Using resting-state intrinsic network connectivity to identify suicide risk in mood disorders

AbstractBackgroundLittle is known about the neural substrates of suicide risk in mood disorders. Improving the identification of biomarkers of suicide risk, as indicated by a history of suicide-related behavior (SB), could lead to more targeted treatments to reduce risk.MethodsParticipants were 18 young adults with a mood disorder with a history of SB (as indicated by endorsing a past suicide attempt), 60 with a mood disorder with a history of suicidal ideation (SI) but not SB, 52 with a mood disorder with no history of SI or SB (MD), and 82 healthy comparison participants (HC). Resting-state functional connectivity within and between intrinsic neural networks, including cognitive control network (CCN), salience and emotion network (SEN), and default mode network (DMN), was compared between groups.ResultsSeveral fronto-parietal regions (k > 57, p < 0.005) were identified in which individuals with SB demonstrated distinct patterns of connectivity within (in the CCN) and across networks (CCN-SEN and CCN-DMN). Connectivity with some of these same regions also distinguished the SB group when participants were re-scanned after 1–4 months. Extracted data defined SB group membership with good accuracy, sensitivity, and specificity (79–88%).ConclusionsThese results suggest that individuals with a history of SB in the context of mood disorders may show reliably distinct patterns of intrinsic network connectivity, even when compared to those with mood disorders without SB. Resting-state fMRI is a promising tool for identifying subtypes of patients with mood disorders who may be at risk for suicidal behavior.

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Using Resting State Intrinsic Network Connectivity to Identify Suicide Risk in Mood Disorders

International Journal of Psychophysiology ◽

10.1016/j.ijpsycho.2018.07.436 ◽

2018 ◽

Vol 131 ◽

pp. S165

Author(s):

J.P. Stange ◽

L. Jenkins ◽

S. Pocius ◽

K. Kreutzer ◽

K. Bessette ◽

...

Keyword(s):

Mood Disorders ◽

Resting State ◽

Suicide Risk ◽

Network Connectivity

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Impact of Binge Drinking During College on Resting State Functional Connectivity

10.1101/2021.02.09.430381 ◽

2021 ◽

Author(s):

Tien T. Tong ◽

Jatin G. Vaidya ◽

John R. Kramer ◽

Samuel Kuperman ◽

Douglas R. Langbehn ◽

...

Keyword(s):

Functional Connectivity ◽

Binge Drinking ◽

Resting State ◽

Brain Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Brain Regions ◽

Resting State Functional Connectivity ◽

Functional Connection ◽

Level Analysis

AbstractAimThe current study aimed to examine the longitudinal effects of standard binge drinking (4+/5+ drinks for females/males in 2 hours) and extreme binge drinking (8+/10+ drinks for females/males in 2 hours) on resting state functional connectivity.Method119 college students with distinct alcohol bingeing patterns (35 non-bingeing controls, 44 standard bingers, and 40 extreme bingers) were recruited to ensure variability in bingeing frequency. Resting state fMRI scans were obtained at time 1 when participants were college freshmen and sophomores and again approximately two years later. On four occasions during the 2-year period between scans, participants reported monthly standard and extreme binge drinking for the past 6 months. Association between bingeing and change in functional connectivity was studied using both network-level and edge-level analysis. Network connectivity was calculated by aggregating multiple edges (a functional connection between any two brain regions) affiliated with the same network. The network-level analysis used mixed-effects models to assess the association between standard/extreme binge drinking and change in network connectivity, focusing on canonical networks often implicated in substance misuse. On the other hand, the edge-level analysis tested the relationship between bingeing and change in whole-brain connectivity edges using connectome-based predictive modeling (CPM).ResultsFor network-level analysis, higher standard bingeing was associated with a decrease in connectivity between Default Mode Network-Ventral Attention Network (DMN-VAN) from time 1 to time 2, controlling for the initial binge groups at time 1, longitudinal network changes, in-scanner motion and other demographic covariates. For edge-level analysis, the CPM failed to identify a generalizable predictive model of cumulative standard/extreme bingeing from change in connectivity edges.ConclusionsOur findings suggest that binge drinking is associated with abnormality in networks implicated in attention allocation and self-focused processes, which, in turn, have been implicated in rumination, craving, and relapse. More extensive alterations in functional connectivity might be observed with heavier or longer binge drinking pattern.

