The DOI matches its Crossref record; 196 of the 196 references it cites were found in a registry or an index.
Common and distinct networks underlying reward valence and processing stages: A meta-analysis of functional neuroimaging studies
Open accessAbstract
To better understand the reward circuitry in human brain, we conducted activation likelihood estimation (ALE) and parametric voxel-based meta-analyses (PVM) on 142 neuroimaging studies that examined brain activation in reward-related tasks in healthy adults. We observed several core brain areas that participated in reward-related decision making, including the nucleus accumbens (NAcc), caudate, putamen, thalamus, orbitofrontal cortex (OFC), bilateral anterior insula, anterior cingulate cortex (ACC) and posterior cingulate cortex (PCC), as well as cognitive control regions in the inferior parietal lobule and prefrontal cortex (PFC). The NAcc was commonly activated by both positive and negative rewards across various stages of reward processing (e.g., anticipation, outcome, and evaluation). In addition, the medial OFC and PCC preferentially responded to positive rewards, whereas the ACC, bilateral anterior insula, and lateral PFC selectively responded to negative rewards. Reward anticipation activated the ACC, bilateral anterior insula, and brain stem, whereas reward outcome more significantly activated the NAcc, medial OFC, and amygdala. Neurobiological theories of reward-related decision making should therefore take distributed and interrelated representations of reward valuation and valence assessment into account.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Nevertheless, we are confident that the receipt-related effects reported here are robust and reliable based on the size of the sample we used as well as the clear main task effect of receipt (irrespective of group) that is in line with previous reports 47.”
From Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings (2015) · cites this paper as [47]
The cited source says what the claim attributes to it.
In the citing paper
“Indeed, striatal structures are known to show biphasic responses, making them capable of responding to positive (reward) as well as negative (punishment) stimulation.47”
From Increased neural responses to reward in adolescents and young adults with attention-deficit/hyperactivity disorder and their unaffected siblings (2015) · cites this paper as [47]
In the citing paper
“The ACC plays a central role in processing positively valenced emotions [239] and other rewards [240], and in coding value representations of anticipated rewards [241]”
From Reward circuitry dysfunction in psychiatric and neurodevelopmental disorders and genetic syndromes: animal models and clinical findings (2012) · cites this paper as [240]
In the citing paper
“Contrasting adolescent reward processing with the existing ALE of adult reward processing (Liu et al., 2011) reveals increased likelihood for activation in limbic, frontolimbic, and striatal regions in adolescents compared with adults.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“However, in support of our predictions, contrast analyses revealed an increased likelihood for activation in adolescents compared with adults in the bilateral ventral and dorsal striatum, insula, OFC, anterior cingulate cortex (ACC), PCC, frontal opercular regions, paracingulate gyrus, subcallosal cortex; right amygdala, right occipital pole; and left lateral occipital cortex.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al. (2011)
In the citing paper
“These regions overlap the subcortical regions identified in similar ALEs of adult reward processing (Liu et al., 2011; Bartra et al., 2013) and include major dopaminergic projection regions”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“A major finding of the current analysis is that adolescents and adults varied in activations during reward processing as supported by descriptive comparisons between our overall ALE and findings reported by Liu et al. (2011) as well as our contrast analysis.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al. (2011)
In the citing paper
“it has not yet been confirmed that, across studies, adolescents reliably activate the same regions during reward based processing as do adults (McClure et al., 2004; Liu et al., 2011; Bartra et al., 2013).”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“using the coordinates for the overall adult reward processing ALE provided by Liu and colleagues (2011) and the thresholding parameters outlined in their manuscript, we reproduced the findings reported in their study as a validation check of our procedures.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“No significant neural substrates were found in the contrast of negative greater than positive valence processing.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al. (2011)
