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Invasive Computational Psychiatry

  • Ignacio Saez
    Correspondence
    Correspondence should be addressed to: Ignacio Saez, PhD Center for Advanced Therapeutics, Icahn School of Medicine at Mount Sinai
    Affiliations
    Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY

    Department of Neurosurgery, Icahn School of Medicine at Mount Sinai, New York, NY

    Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY
    Search for articles by this author
  • Xiaosi Gu
    Correspondence
    Correspondence should be addressed to: Xiaosi Gu, PhD, Center for Computational Psychiatry, Icahn School of Medicine at Mount Sinai
    Affiliations
    Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY

    Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
    Search for articles by this author

      Abstract

      Computational psychiatry, a relatively new field that seeks to understand psychiatric disorders with formal, computational theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress from intracranial neuroscience. Specifically, invasive techniques like stereoelectroencephalography (sEEG), electrocorticography (ECoG), and deep brain stimulation (DBS) have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we will summarize progress and drawbacks from both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction – invasive computational psychiatry (ICP). The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable with non-invasive techniques in human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (“algorithmic targeting”), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a roadmap for this exciting new field as well as presenting cautions such as ethical concerns and generalization of findings.

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      References

        • Chen C.
        • Takahashi T.
        • Nakagawa S.
        • Inoue T.
        • Kusumi I.
        Reinforcement learning in depression: A review of computational research.
        Neuroscience & Biobehavioral Reviews. 2015; 55: 247-267
        • Husain M.
        • Roiser J.P.
        Neuroscience of apathy and anhedonia: a transdiagnostic approach.
        Nat Rev Neurosci. 2018; 19: 470-484
        • Hitchcock P.
        • Forman E.
        • Rothstein N.
        • Zhang F.
        • Kounios J.
        • Niv Y.
        • et al.
        Rumination derails reinforcement learning with possible implications for ineffective behavior.
        Clinical Psychological Science. 2021; 21677026211051324
        • Bishop S.J.
        • Gagne C.
        Anxiety, depression, and decision making: a computational perspective.
        Annual review of neuroscience. 2018; 41: 371-388
        • Braver T.S.
        • Barch D.M.
        • Cohen J.D.
        Cognition and control in schizophrenia: a computational model of dopamine and prefrontal function.
        Biol Psychiatry. 1999; 46: 312-328
        • Redish A.D.
        Addiction as a computational process gone awry.
        Science. 2004; 306: 1944-1947
        • Huys Q.J.M.
        • Moutoussis M.
        • Williams J.
        Are computational models of any use to psychiatry?.
        Neural Networks. 2011; 24: 544-551
        • Deshpande G.
        • Libero L.E.
        • Sreenivasan K.R.
        • Deshpande H.D.
        • Kana R.K.
        Identification of neural connectivity signatures of autism using machine learning.
        Front Hum Neurosci. 2013; 7: 670
        • Pariyadath V.
        • Stein E.A.
        • Ross T.J.
        Machine learning classification of resting state functional connectivity predicts smoking status.
        Front Hum Neurosci. 2014; 8: 425
        • Mete M.
        • Sakoglu U.
        • Spence J.S.
        • Devous M.D.
        • Sr .,
        • Harris T.S.
        • Adinoff B.
        Successful classification of cocaine dependence using brain imaging: a generalizable machine learning approach.
        BMC Bioinformatics. 2016; 17: 357
        • Steele V.R.
        • Rao V.
        • Calhoun V.D.
        • Kiehl K.A.
        Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders.
        Neuroimage. 2017; 145: 265-273
        • Lawson R.P.
        • Mathys C.
        • Rees G.
        Adults with autism overestimate the volatility of the sensory environment.
        Nat Neurosci. 2017; 20: 1293-1299
        • Powers A.R.
        • Mathys C.
        • Corlett P.R.
        Pavlovian conditioning-induced hallucinations result from overweighting of perceptual priors.
        Science. 2017; 357: 596-600
        • Collins A.G.E.
        • Albrecht M.A.
        • Waltz J.A.
        • Gold J.M.
        • Frank M.J.
        Interactions Among Working Memory, Reinforcement Learning, and Effort in Value-Based Choice: A New Paradigm and Selective Deficits in Schizophrenia.
        Biol Psychiatry. 2017; 82: 431-439
        • Rutledge R.B.
        • Moutoussis M.
        • Smittenaar P.
        • Zeidman P.
        • Taylor T.
        • Hrynkiewicz L.
        • et al.
        Association of Neural and Emotional Impacts of Reward Prediction Errors With Major Depression.
        JAMA Psychiatry. 2017; 74: 790-797
        • Na S.
