scholarly journals Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder

2020 ◽  
Vol 11 ◽  
Author(s):  
Rongxin Zhu ◽  
Shui Tian ◽  
Huan Wang ◽  
Haiteng Jiang ◽  
Xinyi Wang ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Suicide Risk ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Bipolar Ii Disorder ◽  
Individual Level ◽  
Bipolar Ii ◽  
The Individual

Bipolar II disorder (BD-II) major depression episode is highly associated with suicidality, and objective neural biomarkers could be key elements to assist in early prevention and intervention. This study aimed to integrate altered brain functionality in the frontolimbic system and machine learning techniques to classify suicidal BD-II patients and predict suicidality risk at the individual level. A cohort of 169 participants were enrolled, including 43 BD-II depression patients with at least one suicide attempt during a current depressive episode (SA), 62 BD-II depression patients without a history of attempted suicide (NSA), and 64 demographically matched healthy controls (HCs). We compared resting-state functional connectivity (rsFC) in the frontolimbic system among the three groups and explored the correlation between abnormal rsFCs and the level of suicide risk (assessed using the Nurses' Global Assessment of Suicide Risk, NGASR) in SA patients. Then, we applied support vector machines (SVMs) to classify SA vs. NSA in BD-II patients and predicted the risk of suicidality. SA patients showed significantly decreased frontolimbic rsFCs compared to NSA patients. The left amygdala-right middle frontal gyrus (orbital part) rsFC was negatively correlated with NGASR in the SA group, but not the severity of depressive or anxiety symptoms. Using frontolimbic rsFCs as features, the SVMs obtained an overall 84% classification accuracy in distinguishing SA and NSA. A significant correlation was observed between the SVMs-predicted NGASR and clinical assessed NGASR (r = 0.51, p = 0.001). Our results demonstrated that decreased rsFCs in the frontolimbic system might be critical objective features of suicidality in BD-II patients, and could be useful for objective prediction of suicidality risk in individuals.

Reduced interhemispheric resting-state functional connectivity in unmedicated bipolar II disorder

2015 ◽  
Vol 132 (5) ◽  
pp. 400-407 ◽  
Author(s):  
Y. Wang ◽  
S. Zhong ◽  
Y. Jia ◽  
Z. Zhou ◽  
Q. Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Resting State Functional Connectivity ◽  
Bipolar Ii Disorder ◽  
Bipolar Ii

scholarly journals Machine Learning Analytics of Resting-State Functional Connectivity Predicts Survival Outcomes of Glioblastoma Multiforme Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Bidhan Lamichhane ◽  
Andy G. S. Daniel ◽  
John J. Lee ◽  
Daniel S. Marcus ◽  
Joshua S. Shimony ◽  
...  
Keyword(s):  
Machine Learning ◽  
Glioblastoma Multiforme ◽  
Functional Connectivity ◽  
Resting State ◽  
Learning Analytics ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Term Survival ◽  
Long Term Survival

Glioblastoma multiforme (GBM) is the most frequently occurring brain malignancy. Due to its poor prognosis with currently available treatments, there is a pressing need for easily accessible, non-invasive techniques to help inform pre-treatment planning, patient counseling, and improve outcomes. In this study we determined the feasibility of resting-state functional connectivity (rsFC) to classify GBM patients into short-term and long-term survival groups with respect to reported median survival (14.6 months). We used a support vector machine with rsFC between regions of interest as predictive features. We employed a novel hybrid feature selection method whereby features were first filtered using correlations between rsFC and OS, and then using the established method of recursive feature elimination (RFE) to select the optimal feature subset. Leave-one-subject-out cross-validation evaluated the performance of models. Classification between short- and long-term survival accuracy was 71.9%. Sensitivity and specificity were 77.1 and 65.5%, respectively. The area under the receiver operating characteristic curve was 0.752 (95% CI, 0.62–0.88). These findings suggest that highly specific features of rsFC may predict GBM survival. Taken together, the findings of this study support that resting-state fMRI and machine learning analytics could enable a radiomic biomarker for GBM, augmenting care and planning for individual patients.

scholarly journals Defining data-driven subgroups of obsessive–compulsive disorder with different treatment responses based on resting-state functional connectivity

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Seoyeon Kwak ◽  
Minah Kim ◽  
Taekwan Kim ◽  
Yoobin Kwak ◽  
Sanghoon Oh ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Obsessive Compulsive Disorder ◽  
Resting State ◽  
Predictive Biomarkers ◽  
Support Vector ◽  
Resting State Functional Connectivity ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Treatment Responses

