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Abstract
The purpose of this review is to highlight the most important aspects of the anatomical and functional uniqueness of the human brain. For this, a comparison is made between our brains and those of our closest ancestors (chimpanzees and bonobos) and human ancestors. During human evolution, several changes occurred in the brain, such as an absolute increase in brain size and number of cortical neurons, in addition to a greater degree of functional lateralization and anatomical asymmetry. Also, the cortical cytoarchitecture became more diversified and there was an increase in the number of intracortical networks and networks extending from the cerebral cortex to subcortical structures, with more neural networks being invested in multisensory and sensory-motor-affective-cognitive integration. These changes permitted more complex, flexible and versatile cognitive abilities and social behavior, such as shared intentionality and symbolic articulated language, which, in turn, made possible the formation of larger social groups and cumulative cultural evolution that are characteristic of our species.
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Affiliation(s)
- José Eymard Homem Pittella
- Universidade Federal de Minas Gerais, Faculdade de Medicina, Departamento de Anatomia Patológica e Medicina Legal, Belo Horizonte MG, Brazil
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Hughes NC, Qian H, Zargari M, Zhao Z, Singh B, Wang Z, Fulton JN, Johnson GW, Li R, Dawant BM, Englot DJ, Constantinidis C, Roberson SW, Bick SK. Reward Circuit Local Field Potential Modulations Precede Risk Taking. bioRxiv 2024:2024.04.10.588629. [PMID: 38645237 PMCID: PMC11030333 DOI: 10.1101/2024.04.10.588629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Risk taking behavior is a symptom of multiple neuropsychiatric disorders and often lacks effective treatments. Reward circuitry regions including the amygdala, orbitofrontal cortex, insula, and anterior cingulate have been implicated in risk-taking by neuroimaging studies. Electrophysiological activity associated with risk taking in these regions is not well understood in humans. Further characterizing the neural signalling that underlies risk-taking may provide therapeutic insight into disorders associated with risk-taking. Eleven patients with pharmacoresistant epilepsy who underwent stereotactic electroencephalography with electrodes in the amygdala, orbitofrontal cortex, insula, and/or anterior cingulate participated. Patients participated in a gambling task where they wagered on a visible playing card being higher than a hidden card, betting $5 or $20 on this outcome, while local field potentials were recorded from implanted electrodes. We used cluster-based permutation testing to identify reward prediction error signals by comparing oscillatory power following unexpected and expected rewards. We also used cluster-based permutation testing to compare power preceding high and low bets in high-risk (<50% chance of winning) trials and two-way ANOVA with bet and risk level to identify signals associated with risky, risk averse, and optimized decisions. We used linear mixed effects models to evaluate the relationship between reward prediction error and risky decision signals across trials, and a linear regression model for associations between risky decision signal power and Barratt Impulsiveness Scale scores for each patient. Reward prediction error signals were identified in the amygdala (p=0.0066), anterior cingulate (p=0.0092), and orbitofrontal cortex (p=6.0E-4, p=4.0E-4). Risky decisions were predicted by increased oscillatory power in high-gamma frequency range during card presentation in the orbitofrontal cortex (p=0.0022), and by increased power following bet cue presentation across the theta-to-beta range in the orbitofrontal cortex ( p =0.0022), high-gamma in the anterior cingulate ( p =0.0004), and high-gamma in the insula ( p =0.0014). Risk averse decisions were predicted by decreased orbitofrontal cortex gamma power ( p =2.0E-4). Optimized decisions that maximized earnings were preceded by decreases within the theta to beta range in orbitofrontal cortex ( p =2.0E-4), broad frequencies in amygdala ( p =2.0E-4), and theta to low-gamma in insula ( p =4.0E-4). Insula risky decision power was associated with orbitofrontal cortex high-gamma reward prediction error signal ( p =0.0048) and with patient impulsivity ( p =0.00478). Our findings identify and help characterize reward circuitry activity predictive of risk-taking in humans. These findings may serve as potential biomarkers to inform the development of novel treatment strategies such as closed loop neuromodulation for disorders of risk taking.