        • Blackmore S.
        • Chung D.
        • O'Brien M.
        • Banker S.M.
        • Heflin M.
        • et al.
        Computational mechanisms underlying illusion of control in delusional individuals.
        Schizophr Res. 2022;
        • Miller K.J.
        • Shenhav A.
        • Ludvig E.A.
        Habits without values.
        Psychological review. 2019; 126: 292
        • Friston K.J.
        The fantastic organ.
        Brain. 2013; 136: 1328-1332
        • Bennett D.
        • Silverstein S.M.
        • Niv Y.
        The Two Cultures of Computational Psychiatry.
        JAMA Psychiatry. 2019; 76: 563-564
        • Daw N.D.
        • Gershman S.J.
        • Seymour B.
        • Dayan P.
        • Dolan R.J.
        Model-based influences on humans' choices and striatal prediction errors.
        Neuron. 2011; 69: 1204-1215
      1. Marr D, Poggio T (1976): From understanding computation to understanding neural circuitry.

        • Camerer C.F.
        • Ho T.-H.
        • Chong J.-K.
        A cognitive hierarchy model of games.
        The Quarterly Journal of Economics. 2004; 119: 861-898
        • Maia T.V.
        • Frank M.J.
        From reinforcement learning models to psychiatric and neurological disorders.
        Nat Neurosci. 2011; 14: 154-162
        • Friston K.J.
        • Harrison L.
        • Penny W.
        Dynamic causal modelling.
        Neuroimage. 2003; 19: 1273-1302
        • Wang X.-J.
        • Krystal J.H.
        Computational psychiatry.
        Neuron. 2014; 84: 638-654
        • Wilson R.C.
        • Collins A.G.E.
        Ten simple rules for the computational modeling of behavioral data.
        eLife. 2019; 8e49547
        • Stephan K.E.
        • Penny W.D.
        • Moran R.J.
        • den Ouden H.E.
        • Daunizeau J.
        • Friston K.J.
        Ten simple rules for dynamic causal modeling.
        Neuroimage. 2010; 49: 3099-3109
        • Glaser J.I.
        • Benjamin A.S.
        • Chowdhury R.H.
        • Perich M.G.
        • Miller L.E.
        • Kording K.P.
        Machine learning for neural decoding.
        Eneuro. 2020; 7
        • Vercio L.L.
        • Amador K.
        • Bannister J.J.
        • Crites S.
        • Gutierrez A.
        • MacDonald M.E.
        • et al.
        Supervised machine learning tools: a tutorial for clinicians.
        Journal of Neural Engineering. 2020; 17062001
        • Hitchcock P.F.
        • Fried E.I.
        • Frank M.J.
        Computational Psychiatry Needs Time and Context.
        Annu Rev Psychol. 2022; 73: 243-270
        • Buzsaki G.
        • Moser E.I.
        Memory, navigation and theta rhythm in the hippocampal-entorhinal system.
        Nat Neurosci. 2013; 16: 130-138
        • Cavanagh J.F.
        • Frank M.J.
        Frontal theta as a mechanism for cognitive control.
        Trends Cogn Sci. 2014; 18: 414-421
        • Ekstrom A.D.
        • Caplan J.B.
        • Ho E.
        • Shattuck K.
        • Fried I.
        • Kahana M.J.
        Human hippocampal theta activity during virtual navigation.
        Hippocampus. 2005; 15: 881-889
        • Kirkby L.A.
        • Luongo F.J.
        • Lee M.B.
        • Nahum M.
        • Van Vleet T.M.
        • Rao V.R.
        • et al.
        An Amygdala-Hippocampus Subnetwork that Encodes Variation in Human Mood.
        Cell. 2018; 175: 1688-1700 e1614
        • Logothetis N.K.
        • Pauls J.
        • Augath M.
        • Trinath T.
        • Oeltermann A.
        Neurophysiological investigation of the basis of the fMRI signal.
        Nature. 2001; 412: 150-157
        • Mukamel R.
        • Gelbard H.
        • Arieli A.
        • Hasson U.
        • Fried I.
        • Malach R.
        Coupling between neuronal firing, field potentials, and FMRI in human auditory cortex.
        Science. 2005; 309: 951-954
        • Leszczynski M.
        • Barczak A.
        • Kajikawa Y.
        • Ulbert I.
        • Falchier A.Y.
        • Tal I.
        • et al.
        Dissociation of broadband high-frequency activity and neuronal firing in the neocortex.
        Sci Adv. 2020; 6eabb0977
        • Bentley W.J.
        • Li J.M.
        • Snyder A.Z.
        • Raichle M.E.
        • Snyder L.H.
