Beta activity in human anterior cingulate cortex mediates reward biases.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
15 Jul 2024
15 Jul 2024
Historique:
received:
15
09
2023
accepted:
07
06
2024
medline:
16
7
2024
pubmed:
16
7
2024
entrez:
15
7
2024
Statut:
epublish
Résumé
The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12-30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.
Identifiants
pubmed: 39009561
doi: 10.1038/s41467-024-49600-7
pii: 10.1038/s41467-024-49600-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5528Subventions
Organisme : NINDS NIH HHS
ID : UH3 NS103549
Pays : United States
Organisme : NIMH NIH HHS
ID : K01 MH116364
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS104953
Pays : United States
Organisme : NINDS NIH HHS
ID : UH3 NS100549
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : MH114854
Informations de copyright
© 2024. The Author(s).
Références
Averbeck, B. B. & Costa, V. D. Motivational neural circuits underlying reinforcement learning. Nat. Neurosci. 20, 505–512 (2017).
pubmed: 28352111
doi: 10.1038/nn.4506
Howard-Jones, P. A. & Jay, T. Reward, learning and games. Curr. Opin. Behav. Sci. 10, 65–72 (2016).
doi: 10.1016/j.cobeha.2016.04.015
Rangel, A. & Hare, T. Neural computations associated with goal-directed choice. Curr. Opin. Neurobiol. 20, 262–270 (2010).
pubmed: 20338744
doi: 10.1016/j.conb.2010.03.001
Kangas, B. D., Wooldridge, L. M., Luc, O. T., Bergman, J. & Pizzagalli, D. A. Empirical validation of a touchscreen probabilistic reward task in rats. Transl. Psychiatry 10, 1–11 (2020).
doi: 10.1038/s41398-020-00969-1
Pizzagalli, D. A., Jahn, A. L. & O’Shea, J. P. Toward an objective characterization of an anhedonic phenotype: a signal-detection approach. Biol. Psychiatry 57, 319–327 (2005).
pubmed: 15705346
pmcid: 2447922
doi: 10.1016/j.biopsych.2004.11.026
Pizzagalli, D. A., Iosifescu, D., Hallett, L. A., Ratner, K. G. & Fava, M. Reduced hedonic capacity in major depressive disorder: evidence from a probabilistic reward task. J. Psychiatr. Res. 43, 76–87 (2008).
pubmed: 18433774
pmcid: 2637997
doi: 10.1016/j.jpsychires.2008.03.001
Rorie, A. E., Gao, J., McClelland, J. L. & Newsome, W. T. Integration of sensory and reward information during perceptual decision-making in Lateral Intraparietal Cortex (LIP) of the Macaque Monkey. PLOS ONE 5, e9308 (2010).
pubmed: 20174574
pmcid: 2824817
doi: 10.1371/journal.pone.0009308
Henschke, J. U. et al. Reward association enhances stimulus-specific representations in primary visual cortex. Curr. Biol. 30, 1866–1880.e5 (2020).
pubmed: 32243857
pmcid: 7237886
doi: 10.1016/j.cub.2020.03.018
O’Doherty, J. P., Cockburn, J. & Pauli, W. M. Learning, reward, and decision making. Annu. Rev. Psychol. 68, 73–100 (2017).
pubmed: 27687119
doi: 10.1146/annurev-psych-010416-044216
Schultz, W. Neuronal reward and decision signals: from theories to data. Physiol. Rev. 95, 853–951 (2015).
pubmed: 26109341
pmcid: 4491543
doi: 10.1152/physrev.00023.2014
Admon, R. & Pizzagalli, D. A. Dysfunctional reward processing in depression. Curr. Opin. Psychol. 4, 114–118 (2015).
pubmed: 26258159
doi: 10.1016/j.copsyc.2014.12.011
Der-Avakian, A., Barnes, S. A., Markou, A. & Pizzagalli, D. A. Translational Assessment of Reward and Motivational Deficits in Psychiatric Disorders. In Translational Neuropsychopharmacology (eds. Robbins, T. W. & Sahakian, B. J.) 231–262 (Springer International Publishing, Cham, 2016). https://doi.org/10.1007/7854_2015_5004 .
