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Predictors of Attrition in Longitudinal Neuroimaging Research: Inhibitory Control, Head Movement, and Resting-State Functional Connectivity.

ABSTRACT: Attrition is a major problem in longitudinal neuroimaging studies, as it may lead to unreliable estimates of the stability of trait-like processes over time, of the identification of risk factors for clinical outcomes, and of the effects of treatment. Identification of characteristics associated with attrition has implications for participant recruitment and participant retention to achieve representative longitudinal samples. We investigated inhibitory control deficits, head motion, and resting-state functional connectivity within the cognitive control network (CCN) as predictors of attrition. Ninety-seven individuals with remitted major depressive disorder or healthy controls completed a functional magnetic resonance imaging scan, which included a go/no-go task and resting-state functional connectivity. Approximately 2 months later, participants were contacted and invited to return for a second scan. Seventeen individuals were lost to follow-up or declined to participate in the follow-up scan. Worse inhibitory control was correlated with greater movement within the scanner, and each predicted a greater likelihood of attrition, with movement mediating the effects of inhibitory control on attrition. Individuals who dropped out of the study exhibited greater movement than nondropouts across 9 of the 14 runs of the scan, with medium-to-large effect sizes. Finally, exploratory analyses suggested that attenuated resting-state connectivity with the CCN (particularly in bilateral dorsolateral prefrontal cortex) was associated with greater likelihood of attrition after accounting for head motion at several levels of analysis. Inhibitory control and movement within the scanner are associated with attrition, and should be considered for strategic oversampling and participant retention strategies to ensure generalizability of results in longitudinal studies.

SUBMITTER: Stange JP

PROVIDER: S-EPMC6249664 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Predictors of Attrition in Longitudinal Neuroimaging Research: Inhibitory Control, Head Movement, and Resting-State Functional Connectivity.

Stange Jonathan P JP Jenkins Lisanne M LM Bessette Katie L KL Kling Leah R LR Bark John S JS Shepard Robert R Hamlat Elissa J EJ DelDonno Sophie S Phan K Luan KL Passarotti Alessandra M AM Ajilore Olusola O Langenecker Scott A SA

Brain connectivity 20181101 9

Attrition is a major problem in longitudinal neuroimaging studies, as it may lead to unreliable estimates of the stability of trait-like processes over time, of the identification of risk factors for clinical outcomes, and of the effects of treatment. Identification of characteristics associated with attrition has implications for participant recruitment and participant retention to achieve representative longitudinal samples. We investigated inhibitory control deficits, head motion, and resting ...[more]

PMID: 30411975

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Project description:BackgroundHyperkinetic movement disorders (HMD) manifest as abnormal and uncontrollable movements. Despite reported involvement of several neural circuits, exact connectivity profiles remain elusive.ObjectivesProviding a comprehensive literature review of resting-state brain connectivity alterations using resting-state fMRI (rs-fMRI). We additionally discuss alterations from the perspective of brain networks, as well as correlations between connectivity and clinical measures.MethodsA systematic review was performed according to PRISMA guidelines and searching PubMed until October 2022. Rs-fMRI studies addressing ataxia, chorea, dystonia, myoclonus, tics, tremor, and functional movement disorders (FMD) were included. The standardized mean difference was used to summarize findings per region in the Automated Anatomical Labeling atlas for each phenotype. Furthermore, the activation likelihood estimation meta-analytic method was used to analyze convergence of significant between-group differences per phenotype. Finally, we conducted hierarchical cluster analysis to provide additional insights into commonalities and differences across HMD phenotypes.ResultsMost articles concerned tremor (51), followed by dystonia (46), tics (19), chorea (12), myoclonus (11), FMD (11), and ataxia (8). Altered resting-state connectivity was found in several brain regions: in ataxia mainly cerebellar areas; for chorea, the caudate nucleus; for dystonia, sensorimotor and basal ganglia regions; for myoclonus, the thalamus and cingulate cortex; in tics, the basal ganglia, cerebellum, insula, and frontal cortex; for tremor, the cerebello-thalamo-cortical circuit; finally, in FMD, frontal, parietal, and cerebellar regions. Both decreased and increased connectivity were found for all HMD. Significant spatial convergence was found for dystonia, FMD, myoclonus, and tremor. Correlations between clinical measures and resting-state connectivity were frequently described.ConclusionKey brain regions contributing to functional connectivity changes across HMD often overlap. Possible increases and decreases of functional connections of a specific region emphasize that HMD should be viewed as a network disorder. Despite the complex interplay of physiological and methodological factors, this review serves to gain insight in brain connectivity profiles across HMD phenotypes.

Project description:Despite the heterogeneous symptom presentation and complex etiology of major depressive disorder (MDD), functional neuroimaging studies have shown with remarkable consistency that dysfunction in mesocorticolimbic brain systems are central to the disorder. Relatively less research has focused on the identification of biological markers of response to antidepressant treatment that would serve to improve the personalized delivery of empirically supported antidepressant interventions. In the present study, we investigated whether resting-state functional brain connectivity (rs-fcMRI) predicted response to Behavioral Activation Treatment for Depression, an empirically validated psychotherapy modality designed to increase engagement with rewarding stimuli and reduce avoidance behaviors. Twenty-three unmedicated outpatients with MDD and 20 matched nondepressed controls completed rs-fcMRI scans after which the MDD group received an average of 12 sessions of psychotherapy. The mean change in Beck Depression Inventory-II scores after psychotherapy was 12.04 points, a clinically meaningful response. Resting-state neuroimaging data were analyzed with a seed-based approach to investigate functional connectivity with four canonical resting-state networks: the default mode network, the dorsal attention network, the executive control network, and the salience network. At baseline, the MDD group was characterized by relative hyperconnectivity of multiple regions with precuneus, anterior insula, dorsal anterior cingulate cortex (dACC), and left dorsolateral prefrontal cortex seeds and by relative hypoconnectivity with intraparietal sulcus, anterior insula, and dACC seeds. Additionally, connectivity of the precuneus with the left middle temporal gyrus and connectivity of the dACC with the parahippocampal gyrus predicted the magnitude of pretreatment MDD symptoms. Hierarchical linear modeling revealed that response to psychotherapy in the MDD group was predicted by pretreatment connectivity of the right insula with the right middle temporal gyrus and the left intraparietal sulcus with the orbital frontal cortex. These results add to the nascent body of literature investigating pretreatment rs-fcMRI predictors of antidepressant treatment response and is the first study to examine rs-fcMRI predictors of response to psychotherapy.

Dataset Information

Predictors of Attrition in Longitudinal Neuroimaging Research: Inhibitory Control, Head Movement, and Resting-State Functional Connectivity.

Publications

Predictors of Attrition in Longitudinal Neuroimaging Research: Inhibitory Control, Head Movement, and Resting-State Functional Connectivity.

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