        Oxygen Level and LFP in Task-Positive and Task-Negative Areas: Bridging BOLD fMRI and Electrophysiology.
        Cereb Cortex. 2016; 26: 346-357
        • Engell A.D.
        • Huettel S.
        • McCarthy G.
        The fMRI BOLD signal tracks electrophysiological spectral perturbations, not event-related potentials.
        Neuroimage. 2012; 59: 2600-2606
        • Benar C.G.
        • Grova C.
        • Kobayashi E.
        • Bagshaw A.P.
        • Aghakhani Y.
        • Dubeau F.
        • et al.
        EEG-fMRI of epileptic spikes: concordance with EEG source localization and intracranial EEG.
        Neuroimage. 2006; 30: 1161-1170
        • Ojemann G.A.
        • Ojemann J.
        • Ramsey N.F.
        Relation between functional magnetic resonance imaging (fMRI) and single neuron, local field potential (LFP) and electrocorticography (ECoG) activity in human cortex.
        Front Hum Neurosci. 2013; 7: 34
        • Zaghloul K.A.
        • Blanco J.A.
        • Weidemann C.T.
        • McGill K.
        • Jaggi J.L.
        • Baltuch G.H.
        • et al.
        Human substantia nigra neurons encode unexpected financial rewards.
        Science. 2009; 323: 1496-1499
        • Zheng J.
        • Schjetnan A.G.P.
        • Yebra M.
        • Gomes B.A.
        • Mosher C.P.
        • Kalia S.K.
        • et al.
        Neurons detect cognitive boundaries to structure episodic memories in humans.
        Nat Neurosci. 2022; 25: 358-368
        • Ekstrom A.D.
        • Kahana M.J.
        • Caplan J.B.
        • Fields T.A.
        • Isham E.A.
        • Newman E.L.
        • et al.
        Cellular networks underlying human spatial navigation.
        Nature. 2003; 425: 184-188
        • Jacobs J.
        • Weidemann C.T.
        • Miller J.F.
        • Solway A.
        • Burke J.F.
        • Wei X.X.
        • et al.
        Direct recordings of grid-like neuronal activity in human spatial navigation.
        Nat Neurosci. 2013; 16: 1188-1190
        • Paulk A.C.
        • Kfir Y.
        • Khanna A.R.
        • Mustroph M.L.
        • Trautmann E.M.
        • Soper D.J.
        • et al.
        Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex.
        Nat Neurosci. 2022; 25: 252-263
        • Carmena J.M.
        • Lebedev M.A.
        • Crist R.E.
        • O'Doherty J.E.
        • Santucci D.M.
        • Dimitrov D.F.
        • et al.
        Learning to control a brain-machine interface for reaching and grasping by primates.
        PLoS Biol. 2003; 1: E42
        • Kishida K.T.
        • Saez I.
        • Lohrenz T.
        • Witcher M.R.
        • Laxton A.W.
        • Tatter S.B.
        • et al.
        Subsecond dopamine fluctuations in human striatum encode superposed error signals about actual and counterfactual reward.
        Proc Natl Acad Sci U S A. 2016; 113: 200-205
        • Kishida K.T.
        • Sandberg S.G.
        • Lohrenz T.
        • Comair Y.G.
        • Saez I.
        • Phillips P.E.
        • et al.
        Sub-second dopamine detection in human striatum.
        PLoS One. 2011; 6e23291
        • Moran R.J.
        • Kishida K.T.
        • Lohrenz T.
        • Saez I.
        • Laxton A.W.
        • Witcher M.R.
        • et al.
        The Protective Action Encoding of Serotonin Transients in the Human Brain.
        Neuropsychopharmacology. 2018; 43: 1425-1435
        • Saez I.
        • Lin J.
        • Stolk A.
        • Chang E.
        • Parvizi J.
        • Schalk G.
        • et al.
        Encoding of Multiple Reward-Related Computations in Transient and Sustained High-Frequency Activity in Human OFC.
        Curr Biol. 2018; 28: 2889-2899 e2883
        • Gueguen M.C.M.
        • Lopez-Persem A.
        • Billeke P.
        • Lachaux J.P.
        • Rheims S.
        • Kahane P.
        • et al.
        Anatomical dissociation of intracerebral signals for reward and punishment prediction errors in humans.
        Nat Commun. 2021; 12: 3344
        • Cecchi R.
        • Vinckier F.
        • Hammer J.
        • Marusic P.
        • Nica A.
        • Rheims S.
        • et al.
        Intracerebral mechanisms explaining the impact of incidental feedback on mood state and risky choice.
        Elife. 2022; 11
        • Lopez-Persem A.