In the citing paper
“Notably, previous studies on adolescent reward processing have relied on studies of adult reward processing (Liu et al., 2011) for guiding hypothesis testing and ROI analyses (e.g., Lorenz et al., 2014).”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“Notably, Liu et al. (2011) also reported strong activation likelihoods for numerous regions of the dorsal and ventral prefrontal cortex as well as superior and inferior parietal areas.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al., 2011
In the citing paper
“Liu et al. (2011) found a significant likelihood of nucleus accumbens and OFC activation in adults’ processing of reward outcomes versus anticipation.”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Liu et al. (2011)
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
How do you feel — now? The anterior insula and human awareness
(Bud) Craig, A. D. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2555 · Cited by 6,657 · cited 1×
“N IH -PA Author M anuscript N IH -PA Author M anuscript N IH -PA Author M anuscript interoception, emotion and empathy (Craig, 2002, 2009; Gu et al., 2010; Phan et al., 2002), and risk and uncertainty assessment (Critchley et al., 2001; Kuhnen and Knutson, 2005; Paulus et al., 2003), lending its…”
How do you feel? Interoception: The sense of the physiological condition of the body
Craig, A. D. (2002) · Nature Reviews Neuroscience · doi:10.1038/nrn894 · Cited by 6,006
The Neural Basis of Decision Making
Gold, J. I., & Shadlen, M. N. (2007) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.113038 · Cited by 3,764
Functional neuroanatomy of emotion: A meta-analysis of emotion activation studies in PET and fMRI
Phan, K. L., Wager, T., Taylor, S. F., & Liberzon, I. (2002) · NeuroImage · doi:10.1006/nimg.2002.1087 · Cited by 3,502
The neural basis of economic decision-making in the Ultimatum Game.
Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003) · Science · doi:10.1126/science.1082976 · Cited by 3,375
Anticipation of Increasing Monetary Reward Selectively Recruits Nucleus Accumbens
Knutson, B., Adams, C. M., Fong, G. W., & Hommer, D. (2001) · The Journal of Neuroscience · doi:10.1523/jneurosci.21-16-j0002.2001 · Cited by 2,244
Cortical substrates for exploratory decisions in humans
Daw, N. D., O'Doherty, J. P., Dayan, P., Seymour, B., & Dolan, R. J. (2006) · Nature · doi:10.1038/nature04766 · Cited by 2,224
The human orbitofrontal cortex: linking reward to hedonic experience
Kringelbach, M. L. (2005) · Nature Reviews Neuroscience · doi:10.1038/nrn1747 · Cited by 2,180
Cited by
Sorted by citations
The expected value of control: an integrative theory of anterior cingulate cortex function
Shenhav, A., Botvinick, M. M., & Cohen, J. D. (2013) · Neuron · doi:10.1016/j.neuron.2013.07.007 · Cited by 2,321
The integration of negative affect, pain and cognitive control in the cingulate cortex
Shackman, A. J., Salomons, T. V., Slagter, H. A., Fox, A. S., Winter, J. J., & Davidson, R. J. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn2994 · Cited by 2,082
The ventral visual pathway: an expanded neural framework for the processing of object quality
Kravitz, D. J., Saleem, K. S., Baker, C. I., Ungerleider, L. G., & Mishkin, M. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.011 · Cited by 1,204
Hiser, J., & Koenigs, M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.10.030 · Cited by 972
Motivation and cognitive control: from behavior to neural mechanism
Botvinick, M., & Braver, T. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015044 · Cited by 909
Dynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791
Sescousse, G., Caldú, X., Segura, B., & Dreher, J.-C. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.02.002 · Cited by 782
Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., & Barrett, L. F. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv001 · Cited by 713
Informatic parcellation of the network involved in the computation of subjective value
Clithero, J. A., & Rangel, A. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nst106 · Cited by 706
Evidence for a large-scale brain system supporting allostasis and interoception in humans
Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Feldman Barrett, L. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0069 · Cited by 641
Ashok, A. H., Marques, T. R., Jauhar, S., Nour, M. M., Goodwin, G. M., Young, A. H., & Howes, O. D. (2017) · Molecular Psychiatry · doi:10.1038/mp.2017.16 · Cited by 546
Walsh, M. M., & Anderson, J. R. (2012) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2012.05.008 · Cited by 577
Jasinska, A. J., Stein, E. A., Kaiser, J., Naumer, M. J., & Yalachkov, Y. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.10.013 · Cited by 513
Pelowski, M. J., Markey, P. S., Forster, M., Gerger, G., & Leder, H. (2017) · Physics of Life Reviews · doi:10.1016/j.plrev.2017.02.003 · Cited by 492
Brain circuit dysfunction in post-traumatic stress disorder: from mouse to man
Fenster, R. J., Lebois, L. A. M., Ressler, K. J., & Suh, J. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0039-7 · Cited by 456
Prediction error in reinforcement learning: A meta-analysis of neuroimaging studies
Garrison, J. R., Erdeniz, B., & Done, J. (2013) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.03.023 · Cited by 460
Oldham, S., Murawski, C., Fornito, A., Youssef, G. J., Yücel, M., & Lorenzetti, V. (2018) · Human Brain Mapping · doi:10.1002/hbm.24184 · Cited by 449
Plichta, M. M., & Scheres, A. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.07.012 · Cited by 424
Impact of contour on aesthetic judgments and approach-avoidance decisions in architecture
Vartanian, O. A., Navarrete, G., Chatterjee, A. K., Fich, L. B., Leder, H., Modroño, C., Nadal, M., Rostrup, N., & Skov, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1301227110 · Cited by 417
A neural reward prediction error revealed by a meta-analysis of ERPs using great grand averages
Sambrook, T. D., & Goslin, J. (2015) · Psychological Bulletin · doi:10.1037/bul0000006 · Cited by 411
Neural substrates of smoking cue reactivity: A meta-analysis of fMRI studies
Engelmann, J. M., Versace, F., Robinson, J. D., Minnix, J. A., Lam, C. Y., Cui, Y., Brown, V. L., & Cinciripini, P. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.024 · Cited by 394
Dinur-Klein, L., Dannon, P., Hadar, A., Rosenberg, O., Roth, Y., Kotler, M., & Zangen, A. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2014.05.020 · Cited by 367
Beyond the FRN: Broadening the time-course of EEG and ERP components implicated in reward processing
Glazer, J. E., Kelley, N. J., Pornpattananangkul, N., Mittal, V. A., & Nusslock, R. (2018) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2018.02.002 · Cited by 381
Chatterjee, A. K., & Vartanian, O. A. (2016) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13035 · Cited by 379
Zhang, W.-N., Chang, S.-H., Guo, L.-Y., Zhang, K.-L., & Wang, J. (2013) · Journal of Affective Disorders · doi:10.1016/j.jad.2013.06.039 · Cited by 355
Decision making in the ageing brain: changes in affective and motivational circuits
Samanez-Larkin, G. R., & Knutson, B. D. (2015) · Nature reviews. Neuroscience · doi:10.1038/nrn3917 · Cited by 352
Can Pornography be Addictive? An fMRI Study of Men Seeking Treatment for Problematic Pornography Use
Gola, M., Wordecha, M., Sescousse, G., Lew-Starowicz, M., Kossowski, B., Wypych, M., Makeig, S., Potenza, M. N., & Marchewka, A. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.78 · Cited by 299
Dichter, G. S., Damiano, C. A., & Allen, J. A. (2012) · Journal of Neurodevelopmental Disorders · doi:10.1186/1866-1955-4-19 · Cited by 299
Silverman, M. H., Jedd, K., & Luciana, M. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.07.083 · Cited by 277
Contextual modulation of value signals in reward and punishment learning
Palminteri, S., Khamassi, M., Joffily, M., & Coricelli, G. (2015) · Nature Communications · doi:10.1038/ncomms9096 · Cited by 273
Becker, M. P. I., Nitsch, A. M., Miltner, W. H. R., & Straube, T. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.3684-13.2014 · Cited by 281
Lieberman, M. D., Straccia, M. A., Meyer, M. L., Du, M., & Tan, K. M. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.021 · Cited by 280
Alcohol, stress, and glucocorticoids: From risk to dependence and relapse in alcohol use disorders
Blaine, S. K., & Sinha, R. (2017) · Neuropharmacology · doi:10.1016/j.neuropharm.2017.01.037 · Cited by 257
“This positive valence is further encoded by neuroplasticity in the afferent connections of the NAcc to PFC; over repeated use, these connections become overly sensitive to alcohol and increase the motivational drive to consume alcohol (Liu et al., 2011).”
