well as rodent structural plasticity research, in that it provides a tool to screen for neuronal plasticity across the whole brain in the mouse while also providing a direct link between human and mouse studies.

Performing label-fusion-based segmentation using multiple automatically generated templates.

Classically, model‐based segmentation procedures match magnetic resonance imaging (MRI) volumes to an expertly labeled atlas using nonlinear registration. The accuracy of these techniques are limited due to atlas biases, misregistration, and resampling error. Multi‐atlas‐based approaches are used as a remedy and involve matching each subject to a number of manually labeled templates. This approach yields numerous independent segmentations that are fused using a voxel‐by‐voxel label‐voting procedure. In this article, we demonstrate how the multi‐atlas approach can be extended to work with input atlases that are unique and extremely time consuming to construct by generating a library of multiple automatically generated templates of different brains (MAGeT Brain). We demonstrate the efficacy of our method for the mouse and human using two different nonlinear registration algorithms (ANIMAL and ANTs). The input atlases consist a high‐resolution mouse brain atlas and an atlas of the human basal ganglia and thalamus derived from serial histological data. MAGeT Brain segmentation improves the identification of the mouse anterior commissure (mean Dice Kappa values (κ = 0.801), but may be encountering a ceiling effect for hippocampal segmentations. Applying MAGeT Brain to human subcortical structures improves segmentation accuracy for all structures compared to regular model‐based techniques (κ = 0.845, 0.752, and 0.861 for the striatum, globus pallidus, and thalamus, respectively). Experiments performed with three manually derived input templates suggest that MAGeT Brain can approach or exceed the accuracy of multi‐atlas label‐fusion segmentation (κ = 0.894, 0.815, and 0.895 for the striatum, globus pallidus, and thalamus, respectively). Hum Brain Mapp 34:2635–2654, 2013.

Clustering autism: using neuroanatomical differences in 26 mouse models to gain insight into the heterogeneity

Autism is a heritable disorder, with over 250 associated genes identified to date, yet no single gene accounts for >1–2% of cases. The clinical presentation, behavioural symptoms, imaging and histopathology findings are strikingly heterogeneous. A more complete understanding of autism can be obtained by examining multiple genetic or behavioural mouse models of autism using magnetic resonance imaging (MRI)-based neuroanatomical phenotyping. Twenty-six different mouse models were examined and the consistently found abnormal brain regions across models were parieto-temporal lobe, cerebellar cortex, frontal lobe, hypothalamus and striatum. These models separated into three distinct clusters, two of which can be linked to the under and over-connectivity found in autism. These clusters also identified previously unknown connections between Nrxn1α, En2 and Fmr1; Nlgn3, BTBR and Slc6A4; and also between X monosomy and Mecp2. With no single treatment for autism found, clustering autism using neuroanatomy and identifying these strong connections may prove to be a crucial step in predicting treatment response.

MRI Phenotyping of Genetically Altered Mice

The laboratory mouse, with its genetic similarity to humans and rich set of tools for manipulating its genome, has emerged as one of the key models for experimental investigation of the genotype/phenotype relationships in mammals. Recent innovations have made MRI an increasingly popular tool for examining the phenotype of genetically altered mice. Advances in field strengths, mouse handling, image analysis and statistics have contributed greatly in this regard. In this chapter, we illustrate the methods necessary to achieve high-throughput phenotyping of genetically altered mice using multiple-mouse MRI combined with advanced image analysis techniques and statistics.

Behavioral Abnormalities and Circuit Defects in the Basal Ganglia of a Mouse Model of 16p11.2 Deletion Syndrome

A deletion on human chromosome 16p11.2 is associated with autism spectrum disorders. We deleted the syntenic region on mouse chromosome 7F3. MRI and high-throughput single-cell transcriptomics revealed anatomical and cellular abnormalities, particularly in cortex and striatum of juvenile mutant mice (16p11(+/-)). We found elevated numbers of striatal medium spiny neurons (MSNs) expressing the dopamine D2 receptor (Drd2(+)) and fewer dopamine-sensitive (Drd1(+)) neurons in deep layers of cortex. Electrophysiological recordings of Drd2(+) MSN revealed synaptic defects, suggesting abnormal basal ganglia circuitry function in 16p11(+/-) mice. This is further supported by behavioral experiments showing hyperactivity, circling, and deficits in movement control. Strikingly, 16p11(+/-) mice showed a complete lack of habituation reminiscent of what is observed in some autistic individuals. Our findings unveil a fundamental role of genes affected by the 16p11.2 deletion in establishing the basal ganglia circuitry and provide insights in the pathophysiology of autism.

Identification of a Functional Connectome for Long-Term Fear Memory in Mice

Long-term memories are thought to depend upon the coordinated activation of a broad network of cortical and subcortical brain regions. However, the distributed nature of this representation has made it challenging to define the neural elements of the memory trace, and lesion and electrophysiological approaches provide only a narrow window into what is appreciated a much more global network. Here we used a global mapping approach to identify networks of brain regions activated following recall of long-term fear memories in mice. Analysis of Fos expression across 84 brain regions allowed us to identify regions that were co-active following memory recall. These analyses revealed that the functional organization of long-term fear memories depends on memory age and is altered in mutant mice that exhibit premature forgetting. Most importantly, these analyses indicate that long-term memory recall engages a network that has a distinct thalamic-hippocampal-cortical signature. This network is concurrently integrated and segregated and therefore has small-world properties, and contains hub-like regions in the prefrontal cortex and thalamus that may play privileged roles in memory expression.

