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  • PI: Marek Kubicki, MD, PhD, Contact: kubicki at bwh.harvard.edu
  • NA-MIC Engineering Contact: Brad Davis, Kitware
  • NA-MIC Algorithms Contact: Polina Golland, MIT
  • Affiliation/Institution: Harvard Medical School, Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital
  • Science: VCFS is a genetic disorder characterized by a deletion of a small piece of chromosome-22. The features of this syndrome include deficits in neurological psychomotor and perceptual skills, as well as in cognitive domains such as learning and memory. Most importantly, up to 30% of VCFS patients develop schizophrenia, making it the most commonly known single risk factor for the development of psychosis and a unique model for studying neurodevelopmental changes leading to psychotic deficits. We plan to collect new, high resolution DTI, structural and fMRI data, and apply existing NAMIC tools, as well as help to develop new tools to investigate the contribution of genetic variation to brain and behavioral/cognitive abnormalities, thus bridging the gap between neuroimaging studies and genetics.
  • Benefits to NA-MIC: The NAMIC community will gain access to de-identified imaging data (new, high resolution structural and diffusion data acquired on the 3T magnet at Brigham and Women’s Hospital). Unlike in schizophrenia, subjects with VCFS have concrete cognitive abnormalities, in addition to a well defined chromosomal abnormality, which, taken together, will make it easier to establish scientific protocols that reveal associated anatomical and functional brain abnormalities in this disorder. Interestingly, some anatomical abnormalities will be shared between VCFS and schizophrenia (e.g., connections within working memory circuits), and some will be not (e.g., sensory and motor paths). Neuropsychological and genetic data will also be collected for each individual as part of separate collaboration between PI and the Children's Hospital("Investigation of Genotype/Phenotype Correlations in Velocardiofacial and DiGeorge Syndromes”) and de-dentified dataset containing neuropsychological tests, clinical evaluations and genetic data will be provided to the PI. The NAMIC project with thus enable the PI to apply NAMIC tools to imaging VCFS data, a genetic disorder that is viewed as a genetically mediated subtype of schizophrenia. To date, there have only been a small number of neuroimaging studies of this disorder and no studies have combined neurocognitive, neuroimaging, and genetic investigations in the same study. Importantly, this research will also increase our understanding of schizophrenia, and will help establish a multimodal research project involving an important collaboration between computer scientists, cognitive neuroscientists, radiologists, psychiatrists, and geneticists. The focus on imaging and genes also affords a new window of opportunity for defining further the new area of “imaging genomics”.

Research Goals

Clinical and Scientific Relevance: The main goal of this application is to characterize “intermediate phenotypes”, such as anatomic and functional abnormalities in the brain of patients with velocardiofacial syndrome (VCFS), and to link this information with cognitive/behavioral deficits in schizophrenia and with genetic variations. This area of research represents a new frontier for making schizophrenia more tractable, as well as for shedding new light on the etiology of this devastating disorder.

We propose to study patients with VCFS, a genetic disorder characterized by a deletion of a small piece of chromosome-22, in order to understand this disorder further as well as to evaluate VCFS as a genetic model for schizophrenia. The features of this syndrome include, among other things, deficits in neurological psychomotor and perceptual skills, as well as cognitive deficits in learning and memory. Importantly, up to 30% of VCFS patients develop schizophrenia, making it the most commonly known single risk factor for the development of psychosis and a unique model for studying neurodevelopmental changes leading to psychotic deficits. The deletion region contains about 30 to 40 genes, but only 3- PRODH, RTN4R and COMT have thus far been related to schizophrenia. COMT (catecholamine-O-methyl transferease) is a gene that is important for the catabolism of dopamine and maintaining appropriate levels of dopamine in the human brain, and several studies have associated this gene, or rather different COMT genotypes, with dorso-lateral prefrontal cortex and working memory, which are known to be disrupted in schizophrenia. RTN4R (known also as No-Go-66 receptor) gene, on the other hand, is much less studied than the COMT gene, but might be equally important in studies investigating schizophrenia. This gene mediates axonal growth inhibition and may play a role in regulating axonal regeneration and plasticity in the adult central nervous system. Since dopamine regulation, as well as neuronal plasticity, are both investigated in schizophrenia, VCFS seems to be the perfect genetic model for schizophrenia.