Reward functioning in PTSD: a systematic review exploring the mechanisms underlying anhedonia
Nawijn, L., van Zuiden, M., Frijling, J. L., Koch, S. B. J., Veltman, D. J., & Olff, M. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.01.019 · Cited by 250
Effects of stress on decisions under uncertainty: A meta-analysis.
Starcke, K., & Brand, M. (2016) · Psychological Bulletin · doi:10.1037/bul0000060 · Cited by 273
van Duijvenvoorde, A. C. K., Peters, S., Braams, B. R., & Crone, E. A. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.06.037 · Cited by 258
The neural correlates of subjective pleasantness
Kühn, S., & Gallinat, J. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.065 · Cited by 238
Chase, H. W., Kumar, P., Eickhoff, S. B., & Dombrovski, A. Y. (2015) · Cognitive Affective & Behavioral Neuroscience · doi:10.3758/s13415-015-0338-7 · Cited by 225
Pace-Schott, E. F., Amole, M. C., Aue, T., Balconi, M., Bylsma, L. M., Critchley, H., Demaree, H. A., Friedman, B. H., Gooding, A. E. K., Gosseries, O., et al. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.05.002 · Cited by 221
“, 2011), another key area for reward processing (Liu et al., 2011).”
Erdeniz, B., Rohe, T., Done, J., & Seidler, R. D. (2013) · Frontiers in Neuroscience · doi:10.3389/fnins.2013.00116 · Cited by 215
A universal role of the ventral striatum in reward-based learning: Evidence from human studies
Daniel, R., & Pollmann, S. (2014) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2014.05.002 · Cited by 225
Foti, D., Carlson, J. M., Sauder, C. L., & Proudfit, G. H. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.058 · Cited by 217
Ersche, K. D., Williams, G. B., Robbins, T. W., & Bullmore, E. T. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2013.02.017 · Cited by 205
Common and distinct neural correlates of personal and vicarious reward: A quantitative meta-analysis
Morelli, S. A., Sacchet, M. D., & Zaki, J. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.12.056 · Cited by 213
Overlapping neural systems represent cognitive effort and reward anticipation
Vassena, E., Silvetti, M., Boehler, C. N., Achten, E., Fias, W., & Verguts, T. (2014) · PLoS ONE · doi:10.1371/journal.pone.0091008 · Cited by 200
Novak, K. D., & Foti, D. (2015) · Psychophysiology · doi:10.1111/psyp.12504 · Cited by 205
Social “wanting” dysfunction in autism: neurobiological underpinnings and treatment implications
Kohls, G., Chevallier, C., Troiani, V., & Schultz, R. T. (2012) · Journal of Neurodevelopmental Disorders · doi:10.1186/1866-1955-4-10 · Cited by 197
Reappraising striatal D1- and D2-neurons in reward and aversion
Soares‐Cunha, C., Coimbra, B., Sousa, N., & Rodrigues, A. J. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.05.021 · Cited by 203
Subcortical structural connectivity of insular subregions
Ghaziri, J., Tucholka, A., Girard, G., Boucher, O., Houde, J., Descoteaux, M., Obaïd, S., Gilbert, G., Rouleau, I., & Nguyen, D. K. (2018) · Scientific Reports · doi:10.1038/s41598-018-26995-0 · Cited by 190
Imbalance in the sensitivity to different types of rewards in pathological gambling
Sescousse, G., Barbalat, G., Domenech, P., & Dreher, J. (2013) · Brain · doi:10.1093/brain/awt126 · Cited by 202
MTAC, Wilson, R. P., Colizzi, M., Bossong, M. G., Allen, P., Kempton, M. J., & Bhattacharyya, S. (2018) · Neuropsychology Review · doi:10.1007/s11065-018-9385-5 · Cited by 196
Decision Making: The Neuroethological Turn
Pearson, J. M., Watson, K. K., & Platt, M. L. (2014) · Neuron · doi:10.1016/j.neuron.2014.04.037 · Cited by 197
Foti, D., Weinberg, A., Bernat, E. M., & Proudfit, G. H. (2014) · Clinical Neurophysiology · doi:10.1016/j.clinph.2014.08.025 · Cited by 193