Abstract Characterization of obsessive–compulsive disorder (OCD), like other psychiatric disorders, suffers from heterogeneities in its symptoms and therapeutic responses, and identification of more homogeneous subgroups may help to resolve the heterogeneity. We aimed to identify the OCD subgroups based on resting-state functional connectivity (rsFC) and to explore their differences in treatment responses via a multivariate approach. From the resting-state functional MRI data of 107 medication-free OCD patients and 110 healthy controls (HCs), we selected rsFC features, which discriminated OCD patients from HCs via support vector machine (SVM) analyses. With the selected brain features, we subdivided OCD patients into subgroups using hierarchical clustering analyses. We identified 35 rsFC features that achieved a high sensitivity (82.74%) and specificity (76.29%) in SVM analyses. The OCD patients were subdivided into two subgroups, which did not show significant differences in their demographic and clinical backgrounds. However, one of the OCD subgroups demonstrated more impaired rsFC that was involved either within the default mode network (DMN) or between DMN brain regions and other network regions. This subgroup also showed both lower improvements in symptom severity in the 16-week follow-up visit and lower responder percentage than the other subgroup. Our results highlight that not only abnormalities within the DMN but also aberrant rsFC between the DMN and other networks may contribute to the treatment response and support the importance of these neurobiological alterations in OCD patients. We suggest that abnormalities in these connectivity may play predictive biomarkers of treatment response, and aid to build more optimal treatment strategies.

scholarly journals Interpretable Learning Approaches in Resting-State Functional Connectivity Analysis: The Case of Autism Spectrum Disorder

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Jinlong Hu ◽  
Lijie Cao ◽  
Tenghui Li ◽  
Bin Liao ◽  
Shoubin Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Piecewise Linear ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fmri Data ◽  
Support Vector ◽  
Resting State Functional Connectivity

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation.

scholarly journals Baseline Resting-State Functional Connectivity Determines Subsequent Pain Ratings to a Tonic Ecologically Valid Experimental Model of Orofacial Pain

2020 ◽  
Author(s):  
Lizbeth J. Ayoub ◽  
Mary Pat McAndrews ◽  
Alexander J. Barnett ◽  
Ka Chun Jeremy Ho ◽  
Iacopo Cioffi ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Subjective Experience ◽  
Ecological Validity ◽  
Real Life ◽  
Grey Matter Volume ◽  
Resting State Functional Connectivity ◽  
Pain Ratings ◽  
Pain Experiences ◽  
The Individual

ABSTRACTPain is a subjective experience with significant individual differences. Laboratory studies investigating pain thresholds and experimental acute pain have identified structural and functional neural correlates. However, these types of pain stimuli have limited ecological validity to real-life pain experiences. Here, we use an orthodontic procedure—the insertion of an elastomeric separator between teeth—which typically induces mild to moderate pain that peaks within 2 days and lasts several days. We aimed to determine whether the baseline structure and resting-state functional connectivity (rsFC) of key regions along the trigeminal nociceptive and pain modulatory pathways correlate with subsequent peak pain ratings. Twenty-six healthy individuals underwent structural and resting-state functional (rs-fMRI) scanning prior to the placement of a separator between the first and second molars, which was kept in place for five days. Participants recorded pain ratings three times daily on a 100-mm visual analogue scale. Peak pain was not significantly correlated with diffusion metrics of the trigeminal nerve, or grey matter volume of any brain region. Peak pain did, however, positively correlate with baseline rsFC between the thalamus contralateral to the separator and bilateral insula, and negatively correlated with connectivity between the periaqueductal gray (PAG) and core nodes of the default mode network (medial prefrontal and posterior cingulate cortices). The ascending (thalamic) nociceptive and the descending (PAG) pain modulatory pathways at baseline each explained unique variation in peak pain intensity ratings. In sum, pre-interventional functional neural architecture of both systems determined the individual pain experience to a subsequent ecologically valid pain stimulus.

scholarly journals Altered Static and Dynamic Interhemispheric Resting-State Functional Connectivity in Patients With Thyroid-Associated Ophthalmopathy

2021 ◽  
Vol 15 ◽  
Author(s):  
Wen Chen ◽  
Hao Hu ◽  
Qian Wu ◽  
Lu Chen ◽  
Jiang Zhou ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Rating Scale ◽  
Area Under The Curve ◽  
Support Vector ◽  
Svm Classifier ◽  
Resting State Functional Connectivity ◽  
Ophthalmic Manifestations ◽  
Thyroid Associated Ophthalmopathy ◽  
Parietal Lobule