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Resting-State Connectivity Features of Resilience to Childhood Maltreatment in Those With and Without a History of Mood Disorder

Biological Psychiatry ◽

10.1016/j.biopsych.2021.02.452 ◽

2021 ◽

Vol 89 (9) ◽

pp. S177

Author(s):

Leah Thomas ◽

Melinda Westlund Schreiner ◽

Katie Bessette ◽

Rebecca Easter ◽

Alina Dillahunt ◽

...

Keyword(s):

Mood Disorder ◽

Childhood Maltreatment ◽

Resting State ◽

History Of ◽

Resting State Connectivity

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Obtaining a Family Psychiatric History: Is it Worth the Effort?

The Canadian Journal of Psychiatry ◽

10.1177/070674379303800904 ◽

1993 ◽

Vol 38 (9) ◽

pp. 590-594 ◽

Cited By ~ 2

Author(s):

Ronald A. Remick ◽

Adele D. Sadovnick ◽

Boris Gimbarzevsky ◽

Raymond W. Lam ◽

Athanasios P. Zis ◽

...

Keyword(s):

Family History ◽

Psychiatric Disorder ◽

Mood Disorder ◽

Mood Disorders ◽

Medical Records ◽

Psychiatric History ◽

First Degree Relatives ◽

History Of ◽

Generated Family ◽

Index Cases

The purpose of this study was to determine whether, for first-degree relatives of patients presenting to a mood disorders clinic, family history information on psychiatric conditions collected by a psychiatrist and incorporated into the patient's medical records is as informative as that gathered during an interview specifically designed to collect family history data. The study group consisted of 472 first-degree relatives of 78 randomly selected index cases from a large mood disorders genetic database. Family history of psychiatric disorders recorded in regular psychiatric medical records (“clinician history”), and data obtained by a genetic counsellor administering specific family psychiatric history questionnaires to patients and multiple family informants (“family history”) were compared using a kappa statistic. Good agreement between the two methods on the presence or absence of a psychiatric disorder was found among first-degree relatives of index cases, but poor agreement was found with respect to the presence or absence of a specific mood disorder diagnosis(es) in a relative. The results suggest that a clinician-generated family psychiatric history is sensitive to the presence or absence of a psychiatric disorder when compared to a more structured detailed genetic interview. However, for research purposes, a clinician-generated family psychiatric history of a specific mood disorder diagnosis, without supporting collateral information, may not be reliable for use in supporting a mood disorder diagnosis in a patient and/or his relatives.

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Local GABA concentration is related to network-level resting functional connectivity

eLife ◽

10.7554/elife.01465 ◽

2014 ◽

Vol 3 ◽

Cited By ~ 86

Author(s):

Charlotte J Stagg ◽

Velicia Bachtiar ◽

Ugwechi Amadi ◽

Christel A Gudberg ◽

Andrei S Ilie ◽

...

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Primary Motor Cortex ◽

Empirical Studies ◽

Network Connectivity ◽

Resting State Fmri ◽

Resonance Spectroscopy ◽

Resting State Networks ◽

Inhibitory Neurotransmitter ◽

Motor Network

Anatomically plausible networks of functionally inter-connected regions have been reliably demonstrated at rest, although the neurochemical basis of these ‘resting state networks’ is not well understood. In this study, we combined magnetic resonance spectroscopy (MRS) and resting state fMRI and demonstrated an inverse relationship between levels of the inhibitory neurotransmitter GABA within the primary motor cortex (M1) and the strength of functional connectivity across the resting motor network. This relationship was both neurochemically and anatomically specific. We then went on to show that anodal transcranial direct current stimulation (tDCS), an intervention previously shown to decrease GABA levels within M1, increased resting motor network connectivity. We therefore suggest that network-level functional connectivity within the motor system is related to the degree of inhibition in M1, a major node within the motor network, a finding in line with converging evidence from both simulation and empirical studies.

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Altered hypothalamus functional connectivity with limbic system in fibromyalgia and the modulation effect of intervention

10.21203/rs.2.20627/v1 ◽

2020 ◽

Author(s):

Jian Kong ◽

Yiting Huang ◽

Jiao Liu ◽

Siyi Yu ◽

Ming Cheng ◽

...