We propose to combine genetic data and sophisticated neuroimaging data analysis. Gray matter regions previously implicated in both VCFS and schizophrenia research (frontal and temporal regions), as well as regions more commonly observed in schizophrenia (medial temporal and subcortical structures), will be delineated and analyzed including shape analysis. In addition, fiber tracts that show abnormalities in schizophrenia, such as the fornix, the cingulum bundle, the uncinate fasciculus, the corpus callosum (also implicated in VCFS studies), and the internal capsule, will be investigated using Diffusion Tensor Imaging, a technique sensitive to white matter fiber integrity. In addition, statistical as well as anatomical relationships between gray and white matter pathology, including anatomical connections between gray matter areas affected in VCFS and schizophrenia and cognitive and clinical symptoms will also be evaluated.

NAMIC will have access to 15 scans of VCFS patients per year for a total of 30 subjects over the two-year grant period. Half of these subjects will have severe psychiatric symptoms, with a diagnosis of schizophrenia or schizophrenia-like symptoms, and half will have either no psychiatric symptoms or minimal symptoms and no Axis I disorder determined by standardized diagnostic interview. While not part of this project, we will have access to patients with schizophrenia and controls from a larger study of schizophrenia. We thus plan to evaluate N=30 control group, which will consist of healthy controls and schizophrenia subjects without VCFS, matched for age, gender, handedness, parental socio-economic status and IQ. The later control group is important, since most schizophrenia studies exclude low IQ participants, thus results of majority of schizophrenia studies might not be comparable with our VCFS population, which is characterized by IQ below normal.

The specific aims of this project are:

  • To detect and localize possible gray and white matter disruptions common to VCFS and schizophrenia.
  • To investigate the relationship among anatomical and between anatomical and functional abnormalities (later measured by clinical symptoms and neuropsychological tests).
  • To determine the extent to which anatomical as well as functional abnormalities observed in schizophrenia and anatomical and functional differences between VCFS patients with and without schizophrenia can be explained by VCFS genotype. (latter involving genetic haplotype analysis)

Data description

What kind of image data is associated with these goals (modality, resolution, quantity)?

To date, DTI and Structural Data on the 1.5Tesla magnet has been collected for 6 VCFS patients. Children's Hospital obtained the IRB approval to share deidentified imaging data with NAMIC community, as well as another IRB to rescan these subjects, and to start collecting further prospective data on 3Tesla magnet, where the following scan parameters will be used:

  • Diffusion Tensor MRI: DTI scans will be acquired from a 3 Tesla GE system (General Electric Medical Systems, Milwaukee, WI) using an echo planar imaging (EPI) DTI Tensor sequence. We will use a double echo option in order to reduce eddy-current related distortions (Heid 2000; Alexander 1997). To reduce the impact of EPI spatial distortion, we will use an 8 Channel coil that will allow us to perform parallel imaging using ASSET (Array Spatial Sensitivity Encoding Techniques, GE) with a SENSE-factor (speed-up) of 2. We will acquire 51 directions with b=900, 8 baseline scans with b=0. The original GE sequence has been modified, in order to increase spatial resolution, and to further minimize image artifacts. The following scan parameters will be used: TR 17000 ms, TE 78 ms, FOV 24 cm, 144 x 144 encoding steps, 1.7-mm slice thickness. We will acquire 81 axial slices covering the whole brain. The total scan time for the sequence will be 17 minutes. After reconstruction, the diffusion-weighted images will be transferred to Linux workstations, where eigenvalues, eigenvectors and anisotropy indices of the diffusion tensor will be calculated.
  • Structural MRI: For the Structural MRI volume and shape measurements, images will be acquired also using a 3T GE scanner (GE Medical Systems, Milwaukee). The acquisition protocol will include two MRI pulse sequences. The first sequence results in contiguous spoiled gradient-recalled acquisition (FastSPGR) with the following parameters; TR=7.5ms, TE=3ms, 15 degree flip angle, 25.6cm2 field of view, NEX=1.0, matrix=256x256. Similar to DTI, we will use an 8 Channel coil that will allow us to perform parallel imaging using ASSET (Array Spatial Sensitivity Encoding Techniques, GE) with a SENSE-factor (speed-up) of 2. The voxel dimensions are 1x1x1 mm. Data will be formatted in the axial plane and analyzed as 186 axial slices, and a total scan time is 4 minutes. The second acquisition sequence (Fast Spin Echo- FSE) produces an axial series of contiguous T2-weighted images (TR=6200ms, TE=103ms, Echo Train Length=16; 25.6cm2 field of view, interleaved acquisitions with 2.0mm slice thickness). The voxel dimensions are 1x1x2.0 mm. This latter pulse sequence is used to measure the volume of the total intracranial contents, used then as an independent factor in a regression procedure to account for the effect of head/brain size.