Pizzagalli, D. A. Depression, stress, and anhedonia: toward a synthesis and integrated model. Annu. Rev. Clin. Psychol. 10, 393–423 (2014).
pubmed: 24471371
pmcid: 3972338
doi: 10.1146/annurev-clinpsy-050212-185606
Safra, L., Chevallier, C. & Palminteri, S. Depressive symptoms are associated with blunted reward learning in social contexts. PLOS Comput. Biol. 15, e1007224 (2019).
pubmed: 31356594
pmcid: 6699715
doi: 10.1371/journal.pcbi.1007224
Whitton, A. E., Treadway, M. T. & Pizzagalli, D. A. Reward processing dysfunction in major depression, bipolar disorder and schizophrenia. Curr. Opin. Psychiatry 28, 7–12 (2015).
pubmed: 25415499
pmcid: 4277233
doi: 10.1097/YCO.0000000000000122
Park, L. T. & Zarate, C. A. Depression in the primary care setting. N. Engl. J. Med. 380, 559–568 (2019).
pubmed: 30726688
pmcid: 6727965
doi: 10.1056/NEJMcp1712493
Williams, L. M. Precision psychiatry: a neural circuit taxonomy for depression and anxiety. Lancet Psychiatry 3, 472–480 (2016).
pubmed: 27150382
pmcid: 4922884
doi: 10.1016/S2215-0366(15)00579-9
Alexander, L. et al. Fractionating blunted reward processing characteristic of anhedonia by over-activating primate subgenual anterior cingulate cortex. Neuron 101, 307–320.e6 (2019).
pubmed: 30528065
pmcid: 6344231
doi: 10.1016/j.neuron.2018.11.021
Keren, H. et al. Reward processing in depression: a conceptual and meta-analytic review across fMRI and EEG studies. Am. J. Psychiatry 175, 1111–1120 (2018).
pubmed: 29921146
pmcid: 6345602
doi: 10.1176/appi.ajp.2018.17101124
Moutoussis, M. et al. Decision-making ability, psychopathology, and brain connectivity. Neuron 109, 2025–2040.e7 (2021).
pubmed: 34019810
pmcid: 8221811
doi: 10.1016/j.neuron.2021.04.019
Vrieze, E. et al. Reduced reward learning predicts outcome in major depressive disorder. Biol. Psychiatry 73, 639–645 (2013).
pubmed: 23228328
doi: 10.1016/j.biopsych.2012.10.014
Widge, A. S. & Miller, E. K. Targeting cognition and networks through neural oscillations: next-generation clinical brain stimulation. JAMA Psychiatry 76, 671–672 (2019).
pubmed: 31116372
pmcid: 7067567
doi: 10.1001/jamapsychiatry.2019.0740
Cohen, M. X., Elger, C. E. & Ranganath, C. Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage 35, 968–978 (2007).
pubmed: 17257860
doi: 10.1016/j.neuroimage.2006.11.056
HajiHosseini, A., Rodríguez-Fornells, A. & Marco-Pallarés, J. The role of beta-gamma oscillations in unexpected rewards processing. NeuroImage 60, 1678–1685 (2012).
pubmed: 22330314
doi: 10.1016/j.neuroimage.2012.01.125
HajiHosseini, A. & Holroyd, C. B. Sensitivity of frontal beta oscillations to reward valence but not probability. Neurosci. Lett. 602, 99–103 (2015).
pubmed: 26149231
doi: 10.1016/j.neulet.2015.06.054
Marco-Pallarés, J., Münte, T. F. & Rodríguez-Fornells, A. The role of high-frequency oscillatory activity in reward processing and learning. Neurosci. Biobehav. Rev. 49, 1–7 (2015).
pubmed: 25464028
doi: 10.1016/j.neubiorev.2014.11.014
Marco-Pallares, J. et al. Human oscillatory activity associated to reward processing in a gambling task. Neuropsychologia 46, 241–248 (2008).
pubmed: 17804025
doi: 10.1016/j.neuropsychologia.2007.07.016
Clark, D. L., Brown, E. C., Ramasubbu, R. & Kiss, Z. H. T. Intrinsic local beta oscillations in the subgenual cingulate relate to depressive symptoms in treatment-resistant depression. Biol. Psychiatry 80, e93–e94 (2016).
pubmed: 27129412
doi: 10.1016/j.biopsych.2016.02.032
Huebl, J. et al. Processing of emotional stimuli is reflected by modulations of beta band activity in the subgenual anterior cingulate cortex in patients with treatment resistant depression. Soc. Cogn. Affect Neurosci. 11, 1290–1298 (2016).