A novel 3D mouse embryo atlas based on micro-CT.

The goal of the International Mouse Phenotyping Consortium (IMPC) is to phenotype targeted knockout mouse strains throughout the whole mouse genome (23,000 genes) by 2021. A significant percentage of the generated mice will be embryonic lethal; therefore, phenotyping methods tuned to the mouse embryo are needed. Methods that are robust, quantitative, automated and high-throughput are attractive owing to the numbers of mice involved. Three-dimensional (3D) imaging is a useful method for characterizing morphological phenotypes. However, tools to automatically quantify morphological information of mouse embryos from 3D imaging have not been fully developed. We present a representative mouse embryo average 3D atlas comprising micro-CT images of 35 individual C57BL/6J mouse embryos at 15.5 days post-coitum. The 35 micro-CT images were registered into a consensus average image with our automated image registration software and 48 anatomical structures were segmented manually. We report the mean and variation in volumes for each of the 48 segmented structures. Mouse organ volumes vary by 2.6-4.2% on a linear scale when normalized to whole body volume. A power analysis of the volume data reports that a 9-14% volume difference can be detected between two classes of mice with sample sizes of eight. This resource will be crucial in establishing baseline anatomical phenotypic measurements for the assessment of mutant mouse phenotypes, as any future mutant embryo image can be registered to the atlas and subsequent organ volumes calculated automatically.

Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse

High-resolution MRI of the mouse brain is gaining prominence in estimating changes in neuroanatomy over time to understand both normal developmental as well as disease processes and mechanisms. These types of experiments, where a change in time is to be captured as accurately as possible using imaging, face multiple experimental design choices. Chief amongst these choices is whether to image ex-vivo, where superior resolution and contrast are available, or in-vivo, where resolution and contrast are lower but the animal can be followed longitudinally. Here we explore this tradeoff by first estimating the sources of variability in anatomical mouse MRI and then, using statistical simulations, provide power analyses of these experiment design choices.

Neurexin-1 and Frontal Lobe White Matter: An Overlapping Intermediate Phenotype for Schizophrenia and Autism Spectrum Disorders

Background Structural variation in the neurexin-1 (NRXN1) gene increases risk for both autism spectrum disorders (ASD) and schizophrenia. However, the manner in which NRXN1 gene variation may be related to brain morphology to confer risk for ASD or schizophrenia is unknown. Method/Principal Findings 53 healthy individuals between 18–59 years of age were genotyped at 11 single nucleotide polymorphisms of the NRXN1 gene. All subjects received structural MRI scans, which were processed to determine cortical gray and white matter lobar volumes, and volumes of striatal and thalamic structures. Each subjects sensorimotor function was also assessed. The general linear model was used to calculate the influence of genetic variation on neural and cognitive phenotypes. Finally, in silico analysis was conducted to assess potential functional relevance of any polymorphisms associated with brain measures. A polymorphism located in the 3′ untranslated region of NRXN1 significantly influenced white matter volumes in whole brain and frontal lobes after correcting for total brain volume, age and multiple comparisons. Follow-up in silico analysis revealed that this SNP is a putative microRNA binding site that may be of functional significance in regulating NRXN1 expression. This variant also influenced sensorimotor performance, a neurocognitive function impaired in both ASD and schizophrenia. Conclusions Our findings demonstrate that the NRXN1 gene, a vulnerability gene for SCZ and ASD, influences brain structure and cognitive function susceptible in both disorders. In conjunction with our in silico results, our findings provide evidence for a neural and cognitive susceptibility mechanism by which the NRXN1 gene confers risk for both schizophrenia and ASD.

Reduced Cortical Volume and Elevated Astrocyte Density in Rats Chronically Treated with Antipsychotic Drugs—Linking Magnetic Resonance Imaging Findings to Cellular Pathology

BACKGROUND Increasing evidence suggests that antipsychotic drugs (APD) might affect brain structure directly, particularly the cerebral cortex. However, the precise anatomical loci of these effects and their underlying cellular basis remain unclear. METHODS With ex vivo magnetic resonance imaging in rats treated chronically with APDs, we used automated analysis techniques to map the regions that show maximal impact of chronic (8 weeks) treatment with either haloperidol or olanzapine on the rat cortex. Guided by these imaging findings, we undertook a focused postmortem investigation with stereology. RESULTS We identified decreases in the volume and thickness of the anterior cingulate cortex (ACC) after chronic APD treatment, regardless of the APD administered. Postmortem analysis confirmed these volumetric findings and demonstrated that chronic APD treatment had no effect on the total number of neurons or S100β+ astrocytes in the ACC. In contrast, an increase in the density of these cells was observed. CONCLUSIONS This study demonstrates region-specific structural effects of chronic APD treatment on the rat cortex, primarily but not exclusively localized to the ACC. At least in the rat, these changes are not due to a loss of either neurons or astrocytes and are likely to reflect a loss of neuropil. Although caution needs to be exerted when extrapolating results from animals to patients, this study highlights the power of this approach to link magnetic resonance imaging findings to their histopathological origins.