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Daood M, Magal N, Peled-Avron L, Nevat M, Ben-Hayun R, Aharon-Peretz J, Tomer R, Admon R. Graph analysis uncovers an opposing impact of methylphenidate on connectivity patterns within default mode network sub-divisions. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2024; 20:15. [PMID: 38902791 PMCID: PMC11191242 DOI: 10.1186/s12993-024-00242-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Accepted: 06/13/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND The Default Mode Network (DMN) is a central neural network, with recent evidence indicating that it is composed of functionally distinct sub-networks. Methylphenidate (MPH) administration has been shown before to modulate impulsive behavior, though it is not yet clear whether these effects relate to MPH-induced changes in DMN connectivity. To address this gap, we assessed the impact of MPH administration on functional connectivity patterns within and between distinct DMN sub-networks and tested putative relations to variability in sub-scales of impulsivity. METHODS Fifty-five right-handed healthy adults underwent two resting-state functional MRI (rs-fMRI) scans, following acute administration of either MPH (20 mg) or placebo, via a randomized double-blind placebo-controlled design. Graph modularity analysis was implemented to fractionate the DMN into distinct sub-networks based on the impact of MPH (vs. placebo) on DMN connectivity patterns with other neural networks. RESULTS MPH administration led to an overall decreased DMN connectivity, particularly with the auditory, cinguloopercular, and somatomotor networks, and increased connectivity with the parietomedial network. Graph analysis revealed that the DMN could be fractionated into two distinct sub-networks, with one exhibiting MPH-induced increased connectivity and the other decreased connectivity. Decreased connectivity of the DMN sub-network with the cinguloopercular network following MPH administration was associated with elevated impulsivity and non-planning impulsiveness. CONCLUSION Current findings highlight the intricate effects of MPH administration on DMN rs-fMRI connectivity, uncovering its opposing impact on distinct DMN sub-divisions. MPH-induced dynamics in DMN connectivity patterns with other neural networks may account for some of the effects of MPH administration on impulsive behavior.
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Affiliation(s)
- Maryana Daood
- School of Psychological Sciences, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, 31905, Israel
- Sakhnin College of Education, Sakhnin, Israel
| | - Noa Magal
- School of Psychological Sciences, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, 31905, Israel
| | - Leehe Peled-Avron
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
- Department of Psychology, Bar-Ilan University, Ramat-Gan, Israel
| | - Michael Nevat
- School of Psychological Sciences, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, 31905, Israel
| | - Rachel Ben-Hayun
- Stroke and Cognition Institute, Rambam Health Care Campus, Haifa, Israel
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Judith Aharon-Peretz
- Stroke and Cognition Institute, Rambam Health Care Campus, Haifa, Israel
- Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | - Rachel Tomer
- School of Psychological Sciences, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, 31905, Israel
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel
| | - Roee Admon
- School of Psychological Sciences, University of Haifa, 199 Aba Khoushy Ave. Mount Carmel, Haifa, 31905, Israel.
- The Integrated Brain and Behavior Research Center (IBBRC), University of Haifa, Haifa, Israel.
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Koloski MF, Terry A, Lee N, Ramanathan DS. Methylphenidate, but not citalopram, decreases impulsive choice in rats performing a temporal discounting task. Front Psychiatry 2024; 15:1385502. [PMID: 38779546 PMCID: PMC11109432 DOI: 10.3389/fpsyt.2024.1385502] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2024] [Accepted: 04/23/2024] [Indexed: 05/25/2024] Open
Abstract
Introduction Drugs targeting monoamine systems remain the most common treatment for disorders with impulse control impairments. There is a body of literature suggesting that drugs affecting serotonin reuptake and dopamine reuptake can modulate distinct aspects of impulsivity - though such tests are often performed using distinct behavioral tasks prohibiting easy comparisons. Methods Here, we directly compare pharmacologic agents that affect dopamine (methylphenidate) vs serotonin (citalopram) manipulations on choice impulsivity in a temporal discounting task where rats could choose between a small, immediate reward or a large reward delayed at either 2 or 10s. In control conditions, rats preferred the large reward at a small (2s) delay and discounted the large reward at a long (10s) delay. Results Methylphenidate, a dopamine transport inhibitor that blocks reuptake of dopamine, dose-dependently increased large reward preference in the long delay (10s) block. Citalopram, a selective serotonin reuptake inhibitor, had no effect on temporal discounting behavior. Impulsive behavior on the temporal discounting task was at least partially mediated by the nucleus accumbens shell. Bilateral lesions to the nucleus accumbens shell reduced choice impulsivity during the long delay (10s) block. Following lesions, methylphenidate did not impact impulsivity. Discussion Our results suggest that striatal dopaminergic systems modulate choice impulsivity via actions within the nucleus accumbens shell, whereas serotonin systems may regulate different aspects of behavioral inhibition/impulsivity.