Braun, U., Schäfer, A., Bassett, D. S., Rausch, F., Schweiger, J. I., Bilek, E., Erk, S., Romanczuk‐Seiferth, N., Grimm, O., Geiger, L. S., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608819113 · Cited by 189
Cue-reactivity in behavioral addictions: a meta-analysis and methodological considerations
Starcke, K., Antons, S., Trotzke, P., & Brand, M. (2018) · Journal of Behavioral Addictions · doi:10.1556/2006.7.2018.39 · Cited by 180
Reduced Intrinsic Connectivity of Amygdala in Adults with Major Depressive Disorder
Ramasubbu, R., Konduru, N., Cortese, F., Bray, S., Gaxiola‐Valdez, I., & Goodyear, B. G. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00017 · Cited by 192
Computing the Social Brain Connectome Across Systems and States
Alcalá-López, D., Smallwood, J., Jefferies, E., Overwalle, F. V., Vogeley, K. T., Mars, R. B., Turetsky, B. I., Laird, A. R., Fox, P. T., Eickhoff, S. B., et al. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx121 · Cited by 182
Neuroimaging the consciousness of self: Review, and conceptual-methodological framework
Frewen, P., Schroeter, M. L., Riva, G., Cipresso, P., Fairfield, B., Padulo, C., Kemp, A. H., Palaniyappan, L. K., Owolabi, M. O., Kusi‐Mensah, K., et al. (2020) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2020.01.023 · Cited by 186
Fouragnan, E., Retzler, C., & Philiastides, M. G. (2018) · Human Brain Mapping · doi:10.1002/hbm.24047 · Cited by 176
Affective traits link to reliable neural markers of incentive anticipation
Wu, C. C., Samanez-Larkin, G. R., Katovich, K., & Knutson, B. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.055 · Cited by 176
Cho, Y. T., Fromm, S., Guyer, A. E., Detloff, A., Pine, D. S., Fudge, J. L., & Ernst, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.013 · Cited by 173
Dichter, G. S., Kozink, R. V., McClernon, F. J., & Smoski, M. J. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.09.048 · Cited by 170
Prefrontal cortical thinning links to negative symptoms in schizophrenia via the ENIGMA consortium
Walton, E., Hibar, D. P., van Erp, T. G. M., Potkin, S. G., Roiz‐Santiáñez, R., Crespo‐Facorro, B., Suárez‐Pinilla, P., van Haren, N. E. M., de Zwarte, S. M. C., Kahn, R. S., et al. (2017) · Psychological Medicine · doi:10.1017/s0033291717001283 · Cited by 173
Sonuga-Barke, E. J. S., Cortese, S., Fairchild, G. F., & Stringaris, A. K. (2015) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12496 · Cited by 177
Broyd, S. J., Richards, H. J., Helps, S. K., Chronaki, G., Bamford, S., & Sonuga-Barke, E. J. S. (2012) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2012.05.015 · Cited by 174
Reward processing in unipolar and bipolar depression: A functional MRI study
Redlich, R., Dohm, K., Grotegerd, D., Opel, N., Zwitserlood, P., Heindel, W., Arolt, V., Kugel, H., & Dannlowski, U. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.110 · Cited by 164
Pfabigan, D. M., Seidel, E., Sladky, R., Hahn, A., Paul, K., Grahl, A., Küblböck, M., Kraus, C., Hummer, A., Kranz, G. S., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.077 · Cited by 166
Vartanian, O. A., & Skov, M. (2014) · Brain and Cognition · doi:10.1016/j.bandc.2014.03.004 · Cited by 164
Schreuders, E., Braams, B. R., Blankenstein, N. E., Peper, J. S., Güroğlu, B., & Crone, E. A. (2018) · Child Development · doi:10.1111/cdev.13056 · Cited by 163
The functional neuroanatomy of decision making: Prefrontal control of thought and action
Coutlee, C. G., & Huettel, S. A. (2011) · Brain Research · doi:10.1016/j.brainres.2011.05.053 · Cited by 165
Neural Processing of Reward and Punishment in Young People at Increased Familial Risk of Depression
