scholarly journals Resting-State Network Analysis of Suicide Attempt History in the UK Biobank

2022 ◽  
Author(s):  
Matthew F Thompson ◽  
Marjan Ghahramanlou-Holloway ◽  
Mikela A. Murphy ◽  
Kanchana U. Perera ◽  
Chelsie Benca-Bachman ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Suicide Attempt ◽  
Resting State ◽  
Suicide Risk ◽  
Future Research ◽  
Control Group ◽  
Large Sample Size ◽  
Uk Biobank ◽  
Resting State Network ◽  
The Uk

Background: Prior research has identified altered brain structure and function in individuals at risk for self-directed violence thoughts and behaviors. However, these studies have largely utilized healthy controls and findings have been inconsistent. Thus, this study examined differences in resting-state functional network connectivity among individuals with lifetime suicide attempt(s) versus lifetime self-directed violence thoughts alone. Methods: Using data from the UK Biobank, this study utilized a series of linear regressions to compare individuals with lifetime suicide attempt(s) (n = 566) versus lifetime self-directed violence thoughts alone (n = 3,447) on within- and between- network resting-state functional connectivity subnetworks. Results: There were no significant between-group differences for between-network, within-network, or whole-brain functional connectivity after adjusting for age, sex, ethnicity, and body mass index and performing statistical corrections for multiple comparisons. Conclusions: Resting-state network measures may not differentiate between individuals with lifetime suicide attempt(s) and lifetime self-directed violence thoughts alone. Null findings diverge from results reported in smaller neuroimaging studies of suicide risk, but are consistent with null findings in other large-scale studies and meta-analyses. Strengths of the study include its large sample size and stringent control group. Future research on a wider array of imaging, genetic, and psychosocial risk factors can clarify relative contributions of individual and combined variables to suicide risk and inform scientific understanding of ideation-to-action framework.

scholarly journals Motor Sequences - Separating The Sequence From The Motor. A longitudinal rsfMRI Study

2021 ◽  
Author(s):  
ATP Jäger ◽  
JM Huntenburg ◽  
SA Tremblay ◽  
U Schneider ◽  
S Grahl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Motor Learning ◽  
Resting State ◽  
Primary Motor Cortex ◽  
Control Group ◽  
Motor Execution ◽  
Motor Sequence ◽  
Resting State Functional Connectivity ◽  
Fast Learning ◽  
Slow Learning

AbstractIn motor learning, sequence-specificity, i.e. the learning of specific sequential associations, has predominantly been studied using task-based fMRI paradigms. However, offline changes in resting state functional connectivity after sequence-specific motor learning are less well understood. Previous research has established that plastic changes following motor learning can be divided into stages including fast learning, slow learning and retention. A description of how resting state functional connectivity after sequence-specific motor sequence learning (MSL) develops across these stages is missing. This study aimed to identify plastic alterations in whole-brain functional connectivity after learning a complex motor sequence by contrasting an active group who learned a complex sequence with a control group who performed a control task matched for motor execution. Resting state fMRI and behavioural performance were collected in both groups over the course of 5 consecutive training days and at follow-up after 12 days to encompass fast learning, slow learning, overall learning and retention. Between-group interaction analyses showed sequence-specific increases in functional connectivity during fast learning in the sensorimotor territory of the internal segment of right globus pallidus (GPi), and sequence-specific decreases in right supplementary motor area (SMA) in overall learning. We found that connectivity changes in key regions of the motor network including the superior parietal cortex (SPC) and primary motor cortex (M1) were not a result of sequence-specific learning but were instead linked to motor execution. Our study confirms the sequence-specific role of SMA and GPi that has previously been identified in online task-based learning studies in humans and primates, and extends it to resting state network changes after sequence-specific MSL. Finally, our results shed light on a timing-specific plasticity mechanism between GPi and SMA following MSL.

Abstract W P29: Tissue Plasminogen Activator Mediated Reperfusion, and Subsequent Improvement in Resting State Connectivity Correlates with Functional Outcome in Acute Ischemic Stroke Patients

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Souvik Sen ◽  
Johann Fridriksson ◽  
Taylor Hanayik ◽  
Christopher Rorden ◽  
Isabel Hubbard ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Functional Connectivity ◽  
Functional Outcome ◽  
Resting State ◽  
Network Connectivity ◽  
Stroke Patients ◽  
Post Stroke ◽  
Resting State Network ◽  
Tissue Plasminogen

