scholarly journals Altered functional connectivity of the insula in a rat model of recurrent headache

2020 ◽  
Vol 16 ◽  
pp. 174480692092211
Author(s):  
Zhihua Jia ◽  
Shengyuan Yu ◽  
Wenjing Tang ◽  
Dengfa Zhao
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Low Frequency ◽  
Association Cortex ◽  
Temporal Association ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Trigeminovascular System ◽  
Inflammatory Soup

Migraine is a pain disorder accompanied by various symptoms. The insula, a “cortical hub,” is involved in many functions. Few studies have focused on the insula in migraine. We explored the resting-state functional connectivity between the insula and other brain areas in rats subjected to repeated meningeal nociception which was commonly used as animal model of migraine. Inflammatory soup was infused through supradural catheters in conscious rats. The rats were subdivided based on the frequency of the inflammatory soup infusions. Magnetic resonance imaging data were acquired on rats 21 days after inflammatory soup infusion and functional connectivity seeded on the insula was analyzed. In the low-frequency inflammatory soup group, magnetic resonance imaging was performed again 1 h after the glyceryl trinitrate injection following baseline scanning. The cerebellum showed increased functional connectivity with the insula in the inflammatory soup groups. The insula showed increased functional connectivity with the medulla and thalamus in the ictal period in the low-frequency inflammatory soup rats. In the high-frequency inflammatory soup group, several areas showed increased functional connectivity with the insula, including the pons, midbrain, thalamus, temporal association cortex, and retrosplenial, visual, and sensory cortices. Our findings support the hypothesis that the headache phase of migraine depends on the activation and sensitization of the trigeminovascular system, and that the chronification of migraine may be related to higher brain centers and limbic cortices. The insula may be a new target for treatment of migraine.

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.

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