McCabe, C., Woffindale, C., Harmer, C. J., & Cowen, P. J. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.04.034 · Cited by 155
Modulating musical reward sensitivity up and down with transcranial magnetic stimulation
Herrero, E. M., Dagher, A., & Zatorre, R. J. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0241-z · Cited by 160
Gerlach, K. D., Spreng, R. N., Madore, K. P., & Schacter, D. L. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu001 · Cited by 148
Osuch, E., Ford, K., Wrath, A., Bartha, R., & Neufeld, R. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.05.003 · Cited by 144
Olbrich, S., Tränkner, A., Chittka, T., Hegerl, U., & Schönknecht, P. (2014) · Psychiatry Research Neuroimaging · doi:10.1016/j.pscychresns.2014.02.010 · Cited by 147
Predicting personality from network-based resting-state functional connectivity
Nostro, A. D., Müller, V. I., Varikuti, D. P., Pläschke, R. N., Hoffstaedter, F., Langner, R., Patil, K. R., & Eickhoff, S. B. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1651-z · Cited by 149
Annual research review: on the developmental neuropsychology of substance use disorders
J. Conrod, P., & Nikolaou, K. (2016) · Journal of Child Psychology and Psychiatry · doi:10.1111/jcpp.12516 · Cited by 137
Sheth, C. S., McGlade, E., & Yurgelun-Todd, D. A. (2017) · Chronic Stress · doi:10.1177/2470547017715645 · Cited by 147
The neurobiology of alcohol consumption and alcoholism: An integrative history
Tabakoff, B., & Hoffman, P. L. (2013) · Pharmacology Biochemistry and Behavior · doi:10.1016/j.pbb.2013.10.009 · Cited by 135
Inferring affect from fMRI data
Knutson, B. D., Katovich, K., & Suri, G. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.04.006 · Cited by 135
Wu, M., Brockmeyer, T., Hartmann, M., Skunde, M., Herzog, W., & Friederich, H. (2015) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2015.11.017 · Cited by 132
Ryan, N. C., Struve, M., Whelan, R., Banaschewski, T., Barker, G. J., Bokde, A. L. W., Bromberg, U., Büchel, C., Flor, H., Fauth‐Bühler, M., et al. (2014) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2014.13111499 · Cited by 139
Functional brain imaging predicts public health campaign success
Falk, E. B., O’Donnell, M. B., Tompson, S., Gonzalez, R., Cin, S. D., Strecher, V. J., Cummings, K. M., & An, L. C. (2015) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsv108 · Cited by 128
Claus, E. D., Blaine, S. K., Filbey, F. M., Mayer, A. R., & Hutchison, K. E. (2013) · Neuropsychopharmacology · doi:10.1038/npp.2013.134 · Cited by 127
McNaughton, N., DeYoung, C. G., & Corr, P. J. (2016) · Neuroimaging Personality, Social Cognition, and Character · doi:10.1016/b978-0-12-800935-2.00002-6 · Cited by 123
Wagner, U., Galli, L., Schott, B. H., Wold, A., van der Schalk, J., Manstead, A. S. R., Scherer, K. R., & Walter, H. (2014) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsu121 · Cited by 135
Gola, M. K., Draps, M., Marchewka, A., & Sescousse, G. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00402 · Cited by 126
Motivated To Win: Relationship between Anticipatory and Outcome Reward-Related Neural Activity
Pornpattananangkul, N., & Nusslock, R. (2015) · Brain and Cognition · doi:10.1016/j.bandc.2015.09.002 · Cited by 115
Vulnerability to Depression in Youth: Advances From Affective Neuroscience
Kujawa, A., & Burkhouse, K. L. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.09.006 · Cited by 128
Studer, B., Manes, F. F., Humphreys, G. W., Robbins, T. W., & Clark, L. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht197 · Cited by 122
The interpretation of computational model parameters depends on the context
