English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Meyer, S., Mueller, K., Stuke, K., Bisenius, S., Diehl-Schmid, J., Jessen, F., et al. (2017). Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. NeuroImage: Clinical, 14, 656-662. doi:10.1016/j.nicl.2017.02.001.

Item is

Files

show Files
hide Files
:
Meyer_Mueller_2017.pdf (Publisher version), 2MB
Name:
Meyer_Mueller_2017.pdf
Description:
-
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-
License:
-

Locators

show

Creators

show
hide
 Creators:
Meyer, Sebastian1, Author
Mueller, Karsten1, Author           
Stuke, Katharina2, Author           
Bisenius, Sandrine2, Author           
Diehl-Schmid, Janine3, Author
Jessen, Frank4, Author
Kassubek, Jan5, Author
Kornhuber, Johannes6, Author
Ludolph, Albert C.5, Author
Prudlo, Johannes7, 8, Author
Schneider, Anja9, Author
Schümberg, Katharina2, Author           
Yakushev, Igor10, Author
Otto, Markus5, Author
Schroeter, Matthias L.2, 11, Author           
The FTLDc Study Group, Author              
Affiliations:
1Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Departments of Psychiatry and Psychotherapy, TU Munich, Germany, ou_persistent22              
4Department of Psychiatry and Psychotherapy, University Bonn, Germany, ou_persistent22              
5Department of Neurology, Ulm University, Germany, ou_persistent22              
6Department of Psychology and Psychotherapy, Friedrich Alexander University Erlangen, Germany, ou_persistent22              
7Department of Neurology, University Medicine Rostock, Germany, ou_persistent22              
8German Center for Neurodegenerative Diseases, Rostock, Germany, ou_persistent22              
9Department of Psychiatry and Psychotherapy, Georg August University, Goettingen, Germany, ou_persistent22              
10Department of Nuclear Medicine, TU Munich, Germany, ou_persistent22              
11Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              

Content

show
hide
Free keywords: Atrophy; Behavioral variant frontotemporal dementia; Diagnostic criteria; Frontotemporal lobar degeneration; MRI; Pattern classification
 Abstract: Purpose

Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms.
Materials & methods

Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis.
Results

Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 86.5%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach.
Conclusion

Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

Details

show
hide
Language(s): eng - English
 Dates: 2017-01-052016-10-142017-02-032017-02-06
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.nicl.2017.02.001
PMID: 28348957
PMC: PMC5357695
Other: eCollection 2017
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : German Consortium for Frontotemporal Lobar Degeneration
Grant ID : O1GI1007A
Funding program : -
Funding organization : German Federal Ministry of Education and Research (BMBF)
Project name : -
Grant ID : -
Funding program : -
Funding organization : LIFE–Leipzig Research Center for Civilization Diseases, University of Leipzig
Project name : -
Grant ID : -
Funding program : -
Funding organization : European Union (EU)
Project name : -
Grant ID : -
Funding program : European Regional Development Fund
Funding organization : European Commission (EC)
Project name : -
Grant ID : -
Funding program : -
Funding organization : Free State of Saxony
Project name : -
Grant ID : PDF-IRG-1307
Funding program : -
Funding organization : Parkinson's Disease Foundation
Project name : -
Grant ID : 11362
Funding program : -
Funding organization : Michael Fox Foundation
Project name : -
Grant ID : -
Funding program : -
Funding organization : Max-Planck International Research Network on Aging (MaxNetAging)

Source 1

show
hide
Title: NeuroImage: Clinical
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Elsevier
Pages: - Volume / Issue: 14 Sequence Number: - Start / End Page: 656 - 662 Identifier: ISSN: 2213-1582
CoNE: https://pure.mpg.de/cone/journals/resource/2213-1582