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Affiliation(s)
- Miranda F. Koloski
- Mental Health, VA San Diego Medical Center, San Diego, CA, United States
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA, United States
- Department of Psychiatry, University of California-San Diego, San Diego, CA, United States
| | - Alyssa Terry
- Mental Health, VA San Diego Medical Center, San Diego, CA, United States
| | - Noelle Lee
- Mental Health, VA San Diego Medical Center, San Diego, CA, United States
| | - Dhakshin S. Ramanathan
- Mental Health, VA San Diego Medical Center, San Diego, CA, United States
- Center of Excellence for Stress and Mental Health, VA San Diego Medical Center, San Diego, CA, United States
- Department of Psychiatry, University of California-San Diego, San Diego, CA, United States
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Francis AN, Sebille S, Whitfield-Gabrieli S, Camprodon JA. Multimodal 7T imaging reveals enhanced functional coupling between salience and frontoparietal networks in young adult tobacco cigarette smokers. Brain Imaging Behav 2024:10.1007/s11682-024-00882-x. [PMID: 38639847 DOI: 10.1007/s11682-024-00882-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/04/2024] [Indexed: 04/20/2024]
Abstract
Tobacco cigarette smoking is associated with disrupted brain network dynamics in resting brain networks including the Salience (SN) and Fronto parietal (FPN). Unified multimodal methods [Resting state connectivity analysis, Diffusion Tensor Imaging (DTI), neurite orientation dispersion and density imaging (NODDI), and cortical thickness analysis] were employed to test the hypothesis that the impact of cigarette smoking on the balance among these networks is due to alterations in white matter connectivity, microstructural architecture, functional connectivity and cortical thickness (CT) and that these metrics define fundamental differences between people who smoke and nonsmokers. Multimodal analyses of previously collected 7 Tesla MRI data via the Human Connectome Project were performed on 22 people who smoke (average number of daily cigarettes was 10 ± 5) and 22 age- and sex-matched nonsmoking controls. First, functional connectivity analysis was used to examine SN-FPN-DMN interactions between people who smoke and nonsmokers. The anatomy of these networks was then assessed using DTI and CT analyses while microstructural architecture of WM was analyzed using the NODDI toolbox. Seed-based connectivity analysis revealed significantly enhanced within network [p = 0.001 FDR corrected] and between network functional coupling of the salience and R-frontoparietal networks in people who smoke [p = 0.004 FDR corrected]. The network connectivity was lateralized to the right hemisphere. Whole brain diffusion analysis revealed no significant differences between people who smoke and nonsmokers in Fractional Anisotropy, Mean diffusivity and in neurite orienting and density. There were also no significant differences in CT in the hubs of these networks. Our results demonstrate that tobacco cigarette smoking is associated with enhanced functional connectivity, but anatomy is largely intact in young adults. Whether this enhanced connectivity is pre-existing, transient or permanent is not known. The observed enhanced connectivity in resting state networks may contribute to the maintenance of smoking frequency.
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Affiliation(s)
- Alan N Francis
- Department of Neuroscience, University of Texas, Rio Grande Valley, Edinburg, TX, USA.
| | - Sophie Sebille
- Department of Neuroscience GHU Paris Psychiatrie et Neurosciences, Paris, France
| | | | - Joan A Camprodon
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, USA
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4
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Jung WH, Kim E. Different topological patterns in structural covariance networks between high and low delay discounters. Front Psychol 2023; 14:1210652. [PMID: 37711326 PMCID: PMC10498536 DOI: 10.3389/fpsyg.2023.1210652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction People prefer immediate over future rewards because they discount the latter's value (a phenomenon termed "delay discounting," used as an index of impulsivity). However, little is known about how the preferences are implemented in brain in terms of the coordinated pattern of large-scale structural brain networks. Methods To examine this question, we classified high discounting group (HDG) and low discounting group (LDG) in young adults by assessing their propensity for intertemporal choice. We compared global and regional topological properties in gray matter volume-based structural covariance networks between two groups using graph theoretical analysis. Results HDG had less clustering coefficient and characteristic path length over the wide sparsity range than LDG, indicating low network segregation and high integration. In addition, the degree of small-worldness was more significant in HDG. Locally, HDG showed less betweenness centrality (BC) in the parahippocampal gyrus and amygdala than LDG. Discussion These findings suggest the involvement of structural covariance network topology on impulsive choice, measured by delay discounting, and extend our understanding of how impulsive choice is associated with brain morphological features.
