Clinical Applications of Stochastic Dynamic Models of the Brain, Part II: A Review
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Roberts, 2017, Clinical applications of stochastic dynamic models of the brain, part I: A primer, Biol Psychiatry Cogn Neurosci Neuroimaging, 2, 216, 10.1016/j.bpsc.2017.01.010
Wendling, 2016, Computational models of epileptiform activity, J Neurosci Methods, 260, 233, 10.1016/j.jneumeth.2015.03.027
Breakspear, 2006, A unifying explanation of primary generalized seizures through nonlinear brain modeling and bifurcation analysis, Cereb Cortex, 16, 1296, 10.1093/cercor/bhj072
Lopes da Silva, 2003, Epilepsies as dynamical diseases of brain systems: Basic models of the transition between normal and epileptic activity, Epilepsia, 44, 72, 10.1111/j.0013-9580.2003.12005.x
Kramer, 2012, Human seizures self-terminate across spatial scales via a critical transition, Proc Natl Acad Sci U S A, 109, 21116, 10.1073/pnas.1210047110
Suffczynski, 2004, Dynamics of non-convulsive epileptic phenomena modeled by a bistable neuronal network, Neuroscience, 126, 467, 10.1016/j.neuroscience.2004.03.014
Benjamin, 2012, A phenomenological model of seizure initiation suggests network structure may explain seizure frequency in idiopathic generalised epilepsy, J Math Neurosci, 2, 1, 10.1186/2190-8567-2-1
Kim, 2009, Dynamics of epileptic seizures: Evolution, spreading, and suppression, J Theor Biol, 257, 527, 10.1016/j.jtbi.2008.12.009
Suffczynski, 2006, Dynamics of epileptic phenomena determined from statistics of ictal transitions, IEEE Trans Biomed Eng, 53, 524, 10.1109/TBME.2005.869800
Freyer, 2012, A canonical model of multistability and scale-invariance in biological systems, PLoS Comput Biol, 8, e1002634, 10.1371/journal.pcbi.1002634
Jirsa, 2014, On the nature of seizure dynamics, Brain, 137, 2210, 10.1093/brain/awu133
Naze, 2015, Computational modeling of seizure dynamics using coupled neuronal networks: Factors shaping epileptiform activity, PLoS Comput Biol, 11, e1004209, 10.1371/journal.pcbi.1004209
Robinson, 2002, Dynamics of large-scale brain activity in normal arousal states and epileptic seizures, Phys Rev E, 65, 041924, 10.1103/PhysRevE.65.041924
Wendling, 2002, Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition, Eur J Neurosci, 15, 1499, 10.1046/j.1460-9568.2002.01985.x
Scheffer, 2009, Early-warning signals for critical transitions, Nature, 461, 53, 10.1038/nature08227
Cerf, 2004, Criticality and synchrony of fluctuations in rhythmical brain activity: Pretransitional effects in epileptic patients, Biol Cybern, 90, 239, 10.1007/s00422-004-0463-9
Litt, 2001, Epileptic seizures may begin hours in advance of clinical onset: a report of five patients, Neuron, 30, 51, 10.1016/S0896-6273(01)00262-8
McSharry, 2003, Prediction of epileptic seizures: Are nonlinear methods relevant?, Nat Med, 9, 241, 10.1038/nm0303-241
Mormann, 2007, Seizure prediction: The long and winding road, Brain, 130, 314, 10.1093/brain/awl241
Freestone, 2015, Seizure prediction: Science fiction or soon to become reality?, Curr Neurol Neurosci Rep, 15, 1, 10.1007/s11910-015-0596-3
Lopes da Silva FH, Blanes, 2003, Dynamical diseases of brain systems: Different routes to epileptic seizures, IEEE Trans Biomed Eng, 50, 540, 10.1109/TBME.2003.810703
Leblois, 2006, Competition between feedback loops underlies normal and pathological dynamics in the basal ganglia, J Neurosci, 26, 3567, 10.1523/JNEUROSCI.5050-05.2006