pubmed: 27013105
pmcid: 4967800
doi: 10.1093/scan/nsw038
Merkl, A. et al. Modulation of beta-band activity in the subgenual anterior cingulate cortex during emotional empathy in treatment-resistant depression. Cereb. Cortex 26, 2626–2638 (2016).
pubmed: 25994959
doi: 10.1093/cercor/bhv100
Alagapan, S. et al. Cingulate dynamics track depression recovery with deep brain stimulation. Nature 622, 130–138 (2023).
pubmed: 37730990
pmcid: 10550829
doi: 10.1038/s41586-023-06541-3
Rudebeck, P. H. & Murray, E. A. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes. Neuron 84, 1143–1156 (2014).
pubmed: 25521376
pmcid: 4271193
doi: 10.1016/j.neuron.2014.10.049
Heilbronner, S. R. & Hayden, B. Y. Dorsal anterior cingulate cortex: a bottom-up view. Annu. Rev. Neurosci. 39, 149–170 (2016).
pubmed: 27090954
pmcid: 5512175
doi: 10.1146/annurev-neuro-070815-013952
Monosov, I. E., Haber, S. N., Leuthardt, E. C. & Jezzini, A. Anterior cingulate cortex and the control of dynamic behavior in primates. Curr. Biol. 30, R1442–R1454 (2020).
pubmed: 33290716
pmcid: 8197026
doi: 10.1016/j.cub.2020.10.009
Bush, G. et al. Dorsal anterior cingulate cortex: a role in reward-based decision making. Proc. Natl Acad. Sci. 99, 523–528 (2002).
pubmed: 11756669
doi: 10.1073/pnas.012470999
Etkin, A., Büchel, C. & Gross, J. J. The neural bases of emotion regulation. Nat. Rev. Neurosci. 16, 693–700 (2015).
pubmed: 26481098
doi: 10.1038/nrn4044
Hayden, B. Y., Pearson, J. M. & Platt, M. L. Fictive reward signals in the anterior cingulate cortex. Science 324, 948–950 (2009).
pubmed: 19443783
pmcid: 3096846
doi: 10.1126/science.1168488
Otis, J. M. et al. Prefrontal cortex output circuits guide reward seeking through divergent cue encoding. Nature 543, 103–107 (2017).
pubmed: 28225752
pmcid: 5772935
doi: 10.1038/nature21376
Terem, A. et al. Claustral neurons projecting to frontal cortex mediate contextual association of reward. Curr. Biol. 30, 3522–3532.e6 (2020).
pubmed: 32707061
doi: 10.1016/j.cub.2020.06.064
Cheng, W. et al. Increased functional connectivity of the posterior cingulate cortex with the lateral orbitofrontal cortex in depression. Transl. Psychiatry 8, 1–10 (2018).
doi: 10.1038/s41398-018-0139-1
McGovern, R. A. & Sheth, S. A. Role of the dorsal anterior cingulate cortex in obsessive-compulsive disorder: converging evidence from cognitive neuroscience and psychiatric neurosurgery. J. Neurosurg. 126, 132–147 (2017).
pubmed: 27035167
doi: 10.3171/2016.1.JNS15601
Rolls, E. T. et al. Functional connectivity of the anterior cingulate cortex in depression and in health. Cereb. Cortex 29, 3617–3630 (2019).
pubmed: 30418547
doi: 10.1093/cercor/bhy236
Sheth, S. A. et al. Human dorsal anterior cingulate cortex neurons mediate ongoing behavioural adaptation. Nature 488, 218–221 (2012).
pubmed: 22722841
pmcid: 3416924
doi: 10.1038/nature11239
Sheth, S. A. et al. Limbic system surgery for treatment-refractory obsessive-compulsive disorder: a prospective long-term follow-up of 64 patients: clinical article. J. Neurosurg. 118, 491–497 (2013).
pubmed: 23240700
doi: 10.3171/2012.11.JNS12389
Steele, J. D., Christmas, D., Eljamel, M. S. & Matthews, K. Anterior cingulotomy for major depression: clinical outcome and relationship to lesion characteristics. Biol. Psychiatry 63, 670–677 (2008).
pubmed: 17916331
doi: 10.1016/j.biopsych.2007.07.019
Whitton, A. E. et al. Blunted neural responses to reward in remitted major depression: a high-density event-related potential study. Biol. Psychiatry. Cogn. Neurosci. Neuroimagin. 1, 87–95 (2016).