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Affiliation(s)
- Wi Hoon Jung
- Department of Psychology, Gachon University, Seongnam, Republic of Korea
| | - Euitae Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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Seeing the future: Connectome strength and network efficiency in visual network predict individual ability of episodic future thinking. Neuropsychologia 2023; 179:108451. [PMID: 36535422 DOI: 10.1016/j.neuropsychologia.2022.108451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 11/10/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Episodic future thinking (EFT) refers to the critical ability that enables people to construct and pre-experience the vivid mental imagery about future events, which impacts on the decision-making for individuals and group. Although EFT is generally believed to have a visual nature by theorists, little neuroscience evidence has been provided to verify this assumption. Here, by employing the approach of connectome-based predictive modeling (CPM) and graph-theoretical analysis, we analyzed resting-state functional brain image from 191 participants to predict their variability of EFT ability (leave-one-out cross-validation), and validated the results by applying different parcellation schemas and feature selection thresholds. At the connectome strength level, CPM-based analysis revealed that EFT ability could be predicted by the connectome strength of visual network. Besides, at the network level, graph-theoretical analysis showed that EFT ability could be predicted by the network efficiency of visual network. Moreover, these findings were replicated using different parcellation schemas and feature selection thresholds. These results robustly and collectively supported that the visual network might be one of the neural substrates underlying EFT ability from a comprehensive perspective of resting-state functional connectivity strength and the neural network. This study provides indications on how the function of visual network supports EFT ability, and enhances our understanding of the EFT ability from a neural basis perspective.
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Mehta K, Pines A, Adebimpe A, Larsen B, Bassett DS, Calkins ME, Baller E, Gell M, Patrick LM, Gur RE, Gur RC, Roalf DR, Romer D, Wolf DH, Kable JW, Satterthwaite TD. Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525577. [PMID: 36747838 PMCID: PMC9900814 DOI: 10.1101/2023.01.25.525577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA,Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA,Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Monica E. Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica Baller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Martin Gell
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany
| | - Lauren M. Patrick
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel Romer
- Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W. Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
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7
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Yang F, Li X, Hu P. The Resting-State Neural Network of Delay Discounting. Front Psychol 2022; 13:828929. [PMID: 35360605 PMCID: PMC8962669 DOI: 10.3389/fpsyg.2022.828929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Accepted: 02/07/2022] [Indexed: 11/13/2022] Open
Abstract
Delay discounting is a common phenomenon in daily life, which refers to the subjective value of a future reward decreasing as a function of time. Previous studies have identified several cortical regions involved in delay discounting, but the neural network constructed by the cortical regions of delay discounting is less clear. In this study, we employed resting-state functional magnetic resonance imaging (RS-fMRI) to measure the spontaneous neural activity in a large sample of healthy young adults and used the Monetary Choice Questionnaire to directly measure participants’ level of delay discounting. To identify the neural network of delay discounting at rest, we used an individual difference approach to explore brain regions whose spontaneous activities were related to delay discounting across the whole brain. Then, these brain regions served as seeds to identify the neural network of delay discounting. We found that the fractional amplitude of low-frequency fluctuations (fALFF) of the left insula were positively correlated to delay discounting. More importantly, its connectivity to the anterior cingulate cortex was read out for participants’ behavioral performance in the task of delay discounting. In short, our study provides empirical evidence that insula-anterior cingulate cortex connectivity may serve as a part of the neural network for delay discounting.