Background: Intravenous Tissue Plasminogen Activator (TPA) is the only FDA approved medical therapy for acute ischemic stroke (AIS). Prior study suggests that early recanalization is associated with better stroke outcome. Our aim was to correlate task-negative and task-positive (TN/TP) resting state network activity with tissue perfusion and functional outcome, in stroke patients who received TPA. Method: AIS patients were consented and underwent NIH stroke scale (NIHSS) assessment and magnetic resonance imaging (MRI) scans during TPA infusion (baseline) and six hours post stroke. The MRI sequences include contrast-enhanced perfusion weighted image (PWI) and resting state Blood Oxygen Level-Dependent or BOLD (RSB) images acquired using a Siemens Treo 3T MRI scanner. Additionally, the RSB scan and the NIHSS were obtained at a 30-day follow up visit. Results: Fourteen patients (mean age ± SD=63 ±14, 50% male, 50% white, 43% black and 7% others) who qualified for TPA completed the study at baseline and 6 hours post stroke. Of these, 6 patients had valid follow up data at 30 days. Three patients without cerebral ischemia were excluded. A paired samples t-test comparing baseline and 6h post stroke showed a significantly improved TP network t(10)= -4.24 p< 0.05. The resting network connectivity improved from 6 hours post stroke to 30-days follow up, t(5)= -5.35 p< 0.01. Similarly, NIHSS, at 6h post stroke t(10)= 3.62 p< 0.01 and at 30-days follow up t(5)= -3.4 p< 0.01 were significantly better than the NIHSS at baseline. The 6-hours post-stroke perfusion correlated with the resting network connectivity in both the damaged (r=-0.56 p= 0.07) and intact hemispheres (r= -0.57 p= 0.06). Differences in functional connectivity and NIHSS scores from baseline to 6 h were positively correlated (r= 0.56 p=0.07). Conclusion: In this pilot study we found that TPA led to changes in MRI based resting state networks and associated functional outcome. Correlations were found between perfusion, functional connectivity and NIHSS. This suggests that the improvement of resting state network means improved efficiency of brain activity indicated by functional outcome and may be a potential predictive MRI biomarker for TPA response. A larger study is needed to verify this finding.

Inter-Hemispherical Investigations on the Functional Connectivity in Controls and Autism Spectrum Using Resting State fMRI

2020 ◽  
pp. 169-186
Author(s):  
S. Vidhusha ◽  
A. Kavitha
Keyword(s):  
Functional Connectivity ◽  
Granger Causality ◽  
Resting State ◽  
Cognitive Task ◽  
Resting State Fmri ◽  
Autism Spectrum ◽  
Control Group ◽  
Neural Connectivity ◽  
Activation Patterns ◽  
The Brain

Autism spectrum disorders are connected with disturbances of neural connectivity. Functional connectivity is typically examined during a cognitive task, but also exists in the absence of a task. While a number of studies have performed functional connectivity analysis to differentiate controls and autism individuals, this work focuses on analyzing the brain activation patterns not only between controls and autistic subjects, but also analyses the brain behaviour present within autism spectrum. This can bring out more intuitive ways to understand that autism individuals differ individually. This has been performed between autism group relative to the control group using inter-hemispherical analysis. Indications of under connectivity were exhibited by the Granger Causality (GC) and Conditional Granger Causality (CGC) in autistic group. Results show that as connectivity decreases, the GC and CGC values also get decreased. Further, to demark the differences present within the spectrum of autistic individuals, GC and CGC values have been calculated.

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 RESTING STATE NETWORK AND MINDFULNESS IN MOTION PLUS DASH DIET IN AFRICAN AMERICANS WITH MILD COGNITIVE IMPAIRMENT

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S112-S113
Author(s):  
Kathy D Wright ◽  
Klatt Maryanna ◽  
Ingrid Adams ◽  
Cady Block ◽  
Todd Monroe ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Pilot Study ◽  
African Americans ◽  
Resting State ◽  
Mindfulness Practice ◽  
Control Group ◽  
Breathing Exercises ◽  
Resting State Network

Abstract The resting state network (RSN) is a target of interest in neurodegenerative research, with evidence linking functional connectivity of its constituent nodes with mild cognitive impairment and dementia. Given the emerging linkage between Alzheimer’s disease and related dementia disorders (ADRD) and hypertension (HTN), non-pharmacological interventions that promote RSN connectivity and blood pressure are needed. The purpose of this pilot study protocol is to deliver a novel intervention, combining mindfulness and the Dietary Approaches to Stop Hypertension (DASH), to improve RSN connectivity and blood pressure in African American (AA) older adults with MCI and HTN. Thirty-six AAs aged 65 and older will be randomized to mindfulness plus DASH, attention control (non-health related education), or a control group. The Mindfulness in Motion (MIM) plus DASH intervention is delivered in 8-weekly group sessions of 6-10 participants. MIM includes mindful movements from chair/standing, breathing exercises and guided meditation. The DASH intervention uses a critical thinking approach that involves problem solving, goal setting, reflection, and developing self-efficacy. Both components are culturally tailored for older African Americans. Cognitive examination, diet and mindfulness practice surveys, blood pressure, and functional magnetic resonance imaging (RSN) data are collected at baseline and 3 months. Forty-eight AAs were screened and 17 were enrolled (women= 13; men= 4) to date. Of the 17 enrolled, 7 were eligible for neuroimaging. Findings from this pilot study may provide the preliminary evidence that MIM plus DASH may improve RSN connectivity and blood pressure in this population at risk for ADRD.

scholarly journals Significant shared heritability underlies suicide attempt and clinically predicted probability of attempting suicide

