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 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 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.

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 Association of acute depressive symptoms and functional connectivity in emotional processing regions following sport-related concussion

Neurology ◽  
2018 ◽  
Vol 91 (23 Supplement 1) ◽  
pp. S15.1-S15
Author(s):  
Lezlie Espana ◽  
William McCuddy ◽  
Lindsay Nelson ◽  
Birn Rasmus ◽  
Andrew Mayer ◽  
...  
Keyword(s):  
Depressive Symptoms ◽  
Functional Connectivity ◽  
Resting State ◽  
Emotional Processing ◽  
Rating Scale ◽  
Meta Analysis ◽  
Brain Regions ◽  
Partial Recovery ◽  
Resting State Functional Connectivity ◽  
Default Mode

Few studies have examined the physiologic correlates of depressive symptoms following sport-related concussion (SRC), despite the prevalence of these symptoms following brain injury. We hypothesized that concussed athletes would have disrupted resting-state functional connectivity in emotional processing regions compared to controls, and that this disruption would be associated with greater post-concussion symptoms of depression. Forty-three concussed athletes at approximately 1 day (N = 34), 1 week (N = 34), and 1 month (N = 30) post-concussion were evaluated along with 51 healthy athletes assessed at a single visit. Resting-state fMRI was collected on a 3T GE scanner (TR = 2s); depressive symptoms were assessed using the Hamilton Rating Scale for Depression (HAM-D). Emotional processing regions of interest (ROI) were defined using an automated meta-analysis of brain regions associated with the term “emotion”. Fisher-Z transformed correlations were calculated between each ROI. A multivariate approach assessed connectivity by analyzing ROI as simultaneous response variables. Concussed athletes had significantly higher depressive symptoms relative to controls at all time points but showed partial recovery by 1-month post-concussion relative to earlier visits (p's< 0.05). Functional connectivity did not differ between controls and concussed athletes at 1 day or one-week post-concussion. However, concussed athletes had significantly different connectivity in regions associated with emotional processing at 1 month relative to 1 day post-concussion (p = 0.002), and relative to controls (p = 0.003). Follow-up analyses showed that increased connectivity between attention and default mode networks at 1-month post-concussion was common across both analyses. In addition, functional connectivity of emotional processing regions was significantly associated with depressive symptoms at 1 day (p = 0.003) and one-week post-concussion (p = 7 × 10-8), with greater HAM-D scores correlating with decreased connectivity between attention and default mode networks. These results suggest that intrinsic connectivity between default mode and attention regions following SRC may be compensatory in nature.

scholarly journals Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder

2021 ◽  
Author(s):  
Marieke A. G. Martens ◽  
Nicola Filippini ◽  
Catherine J. Harmer ◽  
Beata R. Godlewska
Keyword(s):  
Functional Connectivity ◽  
Treatment Response ◽  
Resting State ◽  
Rating Scale ◽  
Antidepressant Treatment ◽  
Resting State Functional Connectivity ◽  
The Right ◽  
Treatment Responsivity ◽  
Response Biomarkers

Abstract Rational With no available response biomarkers, matching an appropriate antidepressant to an individual can be a lengthy process. Improving understanding of processes underlying treatment responsivity in depression is crucial for facilitating work on response biomarkers. Objectives To identify differences in patterns of pre-treatment resting-state functional connectivity (rsFC) that may underlie response to antidepressant treatment. Methods After a baseline MRI scan, thirty-four drug-free patients with depression were treated with an SSRI escitalopram 10 mg daily for 6 weeks; response was defined as ≥ 50% decrease in Hamilton Depression Rating Scale (HAMD) score. Thirty-one healthy controls had a baseline clinical assessment and scan. Healthy participants did not receive treatment. Results Twenty-one (62%) of patients responded to escitalopram. Treatment responsivity was associated with enhanced rsFC of the right fronto-parietal network (FPN)—with the posterior DMN, somatomotor network (SMN) and somatosensory association cortex. The lack of treatment response was characterized by reduced rsFC: of the bilateral FPN with the contralateral SMN, of the right FPN with the posterior DMN, and of the extended sensorimotor auditory area with the inferior parietal lobule (IPL) and posterior DMN. Reduced rsFC of the posterior DMN with IPL was seen in treatment responders, although only when compared with HC. Conclusions The study supports the role of resting-state networks in response to antidepressant treatment, and in particular the central role of the frontoparietal and default mode networks.

