Image at Imagine
- Jean-Marc Tacchella
- Elza Rechtman
- Volodia Dangouloff-Ros
- Jennifer Boisgontier
- Monica Zilbovicius
- Francis Brunelle
- Hervé Lemaître
- Ana Saitovitch
- Raphael Calmon
Volodia Dangouloff-Ros. New algorithm using Arterial spin labeling (ASL) to classify brain tumors in children: First correlations using quantitative MRI perfusion and neuropathological data. Radiology. March 2016.
Saitovitch A. Tuning Eye-Gaze Perception by Transitory STS Inhibition. Cereb Cortex. 2016.
BRICOUT M, Brain imaging in mitochondrial respiratory chain deficiency: combination of brain MRI features as a useful tool for genotype/phenotype correlations. J Med Genet. 2014 51(7):429-435
Boddaert N. MRI findings in 77 children with non-syndromic autistic disorder. PLoS ONE. 2009;4:e4415.
Zilbovicius M. Autism, the superior temporal sulcus and social perception. Trends Neurosci. 2006 Jul;29(7):359-66.
In the recent past, the team has focused on the brain architecture of developmental diseases using non-invasive multimodal brain imaging techniques. This approach is now a requisite to determine brain phenotypes of rare and developmental diseases, and hence, their genetic architecture.
Our research team’s goal is to implement innovative anatomical and functional multimodal brain imaging methods for studying brain diseases in children and teenagers.
During the last decade, our team has identified brain abnormalities in autism and recognized brain imaging patterns leading to discovery of new mutations responsible for several neurogenetic diseases and metabolic encephalopathies. We are also engaged in the research of mastocytosis, childhood epilepsy, and pediatric brain tumors.
BRIEF OUTLINE OF THE PROJECTS :
- Characterization of radiological phenotype of pediatric genetic diseases using multimodal brain mapping
- Correlation of the radiological phenotypes with genotypes
- Investigation of the pathophysiology and natural history of selected diseases
- Application of brain imaging techniques to the monitoring of new treatments in clinical trials
- Optimization of candidate gene studies using an in-house multimodal database of clinically and radiologically homogeneous sub-groups of patients with encephalopathy and or mental retardation
- Radiogenomic of cerebral tumor.
1. Brain Imaging in neurometabolic and genetic developmental disorders
We have investigated phenotype/genotype correlations between well-known genetic entities and brain imaging patterns such as mitochondrial diseases (ND5, SENDA, Pla2G6), encephalopathies (NBIA), epilepsies (KCNT1), cerebellar diseases (Joubert syndrome [RPGRIP1L], ponto- cerebellar hypolasia [CASK], cerebellar dysplasia[OPHN1]), and in abnormal brain gyration (TUBA1A, TUBB2B). Using multimodal brain imaging, we’ve contributed to the delineation of novel clinical entities such as: defective fatty acid 2 hydroxylase (Fa2H) a neurodegenerative disorders with brain iron accumulation (NBIA), mitochondrial disorders (NUBPL), Ravine syndrome (non coding RNA). We have contributed to unravel the natural history of genetic diseases and to the monitoring of the first clinical trials using brain imaging (Freidreich ataxia and deferiprone). Finally, we have constructed algorithms using neuroimaging features to direct molecular genetic analyses (eg, brain iron accumulation and cerebellar ataxia).
Neurometabolic disorders :
The main goals are :
- To characterize the radiological phenotypes using multimodal high field MRI in patients with neurometabolic disorders
- To correlate genotypes/radiological multimodal MRI phenotypes in patients with neurometabolic disorders with known disease mutations.
- To constitute clinically and radiologically homogeneous sub-groups of mentally retarded/encephalopathy patients for genetic studies and candidate gene approach using a multimodal database.
- To describe the natural history of the disease images.
- To develop multimodal MRI as an endpoint of future clinical trials.
The mast cell proliferation and abnormal activation in mastocytosis result from somatic de novo mutations of the c-kit receptor. We hypothesize that mastocytosis disrupts the brain function and may explain the presence of abnormally frequent neuro-psychiatric symptoms in this disease. Our project for the next 4 years is :
- To characterize anatomo-functional brain abnormalities using multimodal MRI.
- To correlate neuropsychiatric symptoms of mastocytosis with anatomo-functional anomalies described by multimodal MRI.
- To perform clinical trials using multimodal MRI as a marker of the efficacy of new therapeutic strategies and measure the effectiveness of cytoreductive treatments.
- To establish a new “mast cell signature” at the brain level, which may be useful to evaluate diagnosis and treatment of mastocytosis.