Wang, S., Leri, F. & Rizvi, S. J. Anhedonia as a central factor in depression: neural mechanisms revealed from preclinical to clinical evidence. Prog. Neuropsychopharmacol. Biol. Psychiatry 110, 110289 (2021).
pubmed: 33631251
doi: 10.1016/j.pnpbp.2021.110289
American Psychiatric Publishing, Inc. Diagnostic and Statistical Manual of Mental Disorders. DSM Library https://dsm.psychiatryonline.org/doi/book/10.1176/appi.books.9780890425596 (2013).
Dowd, E. C., Frank, M. J., Collins, A., Gold, J. M. & Barch, D. M. Probabilistic reinforcement learning in patients with schizophrenia: relationships to anhedonia and avolition. Biol. Psychiatry Cogn. Neurosci. Neuroimagin. 1, 460–473 (2016).
Husain, M. & Roiser, J. P. Neuroscience of apathy and anhedonia: a transdiagnostic approach. Nat. Rev. Neurosci. 19, 470–484 (2018).
pubmed: 29946157
doi: 10.1038/s41583-018-0029-9
Stull, S. W., Bertz, J. W., Epstein, D. H., Bray, B. C. & Lanza, S. T. Anhedonia and substance use disorders by type, severity, and with mental health disorders. J. Addict. Med. 16, e150–e156 (2022).
pubmed: 34282082
pmcid: 8761228
doi: 10.1097/ADM.0000000000000891
Ward, J. et al. Novel genome-wide associations for anhedonia, genetic correlation with psychiatric disorders, and polygenic association with brain structure. Transl. Psychiatry 9, 1–9 (2019).
doi: 10.1038/s41398-019-0635-y
Insel, T. et al. Research Domain Criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).
pubmed: 20595427
doi: 10.1176/appi.ajp.2010.09091379
Morris, B. H., Bylsma, L. M., Yaroslavsky, I., Kovacs, M. & Rottenberg, J. Reward learning in pediatric depression and anxiety: preliminary findings in a high-risk sample. Depress. Anxiety 32, 373–381 (2015).
pubmed: 25826304
pmcid: 4409509
doi: 10.1002/da.22358
O’Doherty, J. P. Reward representations and reward-related learning in the human brain: insights from neuroimaging. Curr. Opin. Neurobiol. 14, 769–776 (2004).
pubmed: 15582382
doi: 10.1016/j.conb.2004.10.016
Rangel, A., Camerer, C. & Montague, P. R. A framework for studying the neurobiology of value-based decision making. Nat. Rev. Neurosci. 9, 545–556 (2008).
pubmed: 18545266
pmcid: 4332708
doi: 10.1038/nrn2357
Fatahi, Z., Haghparast, A., Khani, A. & Kermani, M. Functional connectivity between anterior cingulate cortex and orbitofrontal cortex during value-based decision making. Neurobiol. Learn. Mem. 147, 74–78 (2018).
pubmed: 29191756
doi: 10.1016/j.nlm.2017.11.014
Kahnt, T., Heinzle, J., Park, S. Q. & Haynes, J.-D. The neural code of reward anticipation in human orbitofrontal cortex. Proc. Natl Acad. Sci. 107, 6010–6015 (2010).
pubmed: 20231475
pmcid: 2851854
doi: 10.1073/pnas.0912838107
Malvaez, M., Shieh, C., Murphy, M. D., Greenfield, V. Y. & Wassum, K. M. Distinct cortical–amygdala projections drive reward value encoding and retrieval. Nat. Neurosci. 22, 762–769 (2019).
pubmed: 30962632
pmcid: 6486448
doi: 10.1038/s41593-019-0374-7
Rudebeck, P. H. & Rich, E. L. Orbitofrontal cortex. Curr. Biol. 28, R1083–R1088 (2018).
pubmed: 30253144
pmcid: 9253859
doi: 10.1016/j.cub.2018.07.018
Newson, J. J. & Thiagarajan, T. C. EEG frequency bands in psychiatric disorders: a review of resting state studies. Front. Hum. Neurosci. 12, 521 (2019).