scholarly journals Resting-state functional connectivity of neural circuits associated with primary and secondary rewards in patients with bipolar disorder

2020 ◽  
Vol 15 (7) ◽  
pp. 755-763
Author(s):  
Jing Shi ◽  
Hua Guo ◽  
Sijia Liu ◽  
Wei Xue ◽  
Fengmei Fan ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Resting State ◽  
Ventral Striatum ◽  
Neural Circuits ◽  
Rating Scale ◽  
Region Of Interest ◽  
Resting State Functional Connectivity ◽  
Young Mania ◽  
Scale Scores

Abstract Objective We used resting-state functional connectivity (rsFC) to evaluate the integrity of the neural circuits associated with primary and secondary rewards in bipolar disorder (BD) with different mood phases. Methods Sixty patients with BD [21 patients with depressive episode of BD (BDD) and 41 patients with maniac episode of BD (BDM)] and 42 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging. rsFC was assessed using region of interest-wise analyses. Results Attenuation of rsFC at the orbitofrontal cortex (OFC) and the left ventral striatum (LVS) was observed in the secondary reward circuit of patients with BD compared to that of HCs. Among BDD, BDM and HCs, the rsFC between OFC and LVS in BDM was intermediate, while the rsFC between OFC and right ventral striatum/right amygdala in BDM was the highest; the corresponding rsFC values in BDD were the lowest. Furthermore, a positive correlation was found between rsFC and Young Mania Rating Scale scores in BDM. Conclusions This study suggests that there may be an abnormal rsFC between OFC and LVS in the second reward of patients with BD and the discrepant patterns of rsFC may exist between different mood states in patients with BD.

scholarly journals Dysbalanced Resting-State Functional Connectivity Within the Praxis Network Is Linked to Gesture Deficits in Schizophrenia

2020 ◽  
Vol 46 (4) ◽  
pp. 905-915 ◽  
Author(s):  
Florian Wüthrich ◽  
Petra V Viher ◽  
Katharina Stegmayer ◽  
Andrea Federspiel ◽  
Stephan Bohlhalter ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Precentral Gyrus ◽  
Healthy Controls ◽  
Resting State Functional Connectivity ◽  
Neural Basis ◽  
Course Of Illness ◽  
Parietal Lobule

Abstract Patients with schizophrenia frequently present deficits in gesture production and interpretation, greatly affecting their communication skills. As these gesture deficits can be found early in the course of illness and as they can predict later outcomes, exploring their neural basis may lead to a better understanding of schizophrenia. While gesturing has been reported to rely on a left lateralized network of brain regions, termed praxis network, in healthy subjects and lesioned patients, studies in patients with schizophrenia are sparse. It is currently unclear whether within-network connectivity at rest is linked to gesture deficit. Here, we compared the functional connectivity between regions of the praxis network at rest between 46 patients and 44 healthy controls. All participants completed a validated test of hand gesture performance before resting-state functional magnetic resonance imaging (fMRI) was acquired. Patients performed gestures poorer than controls in all categories and domains. In patients, we also found significantly higher resting-state functional connectivity between left precentral gyrus and bilateral superior and inferior parietal lobule. Likewise, patients had higher connectivity from right precentral gyrus to left inferior and bilateral superior parietal lobule (SPL). In contrast, they exhibited lower connectivity between bilateral superior temporal gyrus (STG). Connectivity between right precentral gyrus and left SPL, as well as connectivity between bilateral STG, correlated with gesture performance in healthy controls. We failed to detect similar correlations in patients. We suggest that altered resting-state functional connectivity within the praxis network perturbs correct gesture planning in patients, reflecting the gesture deficit often seen in schizophrenia.

