scholarly journals Abnormal amplitude of low-frequency fluctuations and functional connectivity of resting-state functional magnetic resonance imaging in patients with leukoaraiosis

2017 ◽  
Vol 7 (6) ◽  
pp. e00714 ◽  
Author(s):  
Rongchuan Cheng ◽  
Honglin Qi ◽  
Yong Liu ◽  
Shifu Zhao ◽  
Chuanming Li ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Resting State ◽  
Low Frequency ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Frequency Fluctuations

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.

scholarly journals Machine Learning based identification of suicidal risk in patients with schizophrenia using multi-level resting state fMRI features

2020 ◽  
Author(s):  
Bartosz Bohaterewicz ◽  
Maria Sobczak Anna ◽  
Igor Podolak ◽  
Bartosz Wójcik ◽  
Dagmara Mętel ◽  
...  
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

Background: Some 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.Methods: Fifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 minutes of 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) was calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine. Results: All groups revealed different internetwork 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.Conclusion: Our 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.

scholarly journals Resting-State Functional Magnetic Resonance Imaging and Functional Connectivity Density Mapping in Patients With Optic Neuritis

2021 ◽  
Vol 15 ◽  
Author(s):  
Ke Song ◽  
Yong Wang ◽  
Mei-Xia Ren ◽  
Jiao Li ◽  
Ting Su ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Optic Neuritis ◽  
Resting State ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Functional Connectivity Density ◽  
The Right

Background: Using resting-state functional connectivity (rsFC), we investigated alternations in spontaneous brain activities reflected by functional connectivity density (FCD) in patients with optic neuritis (ON).Methods: We enrolled 28 patients with ON (18 males, 10 females) and 24 healthy controls (HCs; 16 males, 8 females). All subjects underwent functional magnetic resonance imaging (fMRI) in a quiet state to determine the values of rsFC, long-range FCD (longFCD), and short-range FCD (IFCD). Receiver operating characteristic (ROC) curves were generated to distinguish patients from HCs.Results: The ON group exhibited obviously lower longFCD values in the left inferior frontal gyrus triangle, the right precuneus and the right anterior cingulate, and paracingulate gyri/median cingulate and paracingulate gyri. The left median cingulate and paracingulate gyri and supplementary motor area (SMA) were also significantly lower. Obviously reduced IFCD values were observed in the left middle temporal gyrus/angular gyrus/SMA and right cuneus/SMA compared with HCs.Conclusion: Abnormal neural activities were found in specific brain regions in patients with ON. Specifically, they showed significant changes in rsFC, longFCD, and IFCD values. These may be useful to identify the specific mechanism of change in brain function in ON.

Altered Functional Connectivity within Default Mode Network in Patients with Transient Ischemic Attack: A Resting-State Functional Magnetic Resonance Imaging Study

2019 ◽  
Vol 48 (1-2) ◽  
pp. 61-69 ◽  
Author(s):  
Tingting Zhu ◽  
Lingyu Li ◽  
Yulin Song ◽  
Yu Han ◽  
Chengshu Zhou ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Functional Magnetic Resonance Imaging ◽  
Transient Ischemic Attack ◽  
Default Mode Network ◽  
Resting State ◽  
Functional Magnetic Resonance ◽  
Resonance Imaging ◽  
Ischemic Attack

Default mode network (DMN) is an important functional brain network that supports aspects of cognition. Stroke has been reported to be associated with functional connectivity (FC) impairments within DMN. However, whether FC within DMN changes in transient ischemic attack (TIA), an important risk factor for stroke, remains unclear. Forty-eight TIA patients and 41 age- and sex-matched healthy controls (HCs) were recruited in this study. Using resting-state functional magnetic resonance imaging seed-based FC methods, we examined FC alterations within DMN in TIA patients, tested its associations with clinical information, and further explored the ability of FC abnormalities to predict follow-up ischemic attacks. We found significantly decreased FC of left middle temporal gyrus/angular gyrus both with medial prefrontal cortex (mPFC) and posterior cingulate cortex/precuneus (PCC/Pcu) and significantly decreased FC among each pair of mPFC, left PCC, and right Pcu in patients with TIA as compared with HCs. Moreover, the connectivity between mPFC and left PCC could predict future ischemic attacks of the patients. Collectively, these findings may provide insights into further understanding of the underlying pathological mechanism in TIA, and aberrant FC between the hubs within DMN may provide a reference for the imaging diagnosis and early intervention of TIA.

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