2019 ◽  
Vol 25 (10) ◽  
pp. 2422-2430 ◽  
Author(s):  
Douglas M. Ruderfer ◽  
Colin G. Walsh ◽  
Matthew W. Aguirre ◽  
Yosuke Tanigawa ◽  
Jessica D. Ribeiro ◽  
...  
Keyword(s):  
Genetic Variation ◽  
Suicide Attempt ◽  
Genetic Correlation ◽  
Genetic Profile ◽  
Polygenic Risk Score ◽  
Uk Biobank ◽  
Common Genetic Variation ◽  
Genetic Studies ◽  
Patient Reported ◽  
The Uk

Abstract Suicide accounts for nearly 800,000 deaths per year worldwide with rates of both deaths and attempts rising. Family studies have estimated substantial heritability of suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies has remained a challenge. We utilized two different approaches in independent datasets to characterize the contribution of common genetic variation to suicide attempt. The first is a patient reported suicide attempt phenotype asked as part of an online mental health survey taken by a subset of participants (n = 157,366) in the UK Biobank. After quality control, we leveraged a genotyped set of unrelated, white British ancestry participants including 2433 cases and 334,766 controls that included those that did not participate in the survey or were not explicitly asked about attempting suicide. The second leveraged electronic health record (EHR) data from the Vanderbilt University Medical Center (VUMC, 2.8 million patients, 3250 cases) and machine learning to derive probabilities of attempting suicide in 24,546 genotyped patients. We identified significant and comparable heritability estimates of suicide attempt from both the patient reported phenotype in the UK Biobank (h2SNP = 0.035, p = 7.12 × 10−4) and the clinically predicted phenotype from VUMC (h2SNP = 0.046, p = 1.51 × 10−2). A significant genetic overlap was demonstrated between the two measures of suicide attempt in these independent samples through polygenic risk score analysis (t = 4.02, p = 5.75 × 10−5) and genetic correlation (rg = 1.073, SE = 0.36, p = 0.003). Finally, we show significant but incomplete genetic correlation of suicide attempt with insomnia (rg = 0.34–0.81) as well as several psychiatric disorders (rg = 0.26–0.79). This work demonstrates the contribution of common genetic variation to suicide attempt. It points to a genetic underpinning to clinically predicted risk of attempting suicide that is similar to the genetic profile from a patient reported outcome. Lastly, it presents an approach for using EHR data and clinical prediction to generate quantitative measures from binary phenotypes that can improve power for genetic studies.

scholarly journals Resting State Functional MRI in Neonates with Prenatal Opioid Exposure: Analysis of Thalamocortical Functional Connectivity

2020 ◽  
Vol 3 ◽  
Author(s):  
Zoe Guckien ◽  
Jonathan Dietrich ◽  
Ramana Vishnubhotla ◽  
MaKayla Picklesimer ◽  
Christina Sparks ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Public Health Issue ◽  
Control Group ◽  
Seed Region ◽  
Subcortical Structures ◽  
Neural Basis ◽  
Neurodevelopmental Outcomes ◽  
Caudate Nuclei ◽  
The Right

Background/Objective: Prenatal opioid exposure (POE) is a growing public health issue that can result in premature birth, Neonatal Abstinence Syndrome (NAS), and adverse neurodevelopmental outcomes. However, the neural basis for these findings remains relatively unknown. In this study, we aimed to investigate the neural correlates of POE based on neonatal thalamocortical functional connectivity using resting state functional magnetic resonance imaging (rs-fMRI).     Methods: In this prospective, IRB-approved study, nineteen neonates with POE and twenty opioid naive (ON) controls underwent non-invasive MRI during natural sleep at mean post-menstrual age (PMA) of 44.7 ± 2.6 and 44.6 ± 2.6 weeks respectively. MR imaging included anatomic T2-weighted images and rs-fMRI. General Linear Model (GLM) seed-based whole brain functional connectivity analysis was performed for each subject, with the right and left thalamus as distinct seed regions. Unpaired mixed-effects group analyses between POE and ON groups were conducted for each seed region corrected for PMA and sex.    Results: Thalamic connectivity to cortical and subcortical structures differed in the POE group compared to the ON control group. The POE group exhibited higher functional connectivity to deep gray structures, frontal, medial prefrontal, parietal, occipital, and anterior temporal cortices compared to controls. The POE group exhibited lower connectivity to the nuclei accumbentes, bilateral caudate nuclei, posterior cingulate gyri, superior frontal gyri, insular, and dorsolateral prefrontal cortices.     Conclusion and Potential Impact: Overall, these novel results suggest the presence of opioid exposure-related alterations in thalamic functional connectivity. Given that the thalamus plays a crucial role in early brain development, the described alterations in thalamocortical and thalamic-subcortical connectivity may have implications in stratifying risk and informing treatment for the adverse neurodevelopmental outcomes associated with POE. Future studies should explore the relationship between POE-associated disruptions in thalamic connectivity and developmental outcomes. 

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