scholarly journals Characterization of young and old adult brains: An EEG functional connectivity analysis

2018 ◽  
Author(s):  
Bahar Moezzi ◽  
Latha Madhuri Pratti ◽  
Brenton Hordacre ◽  
Lynton Graetz ◽  
Carolyn Berryman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Older Age ◽  
Support Vector ◽  
Functional Networks ◽  
Svm Classifier ◽  
Age Group ◽  
Beta Band ◽  
Machine Learning Classification ◽  
Functional Connections

Brain connectivity studies have reported that functional networks change with older age. We aim to (1) investigate whether electroencephalography (EEG) data can be used to distinguish between individual functional networks of young and old adults; and (2) identify the functional connections that contribute to this classification. Two eyes-open resting-state EEG recording sessions with 64 electrodes for each of 22 younger adults (19-37 years) and 22 older adults (63-85 years) were conducted. For each session, imaginary coherence matrices in theta, alpha, beta and gamma bands were computed. A range of machine learning classification methods were utilized to distinguish younger and older adult brains. A support vector machine (SVM) classifier was 94% accurate in classifying the brains by age group. We report decreased functional connectivity with older age in theta, alpha and gamma bands, and increased connectivity with older age in beta band. Most connections involving frontal, temporal, and parietal electrodes, and approximately two-thirds of connections involving occipital electrodes, showed decreased connectivity with older age. Just over half of the connections involving central electrodes showed increased connectivity with older age. Functional connections showing decreased strength with older age had significantly longer electrode-to-electrode distance than those that increased with older age. Most of the connections used by the classifier to distinguish participants by age group belonged to the alpha band. Findings suggest a decrease in connectivity in key networks and frequency bands associated with attention and awareness, and an increase in connectivity of the sensorimotor functional networks with ageing during a resting state.

scholarly journals Resting State Functional Connectivity and Outcomes of Psychotherapies for Late-life Depression

2020 ◽  
Author(s):  
Nili Solomonov ◽  
Lindsay W. Victoria ◽  
Katharine Dunlop ◽  
Matteo Respino ◽  
Matthew Hoptman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Rating Scale ◽  
Depression Scale ◽  
Late Life ◽  
Anterior Cingulate ◽  
Middle Temporal Gyrus ◽  
Late Life Depression ◽  
Resting State Functional Connectivity ◽  
Depressive Episodes

Background: Problem solving therapy (PST) and “Engage”, a reward-exposure” based therapy, are important treatment options for late-life depression, given modest efficacy of antidepressants in this disorder. Abnormal function of the reward and default mode networks has been observed during depressive episodes. This study examined whether resting state functional connectivity (rsFC) of reward and DMN circuitries is associated with treatment outcomes. Methods: Thirty-two older adults with major depression (mean age = 72.7) were randomized to 9-weeks of either PST or “Engage”. We assessed rsFC at baseline and Week 6. We placed seeds in three a priori regions of interest: subgenual cingulate (sgACC), dorsal anterior cingulate cortex (dACC), and nucleus accumbens (NAcc). Outcome measures included the Hamilton Depression Rating Scale (HAMD) and the Behavioral Activation for Depression Scale (BADS).Results: In both PST and “Engage”, higher rsFC between the sgACC and middle temporal gyrus at baseline was associated with greater improvement in depression severity (HAMD). Preliminary findings suggested that in “Engage” treated participants, lower rsFC between the dACC and DMPFC at baseline was associated with HAM-D improvement. Finally, in Engage only, increased rsFC from baseline to Week 6 between NAcc and Superior Parietal Cortex was associated with increased BADS scores.Conclusion: The results suggest that patients who present with higher rsFC between the sgACC and a structure within the DMN may benefit from behavioral psychotherapies for late life depression. ‘Engage’ may lead to increased rsFC within the reward system reflecting a reconditioning of the reward systems by reward exposure.

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