Keyword(s):

Functional Connectivity ◽

Limbic System ◽

Resting State ◽

Tai Chi ◽

Effective Connectivity ◽

Resting State Fmri ◽

Trial Registration ◽

Modulation Effect ◽

Resting State Functional Connectivity ◽

Before And After

Abstract Background: This study aims to investigate the resting state functional connectivity (rsFC) changes of the hypothalamus in Fibromyalgia patients and the modulation effect of effective treatments. Methods: Fibromyalgia patients and matched healthy controls (HC’s) were recruited. Resting state fMRI data were collected from fibromyalgia patients before and after a 12-week Tai Chi intervention and once from HC’s. Results: Data analysis showed that fibromyalgia patients displayed significantly decreased medial hypothalamus (MH) rsFC with the thalamus and amygdala when compared to HC’s at baseline. After the intervention, fibromyalgia patients showed increased (normalized) MH rsFC in the thalamus and amygdala. Effective connectivity analysis showed disrupted MH and thalamus interaction in fibromyalgia, which nonetheless could be partially restored by Tai Chi. Conclusions: Elucidating the role of the diencephalon and limbic system in the pathophysiology and development of fibromyalgia may facilitate the development of new treatment methods for this prevalent disorder. Trial registration: Trial registration ClinicalTrials.gov Identifier: NCT02407665. Registered 3 April 2015 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT02407665

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The effect of global signal regression on DCM estimates of noise and effective connectivity from resting state fMRI

10.1101/634063 ◽

2019 ◽

Author(s):

Hannes Almgren ◽

Frederik Van de Steen ◽

Adeel Razi ◽

Karl Friston ◽

Daniele Marinazzo

Keyword(s):

Functional Connectivity ◽

Resting State ◽

Effective Connectivity ◽

Network Connectivity ◽

Resting State Fmri ◽

Measurement Noise ◽

Resting State Networks ◽

Cross Sectional ◽

Global Signal ◽

Leibler Divergence

AbstractThe influence of the global BOLD signal on resting state functional connectivity in fMRI data remains a topic of debate, with little consensus. In this study, we assessed the effects of global signal regression (GSR) on effective connectivity within and between resting-state networks – as estimated with dynamic causal modelling (DCM) for resting state fMRI (rsfMRI). DCM incorporates a forward (generative) model that quantifies the contribution of different types of noise (including global measurement noise), effective connectivity, and (neuro)vascular processes to functional connectivity measurements. DCM analyses were applied to two different designs; namely, longitudinal and cross-sectional designs. In the modelling of longitudinal designs, we included four extensive longitudinal resting state fMRI datasets with a total number of 20 subjects. In the analysis of cross-sectional designs, we used rsfMRI data from 361 subjects from the Human Connectome Project. We hypothesized that (1) GSR would have no discernible impact on effective connectivity estimated with DCM, and (2) GSR would be reflected in the parameters representing global measurement noise. Additionally, we performed comparative analyses of the informative value of data with and without GSR. Our results showed negligible to small effects of GSR on connectivity within small (separately estimated) RSNs. For between-network connectivity, we found two important effects: the effect of GSR on between-network connectivity (averaged over all connections) was negligible to small, while the effect of GSR on individual connections was non-negligible. Contrary to our expectations, we found either no effect (in the longitudinal designs) or a non-specific (cross-sectional design) effect of GSR on parameters representing (global) measurement noise. Data without GSR were found to be more informative than data with GSR; however, in small resting state networks the precision of posterior estimates was greater using data after GSR. In conclusion, GSR is a minor concern in DCM studies; however, individual between-network connections (as opposed to average between-network connectivity) and noise parameters should be interpreted quantitatively with some caution. The Kullback-Leibler divergence of the posterior from the prior, together with the precision of posterior estimates, might offer a useful measure to assess the appropriateness of GSR, when nuancing data features in resting state fMRI.

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Effects of long-term cocaine self-administration on brain resting-state functional connectivity in nonhuman primates

Translational Psychiatry ◽

10.1038/s41398-020-01101-z ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Stephen J. Kohut ◽

Dionyssios Mintzopoulos ◽

Brian D. Kangas ◽

Hannah Shields ◽

Kelly Brown ◽

...