        • Bastin J.
        • Petton M.
        • Abitbol R.
        • Lehongre K.
        • Adam C.
        • et al.
        Four core properties of the human brain valuation system demonstrated in intracranial signals.
        Nat Neurosci. 2020; 23: 664-675
        • Graat I.
        • Figee M.
        • Denys D.
        The application of deep brain stimulation in the treatment of psychiatric disorders.
        Int Rev Psychiatry. 2017; 29: 178-190
        • Greenberg B.D.
        • Malone D.A.
        • Friehs G.M.
        • Rezai A.R.
        • Kubu C.S.
        • Malloy P.F.
        • et al.
        Three-year outcomes in deep brain stimulation for highly resistant obsessive-compulsive disorder.
        Neuropsychopharmacology. 2006; 31: 2384-2393
        • Stangl M.
        • Topalovic U.
        • Inman C.S.
        • Hiller S.
        • Villaroman D.
        • Aghajan Z.M.
        • et al.
        Boundary-anchored neural mechanisms of location-encoding for self and others.
        Nature. 2021; 589: 420-425
        • Friston K.J.
        • Price C.J.
        Degeneracy and redundancy in cognitive anatomy.
        Trends Cogn Sci. 2003; 7: 151-152
        • Sajid N.
        • Parr T.
        • Hope T.M.
        • Price C.J.
        • Friston K.J.
        Degeneracy and Redundancy in Active Inference.
        Cereb Cortex. 2020; 30: 5750-5766
        • Dayan P.
        • Huys Q.J.
        Serotonin, inhibition, and negative mood.
        PLoS Comput Biol. 2008; 4: e4
        • 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. 2008; 43: 76-87
        • Jewett D.L.
        • Fein G.
        • Greenberg M.H.
        A double-blind study of symptom provocation to determine food sensitivity.
        New England Journal of Medicine. 1990; 323: 429-433
        • Nakao T.
        • Nakagawa A.
        • Yoshiura T.
        • Nakatani E.
        • Nabeyama M.
        • Yoshizato C.
        • et al.
        Brain activation of patients with obsessive-compulsive disorder during neuropsychological and symptom provocation tasks before and after symptom improvement: a functional magnetic resonance imaging study.
        Biological psychiatry. 2005; 57: 901-910
        • Li C.T.
        • Chen M.H.
        • Juan C.H.
        • Huang H.H.
        • Chen L.F.
        • Hsieh J.C.
        • et al.
        Efficacy of prefrontal theta-burst stimulation in refractory depression: a randomized sham-controlled study.
        Brain. 2014; 137: 2088-2098
        • Williams N.R.
        • Sudheimer K.D.
        • Bentzley B.S.
        • Pannu J.
        • Stimpson K.H.
        • Duvio D.
        • et al.
        High-dose spaced theta-burst TMS as a rapid-acting antidepressant in highly refractory depression.
        Brain. 2018; 141: e18
        • Fitzgerald P.J.
        • Watson B.O.
        Gamma oscillations as a biomarker for major depression: an emerging topic.
        Transl Psychiatry. 2018; 8: 177
        • Smart O.L.
        • Tiruvadi V.R.
        • Mayberg H.S.
        Multimodal approaches to define network oscillations in depression.
        Biol Psychiatry. 2015; 77: 1061-1070
        • Pillman F.
        Carl Wernicke and the neurobiological paradigm in psychiatry.
        Acta Neuropsychologica. 2007; 5: 246-260
        • Kanner A.M.
        Depression in epilepsy: prevalence, clinical semiology, pathogenic mechanisms, and treatment.
        Biol Psychiatry. 2003; 54: 388-398
        • Kanner A.M.
        • Schachter S.C.
        • Barry J.J.
        • Hesdorffer D.C.
        • Mula M.
        • Trimble M.
        • et al.
        Depression and epilepsy: epidemiologic and neurobiologic perspectives that may explain their high comorbid occurrence.
        Epilepsy Behav. 2012; 24: 156-168
        • Huys Q.J.M.
        • Dayan P.
        A Bayesian formulation of behavioral control.
        Cognition. 2009; 113: 314-328
        • Gradin V.B.
        • Kumar P.
        • Waiter G.
        • Ahearn T.
        • Stickle C.
        • Milders M.
        • et al.
        Expected value and prediction error abnormalities in depression and schizophrenia.
        Brain. 2011; 134: 1751-1764
        • Eldar E.
        • Niv Y.
        Interaction between emotional state and learning underlies mood instability.
        Nature Communications. 2015; 6: 6149
        • Pizzagalli D.A.
        • Holmes A.J.
        • Dillon D.G.