What other kinds of data are involved (non-image)?

For all the subjects, we are also currently collecting:

  • Neuropsychological and Cognitive Data: VCFS patients will be administered a standard battery of cognitive measures in major domains thought to be affected in VCFS and schizophrenic populations, including: general intellectual functioning, verbal intelligence and language functioning, working memory, visuo-spatial and perceptual functioning, executive functioning, and attention among others. In addition, psychiatric diagnostic evaluations will be carried out on all VCFS subjects, and include standardized assessment of general psychiatric symptoms with the Structured Clinical Interview for DSM-IV Axis I (SCID-I) as well as specific measures of positive and negative symptoms of psychosis and measures of schizotypy. It is expected that structural brain abnormalities will correlate with the presence or absence of psychiatric diagnoses in VCFS subjects (e.g., midline defects like cavum septum pellicidum/vergae or enlarged lateral ventricles in psychotic VCFS subjects) and various cognitive measures (e.g., impairment in executive functioning and attention correlated with prefrontal lobe cortex anomalies; arithmetic ability correlated with inferior parietal lobe anomalies; and verbal memory correlated with left superior temporal gyrus, and left amygdale-hippocampal complex).
  • Genetic Measures: As testing every putative genetic variant in every candidate gene that may contribute to the VCFS/DGS phenotype would be prohibitive in terms of time and money, haplotype based approaches offer an alternative. These approaches have contributed to the identification of Mendelian disease genes and are now beginning to help identify susceptibility genes for common, complex diseases. The International HapMap Consortium is constructing genome-wide maps of sequence variation that may facilitate large association studies (The International HapMap Consortium:http://www.hapmap.org/). It is predicted that these approaches will be a powerful tool for common diseases like schizophrenia that are thought to result from a small number of common variants (“common disease-common variant” hypothesis). We propose a pilot investigation of the role of genetic variation found in the 22q11-deleted region using “tag” SNPs that define the haplotype map of this region. Using around two tag SNPs per haplotype block we expect that we will genotype around 160 SNPs for subsequent association studies. Although genes outside 22q11 may have a modifying effect on the phenotypic spectrum, our current focus is on genes within the 22q11 region because we feel it is important to first assess the role of the genes within the 3Mb region of deletion. Once genotyping data are assembled, we will use a case-control study design to determine if any of the genes in the commonly deleted 3Mb region are associated with neuropsychiatric phenotypes. Specifically, we will use the identified tag SNPs to genotype a set of cases (VCFS patients with psychiatric symptoms) and a set of controls (VCFS patients without psychiatric symptoms). Neurocognitive measures, or anatomical abnormalities will also be considered as phenotypes of interest. Both single SNP and haplotype analyses will be undertaken.

What amount of data has been collected to date and what will be collected in the next 12-24 months?

As mentioned above, thus far we have collected data from six VCFS patients, and the same number of matched controls. We plan to rescan these subjects on a 3Tesla magnet as soon as possible. We plan to scan 15 patients per year over the 2 year grant period.

What information would you like to be able to extract from these images that you are currently unable to?

Higher scan resolution and a specific focus on automated measures of brain regions of interest in VCFS, as well as Diffusion Tensor Tractography measures, will allow us to detect more subtle brain abnormalities in patients with VCFS with and without schizophrenia. More specifically, in terms of structural imaging, we are hoping to be able to use reliable automatic brain segmentation, ROI parcellation, and surface model and shape analysis that would be immune to some of the inherent anatomical differences between populations. In terms of diffusion imaging, we are hoping that high resolution diffusion scans will allow us to visualize and to quantify fiber tracts that we were not able to define using low resolution scans. Also, since some of the fiber bundles interconnect multiple, functionally independent brain regions, we are hoping to be able to separate these bundles from the main fiber tracts, and quantify them using new DTI tools developed by our collaborators on the U54. Also, since fiber crossings are currently limiting our ability to track some of the long association fiber bundles, we are hoping to overcome this limitation with the help of Cores 1 and 2.

Image Processing Needs

What kinds of image processing tasks are needed?