Keyword(s):

Functional Connectivity ◽

Cognitive Processing ◽

Resting State ◽

Resting State Fmri ◽

Brain Regions ◽

Anterior Cingulate ◽

Resting State Functional Connectivity ◽

Self Administration ◽

Chronic Cocaine

AbstractLong-term cocaine use is associated with a variety of neural and behavioral deficits that impact daily function. This study was conducted to examine the effects of chronic cocaine self-administration on resting-state functional connectivity of the dorsal anterior cingulate (dACC) and putamen—two brain regions involved in cognitive function and motoric behavior—identified in a whole brain analysis. Six adult male squirrel monkeys self-administered cocaine (0.32 mg/kg/inj) over 140 sessions. Six additional monkeys that had not received any drug treatment for ~1.5 years served as drug-free controls. Resting-state fMRI imaging sessions at 9.4 Tesla were conducted under isoflurane anesthesia. Functional connectivity maps were derived using seed regions placed in the left dACC or putamen. Results show that cocaine maintained robust self-administration with an average total intake of 367 mg/kg (range: 299–424 mg/kg). In the cocaine group, functional connectivity between the dACC seed and regions primarily involved in motoric behavior was weaker, whereas connectivity between the dACC seed and areas implicated in reward and cognitive processing was stronger. In the putamen seed, weaker widespread connectivity was found between the putamen and other motor regions as well as with prefrontal areas that regulate higher-order executive function; stronger connectivity was found with reward-related regions. dACC connectivity was associated with total cocaine intake. These data indicate that functional connectivity between regions involved in motor, reward, and cognitive processing differed between subjects with recent histories of cocaine self-administration and controls; in dACC, connectivity appears to be related to cumulative cocaine dosage during chronic exposure.

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S147. FUNCTIONAL BRAIN CONNECTIVITY DATA IMPROVE CLINICAL OUTCOME PREDICTION IN YOUTH AT RISK FOR PSYCHOSIS

Schizophrenia Bulletin ◽

10.1093/schbul/sbaa031.213 ◽

2020 ◽

Vol 46 (Supplement_1) ◽

pp. S92-S92

Author(s):

Guusje Collin ◽

Alfonso Nieto-Castanon ◽

Martha Shenton ◽

Ofer Pasternak ◽

Sinead Kelly ◽

...

Keyword(s):

Prediction Model ◽

Resting State ◽

Outcome Prediction ◽

Brain Connectivity ◽

Network Connectivity ◽

Resting State Functional Connectivity ◽

Clinical Predictors ◽

Functional Brain ◽

Improved Outcome ◽

Better Than

Abstract Background Improved outcome prediction in individuals at high risk for psychosis may facilitate targeted early intervention. Studies suggest that improved outcome prediction may be achieved through the use of neurocognitive or neuroimaging data, on their own or in addition to clinical data. This study examines whether adding resting-state functional connectivity data to validated clinical predictors of psychosis improve outcome prediction in the prodromal stage. Methods This study involves 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome after one-year follow-up, participants were separated into three outcome categories: good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Resting-state fMRI data were acquired for each participant and processed using the Conn toolbox, including rigorous motion correction. Multinomial logistic regression analysis and leave-one-out cross-validation were used to assess the performance of three prediction models: 1) a clinical-only model using validated clinical predictors from the NAPLS-2 psychosis-risk calculator, 2) an fMRI-only model using measures of functional connectome organization and within/between-network connectivity among established resting-state networks, and 3) a combined clinical and fMRI prediction model. Model performance was assessed using the harmonic mean of the positive predictive value and sensitivity for each outcome category. This F1 measure was compared to expected chance-levels using a permutation test with 1,000 sampled permutations in order to evaluate the statistical significance of the model’s prediction. Results The clinical-only prediction model failed to achieve a significant level of outcome prediction (F1 = 0.32, F1-chance = 0.26 □ 0.06, p = .154). The fMRI-only model did predict clinical outcome to a significant degree (F1 = 0.41, F1-chance = 0.29 □ 0.06, p = .016), but the combined clinical and fMRI prediction model showed the best performance (F1 = 0.46, F1-chance = 0.29 □ 0.06, p < .001). On average, positive predictive values (reflecting the probability that an outcome label predicted by the model was correct) were 39% better than chance-level and 32% better than the clinical-only model. Analyzing the contribution of individual predictor variables showed that GAF functional decline, a family history of psychosis, and performance on the Hopkins Verbal Learning Test were the most influential clinical predictors, whereas modular connectome organization, default-mode and fronto-parietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, cerebellum, and sensorimotor networks were the leading fMRI predictors. Discussion This study’s findings suggest that functional brain abnormalities reflected by alterations in resting-state functional connectivity precede and may drive subsequent changes in clinical functioning. Moreover, the findings show that markers of functional brain connectivity may be useful for improving early identification and clinical decision-making in prodromal psychosis.

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

Frontiers in Psychiatry ◽

10.3389/fpsyt.2020.597770 ◽

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.

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