        • Goetz E.L.
        • Birk J.L.
        • Bogdan R.
        • et al.
        Reduced caudate and nucleus accumbens response to rewards in unmedicated individuals with major depressive disorder.
        Am J Psychiatry. 2009; 166: 702-710
        • Cavanagh J.F.
        • Bismark A.W.
        • Frank M.J.
        • Allen J.J.B.
        Multiple Dissociations Between Comorbid Depression and Anxiety on Reward and Punishment Processing: Evidence From Computationally Informed EEG.
        Comput Psychiatr. 2019; 3: 1-17
        • Mueller E.M.
        • Pechtel P.
        • Cohen A.L.
        • Douglas S.R.
        • Pizzagalli D.A.
        Potentiated processing of negative feedback in depression is attenuated by anhedonia.
        Depress Anxiety. 2015; 32: 296-305
        • Harrison N.A.
        • Voon V.
        • Cercignani M.
        • Cooper E.A.
        • Pessiglione M.
        • Critchley H.D.
        A Neurocomputational Account of How Inflammation Enhances Sensitivity to Punishments Versus Rewards.
        Biol Psychiatry. 2016; 80: 73-81
        • Smith R.
        • Kuplicki R.
        • Feinstein J.
        • Forthman K.L.
        • Stewart J.L.
        • Paulus M.P.
        • et al.
        A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders.
        PLoS computational biology. 2020; 16e1008484
        • Smith R.
        • Khalsa S.S.
        • Paulus M.P.
        An Active Inference Approach to Dissecting Reasons for Nonadherence to Antidepressants.
        Biol Psychiatry Cogn Neurosci Neuroimaging. 2021; 6: 919-934
        • Smith R.
        • Kirlic N.
        • Stewart J.L.
        • Touthang J.
        • Kuplicki R.
        • McDermott T.J.
        • et al.
        Long-term stability of computational parameters during approach-avoidance conflict in a transdiagnostic psychiatric patient sample.
        Scientific reports. 2021; 11: 1-13
        • Simon G.E.
        • VonKorff M.
        • Piccinelli M.
        • Fullerton C.
        • Ormel J.
        An international study of the relation between somatic symptoms and depression.
        New England journal of medicine. 1999; 341: 1329-1335
        • Harshaw C.
        Interoceptive dysfunction: toward an integrated framework for understanding somatic and affective disturbance in depression.
        Psychological bulletin. 2015; 141: 311
        • Segrin C.
        Social skills deficits associated with depression.
        Clinical psychology review. 2000; 20: 379-403
        • Ait Oumeziane B.
        • Jones O.
        • Foti D.
        Neural sensitivity to social and monetary reward in depression: clarifying general and domain-specific deficits.
        Frontiers in behavioral neuroscience. 2019; 13: 199
        • Mayberg H.S.
        • Lozano A.M.
        • Voon V.
        • McNeely H.E.
        • Seminowicz D.
        • Hamani C.
        • et al.
        Deep brain stimulation for treatment-resistant depression.
        Neuron. 2005; 45: 651-660
        • Kennedy S.H.
        • Giacobbe P.
        • Rizvi S.J.
        • Placenza F.M.
        • Nishikawa Y.
        • Mayberg H.S.
        • et al.
        Deep brain stimulation for treatment-resistant depression: follow-up after 3 to 6 years.
        Am J Psychiatry. 2011; 168: 502-510
        • Holtzheimer P.E.
        • Husain M.M.
        • Lisanby S.H.
        • Taylor S.F.
        • Whitworth L.A.
        • McClintock S.
        • et al.
        Subcallosal cingulate deep brain stimulation for treatment-resistant depression: a multisite, randomised, sham-controlled trial.
        Lancet Psychiatry. 2017; 4: 839-849
        • Crowell A.L.
        • Riva-Posse P.
        • Holtzheimer P.E.
        • Garlow S.J.
        • Kelley M.E.
        • Gross R.E.
        • et al.
        Long-Term Outcomes of Subcallosal Cingulate Deep Brain Stimulation for Treatment-Resistant Depression.
        Am J Psychiatry. 2019; 176: 949-956
        • Mayberg H.S.
        • Liotti M.
        • Brannan S.K.
        • McGinnis S.
        • Mahurin R.K.
        • Jerabek P.A.
        • et al.
        Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness.
        Am J Psychiatry. 1999; 156: 675-682
        • Drysdale A.T.
        • Grosenick L.
        • Downar J.
        • Dunlop K.
        • Mansouri F.
        • Meng Y.
        • et al.
        Resting-state connectivity biomarkers define neurophysiological subtypes of depression.
        Nat Med. 2017; 23: 28-38
        • Sani O.G.