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Xiao Z, Chen Z, Chen W, Gao W, He L, Wang Q, Lei X, Qiu J, Feng T, Chen H, Turel O, Bechara A, He Q. OUP accepted manuscript. Cereb Cortex 2022; 32:4605-4618. [PMID: 35059700 PMCID: PMC9383225 DOI: 10.1093/cercor/bhab505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/04/2021] [Accepted: 12/05/2021] [Indexed: 11/14/2022] Open
Abstract
The Coronavirus disease of 2019 (COVID-19) and measures to curb it created population-level changes in male-dominant impulsive and risky behaviors such as violent crimes and gambling. One possible explanation for this is that the pandemic has been stressful, and males, more so than females, tend to respond to stress by altering their focus on immediate versus delayed rewards, as reflected in their delay discounting rates. Delay discounting rates from healthy undergraduate students were collected twice during the pandemic. Discounting rates of males (n=190) but not of females (n=493) increased during the pandemic. Using machine learning, we show that prepandemic functional connectome predict increased discounting rates in males (n=88). Moreover, considering that delay discounting is associated with multiple psychiatric disorders, we found the same neural pattern that predicted increased discounting rates in this study, in secondary datasets of patients with major depression and schizophrenia. The findings point to sex-based differences in maladaptive delay discounting under real-world stress events, and to connectome-based neuromarkers of such effects. They can explain why there was a population-level increase in several impulsive and risky behaviors during the pandemic and point to intriguing questions about the shared underlying mechanisms of stress responses, psychiatric disorders and delay discounting.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | | | - Qinghua He
- Address correspondence to Qinghua He, Faculty of Psychology, Southwest University, 2 Tiansheng Road, 400715 Chongqing, China. , Tel: +86-13647691390
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Bari AA, Sparks H, Levinson S, Wilson B, London ED, Langevin JP, Pouratian N. Amygdala Structural Connectivity Is Associated With Impulsive Choice and Difficulty Quitting Smoking. Front Behav Neurosci 2020; 14:117. [PMID: 32714164 PMCID: PMC7351509 DOI: 10.3389/fnbeh.2020.00117] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 06/11/2020] [Indexed: 11/24/2022] Open
Abstract
Introduction: The amygdala is known to play a role in mediating emotion and possibly addiction. We used probabilistic tractography (PT) to evaluate whether structural connectivity of the amygdala to the brain reward network is associated with impulsive choice and tobacco smoking. Methods: Diffusion and structural MRI scans were obtained from 197 healthy subjects (45 with a history of tobacco smoking) randomly sampled from the Human Connectome database. PT was performed to assess amygdala connectivity with several brain regions. Seed masks were generated, and statistical maps of amygdala connectivity were derived. Connectivity results were correlated with a subject performance both on a delayed discounting task and whether they met specified criteria for difficulty quitting smoking. Results: Amygdala connectivity was spatially segregated, with the strongest connectivity to the hippocampus, orbitofrontal cortex (OFC), and brainstem. Connectivity with the hippocampus was associated with preference for larger delayed rewards, whereas connectivity with the OFC, rostral anterior cingulate cortex (rACC), and insula were associated with preference for smaller immediate rewards. Greater nicotine dependence with difficulty quitting was associated with less hippocampal and greater brainstem connectivity. Scores on the Fagerstrom Test for Nicotine Dependence (FTND) correlated with rACC connectivity. Discussion: These findings highlight the importance of the amygdala-hippocampal-ACC network in the valuation of future rewards and substance dependence. These results will help to identify potential targets for neuromodulatory therapies for addiction and related disorders.
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Affiliation(s)
- Ausaf A Bari
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Hiro Sparks
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Simon Levinson
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Bayard Wilson
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Edythe D London
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine and Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States
| | - Jean-Philippe Langevin
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nader Pouratian
- Department of Neurosurgery, University of California, Los Angeles, Los Angeles, CA, United States
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Cai H, Chen J, Liu S, Zhu J, Yu Y. Brain functional connectome-based prediction of individual decision impulsivity. Cortex 2020; 125:288-298. [PMID: 32113043 DOI: 10.1016/j.cortex.2020.01.022] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 01/15/2020] [Accepted: 01/30/2020] [Indexed: 02/07/2023]
Abstract
Extensive neuroimaging research has attempted to identify neural correlates and predictors of decision impulsivity. However, the nature and extent of decision impulsivity-brain association have varied substantially across studies, likely due to small sample sizes, limited image quality, different imaging measurement selections, and non-specific methodologies. The objective of this study was to develop a reliable predictive model of decision impulsivity-brain relationship in a large sample by applying connectome-based predictive modeling (CPM), a recently developed machine learning approach, to whole-brain functional connectivity data ("neural fingerprints"). For 809 healthy young participants from the Human Connectome Project, high-quality resting-state functional MRI data were utilized to construct brain functional connectome and delay discounting test was used to assess decision impulsivity. Then, CPM with leave-one-out cross-validation was conducted to predict individual decision impulsivity from whole-brain functional connectivity. We found that CPM successfully and reliably predicted the delay discounting scores in novel individuals. Moreover, different feature selection thresholds, parcellation strategies and cross-validation approaches did not significantly influence the prediction results. At the neural level, we observed that the decision impulsivity-associated functional networks included brain regions within default-mode, subcortical, somato-motor, dorsal attention, and visual systems, suggesting that decision impulsivity emerges from highly integrated connections involving multiple intrinsic networks. Our findings not only may expand existing knowledge regarding the neural mechanism of decision impulsivity, but also may present a workable route towards translation of brain imaging findings into real-world economic decision-making.