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Bertocci MA, Rozovsky R, Wolfe M, Abdul-Waalee H, Chobany M, Malgireddy G, Hart JA, Skeba A, Brady T, Lepore B, Versace A, Chase HW, Birmaher B, Phillips ML, Diler RS. Neural markers of mania that distinguish inpatient adolescents with bipolar disorder from those with other psychopathology. Psychiatry Res 2024; 333:115747. [PMID: 38301286 PMCID: PMC10922873 DOI: 10.1016/j.psychres.2024.115747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/14/2024] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
Pediatric bipolar disorder (BD) is difficult to distinguish from other psychiatric disorders, a challenge which can result in delayed or incorrect interventions. Using neuroimaging we aimed to identify neural measures differentiating a rarified sample of inpatient adolescents with BD from other inpatient psychopathology (OP) and healthy adolescents (HC) during a reward task. We hypothesized reduced subcortical and elevated cortical activation in BD relative to other groups, and that these markers will be related to self-reported mania scores. We examined inpatient adolescents with diagnosis of BD-I/II (n = 29), OP (n = 43), and HC (n = 20) from the Inpatient Child and Adolescent Bipolar Spectrum Imaging study. Inpatient adolescents with BD showed reduced activity in right thalamus, left thalamus, and left amygdala, relative to inpatient adolescents with OP and HC. This reduced neural function explained 21% of the variance in past month and 23% of the variance in lifetime mania scores. Lower activity in regions associated with the reward network, during reward processing, differentiates BD from OP in inpatient adolescents and explains >20% of the variance in mania scores. These findings highlight potential targets to aid earlier identification of, and guide new treatment developments for, pediatric BD.
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Affiliation(s)
- Michele A Bertocci
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA.
| | - Renata Rozovsky
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA
| | - Maria Wolfe
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | | | - Mariah Chobany
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | | | - Jonathan A Hart
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Alex Skeba
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Tyler Brady
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Brianna Lepore
- University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA
| | - Henry W Chase
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA
| | - Boris Birmaher
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
| | - Mary L Phillips
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA
| | - Rasim S Diler
- Department of Psychiatry, University of Pittsburgh, 121 Meyran Avenue, 120 Loeffler Building, Pittsburgh, PA 15213, USA; University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA
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Yuan W, Chen M, Wang DW, Li QH, Yin YY, Li B, Wang HR, Hu J, Gong YD, Yuan TF, Yu TG. Computational markers of risky decision-making predict for relapse to alcohol. Eur Arch Psychiatry Clin Neurosci 2024; 274:353-362. [PMID: 37148307 DOI: 10.1007/s00406-023-01602-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 03/29/2023] [Indexed: 05/08/2023]
Abstract
BACKGROUND Relapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with AUD. METHODS Forty-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking status. RESULTS The risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to drink. CONCLUSION Our study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.
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Affiliation(s)
- Wei Yuan
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China
| | - Meng Chen
- Brain and Cognitive Neuroscience Research Center, Liaoning Normal University, Dalian, 116029, China
| | - Duan-Wei Wang
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China
| | - Qian-Hui Li
- Division of Gastroenterology, Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, 518107, China
| | - Yuan-Yuan Yin
- School of Mental Health, Wenzhou Medical University, Wenzhou, 325035, China
| | - Bin Li
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China
| | - Hai-Rong Wang
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China
| | - Ji Hu
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Yuan-Dong Gong
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China.
| | - Ti-Fei Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Brain Health Institute, National Center for Mental Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Co-Innovation Center of Neuroregeneration, Nantong University, Nantong, Jiangsu, China.
| | - Tian-Gui Yu
- Department of Addiction Medicine, Shandong Mental Health Center, Jinan, 250014, China.
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Michael C, Taxali A, Angstadt M, Kardan O, Weigard A, Molloy MF, McCurry KL, Hyde LW, Heitzeg MM, Sripada C. Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis. bioRxiv 2023:2023.11.08.565517. [PMID: 38014302 PMCID: PMC10680554 DOI: 10.1101/2023.11.08.565517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.
