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Velo-Cardio-Facial Syndrome

Velo-Cardio-Facial Syndrome. PRINCIPAL INVESTIGATOR: Marek Kubicki, MD, PhD INVESTIGATORS: Zora Kikinis, PhD Sylvain Bouix, PhD Marc Niethammer, PhD Martha Shenton, PhD Christine Finn, MD Raju Kucherlapati, MD RESEARCH ASSISTANT: Kate Smith, BA. Velo-Cardio-Facial Syndrome (VCFS).

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Velo-Cardio-Facial Syndrome

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  1. Velo-Cardio-Facial Syndrome PRINCIPAL INVESTIGATOR: Marek Kubicki, MD, PhD INVESTIGATORS: Zora Kikinis, PhD Sylvain Bouix, PhD Marc Niethammer, PhD Martha Shenton, PhD Christine Finn, MD Raju Kucherlapati, MD RESEARCH ASSISTANT: Kate Smith, BA

  2. Velo-Cardio-Facial Syndrome (VCFS) Deletion of the fragment of short arm of the 22nd chromosome (single copies of 30 to 45 genes missing) Prevalence 1 in every 4000 newborns Clinical symptoms: "velum" latin meaning soft palate “kardia" greek meaning heart "facial" latin having to do with the face Cognitive symptoms: Psychomotor deficits Learning and memory disabilities Emotional abnormalities (flat affect and poor social interaction). High incidence for schizophrenia and/or bipolar disorder in adult(30%VCFS patients developschizophrenia), and 4-5 genes have been suggested to be related to schizophrenia (COMT- attention, memory, prefrontal function; RTN4R- axonal regeneration and plasticity)

  3. Aims Etiology of schizophrenia and related diseases Prognosis of mental health diseases in VCFS Early intervention

  4. Project Subject recruitment Psychological interview DNA analysis, genotyping of the 22q11.2 region Brain imaging (MRI, DTI and fMRI) Analysis of imaging data, and genetic correlations

  5. Imaging Data Available Two stages: Available now: 7 VCFS and 7 matched control cases scanned on 1.5 T magnet (DTI and structural scans) 15 schizophrenics and 15 control cases scanned on 3T magnet (DTI and fMRI) plus 1.5T chronic schizophrenia data (DTI and structural scans) Available by the end of the year: 15 VCFS and 15 matched control cases scanned on 3T magnet (DTI, structural scans and fMRI)

  6. Hypotheses • Regions that we want to study with MRI • DLPC (executive function, memory) • Orbital Frontal Gyrus (emotion) • Cingulate Gyrus (attention, emotion) • Hippocampus (memory, learning) • Tracts that we want to study with DTI (frontal-temporal connections): • Fornix (memory) • Arcuate Fasciculus (language) • Cingulum Bundle (attention) • Uncinate Fasciculus (emotion, affective flattening) • Networks that want to study with fMRI : • Memory, attention, emotion, language (semantic processing)

  7. Specific Requirements • Automatic DLPC segmentation. • Automatic segmentation of other structures (orbital frontal cortex, cingulate gyrus, hippocampus). • Tools for measuring anatomical connectivity between these regions (optimal path analysis, stochastic tractography). • “Default network” and “rest” fMRI analysis. • Anatomical (DTI), and functional (fMRI) connectivity analysis.

  8. Progress So Far • Automatic DLPC segmentation. • John and Brad finished the slicer2 module, its being tested and used for first episode schizophrenia study now. • Automatic segmentation of other structures (orbital frontal cortex, cingulate gyrus, hippocampus). • We have limited experience with freesurfer and cingulate, have not tried Killian’s segmentation for anything other than STG. • Tools for measuring anatomical connectivity between these regions (optimal path analysis, stochastic tractography). • Optimal Path Analysis- we used it, but its not yet in the slicer, there might be some problems with it. Same with Stochastic tractography. • “Default network” fMRI analysis, functional connectivity analysis. • Polina and Sandy might have some tools, but we have not tried them yet.

  9. Potential Challenges for Segmentation/Registration • Different brain shape • Thicker skull • Brain atrophy • Congenital abnormalities • Brain asymmetry • White matter lesions

  10. Segmentation/Registration Beneficial algorithms/software: • Automatic segmentation procedures tailored for VCFS (atlas?). • Automatic parameterization of segmentation algorithm (e.g., optimal parameter selection for EM segmenter based on training data). • Robust and easy to use registration. Capabilities for batch processing.

  11. VCFS Schizophrenia Candidate Genes COMT (controls dopamine degradation in prefrontal cortex, related to attention and memory) RTN4R(also known as Nogo 66, related toaxonal regeneration and plasticity) PRODH ZDH8 SNAP29 TBX1

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