Format

Send to

Choose Destination
Alzheimers Dement (Amst). 2019 Aug 28;11:588-598. doi: 10.1016/j.dadm.2019.06.002. eCollection 2019 Dec.

Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging.

Author information

1
Multimedia Signal Processing Group - Neuroimage Division, French-Argentine International Center for Information and Systems Sciences (CIFASIS) - National Scientific and Technical Research Council (CONICET), Rosario, Argentina.
2
Laboratory of Neuroimaging and Neuroscience (LANEN), INECO Foundation Rosario, Rosario, Argentina.
3
Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires, Argentina.
4
National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina.
5
Faculty of Education, National University of Cuyo (UNCuyo), Mendoza, Argentina.
6
Centre of Excellence in Cognition and its Disorders, Australian Research Council (ARC), Sydney, Australia.
7
The University of Sydney, Brain and Mind Centre and Clinical Medical School, Sydney, Australia.
8
Memory and Aging Center, University of California, San Francisco, San Francisco, CA, USA.
9
Department of Neurology, Memory Aging Center, University of California, San Francisco, CA, USA.
10
The University of Sydney, Brain and Mind Centre and School of Psychology, Sydney, Australia.
11
Medical School, Aging Institute, Psychiatry and Mental Health, Pontificia Universidad Javeriana (PUJ), Bogotá, Colombia.
12
Memory and Balance Clinic, Buenos Aires, Argentina.
13
Department of Neurology, Dr Cesar Milstein Hospital, Buenos Aires, Argentina.
14
Departamento de Estudios Psicológicos, Universidad Icesi, Cali, Colombia (eduar).
15
Radiology, Hospital Universitario San Ignacio (HUSI), Bogotá, Colombia.
16
Pontificia Universidad Javeriana, Departments of Physiology and Psychiatry - Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia.
17
Universidad Autónoma del Caribe, Barranquilla, Colombia.
18
Center for Social and Cognitive Neuroscience (CSCN), School of Psychology, Universidad Adolfo Ibáñez, Santiago, Chile.

Abstract

Introduction:

Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem.

Methods:

We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier.

Results:

Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).

Discussion:

This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.

KEYWORDS:

Classifiers; Data-driven computational approaches; Dementia; Neuroimaging; bvFTD

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center