Luca Dodero

Neuroimaging Evidence of Major Morpho-Anatomical and Functional Abnormalities in the BTBR T+TF/J Mouse Model of Autism

BTBR T+tf/J (BTBR) mice display prominent behavioural deficits analogous to the defining symptoms of autism, a feature that has prompted a widespread use of the model in preclinical autism research. Because neuro-behavioural traits are described with respect to reference populations, multiple investigators have examined and described the behaviour of BTBR mice against that exhibited by C57BL/6J (B6), a mouse line characterised by high sociability and low self-grooming. In an attempt to probe the translational relevance of this comparison for autism research, we used Magnetic Resonance Imaging (MRI) to map in both strain multiple morpho-anatomical and functional neuroimaging readouts that have been extensively used in patient populations. Diffusion tensor tractography confirmed previous reports of callosal agenesis and lack of hippocampal commissure in BTBR mice, and revealed a concomitant rostro-caudal reorganisation of major cortical white matter bundles. Intact inter-hemispheric tracts were found in the anterior commissure, ventro-medial thalamus, and in a strain-specific white matter formation located above the third ventricle. BTBR also exhibited decreased fronto-cortical, occipital and thalamic gray matter volume and widespread reductions in cortical thickness with respect to control B6 mice. Foci of increased gray matter volume and thickness were observed in the medial prefrontal and insular cortex. Mapping of resting-state brain activity using cerebral blood volume weighted fMRI revealed reduced cortico-thalamic function together with foci of increased activity in the hypothalamus and dorsal hippocampus of BTBR mice. Collectively, our results show pronounced functional and structural abnormalities in the brain of BTBR mice with respect to control B6 mice. The large and widespread white and gray matter abnormalities observed do not appear to be representative of the neuroanatomical alterations typically observed in autistic patients. The presence of reduced fronto-cortical metabolism is of potential translational relevance, as this feature recapitulates previously-reported clinical observations.

Dominant β-catenin mutations cause intellectual disability with recognizable syndromic features.

The recent identification of multiple dominant mutations in the gene encoding β-catenin in both humans and mice has enabled exploration of the molecular and cellular basis of β-catenin function in cognitive impairment. In humans, β-catenin mutations that cause a spectrum of neurodevelopmental disorders have been identified. We identified de novo β-catenin mutations in patients with intellectual disability, carefully characterized their phenotypes, and were able to define a recognizable intellectual disability syndrome. In parallel, characterization of a chemically mutagenized mouse line that displays features similar to those of human patients with β-catenin mutations enabled us to investigate the consequences of β-catenin dysfunction through development and into adulthood. The mouse mutant, designated batface (Bfc), carries a Thr653Lys substitution in the C-terminal armadillo repeat of β-catenin and displayed a reduced affinity for membrane-associated cadherins. In association with this decreased cadherin interaction, we found that the mutation results in decreased intrahemispheric connections, with deficits in dendritic branching, long-term potentiation, and cognitive function. Our study provides in vivo evidence that dominant mutations in β-catenin underlie losses in its adhesion-related functions, which leads to severe consequences, including intellectual disability, childhood hypotonia, progressive spasticity of lower limbs, and abnormal craniofacial features in adults.

Altered functional connectivity networks in acallosal and socially impaired BTBR mice

Abstract Agenesis of the corpus callosum (AgCC) is a congenital condition associated with wide-ranging emotional and social impairments often overlapping with the diagnostic criteria for autism. Mapping functional connectivity in the acallosal brain can help identify neural correlates of the deficits associated with this condition, and elucidate how congenital white matter alterations shape the topology of large-scale functional networks. By using resting-state BOLD functional magnetic resonance imaging (rsfMRI), here we show that acallosal BTBR T+tpr3tf/J (BTBR) mice, an idiopathic model of autism, exhibit impaired intra-hemispheric connectivity in fronto-cortical, but not in posterior sensory cortical areas. We also document profoundly altered subcortical and intra-hemispheric connectivity networks, with evidence of marked fronto-thalamic and striatal disconnectivity, along with aberrant spatial extension and strength of ipsilateral and local connectivity. Importantly, inter-hemispheric tracing of monosynaptic connections in the primary visual cortex using recombinant rabies virus confirmed the absence of direct homotopic pathways between posterior cortical areas of BTBR mice, suggesting a polysynaptic origin for the synchronous rsfMRI signal observed in these regions. Collectively, the observed long-range connectivity impairments recapitulate hallmark neuroimaging findings in autism, and are consistent with the behavioral phenotype of BTBR mice. In contrast to recent rsfMRI studies in high functioning AgCC individuals, the profound fronto-cortical and subcortical disconnectivity mapped suggest that compensatory mechanism may not necessarily restore the full connectional topology of the brain, resulting in residual connectivity alterations that serve as plausible substrates for the cognitive and emotional deficits often associated with AgCC.

