Low frequency analysis of acoustical parameters of emotional speech for use with functional magnetic resonance imaging

2016 ◽  
Vol 140 (4) ◽  
pp. 3237-3238
Author(s):  
Peter M. Moriarty ◽  
Michelle C. Vigeant ◽  
Pan Liu ◽  
Rick Gilmore ◽  
Rachel Wolf ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Magnetic Resonance Imaging ◽  
Frequency Analysis ◽  
Low Frequency ◽  
Emotional Speech ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Acoustical Parameters

scholarly journals Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features

2021 ◽  
Vol 14 ◽  
Author(s):  
Bartosz Bohaterewicz ◽  
Anna M. Sobczak ◽  
Igor Podolak ◽  
Bartosz Wójcik ◽  
Dagmara Mȩtel ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Low Frequency ◽  
Suicidal Risk ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging

BackgroundSome studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR.MethodsFifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine.ResultsAll groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures.ConclusionOur findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.

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