MRI Biomarkers of Patients with Tuberous Sclerosis Complex and Autism Tuberous sclerosis complex (TSC) is an autosomal dominant disease characterized by the presence of benign tumors, called hamartomas, which can affect virtually every organ system of the body, including the brain. The prognosis for individuals with TSC varies in accordance with the severity of the specific symptoms. While severe manifestations may be seen in individuals diagnosed in childhood, mild forms of the disease may be observed in men and women diagnosed in adulthood. The cause of neurological deficits in TSC patients is a key unresolved question. Our key hypothesis is that the development of autistic spectrum disorders (ASD) in TSC patients is a consequence of abnormal white matter development and maturation. This hypothesis is supported by both animal model findings of axonal miswiring and hypomyelination, and studies with TSC patients using diffusion imaging that have identified brain structural changes consistent with aberrant connectivity and loss of myelination. These suggest that adverse cognitive/social/behavioral outcomes may be due to alterations in white matter connectivity and microstructural integrity, not the cortical tubers that are the most obviou brain abnormalities in TSC. TSC is a genetic disorder with a well understood genetic basis for abnormal brain development, for which brain modifying drug therapy is currently available. The ability to characterize brain abnormalities in TSC with and without ASD will be crucial to the development of a drug therapy for ASD in TSC. Our overall objective is to identify the brain changes that are associated with ASD in patients with TSC, by the evaluation of advanced MRI of healthy controls, ASD patients without TSC, and TSC patients with and without ASD. We propose to recruit a cohort of children, aged 5-10 years old, and to carry out comprehensive MRI, image analysis and cognitive phenotyping. We propose to study these children longitudinally for five years. We propose to develop and evaluate a set of quantitative anatomic and diffusion MRI measures that characterize white matter, cortical and subcortical gray matter, and harmatomas. In order to improve the accuracy and reliability of the MRI measures, we will develop novel algorithms for MRI analysis of these subjects building on our own recent work, implement open source software tools to apply these algorithms, and validate these tools in comparison to conventional analysis strategies. We will distribute the imaging data and these software tools to the imaging community. The primary outcome will be the development for the first time of a capability discriminate between controls, patients with ASD without TSC, TSC patients without ASD and TSC patients with ASD.

Public Health Relevance

Tuberous Sclerosis Complex (TSC) is a neurocutaneous autosomal dominant disorder involving mutations of the TSC1 or TSC2 genes. It is characterized by the presence of benign tumors called hamartomas in multiple organ systems, including the brain (where hamartomas are known as cortical tubers). TSC affects people of all ethnic groups, races and sex with equal frequency. For TSC patients, adverse cognitive outcomes are common, especially Autism Spectrum Disorders (ASD) which has an incidence of about 50%. Although micro- structural white matter alterations and macro-structural brain tissue abnormalities, including cortical tubers, are common and apparent with sophisticated MRI, the relationship between these brain changes and neurological symptoms remains unclear. Recent data from animal models of TSC and imaging experiments with patients suggests that axonal miswiring and hypomyelination may contribute importantly to the development of cognitive/social/behavioral deficits and ASD. We seek to identify MRI measures that distinguish healthy controls, patients with idiopathic ASD, TSC patients without ASD and TSC patients with ASD. We will develop and validate advanced MRI measures in a longitudinal study of children aged 5-10 years old. TSC is a genetic disorder for which brain modifying drug treatment is currently available. The development of a validated set of MRI measures that uniquely identifies the brain changes that underlie ASD in TSC will be critical enabling technology for drug trials in TSC, and for evaluating response to drug therapy. By imaging from an early age, before brain maturation is complete, it may be possible to predict, for an individual patient, an increased risk of development of ASD, and ultimately to tailor interventions to alter the developmental trajectory.

National Institute of Health (NIH)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Mamounas, Laura
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Children's Hospital Boston
United States
Zip Code
Taquet, Maxime; Scherrer, BenoƮt; Peters, Jurriaan M et al. (2014) A fully Bayesian inference framework for population studies of the brain microstructure. Med Image Comput Comput Assist Interv 17:25-32
Akhondi-Asl, Alireza; Afacan, Onur; Mulkern, Robert V et al. (2014) T(2)-relaxometry for myelin water fraction extraction using wald distribution and extended phase graph. Med Image Comput Comput Assist Interv 17:145-52
Suarez, Ralph O; Taimouri, Vahid; Boyer, Katrina et al. (2014) Passive fMRI mapping of language function for pediatric epilepsy surgical planning: validation using Wada, ECS, and FMAER. Epilepsy Res 108:1874-88
Taquet, Maxime; Scherrer, Benoit; Commowick, Olivier et al. (2014) A mathematical framework for the registration and analysis of multi-fascicle models for population studies of the brain microstructure. IEEE Trans Med Imaging 33:504-17
Akhondi-Asl, Alireza; Hoyte, Lennox; Lockhart, Mark E et al. (2014) A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights. IEEE Trans Med Imaging 33:1997-2009
Suinesiaputra, Avan; Cowan, Brett R; Al-Agamy, Ahmed O et al. (2014) A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images. Med Image Anal 18:50-62
Akhondi-Asl, Alireza; Warfield, Simon K (2013) Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. IEEE Trans Med Imaging 32:1840-52