Eckstein, M. K., Master, S. L., Xia, L., Dahl, R. E., Wilbrecht, L., & Collins, A. G. E. (2022) · eLife · doi:10.7554/elife.75474 · Cited by 124
Appearance Matters: Neural Correlates of Food Choice and Packaging Aesthetics
van der Laan, L. N., de Ridder, D. T. D., Viergever, M. A., & Smeets, P. A. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0041738 · Cited by 122
Worhunsky, P. D., Malison, R. T., Rogers, R. D., & Potenza, M. N. (2014) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2014.09.013 · Cited by 112
Surprise-related activation in the nucleus accumbens interacts with music-induced pleasantness
Shany, O., Singer, N., Gold, B. P., Jacoby, N., Tarrasch, R., Hendler, T., & Granot, R. (2019) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsz019 · Cited by 114
Eckstein, M. K., Wilbrecht, L., & Collins, A. G. E. (2021) · PsyArXiv (OSF Preprints) · doi:10.1016/j.cobeha.2021.06.004 · Cited by 131
Dopamine Modulates Adaptive Prediction Error Coding in the Human Midbrain and Striatum
Diederen, K. M. J., Ziauddeen, H., Vestergaard, M. D., Spencer, T., Schultz, W., & Fletcher, P. C. (2017) · The Journal of Neuroscience · doi:10.1523/jneurosci.1979-16.2016 · Cited by 110
Novak, B. K., Novak, K. D., Lynam, D. R., & Foti, D. (2016) · Biological Psychology · doi:10.1016/j.biopsycho.2016.07.008 · Cited by 117
Liu, J., Cao, L., Li, H., Gao, Y., Bu, X., Liang, K., Bao, W., Zhang, S., Qiu, H., Li, X., et al. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104574 · Cited by 109
Differential neural circuitry and self-interest in realvshypothetical moral decisions
FeldmanHall, O., Dalgleish, T., Thompson, R., Evans, D., Schweizer, S., & Mobbs, D. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nss069 · Cited by 113
von Rhein, D., Cools, R., Zwiers, M. P., van der Schaaf, M., Franke, B., Luman, M., Oosterlaan, J., Heslenfeld, D. J., Hoekstra, P. J., Hartman, C. A., et al. (2015) · Journal of the American Academy of Child & Adolescent Psychiatry · doi:10.1016/j.jaac.2015.02.012 · Cited by 108
Metadata
- Authors
- Liu, X., Hairston, J., Schrier, M., & Fan, J.
- Affiliations
- Chinese Academy of Sciences (CN)
- Icahn School of Medicine at Mount Sinai (US)
- Queens College, CUNY (US)
- Corresponding author
- Liu, Xun
- Venue
- Neuroscience & Biobehavioral Reviews
- Publisher
- Elsevier BV
- Published
- 2011-04
- Volume
- 35
- Issue
- 5
- Pages
- 1219-1236
- ISSN
- 0149-7634 (ISSN-L)
- PMC
- PMC3395003
- OpenAlex
- W2131675069
- Semantic Scholar
- 2ad686808fa437bceed156b160d64b167847da84
- Cited by
- 916
- References
- 196
- Citations by year
- 2026: 33 · 2025: 59 · 2024: 50 · 2023: 58 · 2022: 50 · 2021: 70 · 2020: 69 · 2019: 55 · 2018: 68 · 2017: 71 · 2016: 68 · 2015: 71 · 2014: 96 · 2013: 49 · 2012: 36
- FWCI
- 2.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Neuroscience & Biobehavioral Reviews (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion, cc-by-nc-sa)
- Funders
- National Institute of Mental Health (R21MH083164)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural and Behavioral Psychology Studies
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neurotransmitter Receptor Influence on Behavior
- Social Sciences › Psychology › Clinical Psychology › Gambling Behavior and Treatments
- Keywords
- reward processing, reward valence, reward systems, nucleus accumbens, orbitofrontal cortex, anterior insula, anterior cingulate cortex, posterior cingulate cortex, thalamus, caudate and putamen, reward anticipation, reward outcome, amygdala, cognitive control
- MeSH terms
- Brain, Brain Mapping, Diagnostic Imaging, Humans, Functional Laterality, Neural Pathways, Reward, Likelihood Functions, Databases, Factual
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