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Affiliation(s)
- Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jingyao Chen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Siyu Liu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
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11
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Burnette EM, Grodin EN, Lim AC, MacKillop J, Karno MP, Ray LA. Association between impulsivity and neural activation to alcohol cues in heavy drinkers. Psychiatry Res Neuroimaging 2019; 293:110986. [PMID: 31622796 PMCID: PMC7238761 DOI: 10.1016/j.pscychresns.2019.110986] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 09/16/2019] [Accepted: 09/23/2019] [Indexed: 12/20/2022]
Abstract
This study examines associations between two measures of impulsivity and brain response to alcohol taste cues. Impulsivity is both a risk factor for and a consequence of alcohol use and misuse. Frontostriatal circuits are linked to both impulsivity and addiction-related behaviors, including response to alcohol cues. Non-treatment-seeking heavy drinkers (n = 55) completed (i) an fMRI alcohol taste cue-reactivity paradigm; (ii) the monetary choice questionnaire (MCQ), a measure of choice impulsivity where participants choose between smaller, sooner rewards and larger, delayed rewards; (iii) and the UPPS-P Impulsive Behavior Scale, a self-report measure assessing five impulsivity factors. General linear models identified associations between neural alcohol taste cue-reactivity and impulsivity, adjusting for age, gender, and smoking status. Self-reported sensation seeking was positively associated with alcohol taste cue-elicited activation in frontostriatal regions, such that individuals who reported higher sensation seeking displayed greater neural response to alcohol taste cues. Conversely, delay discounting was negatively associated with activation in frontoparietal regions, such that individuals who reported greater discounting showed less cue-elicited activation. There were no significant associations between other self-reported impulsivity subscales and alcohol taste cue-reactivity. These results indicate that sensation seeking is associated with reward responsivity, while delay discounting is associated with recruitment of self-control circuitry.
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Affiliation(s)
- Elizabeth M Burnette
- Interdepartmental Program for Neuroscience, University of California, Los Angeles, CA, USA
| | - Erica N Grodin
- Department of Psychology, University of California, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, University of California, Los Angeles, CA, USA
| | - Aaron C Lim
- Department of Psychology, University of California, Los Angeles, CA, USA
| | - James MacKillop
- Department of Psychiatry and Behavioral Neurosciences, McMaster University, Hamilton, ON, Canada
| | - Mitchell P Karno
- Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, University of California, Los Angeles, CA, USA
| | - Lara A Ray
- Interdepartmental Program for Neuroscience, University of California, Los Angeles, CA, USA; Department of Psychology, University of California, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, UCLA School of Medicine, University of California, Los Angeles, CA, USA.
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Chen Z, Hu X, Chen Q, Feng T. Altered structural and functional brain network overall organization predict human intertemporal decision-making. Hum Brain Mapp 2019; 40:306-328. [PMID: 30240495 PMCID: PMC6865623 DOI: 10.1002/hbm.24374] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 08/14/2018] [Accepted: 08/15/2018] [Indexed: 11/06/2022] Open
Abstract
Intertemporal decision-making is naturally ubiquitous to us: individuals always make a decision with different consequences occurring at different moments. These choices are invariably involved in life-changing outcomes regarding marriage, education, fertility, long-term well-being, and even public policy. Previous studies have clearly uncovered the neurobiological mechanism of the intertemporal decision in the schemes of regional location or sub-network. However, it still remains unclear how to characterize intertemporal behavior with multimodal whole-brain network metrics to date. Here, we combined diffusion tensor image and resting-state functional connectivity MRI technology, in conjunction with graph-theoretical analysis, to explore the link between topological properties of integrated structural and functional whole-brain networks and intertemporal decision-making. Graph-theoretical analysis illustrated that the participants with steep discounting rates exhibited the decreased global topological organizations including small-world and rich-club regimes in both functional and structural connectivity networks, and reflected the dreadful local topological dynamics in the modularity of functional connectome. Furthermore, in the cross-modalities configuration, the same relationship was predominantly observed for the coupling of structural-functional connectivity as well. Above topological metrics are commonly indicative of the communication pattern of simultaneous global and local parallel information processing, and it thus reshapes our accounts on intertemporal decision-making from functional regional/sub-network scheme to multimodal brain overall organization.
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Affiliation(s)
- Zhiyi Chen
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Xingwang Hu
- Institute of EducationSichuan Normal UniversityChengduChina
| | - Qi Chen
- School of PsychologySouth China Normal UniversityGuangzhouChina
| | - Tingyong Feng
- Faculty of PsychologySouthwest UniversityChongqingChina
- Key Laboratory of Cognition and Personality, Ministry of EducationChongqingChina
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