        • Yang Y.
        • Lee M.B.
        • Dawes H.E.
        • Chang E.F.
        • Shanechi M.M.
        Mood variations decoded from multi-site intracranial human brain activity.
        Nat Biotechnol. 2018; 36: 954-961
        • Scangos K.W.
        • Khambhati A.N.
        • Daly P.M.
        • Makhoul G.S.
        • Sugrue L.P.
        • Zamanian H.
        • et al.
        Closed-loop neuromodulation in an individual with treatment-resistant depression.
        Nat Med. 2021; 27: 1696-1700
        • Sheth S.A.
        • Bijanki K.R.
        • Metzger B.
        • Allawala A.
        • Pirtle V.
        • Adkinson J.A.
        • et al.
        Deep Brain Stimulation for Depression Informed by Intracranial Recordings.
        Biol Psychiatry. 2021;
        • Raij T.
        • Nummenmaa A.
        • Marin M.F.
        • Porter D.
        • Furtak S.
        • Setsompop K.
        • et al.
        Prefrontal Cortex Stimulation Enhances Fear Extinction Memory in Humans.
        Biol Psychiatry. 2018; 84: 129-137
        • Winston Chiong M.K.L.
        • Chang Edward F.
        Neurosurgical Patients as Human Research Subjects: Ethical Considerations in Intracranial Electrophysiology Research.
        Neurosurgery. 2017; 83: 29-37
        • Feinsinger A.
        • Pouratian N.
        • Ebadi H.
        • Adolphs R.
        • Andersen R.
        • Beauchamp M.S.
        • et al.
        Ethical commitments, principles, and practices guiding intracranial neuroscientific research in humans.
        Neuron. 2022; 110: 188-194
        • Vedam-Mai V.
        • Deisseroth K.
        • Giordano J.
        • Lazaro-Munoz G.
        • Chiong W.
        • Suthana N.
        • et al.
        Proceedings of the Eighth Annual Deep Brain Stimulation Think Tank: Advances in Optogenetics, Ethical Issues Affecting DBS Research, Neuromodulatory Approaches for Depression, Adaptive Neurostimulation, and Emerging DBS Technologies.
        Front Hum Neurosci. 2021; 15644593
        • Lee D.J.
        • Lozano C.S.
        • Dallapiazza R.F.
        • Lozano A.M.
        Current and future directions of deep brain stimulation for neurological and psychiatric disorders.
        J Neurosurg. 2019; 131: 333-342
        • Deuschl G.
        • Schade-Brittinger C.
        • Krack P.
        • Volkmann J.
        • Schafer H.
        • Botzel K.
        • et al.
        A randomized trial of deep-brain stimulation for Parkinson's disease.
        N Engl J Med. 2006; 355: 896-908
        • Coenen V.A.
        • Honey C.R.
        • Hurwitz T.
        • Rahman A.A.
        • McMaster J.
        • Burgel U.
        • et al.
        Medial forebrain bundle stimulation as a pathophysiological mechanism for hypomania in subthalamic nucleus deep brain stimulation for Parkinson's disease.
        Neurosurgery. 2009; 64 (discussion 1114-1105): 1106-1114
        • Vercueil L.
        • Pollak P.
        • Fraix V.
        • Caputo E.
        • Moro E.
        • Benazzouz A.
        • et al.
        Deep brain stimulation in the treatment of severe dystonia.
        J Neurol. 2001; 248: 695-700
        • Flora E.D.
        • Perera C.L.
        • Cameron A.L.
        • Maddern G.J.
        Deep brain stimulation for essential tremor: a systematic review.
        Mov Disord. 2010; 25: 1550-1559
        • de Koning P.P.
        • Figee M.
        • van den Munckhof P.
        • Schuurman P.R.
        • Denys D.
        Current status of deep brain stimulation for obsessive-compulsive disorder: a clinical review of different targets.
        Curr Psychiatry Rep. 2011; 13: 274-282
        • Greenberg B.D.
        • Gabriels L.A.
        • Malone Jr., D.A.
        • Rezai A.R.
        • Friehs G.M.
        • Okun M.S.
        • et al.
        Deep brain stimulation of the ventral internal capsule/ventral striatum for obsessive-compulsive disorder: worldwide experience.
        Mol Psychiatry. 2010; 15: 64-79
        • Sturm V.
        • Lenartz D.
        • Koulousakis A.
        • Treuer H.
        • Herholz K.
        • Klein J.C.
        • et al.
        The nucleus accumbens: a target for deep brain stimulation in obsessive-compulsive- and anxiety-disorders.