Xiao, J. et al. Decoding depression severity from intracranial neural activity. Biol. Psychiatry 94, 445–453 (2023).
Adkinson, J. A. et al. Imaging versus electrographic connectivity in human mood-related fronto-temporal networks. Brain Stimul. 15, 554–565 (2022).
pubmed: 35292403
pmcid: 9232982
doi: 10.1016/j.brs.2022.03.002
Allawala, A. et al. A novel framework for network-targeted neuropsychiatric deep brain stimulation. Neurosurgery 89, E116 (2021).
pubmed: 33913499
pmcid: 8279838
doi: 10.1093/neuros/nyab112
Sheth, S. A. et al. Deep brain stimulation for depression informed by intracranial recordings. Biol. Psychiatry 92, 246–251 (2022).
pubmed: 35063186
doi: 10.1016/j.biopsych.2021.11.007
Chase, H. W. et al. Approach and avoidance learning in patients with major depression and healthy controls: relation to anhedonia. Psychol. Med. 40, 433–440 (2010).
pubmed: 19607754
doi: 10.1017/S0033291709990468
Gradin, V. B. et al. Expected value and prediction error abnormalities in depression and schizophrenia. Brain 134, 1751–1764 (2011).
pubmed: 21482548
doi: 10.1093/brain/awr059
Rothkirch, M., Tonn, J., Köhler, S. & Sterzer, P. Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder. Brain 140, 1147–1157 (2017).
pubmed: 28334960
doi: 10.1093/brain/awx025
Whitmer, A. J., Frank, M. J. & Gotlib, I. H. Sensitivity to reward and punishment in major depressive disorder: effects of rumination and of single versus multiple experiences. Cognit. Emot. 26, 1475–1485 (2012).
doi: 10.1080/02699931.2012.682973
Gabbay, V. et al. Anhedonia, but not irritability, is associated with illness severity outcomes in adolescent major depression. J. Child Adolesc. Psychopharmacol. 25, 194–200 (2015).
pubmed: 25802984
pmcid: 4403015
doi: 10.1089/cap.2014.0105
Der-Avakian, A., D’Souza, M. S., Pizzagalli, D. A. & Markou, A. Assessment of reward responsiveness in the response bias probabilistic reward task in rats: implications for cross-species translational research. Transl. Psychiatry 3, e297 (2013).
pubmed: 23982629
pmcid: 3756297
doi: 10.1038/tp.2013.74
Iturra‑Mena, A. M., Kangas, B. D., Luc, O. T., Potter, D. & Pizzagalli, D. A. Electrophysiological signatures of reward learning in the rodent touchscreen-based Probabilistic Reward Task. Neuropsychopharmacology 48, 700–709 (2023).
pubmed: 36646816
pmcid: 9938210
doi: 10.1038/s41386-023-01532-4
Sailer, U., Wurm, F. & Pfabigan, D. M. Social and non-social feedback stimuli lead to comparable levels of reward learning and reward responsiveness in an online probabilistic reward task. Behav. Res. https://doi.org/10.3758/s13428-023-02255-6 (2023).
Wilkinson, M. P., Slaney, C. L., Mellor, J. R. & Robinson, E. S. J. Investigation of reward learning and feedback sensitivity in non-clinical participants with a history of early life stress. PLOS ONE 16, e0260444 (2021).
pubmed: 34890390
pmcid: 8664195
doi: 10.1371/journal.pone.0260444
Bouthour, W. et al. Biomarkers for closed-loop deep brain stimulation in Parkinson disease and beyond. Nat. Rev. Neurol. 15, 343–352 (2019).
pubmed: 30936569
doi: 10.1038/s41582-019-0166-4
Lozano, A. M. et al. Deep brain stimulation: current challenges and future directions. Nat. Rev. Neurol. 15, 148–160 (2019).
pubmed: 30683913
pmcid: 6397644
doi: 10.1038/s41582-018-0128-2
Vergunst, F. K. et al. Longitudinal course of symptom severity and fluctuation in patients with treatment-resistant unipolar and bipolar depression. Psychiatry Res. 207, 143–149 (2013).
pubmed: 23601791
doi: 10.1016/j.psychres.2013.03.022
Cagnan, H., Denison, T., McIntyre, C. & Brown, P. Emerging technologies for improved deep brain stimulation. Nat. Biotechnol. 37, 1024–1033 (2019).
