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      Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

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          Abstract

          Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain’s network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants’ compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

          Author Summary

          The distinction of a neurodegenerative condition against multiple related diseases simultaneously, with overlapping features and high levels of heterogeneity in behavioral, clinical, and neuropathological markers, remains a challenge. Here, we combined structural and functional connectivity markers with diverse methods including graph theory and cognitive markers to perform a multiclass classification of five FTD variants. An optimum set of features was obtained through progressive feature elimination by removing redundant and uninformative variables. Our results for the simultaneous multiclass categorization of each FTD variant and healthy controls achieved a performance up to an area under the curve of 0.95. This approach can help develop clinical decision support tools to detect specific affectations in the context of overlapping neurodegenerative diseases.

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          A power primer.

          One possible reason for the continued neglect of statistical power analysis in research in the behavioral sciences is the inaccessibility of or difficulty with the standard material. A convenient, although not comprehensive, presentation of required sample sizes is provided here. Effect-size indexes and conventional values for these are given for operationally defined small, medium, and large effects. The sample sizes necessary for .80 power to detect effects at these levels are tabled for eight standard statistical tests: (a) the difference between independent means, (b) the significance of a product-moment correlation, (c) the difference between independent rs, (d) the sign test, (e) the difference between independent proportions, (f) chi-square tests for goodness of fit and contingency tables, (g) one-way analysis of variance, and (h) the significance of a multiple or multiple partial correlation.
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            Statistical Power Analysis for the Behavioral Sciences

            <i>Statistical Power Analysis</i> is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: <br> * a chapter covering power analysis in set correlation and multivariate methods;<br> * a chapter considering effect size, psychometric reliability, and the efficacy of "qualifying" dependent variables and;<br> * expanded power and sample size tables for multiple regression/correlation.<br>
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              Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.

              An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute (MNI) (D. L. Collins et al., 1998, Trans. Med. Imag. 17, 463-468) was performed. The MNI single-subject main sulci were first delineated and further used as landmarks for the 3D definition of 45 anatomical volumes of interest (AVOI) in each hemisphere. This procedure was performed using a dedicated software which allowed a 3D following of the sulci course on the edited brain. Regions of interest were then drawn manually with the same software every 2 mm on the axial slices of the high-resolution MNI single subject. The 90 AVOI were reconstructed and assigned a label. Using this parcellation method, three procedures to perform the automated anatomical labeling of functional studies are proposed: (1) labeling of an extremum defined by a set of coordinates, (2) percentage of voxels belonging to each of the AVOI intersected by a sphere centered by a set of coordinates, and (3) percentage of voxels belonging to each of the AVOI intersected by an activated cluster. An interface with the Statistical Parametric Mapping package (SPM, J. Ashburner and K. J. Friston, 1999, Hum. Brain Mapp. 7, 254-266) is provided as a freeware to researchers of the neuroimaging community. We believe that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain in which deformations are well known. However, this tool does not alleviate the need for more sophisticated labeling strategies based on anatomical or cytoarchitectonic probabilistic maps.
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                Author and article information

                Contributors
                Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role: Role:
                Role: Role: Role: Role: Role: Role: Role: Role: Role:
                Journal
                Netw Neurosci
                Netw Neurosci
                netn
                Network Neuroscience
                MIT Press (One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA journals-info@mit.edu )
                2472-1751
                2023
                01 January 2023
                : 7
                : 1
                : 322-350
                Affiliations
                [1]Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Santiago de Chile, Chile
                [2]Center for Social and Cognitive Neuroscience, School of Psychology, Universidad Adolfo Ibañez, Santiago de Chile, Chile
                [3]Cognitive Neuroscience Center, Universidad de San Andres, Buenos Aires, Argentina
                [4]Global Brain Health Institute, University of California San Francisco, San Francisco, CA, USA
                [5]Trinity College Dublin, Dublin, Ireland
                [6]Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                [†]

                First authors.

                Handling Editor: Mikail Rubinov

                Author information
                https://orcid.org/0000-0003-1731-8325
                Article
                netn_a_00285
                10.1162/netn_a_00285
                10270711
                37333999
                a37c86da-053d-4608-8a70-6dd61f53454b
                © 2022 Massachusetts Institute of Technology

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For a full description of the license, please visit https://creativecommons.org/licenses/by/4.0/.

                History
                : 14 April 2022
                : 03 October 2022
                Page count
                Pages: 29
                Funding
                Funded by: Takeda Pharmaceutical Company, DOI 10.13039/100008373;
                Award ID: CW2680521
                Award Recipient :
                Categories
                Research Article
                Custom metadata
                Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023). Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference. Network Neuroscience, 7(1), 322–350. https://doi.org/10.1162/netn_a_00285

                multiclass classification,ftd,ftd variants,connectivity,machine learning

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