Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Standard

Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. / Erleben, Lene Lillemark; Sørensen, Lauge; Mysling, Peter; Pai, Akshay Sadananda Uppinakudru; Dam, Erik B.; Nielsen, Mads.

Medical Imaging 2013: image processing . red. / Sebastien Ourselin; David R. Haynor. SPIE - International Society for Optical Engineering, 2013. 866926 (Progress in Biomedical Optics and Imaging; Nr. 36, Bind 14).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Erleben, LL, Sørensen, L, Mysling, P, Pai, ASU, Dam, EB & Nielsen, M 2013, Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. i S Ourselin & DR Haynor (red), Medical Imaging 2013: image processing ., 866926, SPIE - International Society for Optical Engineering, Progress in Biomedical Optics and Imaging, nr. 36, bind 14, Medical Imaging 2013, Lake Buena Vista, USA, 10/02/2013. https://doi.org/10.1117/12.2007600

APA

Erleben, L. L., Sørensen, L., Mysling, P., Pai, A. S. U., Dam, E. B., & Nielsen, M. (2013). Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. I S. Ourselin, & D. R. Haynor (red.), Medical Imaging 2013: image processing [866926] SPIE - International Society for Optical Engineering. Progress in Biomedical Optics and Imaging, Nr. 36, Bind. 14 https://doi.org/10.1117/12.2007600

Vancouver

Erleben LL, Sørensen L, Mysling P, Pai ASU, Dam EB, Nielsen M. Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. I Ourselin S, Haynor DR, red., Medical Imaging 2013: image processing . SPIE - International Society for Optical Engineering. 2013. 866926. (Progress in Biomedical Optics and Imaging; Nr. 36, Bind 14). https://doi.org/10.1117/12.2007600

Author

Erleben, Lene Lillemark ; Sørensen, Lauge ; Mysling, Peter ; Pai, Akshay Sadananda Uppinakudru ; Dam, Erik B. ; Nielsen, Mads. / Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI. Medical Imaging 2013: image processing . red. / Sebastien Ourselin ; David R. Haynor. SPIE - International Society for Optical Engineering, 2013. (Progress in Biomedical Optics and Imaging; Nr. 36, Bind 14).

Bibtex

@inproceedings{d786e8cc0bac46779fc4004ef736544a,
title = "Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI",
abstract = "This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using Free Surfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.",
author = "Erleben, {Lene Lillemark} and Lauge S{\o}rensen and Peter Mysling and Pai, {Akshay Sadananda Uppinakudru} and Dam, {Erik B.} and Mads Nielsen",
year = "2013",
doi = "10.1117/12.2007600",
language = "English",
isbn = "9780819494436",
series = "Progress in Biomedical Optics and Imaging",
publisher = "SPIE - International Society for Optical Engineering",
number = "36",
editor = "Sebastien Ourselin and Haynor, {David R.}",
booktitle = "Medical Imaging 2013",
note = "null ; Conference date: 10-02-2013 Through 12-02-2013",

}

RIS

TY - GEN

T1 - Morphometric connectivity analysis to distinguish normal, mild cognitive impaired, and Alzheimer subjects based on brain MRI

AU - Erleben, Lene Lillemark

AU - Sørensen, Lauge

AU - Mysling, Peter

AU - Pai, Akshay Sadananda Uppinakudru

AU - Dam, Erik B.

AU - Nielsen, Mads

PY - 2013

Y1 - 2013

N2 - This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using Free Surfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.

AB - This work investigates a novel way of looking at the regions in the brain and their relationship as possible markers to classify normal control (NC), mild cognitive impaired (MCI), and Alzheimer Disease (AD) subjects. MRI scans from a subset of 101 subjects from the ADNI study at baseline was used for this study. 40 regions in the brain including hippocampus, amygdala, thalamus, white, and gray matter were segmented using Free Surfer. From this data, we calculated the distance between the center of mass of each region, the normalized number of voxels and the percentage volume and surface connectivity shared between the regions. These markers were used for classification using a linear discriminant analysis in a leave-one-out manner. We found that the percentage of surface and volume connectivity between regions gave a significant classification between NC and AD and borderline significant between MCI and AD even after correction for whole brain volume at baseline. The results show that the morphometric connectivity markers include more information than whole brain volume or distance markers. This suggests that one can gain additional information by combining morphometric connectivity markers with traditional volume and shape markers.

U2 - 10.1117/12.2007600

DO - 10.1117/12.2007600

M3 - Article in proceedings

SN - 9780819494436

T3 - Progress in Biomedical Optics and Imaging

BT - Medical Imaging 2013

A2 - Ourselin, Sebastien

A2 - Haynor, David R.

PB - SPIE - International Society for Optical Engineering

Y2 - 10 February 2013 through 12 February 2013

ER -

ID: 169383017