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Kumpf U, Soldini A, Burkhardt G, Bulubas L, Dechantsreiter E, Eder J, Padberg F, Palm U. Association between Mood and Sensation Seeking Following rTMS. Brain Sci 2023; 13:1265. [PMID: 37759866 PMCID: PMC10527256 DOI: 10.3390/brainsci13091265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/29/2023] Open
Abstract
Previous studies investigating mood changes in healthy subjects after prefrontal repetitive transcranial magnetic stimulation (rTMS) have shown largely inconsistent results. This may be due to methodological issues, considerable inter-individual variation in prefrontal connectivity or other factors, e.g., personality traits. This pilot study investigates whether mood changes after rTMS are affected by personality parameters. In a randomized cross-over design, 17 healthy volunteers received three sessions of 1 Hz rTMS to Fz, F3 and T3 (10/20 system). The T3 electrode site served as the control condition with the coil angled 45° to the scalp. Subjective mood was rated at baseline and after each condition. Personality traits were assessed using the NEO Five-Factor Inventory (NEO-FFI) and the Sensation Seeking Scale (SSS). For all conditions, a significant association between mood changes towards a deterioration in mood and SSS scores was observed. There were no differences between conditions and no correlations between mood changes and NEO-FFI. The data show that sensation-seeking personality has an impact on subjective mood changes following prefrontal rTMS in all conditions. Future studies investigating the effects of rTMS on emotional paradigms should include individual measures of sensation-seeking personality. The pre-selection of subjects according to personality criteria may reduce the variability in results.
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Affiliation(s)
| | | | | | | | | | | | | | - Ulrich Palm
- Department of Psychiatry and Psychotherapy, Ludwig Maximilians University Munich, Nussbaumstr. 7, 80336 Munich, Germany; (U.K.)
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Rolls ET. Emotion, motivation, decision-making, the orbitofrontal cortex, anterior cingulate cortex, and the amygdala. Brain Struct Funct 2023:10.1007/s00429-023-02644-9. [PMID: 37178232 DOI: 10.1007/s00429-023-02644-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/12/2023] [Indexed: 05/15/2023]
Abstract
The orbitofrontal cortex and amygdala are involved in emotion and in motivation, but the relationship between these functions performed by these brain structures is not clear. To address this, a unified theory of emotion and motivation is described in which motivational states are states in which instrumental goal-directed actions are performed to obtain rewards or avoid punishers, and emotional states are states that are elicited when the reward or punisher is or is not received. This greatly simplifies our understanding of emotion and motivation, for the same set of genes and associated brain systems can define the primary or unlearned rewards and punishers such as sweet taste or pain. Recent evidence on the connectivity of human brain systems involved in emotion and motivation indicates that the orbitofrontal cortex is involved in reward value and experienced emotion with outputs to cortical regions including those involved in language, and is a key brain region involved in depression and the associated changes in motivation. The amygdala has weak effective connectivity back to the cortex in humans, and is implicated in brainstem-mediated responses to stimuli such as freezing and autonomic activity, rather than in declarative emotion. The anterior cingulate cortex is involved in learning actions to obtain rewards, and with the orbitofrontal cortex and ventromedial prefrontal cortex in providing the goals for navigation and in reward-related effects on memory consolidation mediated partly via the cholinergic system.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.
- Department of Computer Science, University of Warwick, Coventry, UK.
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Kulkarni KR, Schafer M, Berner LA, Fiore VG, Heflin M, Hutchison K, Calhoun V, Filbey F, Pandey G, Schiller D, Gu X. An Interpretable and Predictive Connectivity-Based Neural Signature for Chronic Cannabis Use. Biol Psychiatry Cogn Neurosci Neuroimaging 2023; 8:320-330. [PMID: 35659965 PMCID: PMC9708942 DOI: 10.1016/j.bpsc.2022.04.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/10/2022] [Accepted: 04/27/2022] [Indexed: 11/27/2022]
Abstract
BACKGROUND Cannabis is one of the most widely used substances in the world, with usage trending upward in recent years. However, although the psychiatric burden associated with maladaptive cannabis use has been well established, reliable and interpretable biomarkers associated with chronic use remain elusive. In this study, we combine large-scale functional magnetic resonance imaging with machine learning and network analysis and develop an interpretable decoding model that offers both accurate prediction and novel insights into chronic cannabis use. METHODS Chronic cannabis users (n = 166) and nonusing healthy control subjects (n = 124) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic users and nonusers based on whole-brain functional connectivity. Network analysis was used to identify the most predictive regions and communities. RESULTS We obtained high (∼80% out-of-sample) accuracy across 4 different classification models, demonstrating that task-evoked connectivity can successfully differentiate chronic cannabis users from nonusers. We also identified key predictive regions implicating motor, sensory, attention, and craving-related areas, as well as a core set of brain networks that contributed to successful classification. The most predictive networks also strongly correlated with cannabis craving within the chronic user group. CONCLUSIONS This novel approach produced a neural signature of chronic cannabis use that is both accurate in terms of out-of-sample prediction and interpretable in terms of predictive networks and their relation to cannabis craving.