pubmed: 31477926
pmcid: 6877347
doi: 10.1038/s41587-019-0244-6
Little, S. et al. Adaptive deep brain stimulation in advanced Parkinson disease. Ann. Neurol. 74, 449–457 (2013).
pubmed: 23852650
pmcid: 3886292
doi: 10.1002/ana.23951
Rosa, M. et al. Adaptive deep brain stimulation in a freely moving Parkinsonian patient. Mov. Disord. 30, 1003–1005 (2015).
pubmed: 25999288
pmcid: 5032989
doi: 10.1002/mds.26241
Chudasama, Y. et al. The role of the anterior cingulate cortex in choices based on reward value and reward contingency. Cereb. Cortex 23, 2884–2898 (2013).
pubmed: 22944530
doi: 10.1093/cercor/bhs266
Fox, K. C. R. et al. Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain. Nat. Hum. Behav. 4, 1039–1052 (2020).
pubmed: 32632334
pmcid: 7572705
doi: 10.1038/s41562-020-0910-1
Parvizi, J., Rangarajan, V., Shirer, W. R., Desai, N. & Greicius, M. D. The will to persevere induced by electrical stimulation of the human cingulate Gyrus. Neuron 80, 1359–1367 (2013).
pubmed: 24316296
doi: 10.1016/j.neuron.2013.10.057
Holroyd, C. B. & Umemoto, A. The research domain criteria framework: the case for anterior cingulate cortex. Neurosci. Biobehav. Rev. 71, 418–443 (2016).
pubmed: 27693229
doi: 10.1016/j.neubiorev.2016.09.021
Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).
pubmed: 23889930
pmcid: 3767969
doi: 10.1016/j.neuron.2013.07.007
Polanía, R., Krajbich, I., Grueschow, M. & Ruff, C. C. Neural oscillations and synchronization differentially support evidence accumulation in perceptual and value-based decision making. Neuron 82, 709–720 (2014).
pubmed: 24811387
doi: 10.1016/j.neuron.2014.03.014
Bogaerts, L., Richter, C. G., Landau, A. N. & Frost, R. Beta-band activity is a signature of statistical learning. J. Neurosci. 40, 7523–7530 (2020).
pubmed: 32826312
pmcid: 7511193
doi: 10.1523/JNEUROSCI.0771-20.2020
Espenhahn, S. et al. Cortical beta oscillations are associated with motor performance following visuomotor learning. NeuroImage 195, 340–353 (2019).
pubmed: 30954709
doi: 10.1016/j.neuroimage.2019.03.079
Fries, P. Rhythms for cognition: communication through coherence. Neuron 88, 220–235 (2015).
pubmed: 26447583
pmcid: 4605134
doi: 10.1016/j.neuron.2015.09.034
Ghiani, A., Maniglia, M., Battaglini, L., Melcher, D. & Ronconi, L. Binding mechanisms in visual perception and their link with neural oscillations: a review of evidence from tACS. Front. Psychol. 12, 643677 (2021).
pubmed: 33828509
pmcid: 8019716
doi: 10.3389/fpsyg.2021.643677
Lundqvist, M. et al. Gamma and beta bursts underlie working memory. Neuron 90, 152–164 (2016).
pubmed: 26996084
pmcid: 5220584
doi: 10.1016/j.neuron.2016.02.028
Lundqvist, M., Herman, P., Warden, M. R., Brincat, S. L. & Miller, E. K. Gamma and beta bursts during working memory readout suggest roles in its volitional control. Nat. Commun. 9, 394 (2018).
pubmed: 29374153
pmcid: 5785952
doi: 10.1038/s41467-017-02791-8
Fischl, B. FreeSurfer. NeuroImage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Joshi, A. et al. Unified framework for development, deployment and robust testing of neuroimaging algorithms. Neuroinformatics 9, 69–84 (2011).
pubmed: 21249532
pmcid: 3066099
doi: 10.1007/s12021-010-9092-8
Bastos, A. M. & Schoffelen, J.-M. A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front. Syst. Neurosci. 9, 175 (2015).
pubmed: 26778976
Magnotti, J. F., Wang, Z. & Beauchamp, M. S. RAVE: comprehensive open-source software for reproducible analysis and visualization of intracranial EEG data. NeuroImage 223, 117341 (2020).
pubmed: 32920161
doi: 10.1016/j.neuroimage.2020.117341