Childhood epilepsy :
This project aims at providing accurate delimitation of the epileptogenic region and/or lesion to be resected by neurosurgery in refractory childhood epilepsies. This is not well done by anatomical MRI. Therefore we shall use functional MRI with non invasive ASL (arterial spin labeling) to measure quantitative cerebral blood flow (CBF). Presently, the gold standard is PET (used to detect the hypometabolism of the epileptogenic zone but with radioactive tracer). With the same aim, high resolution EEG with dipole source localization is under development with encouraging results in adults. Our project is :
- To validate CBF measurements with MRI and ASL methodology as compared to PET.
- To validate the use of High Resolution EEG coupled to MRI/ASL.
- To find correlations between radiological phenotypes and genotypes in childhood epilepsy (eg, Dravet (SCN1A mutation), Munc18 (STXBP1 mutations) etc).
- To develop computer-assisted epilepsy localization using dynamic texture analysis (cf project 1).
Paediatric brain tumors :
Our project is :
- To look for correlation between genomic, histopathological and MRI features in pediatric brain tumors (location, perfusion, diffusion and spectroscopy)
- To perform clinical trials using multimodal MRI with diffusion, perfusion and ASL as markers of the efficacy of novel therapies (such as Bevacizumab, Convection Enhancement Therapy…).
- To understand the local impact of a brain lesion and its repercussion on the whole brain using multimodal MRI through the associative fibers (ASL, tractography, functional MRI). Our research includes the cognitive impact of temporal and posterior fossa arachnoid cysts as well as the predictive factors for akinetic mutism in posterior fossa tumors by using multimodal MRI and eye-tracking prior to and after surgery.
2. Brain Imaging in Autism
Identifying anatomo-functional brain anomalies is a great challenge in understanding autism. Using multimodal brain imaging, we showed the existence of anatomical and functional abnormalities of the superior temporal sulcus (STS) in autism. The STS is known to be a critical region for social cognition. It is implicated in several steps of social interactions: auditory and visual social perceptions (eye gazes and voice perception) and more complex social cognition processes. The main results obtained in autism are: i) At rest, we have described for the first time a localized cortical anomaly of the STS using PET and MRI; the degree of STS abnormality was correlated with autism clinical severity; ii) Using functional MRI, we have reported an absence of activation of the STS voice area iii) In addition, we described an unexpectedly high rate (40%) of MRI abnormalities, mainly localized in the temporal area, illustrating the importance of including MRI studies in clinical evaluation of autism.; iiii) Using multivariate classification of PET images, we have recently shown that it is possible to classify images of children with autism according to diagnosis (sensitivity of 88%, specificity of 75%, correct classification rate of 86%). This suggests that rest cerebral blood flow (CBF) images may be a biomarker of autism.
Based on our previous results showing STS anomalies in autism, our research project are :
- To use MRI/ASL- cerebral blood flow images as a biomarker of autism. Using multivariate classification of PET images of cerebral blood flow (CBF), we have recently shown that it is possible to classify images of children with autism according to diagnosis (Duchesnay, 11). We want to reproduce this results using MRI-ASL that could replace the PET.
- To use eye tracking to perform quantitative evaluation of social cognition in children with autism. Eye-tracking is a non-invasive method giving accurate information about how the subject has access to visual stimuli in a given social situation. Our goal is to quantify the differences in social perception between autistic children and controls. In addition, we shall study the impact of the severity of autism in social perception. Finally, social perception parameters will be correlated to anatomical and functional MRI data.
- To document the involvment of the STS in social perception with rTMS (repetitive transcranial magnetic stimulation). rTMS is a non-invasive and painless technique used in both cognitive and therapeutic research in neuropsychiatric disorders. It consists on applying a magnetic pulse to the brain through the skull by placing a coil on the surface of the head. These magnetic fields induce an electric field, modifying activity of neurons within the target area, in an artificial and transient way. This allows interacting with human brain in action. We want to use rTMS to induce changes in social cognitive performance in order to better clarify the role of the STS region. This will be done by exciting or inhibiting the STS in healthy subjects and in subjects with autism. During each stimulation / inhibition, the impact on social cognition is measured using two parameters: the perception of faces, as measured by the eye-tracking method and the perception of the human voice. rTMS may become an innovative therapeutic strategy in autism.
- To implement a multidisciplinary database. We are currently implementing a state-of-art data-base (presently 300 autistic children) allowing storage for neuroimaging, genetic and clinical data. The overall objective of this data-base is to provide a multidisciplinary infrastructure for large-scale clinical studies that combine genomic, neuroimaging and clinical data.