        J Chem Neuroanat. 2003; 26: 293-299
        • Bewernick B.H.
        • Hurlemann R.
        • Matusch A.
        • Kayser S.
        • Grubert C.
        • Hadrysiewicz B.
        • et al.
        Nucleus accumbens deep brain stimulation decreases ratings of depression and anxiety in treatment-resistant depression.
        Biol Psychiatry. 2010; 67: 110-116
        • Hodaie M.
        • Wennberg R.A.
        • Dostrovsky J.O.
        • Lozano A.M.
        Chronic anterior thalamus stimulation for intractable epilepsy.
        Epilepsia. 2002; 43: 603-608
        • Dougherty D.D.
        • Rezai A.R.
        • Carpenter L.L.
        • Howland R.H.
        • Bhati M.T.
        • O'Reardon J.P.
        • et al.
        A Randomized Sham-Controlled Trial of Deep Brain Stimulation of the Ventral Capsule/Ventral Striatum for Chronic Treatment-Resistant Depression.
        Biol Psychiatry. 2015; 78: 240-248
        • Malone Jr., D.A.
        • Dougherty D.D.
        • Rezai A.R.
        • Carpenter L.L.
        • Friehs G.M.
        • Eskandar E.N.
        • et al.
        Deep brain stimulation of the ventral capsule/ventral striatum for treatment-resistant depression.
        Biol Psychiatry. 2009; 65: 267-275
        • Sartorius A.
        • Kiening K.L.
        • Kirsch P.
        • von Gall C.C.
        • Haberkorn U.
        • Unterberg A.W.
        • et al.
        Remission of major depression under deep brain stimulation of the lateral habenula in a therapy-refractory patient.
        Biol Psychiatry. 2010; 67: e9-e11
        • Schlaepfer T.E.
        • Bewernick B.H.
        • Kayser S.
        • Madler B.
        • Coenen V.A.
        Rapid effects of deep brain stimulation for treatment-resistant major depression.
        Biol Psychiatry. 2013; 73: 1204-1212
        • Luigjes J.
        • van den Brink W.
        • Feenstra M.
        • van den Munckhof P.
        • Schuurman P.R.
        • Schippers R.
        • et al.
        Deep brain stimulation in addiction: a review of potential brain targets.
        Mol Psychiatry. 2012; 17: 572-583
        • Muller U.J.
        • Voges J.
        • Steiner J.
        • Galazky I.
        • Heinze H.J.
        • Moller M.
        • et al.
        Deep brain stimulation of the nucleus accumbens for the treatment of addiction.
        Ann N Y Acad Sci. 2013; 1282: 119-128
        • Schrock L.E.
        • Mink J.W.
        • Woods D.W.
        • Porta M.
        • Servello D.
        • Visser-Vandewalle V.
        • et al.
        Tourette syndrome deep brain stimulation: a review and updated recommendations.
        Mov Disord. 2015; 30: 448-471
        • Corripio I.
        • Roldan A.
        • McKenna P.
        • Sarro S.
        • Alonso-Solis A.
        • Salgado L.
        • et al.
        Target selection for deep brain stimulation in treatment resistant schizophrenia.
        Prog Neuropsychopharmacol Biol Psychiatry. 2022; 112110436
        • Harat M.
        • Rudas M.
        • Zielinski P.
        • Birska J.
        • Sokal P.
        Nucleus accumbens stimulation in pathological obesity.
        Neurol Neurochir Pol. 2016; 50: 207-210
        • Wang J.
        • Chang C.
        • Geng N.
        • Wang X.
        • Gao G.
        Treatment of intractable anorexia nervosa with inactivation of the nucleus accumbens using stereotactic surgery.
        Stereotact Funct Neurosurg. 2013; 91: 364-372
        • Crone N.E.
        • Sinai A.
        • Korzeniewska A.
        High-frequency gamma oscillations and human brain mapping with electrocorticography.
        Prog Brain Res. 2006; 159: 275-295
        • Mesgarani N.
        • Chang E.F.
        Selective cortical representation of attended speaker in multi-talker speech perception.
        Nature. 2012; 485: 233-236
        • Pasley B.N.
        • David S.V.
        • Mesgarani N.
        • Flinker A.
        • Shamma S.A.
        • Crone N.E.
        • et al.
        Reconstructing speech from human auditory cortex.
        PLoS Biol. 2012; 10e1001251
        • Crone N.E.
        • Miglioretti D.L.
        • Gordon B.
        • Lesser R.P.
        Functional mapping of human sensorimotor cortex with electrocorticographic spectral analysis. II. Event-related synchronization in the gamma band.
        Brain : a journal of neurology. 1998; 121: 2301-2315
        • Skarpaas T.L.