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Affiliation(s)
- Kaustubh R Kulkarni
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Schafer
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Laura A Berner
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Vincenzo G Fiore
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matt Heflin
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Kent Hutchison
- Institute for Cognitive Science, University of Colorado, Boulder, Colorado
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, Georgia
| | - Francesca Filbey
- Center for BrainHealth, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, Texas
| | - Gaurav Pandey
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Daniela Schiller
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Xiaosi Gu
- Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York.
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Rolls ET, Deco G, Huang CC, Feng J. The human orbitofrontal cortex, vmPFC, and anterior cingulate cortex effective connectome: emotion, memory, and action. Cereb Cortex 2022; 33:330-356. [PMID: 35233615 DOI: 10.1093/cercor/bhac070] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 02/02/2022] [Accepted: 02/03/2022] [Indexed: 01/17/2023] Open
Abstract
The human orbitofrontal cortex, ventromedial prefrontal cortex (vmPFC), and anterior cingulate cortex are involved in reward processing and thereby in emotion but are also implicated in episodic memory. To understand these regions better, the effective connectivity between 360 cortical regions and 24 subcortical regions was measured in 172 humans from the Human Connectome Project and complemented with functional connectivity and diffusion tractography. The orbitofrontal cortex has effective connectivity from gustatory, olfactory, and temporal visual, auditory, and pole cortical areas. The orbitofrontal cortex has connectivity to the pregenual anterior and posterior cingulate cortex and hippocampal system and provides for rewards to be used in memory and navigation to goals. The orbitofrontal and pregenual anterior cortex have connectivity to the supracallosal anterior cingulate cortex, which projects to midcingulate and other premotor cortical areas and provides for action-outcome learning including limb withdrawal or flight or fight to aversive and nonreward stimuli. The lateral orbitofrontal cortex has outputs to language systems in the inferior frontal gyrus. The medial orbitofrontal cortex connects to the nucleus basalis of Meynert and the pregenual cingulate to the septum, and damage to these cortical regions may contribute to memory impairments by disrupting cholinergic influences on the neocortex and hippocampus.
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Affiliation(s)
- Edmund T Rolls
- Oxford Centre for Computational Neuroscience, Oxford, UK.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain.,Cognition, Pompeu Fabra University, Barcelona, Spain.,Institució Catalana de la Recerca i Estudis Avançats (ICREA), Universitat Pompeu Fabra, Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Chu-Chung Huang
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China
| | - Jianfeng Feng
- Department of Computer Science, University of Warwick, Coventry CV4 7AL, UK.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai 200433, China
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van Duijvenvoorde ACK, van Hoorn J, Blankenstein NE. Risks and rewards in adolescent decision-making. Curr Opin Psychol 2022; 48:101457. [PMID: 36088823 DOI: 10.1016/j.copsyc.2022.101457] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 07/27/2022] [Accepted: 07/29/2022] [Indexed: 01/28/2023]
Abstract
Adolescent decision-making has been characterized as risky, and a heightened reward sensitivity may be one of the aspects contributing to riskier choice-behavior. Previous studies have targeted reward-sensitivity in adolescence and the neurobiological mechanisms of reward processing in the adolescent brain. In recent examples, researchers aim to disentangle the contributions of risk- and reward-sensitivity to adolescent risk-taking. Here, we discuss recent findings of adolescent's risk preferences and the associated neural mechanisms. We highlight potential frameworks that target individual differences in risk preferences in an effort to understand adolescent risk-taking, and with an ultimate goal of leveraging undesirable levels of risk taking.
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Affiliation(s)
- Anna C K van Duijvenvoorde
- Leiden University, Dept of Developmental and Educational Psychology, Wassenaarseweg 52, 2333 AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands.
| | - Jorien van Hoorn
- Leiden University, Dept of Developmental and Educational Psychology, Wassenaarseweg 52, 2333 AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands; Levvel, Academic Center for Child- and Adolescent Psychiatry, Amsterdam, the Netherlands
| | - Neeltje E Blankenstein
- Leiden University, Dept of Developmental and Educational Psychology, Wassenaarseweg 52, 2333 AK Leiden, the Netherlands; Leiden Institute for Brain and Cognition, Leiden, the Netherlands
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