Application of the functionally homogeneous regions (FHR) method to identify the most informative regions of the human brain for binary classification of schizophrenia based on resting-state functional MRI data

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The article presents results of the analysis of the most informative brain regions for diagnosing schizophrenia based on resting-state functional MRI data using method of functionally homogeneous regions (FHR) previously developed by the authors and the CONN functional atlas. The analysis was performed using fMRI data from 32 subjects diagnosed with schizophrenia and 36 subjects from the control group obtained on Siemens tomograph. Data from 19 subjects diagnosed with schizophrenia and 29 subjects from the control group obtained on General Electric MRI scanner were used for verification. Eight most informative regions were identified. The analysis of the identified regions showed that changing the composition of the training group significantly affects the list of the most significant regions. At the same time the analysis of the identified most significant regions for repeatability with varying the composition of subjects showed that out of the eight identified most significant regions, four have repeatability higher than 70%, two have repeatability from 50% to 70%, and two have repeatability from 30% to 50%. This may indicate that the identified regions are not random and opens up prospects for further in-depth analysis and determination of their significance in diagnosing schizophrenia. Verification carried out on data from General Electric MRI scanner partially confirmed the heightened importance of the identified regions for the classification of schizophrenia pathology, but no perfect match was achieved on datasets from different MRI scanners.

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作者简介

A. Poyda

National Research Center “Kurtchatov Institute”

编辑信件的主要联系方式.
Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

S. Kozlov

National Research Center “Kurtchatov Institute”

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

A. Zhemchuzhnikov

National Research Center “Kurtchatov Institute”

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

V. Orlov

National Research Center “Kurtchatov Institute”

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

S. Kartashov

National Research Center “Kurtchatov Institute”

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

L. Bravve

Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

М. Kaydan

Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

G. Kostyuk

Psychiatric Hospital no. 1 Named after N.A. Alexeev of the Department of Health of Moscow

Email: Poyda_AA@nrcki.ru
俄罗斯联邦, Moscow

参考

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1. JATS XML
2. Fig. 1. Classification accuracy depending on the maximum number of features/regions.

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3. Fig. 2. Probability of regions being among the 8 most significant ones based on the weights calculated by the ExtraTrees algorithm (Siemens MRI scanner data). The values on the horizontal axis correspond to the regions number in the CONN atlas.

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4. Fig. 3. Position of the region in the ordered list of their significance averaged on 5000 iterations (Siemens MRI scanner data).

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5. Fig. 4. Dependence of classification accuracy on feature removal (Siemens MRI scanner data): blue column – without feature removal; orange – removal of random 8 features; gray – removal of 8 features corresponding to the selected most significant regions.

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6. Fig. 5. (а) Probability of regions being among the 8 most significant ones based on the weights calculated by the ExtraTrees algorithm. The values on the horizontal axis correspond to the regions number in the CONN atlas. Red color – regions obtained on General Electric dataset, blue color – regions obtained on Siemens dataset. (б) Position of the region in the ordered list of their significance averaged on 5000 iterations. Red color – regions obtained on General Electric dataset, blue color – regions obtained on Siemens dataset.

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7. Fig. 6. Dependence of FHR on the angle of magnetization deviation (General Electric tomograph, one subject).

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