Tag: Software

Lateral ventricles are reliable and sensitive indicators of brain atrophy and disease progression in behavioral variant frontotemporal dementia (bvFTD). VentRa takes a comma separated (.csv) file providing the path for the raw T1-weighted images as well as age and sex of the subjects as input, and provides preprocessed images along with ventricle segmentations, QC files for the segmentations, as well as a .csv file including the diagnosis (based on the classifier trained on bvFTD vs the mixed group data) along with all the extracted ventricle features: i.e. total ventricle volume, ventricle volumes in each lobe and hemisphere,anterior-posterior ratio (APR), left-right temporal lobe ratio (LRTR), and left-right frontal ratio (LRFR).

VentRa Tool

For more details, see:

Ana L. Manera,  Mahsa Dadar, D. Louis Collins, Simon Ducharme, “Ventricle shape features as a reliable differentiator between the behavioral variant frontotemporal dementia and other dementias”, arXiv. https://arxiv.org/abs/2103.03065

Neurodegenerative diseases such as Alzheimer’s disease (AD) commonly coexist with cerebrovascular disease in the elderly population. Cerebral small vessel disease (SVD) is the most common vascular cause of dementia and a major contributor to mixed dementia. SVD frequently coexists with AD and can increase the cognitive and physical deficits caused by neurodegeneration. White matter hyperintensities (WMHs) are considered to be one of the major signs of SVD on MRI and are associated with neurological and cognitive symptoms and physical difficulties. We have developed automated tools for segmentation of WMHs in Alzheimer’s patients using multiple contrasts of MR images­­. We use these segmentations to study the effect of WMHs in AD.

WMH Segmentation Pipeline:

https://www.dropbox.com/sh/zbbqjjo1ilzuun2/AABWN17N2fyzi8p3aSfiA0fEa?dl=0

Docker Container:

docker pull nistmni/wmhchallenge_2017

Execution method:

     CONTAINERID=$(docker run -dit --name wmh_test -v
      $(pwd)/Docker_Files/${input}/orig:/input/orig:ro -v
      $(pwd)/Docker_Files/${input}/pre:/input/pre:ro -i -v /output
    nistmni/wmhchallenge_2017 )
      docker exec $CONTAINERID "/home/nistmni/run.sh"
      docker cp $CONTAINERID:/output result
      docker stop $CONTAINERID
      docker rm -v $CONTAINERID

Journal Publications:                                                                                                                                      

1. Dadar, M., Maranzano, J., Ducharme, S., Collins, D. L., & Alzheimer’s Disease Neuroimaging Initiative. (2019). “White Matter in Different Regions Evolve Differently During Progression to Dementia”. Neurobiology of Aging.
2. Dadar M., Zeighami Y., Yau Y., Fereshtehnejad S.M., Maranzano J., Postuma R.B., Dagher A., Collins D.L. (2018), “White Matter Hyperintensities Are Linked to Cognitive Decline in de Novo Parkinson’s Disease Patients”. NeuroImage: Clinical, 20, 892-900.
3. Dadar, M., Fonov, V. S., Collins, D. L., & Alzheimer’s Disease Neuroimaging Initiative. (2018). A comparison of publicly available linear MRI stereotaxic registration techniques. NeuroImage, 174, 191-200.
4. Dadar, M., Maranzano, J., Ducharme, S., Carmichael, O. T., Decarli, C., Collins, D. L., & Alzheimer’s Disease Neuroimaging Initiative. (2018). Validation of T 1w‐based segmentations of white matter hyperintensity volumes in large‐scale datasets of aging. Human brain mapping, 39(3), 1093-1107.
5. Dadar, M., Maranzano, J.,…, Collins, D.L. & Alzheimer’s Disease Neuroimaging Initiative. (2017). Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging. NeuroImage, 157, 233-249.
6. Dadar, M., Pascoal, T. A., Manitsirikul, S., Misquitta, K., … & Collins, D. L. (2017). Validation of a regression technique for segmentation of white matter hyperintensities in Alzheimer’s disease. IEEE transactions on medical imaging, 99, 1-1.