  1. Structural Data Analysis:
    • Brain Tissue Classification: Standard segmentation algorithms, may not work optimally on the VCFS data set as the shape of the head and brain may be quite different from a normal brain. We will need help to build a new atlas specific to this population.
    • Cortical Gray Matter Parcellation: A fully automated algorithm which can perform neuroanatomical structures parcellation would be ideal. Principal regions of interests are temporal lobe, sub cortex and frontal lobe.
    • Cortical Surface Extraction: We would like to have the ability to extract two well-formed surfaces (i.e., no holes or handles), one representing the interface between gray and white matter and one the interface between gray matter and cortico-spinal fluid.
    • Morphometric Analysis Tools: Although volume differences are still central to neuroimaging analysis, we wish to further explore the shape of the anatomical structures. Shape measures that are intuitive and easy to interpret yet robust and powerful statistically are desired. Also a full cortical thickness measurement analysis tool would be extremely useful.
  1. Diffusion Data Analysis:
    • Preprocessing Data: The 3T diffusion weighted EPI images that we acquired display a number of undesirable features. They are noisy, are very sensitive to motion artifacts and are distorted due to susceptibility artifacts. Tools to correct the image distortions and smooth the data are needed.
    • Fiber Tractography: The current fiber tractography tools available to us do not allow us to easily do full brain tractography and efficiently display of the tracts. Better seeding strategies and more memory and CPU efficient algorithms for visualization are needed. In addition, in order to visualize, and to quantify some of the long association fiber tracts, a method to solve fiber crossings is required.
    • DWI to DWI Registration: current techniques based on scalar value data to perform linear and non linear DWI to DWI (or DTI to DTI) coregistration have not led to satisfactory results. We would like a tool to allow us to perform inter and intra subject diffusion data registration.
    • White Matter Atlas: Population specific unbiased white matter atlas generation would be greatly helpful to segment and analyze diffusion data.
    • Connectivity Analysis: given network of neuroanatomical grey matter structures, one would ideally be able to measure the “strength” of the fiber bundles connecting them. Current optimal path tracking techniques have shown promising results but are still in early development stages. Having the ability to quantify the properties of a connection between two or more regions of the brain would be of great value.
  1. Data Fusion:
    • Multi-modality Non-linear Registration: We would like to be able to coregister all structural data with the diffusion data so that we can better correlate diffusion and morphometry.

The State of Our Image Processing Technology

  • Tools We Are Using: Our main tool to analyze our data is the 3D slicer. We use it for tissue classification, registration, filtering, manual drawing and diffusion data analysis. Our main file format is NRRD and we use its associated software suite to manipulate our data. We also use Matlab for quick tests of ideas and shell scripts for batch processing.
  • Successes and Shortcomings:
    • The analysis of structural data has been successful, both for segmentation, registration and manual drawing. We are missing a cortical surface extraction algorithm so that we can perform cortical thickness measurement. Shape analysis tools are also lacking in Slicer although they exist as outside programs.
    • The DTMRI module in the slicer is one of the most advanced tools we have come across for the analysis of diffusion imaging. It allows us to do many types of analyses. It has a few shortcomings. One is the inability to handle very large numbers of fibers, and another is the lack of robust DWI to DWI registration. Optimal path analysis and stochastic tractography can be run outside the slicer, but these tools are not yet incorporated in the slicer.

What Will We Do with the NA-MIC Kit?

What are our plans with 3D Slicer, version 3?

We will continue to use Slicer for most of our processing steps, for both structural, as well as diffusion data analysis. We are hoping that some of the methods we are currently using outside of slicer will become part of slicer 3 (i.e., cortical thickness analysis, shape analysis tools, DWI registration, optimal path analysis and stochastic tractography, to name just a few).

In What Way and in What Direction Will Our Research Drive NA-MIC?

We will continue to ask clinical questions, with the hope that the technology that is now lacking will be developed to answer them. NA-MIC has already demonstrated that most of the tools are efficient in detecting subtle anatomical abnormalities in schizophrenia, and now, by introducing another, overlapping but different population, with new scans and new questions, we will introduce new post processing challenges that will further refine and hopefully make NA-MIC tools more reliable and robust.

What Do We Hope to Gain From This Project?

We are hoping to get access to new, exciting processing tools that would allow us to test our main clinical hypothesis: to what extent anatomical as well as functional abnormalities observed in schizophrenia and anatomical and functional differences between VCFS patients with and without schizophrenia can be explained by VCFS genotype.