Dysfunctional dopaminergic neurotransmission in asocial BTBR mice

Autism spectrum disorders (ASD) are neurodevelopmental conditions characterized by pronounced social and communication deficits and stereotyped behaviours. Recent psychosocial and neuroimaging studies have highlighted reward-processing deficits and reduced dopamine (DA) mesolimbic circuit reactivity in ASD patients. However, the neurobiological and molecular determinants of these deficits remain undetermined. Mouse models recapitulating ASD-like phenotypes could help generate hypotheses about the origin and neurophysiological underpinnings of clinically relevant traits. Here we used functional magnetic resonance imaging (fMRI), behavioural and molecular readouts to probe dopamine neurotransmission responsivity in BTBR T+ Itpr3tf/J mice (BTBR), an inbred mouse line widely used to model ASD-like symptoms owing to its robust social and communication deficits, and high level of repetitive stereotyped behaviours. C57BL/6J (B6) mice were used as normosocial reference comparators. DA reuptake inhibition with GBR 12909 produced significant striatal DA release in both strains, but failed to elicit fMRI activation in widespread forebrain areas of BTBR mice, including mesolimbic reward and striatal terminals. In addition, BTBR mice exhibited no appreciable motor responses to GBR 12909. DA D1 receptor-dependent behavioural and signalling responses were found to be unaltered in BTBR mice, whereas dramatic reductions in pre- and postsynaptic DA D2 and adenosine A2A receptor function was observed in these animals. Overall these results document profoundly compromised DA D2-mediated neurotransmission in BTBR mice, a finding that is likely to have a role in the distinctive social and behavioural deficits exhibited by these mice. Our results call for a deeper investigation of the role of dopaminergic dysfunction in mouse lines exhibiting ASD-like phenotypes, and possibly in ASD patient populations.

Kernel-based classification for brain connectivity graphs on the Riemannian manifold of positive definite matrices

An important task in connectomics studies is the classification of connectivity graphs coming from healthy and pathological subjects. In this paper, we propose a mathematical framework based on Riemannian geometry and kernel methods that can be applied to connectivity matrices for the classification task. We tested our approach using different real datasets of functional and structural connectivity, evaluating different metrics to describe the similarity between graphs. The empirical results obtained clearly show the superior performance of our approach compared with baseline methods, demonstrating the advantages of our manifold framework and its potential for other applications.

Group-Wise Functional Community Detection through Joint Laplacian Diagonalization

There is a growing conviction that the understanding of the brain function can come through a deeper knowledge of the network connectivity between different brain areas. Resting state Functional Magnetic Resonance Imaging (rs-fMRI) is becoming one of the most important imaging modality widely used to understand network functionality. However, due to the variability at subject scale, mapping common networks across individuals is by now a real challenge. In this work we present a novel approach to group-wise community detection, i.e. identification of functional coherent sub-graphs across multiple subjects. This approach is based on a joint diagonalization of two or more graph Laplacians, aiming at finding a common eigenspace across individuals, over which clustering in fewer dimension can then be applied. This allows to identify common sub-networks across different graphs. We applied our method to rs-fMRI dataset of mouse brain finding most important sub-networks recently described in literature.

Automatic White Matter Fiber Clustering Using Dominant Sets

We present an unsupervised approach based on the Dominant Sets framework to automatically segment the white matter fibers into bundles. This framework, rooted in the Game Theory, allows for the automatic determination of the number of clusters from the data itself, without any prior assumption. The clustered bundles are a key information for the generation of unbiased structural connectivity atlases. We have thoroughly validated our algorithm both quantitatively and qualitatively. Indeed, we used biologically plausible synthetic datasets to numerically validate the performance in terms of Precision, Recall and other measures employed in the literature. We also evaluated the algorithm on a real Diffusion Tensor Imaging tractography of a whole mouse brain obtaining promising results. In fact, some of the most prominent brain structures determined by the algorithm correspond to white matter expected anatomy.

Kernel-Based Analysis of Functional Brain Connectivity on Grassmann Manifold

Functional Magnetic Resonance Imaging (fMRI) is widely adopted to measure brain activity, aiming at studying brain functions both in healthy and pathological subjects. Discrimination and identification of functional alterations in the connectivity, characterizing mental disorders, are getting increasing attention in neuroscience community.

Automated multi-subject fiber clustering of mouse brain using dominant sets

Mapping of structural and functional connectivity may provide deeper understanding of brain function and disfunction. Diffusion Magnetic Resonance Imaging (DMRI) is a powerful technique to non-invasively delineate white matter (WM) tracts and to obtain a three-dimensional description of the structural architecture of the brain. However, DMRI tractography methods produce highly multi-dimensional datasets whose interpretation requires advanced analytical tools. Indeed, manual identification of specific neuroanatomical tracts based on prior anatomical knowledge is time-consuming and prone to operator-induced bias. Here we propose an automatic multi-subject fiber clustering method that enables retrieval of group-wise WM fiber bundles. In order to account for variance across subjects, we developed a multi-subject approach based on a method known as Dominant Sets algorithm, via an intra- and cross-subject clustering. The intra-subject step allows us to reduce the complexity of the raw tractography data, thus obtaining homogeneous neuroanatomically-plausible bundles in each diffusion space. The cross-subject step, characterized by a proper space-invariant metric in the original diffusion space, enables the identification of the same WM bundles across multiple subjects without any prior neuroanatomical knowledge. Quantitative analysis was conducted comparing our algorithm with spectral clustering and affinity propagation methods on synthetic dataset. We also performed qualitative analysis on mouse brain tractography retrieving significant WM structures. The approach serves the final goal of detecting WM bundles at a population level, thus paving the way to the study of the WM organization across groups.

Joint laplacian diagonalization for multi-modal brain community detection

In this paper we present a novel approach to group-wise multi-modal community detection, i.e. identification of coherent sub-graphs across multiple subjects with strong correlation across modalities. This approach is based on joint diagonalization of two or more graph Laplacians aiming at finding a common eigenspace across individuals, over which spectral clustering in fewer dimension is then applied. The method allows to identify common sub-networks across different graphs. We applied our method on 40 multi-modal structural and functional healthy subjects, finding well known sub-networks described in literature. Our experiments revealed that detected multi-modal brain sub-networks improve the consistency of group-wise unimodal community detection.