Purpose: Thyroid-associated ophthalmopathy (TAO) is a debilitating and sight-threatening autoimmune disease that severely impairs patients’ quality of life. Besides the most common ophthalmic manifestations, the emotional and psychiatric disturbances are also usually observed in clinical settings. This study was to investigate the interhemispheric functional connectivity alterations in TAO patients using resting-state functional magnetic resonance imaging (rs-fMRI).Methods: Twenty-eight TAO patients and 22 healthy controls (HCs) underwent rs-fMRI scans. Static and dynamic voxel-mirrored homotopic connectivity (VMHC) values were calculated and compared between the two groups. A linear support vector machine (SVM) classifier was used to examine the performance of static and dynamic VMHC differences in distinguishing TAOs from HCs.Results: Compared with HCs, TAOs showed decreased static VMHC in lingual gyrus (LG)/calcarine (CAL), middle occipital gyrus, postcentral gyrus, superior parietal lobule, inferior parietal lobule, and precuneus. Meanwhile, TAOs demonstrated increased dynamic VMHC in orbitofrontal cortex (OFC). In TAOs, static VMHC in LG/CAL was positively correlated with visual acuity (r = 0.412, P = 0.036), whilst dynamic VMHC in OFC was positively correlated with Hamilton Anxiety Rating Scale (HARS) score (r = 0.397, P = 0.044) and Hamilton Depression Rating Scale (HDRS) score (r = 0.401, P = 0.042). The SVM model showed good performance in distinguishing TAOs from HCs (area under the curve, 0.971; average accuracy, 94%).Conclusion: TAO patients had altered static and dynamic VMHC in the occipital, parietal, and orbitofrontal areas, which could serve as neuroimaging prediction markers of TAO.

scholarly journals Individual prediction of trauma-focused psychotherapy response in youth with posttraumatic stress disorder using resting-state functional connectivity

2021 ◽  
Author(s):  
Paul Zhutovsky ◽  
Jasper B. Zantvoord ◽  
Judith B.M. Ensink ◽  
Rosanne Op den Kelder ◽  
Ramon J.L. Lindauer ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Functional Connectivity ◽  
Posttraumatic Stress ◽  
Resting State ◽  
Prediction Models ◽  
Stress Disorder ◽  
Network Connectivity ◽  
Support Vector ◽  
Permutation Testing ◽  
Resting State Functional Connectivity

AbstractRandomized controlled trials have shown efficacy of trauma-focused psychotherapies in youth with posttraumatic stress disorder (PTSD). However, response varies considerably among individuals. Currently, no biomarkers are available to assist clinicians in identifying youth who are most likely to benefit from treatment. In this study, we investigated whether resting-state functional magnetic resonance imaging (rs-fMRI) could distinguish between responders and non-responders on the group- and individual patient level.Pre-treatment rs-fMRI was recorded in 40 youth (ages 8-17 years) with (partial) PTSD before trauma-focused psychotherapy. Change in symptom severity from pre- to post-treatment was assessed using the Clinician-Administered PTSD scale for Children and Adolescents to divide participants into responders (≥30% symptom reduction) and non-responders. Functional networks were identified using meta-independent component analysis. Group-differences within- and between-network connectivity between responders and non-responders were tested using permutation testing. Individual predictions were made using multivariate, cross-validated support vector machine classification.A network centered on the bilateral superior temporal gyrus predicted treatment response for individual patients with 76.17% accuracy (pFWE = 0.018, 87.14% sensitivity, 65.20% specificity, area-under-receiver-operator-curve of 0.82). Functional connectivity between the frontoparietal and sensorimotor network was significantly stronger in non-responders (pFWE = 0.012) on the group-level. Within-network connectivity was not significantly different between groups.This study provides proof-of-concept evidence for the feasibility to predict trauma-focused psychotherapy response in youth with PTSD at an individual-level. Future studies are required to test if larger cohorts could increase accuracy and to test further generalizability of the prediction models.

Migraine classification using magnetic resonance imaging resting-state functional connectivity data

Cephalalgia ◽  
2016 ◽  
Vol 37 (9) ◽  
pp. 828-844 ◽  
Author(s):  
Catherine D Chong ◽  
Nathan Gaw ◽  
Yinlin Fu ◽  
Jing Li ◽  
Teresa Wu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Functional Connectivity ◽  
Resting State ◽  
Disease Burden ◽  
Brain Mri ◽  
Machine Learning Techniques ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Functional Connections ◽  
Healthy Control

Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

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