        • Jarosiewicz B.
        • Morrell M.J.
        Brain-responsive neurostimulation for epilepsy (RNS((R)) System).
        Epilepsy Res. 2019; 153: 68-70
        • Scangos K.W.
        • Makhoul G.S.
        • Sugrue L.P.
        • Chang E.F.
        • Krystal A.D.
        State-dependent responses to intracranial brain stimulation in a patient with depression.
        Nat Med. 2021; 27: 229-231
        • Waltz J.A.
        • Frank M.J.
        • Robinson B.M.
        • Gold J.M.
        Selective reinforcement learning deficits in schizophrenia support predictions from computational models of striatal-cortical dysfunction.
        Biological psychiatry. 2007; 62: 756-764
        • Gillan C.M.
        • Papmeyer M.
        • Morein-Zamir S.
        • Sahakian B.J.
        • Fineberg N.A.
        • Robbins T.W.
        • et al.
        Disruption in the balance between goal-directed behavior and habit learning in obsessive-compulsive disorder.
        Am J Psychiat. 2011; 168: 718-726
        • Adams R.A.
        • Stephan K.E.
        • Brown H.R.
        • Frith C.D.
        • Friston K.J.
        The computational anatomy of psychosis.
        Front Psychiatry. 2013; 4: 47
        • Gu X.
        • Filbey F.
        A bayesian observer model of drug craving.
        JAMA Psychiatry. 2017; 74: 419-420
      2. Gu X (2018): Incubation of craving: A Bayesian Account Neuropsychopharmacology.

        • Ognibene D.
        • Fiore V.G.
        • Gu X.
        Addiction beyond pharmacological effects: The role of environment complexity and bounded rationality.
        Neural Netw. 2019; 116: 269-278
        • Garvert M.M.
        • Moutoussis M.
        • Kurth-Nelson Z.
        • Behrens T.E.
        • Dolan R.J.
        Learning-induced plasticity in medial prefrontal cortex predicts preference malleability.
        Neuron. 2015; 85: 418-428
        • Na S.
        • Chung D.
        • Hula A.
        • Perl O.
        • Jung J.
        • Heflin M.
        • et al.
        Humans use forward thinking to exploit social controllability.
        Elife. 2021; 10
        • Banker S.M.
        • Na S.
        • Beltrán J.
        • Koenigsberg H.W.
        • Foss-Feig J.H.
        • Gu X.
        • et al.
        Disrupted computations of social control in individuals with obsessive-compulsive and misophonia symptoms.
        iScience. 2022; 25104617
        • Xiang T.
        • Lohrenz T.
        • Montague P.R.
        Computational substrates of norms and their violations during social exchange.
        J Neurosci. 2013; 33: 1099-1108a
        • Gu X.
        • Wang X.
        • Hula A.
        • Wang S.
        • Xu S.
        • Lohrenz T.M.
        • et al.
        Necessary, yet dissociable contributions of the insular and ventromedial prefrontal cortices to norm adaptation: computational and lesion evidence in humans.
        J Neurosci. 2015; 35: 467-473
        • Craig A.B.
        • Grossman E.
        • Krichmar J.L.
        Investigation of autistic traits through strategic decision-making in games with adaptive agents.
        Scientific reports. 2017; 7: 1-11
        • Khalil R.
        • Tindle R.
        • Boraud T.
        • Moustafa A.A.
        • Karim A.A.
        Social decision making in autism: On the impact of mirror neurons, motor control, and imitative behaviors.
        CNS neuroscience & therapeutics. 2018; 24: 669-676
        • Will G.-J.
        • Rutledge R.B.
        • Moutoussis M.
        • Dolan R.J.
        Neural and computational processes underlying dynamic changes in self-esteem.
        Elife. 2017; 6e28098
        • Koban L.
        • Schneider R.
        • Ashar Y.K.
        • Andrews-Hanna J.R.
        • Landy L.
        • Moscovitch D.A.
        • et al.
        Social anxiety is characterized by biased learning about performance and the self.
        Emotion. 2017; 17: 1144
        • Siegel J.Z.
        • Curwell-Parry O.
        • Pearce S.
        • Saunders K.E.
        • Crockett M.J.
        A computational phenotype of disrupted moral inference in borderline personality disorder.
        Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 2020; 5: 1134-1141
        • Henco L.
        • Diaconescu A.O.
        • Lahnakoski J.M.
        • Brandi M.-L.
        • Hörmann S.
        • Hennings J.
        • et al.
        Aberrant computational mechanisms of social learning and decision-making in schizophrenia and borderline personality disorder.
        PLoS computational biology. 2020; 16e1008162