Van Albada, 2009, Mean-field modeling of the basal ganglia-thalamocortical system. II: Dynamics of parkinsonian oscillations, J Theor Biol, 257, 664, 10.1016/j.jtbi.2008.12.013
Frank, 2001, Interactions between frontal cortex and basal ganglia in working memory: A computational model, Cogn Affect Behav Neurosci, 1, 137, 10.3758/CABN.1.2.137
Frank, 2005, Dynamic dopamine modulation in the basal ganglia: A neurocomputational account of cognitive deficits in medicated and nonmedicated Parkinsonism, J Cogn Neurosci, 17, 51, 10.1162/0898929052880093
Frank, 2004, By carrot or by stick: Cognitive reinforcement learning in parkinsonism, Science, 306, 1940, 10.1126/science.1102941
Rubin, 2004, High frequency stimulation of the subthalamic nucleus eliminates pathological thalamic rhythmicity in a computational model, J Comput Neurosci, 16, 211, 10.1023/B:JCNS.0000025686.47117.67
Meijer, 2011, From Parkinsonian thalamic activity to restoring thalamic relay using deep brain stimulation: New insights from computational modeling, J Neural Eng, 8, 066005, 10.1088/1741-2560/8/6/066005
Schiff, 2010, Toward model-based control of Parkinson’s disease, Philos Trans R Soc Lond B Math Phys Eng Sci, 368, 2269, 10.1098/rsta.2010.0050
Popovych, 2014, Control of abnormal synchronization in neurological disorders, Front Neurol, 5, 268, 10.3389/fneur.2014.00268
Feng, 2007, Optimal deep brain stimulation of the subthalamic nucleus -- a computational study, J Comput Neurosci, 23, 265, 10.1007/s10827-007-0031-0
Tass, 2009, Long-lasting desynchronization in rat hippocampal slice induced by coordinated reset stimulation, Phys Rev E, 80, 011902, 10.1103/PhysRevE.80.011902
Wang, 2016, Coordinated reset deep brain stimulation of subthalamic nucleus produces long-lasting, dose-dependent motor improvements in the 1-methyl-4-phenyl-1, 2, 3, 6-tetrahydropyridine non-human primate model of parkinsonism, Brain Stim, 9, 609, 10.1016/j.brs.2016.03.014
Rosin, 2011, Closed-loop deep brain stimulation is superior in ameliorating parkinsonism, Neuron, 72, 370, 10.1016/j.neuron.2011.08.023
Little, 2013, Adaptive deep brain stimulation in advanced Parkinson disease, Ann Neurol, 74, 449, 10.1002/ana.23951
Tass, 2007
Tass, 2001, Desynchronizing double-pulse phase resetting and application to deep brain stimulation, Biol Cybern, 85, 343, 10.1007/s004220100268
Tass, 2003, Obsessive-compulsive disorder: Development of demand-controlled deep brain stimulation with methods from stochastic phase resetting, Neuropsychopharmacology, 28, S27, 10.1038/sj.npp.1300144
Tass, 2012, Unlearning tinnitus-related cerebral synchrony with acoustic coordinated reset stimulation: Theoretical concept and modelling, Biol Cybern, 106, 27, 10.1007/s00422-012-0479-5
Niedermeyer, 1999, The burst-suppression electroencephalogram, Clin Electroencephalogr, 30, 99, 10.1177/155005949903000305
Roberts, 2014, Scale-free bursting in human cortex following hypoxia at birth, J Neurosci, 34, 6557, 10.1523/JNEUROSCI.4701-13.2014
Sethna, 2001, Crackling noise, Nature, 410, 242, 10.1038/35065675
Iyer, 2014, Novel features of early burst suppression predict outcome after birth asphyxia, Ann Clin Transl Neurol, 1, 209, 10.1002/acn3.32
Iyer, 2015, Early detection of preterm intraventricular hemorrhage from clinical electroencephalography, Crit Care Med, 43, 2219, 10.1097/CCM.0000000000001190
Iyer, 2015, Cortical burst dynamics predict clinical outcome early in extremely preterm infants, Brain, 138, 2206, 10.1093/brain/awv129
Ching, 2012, A neurophysiological–metabolic model for burst suppression, Proc Natl Acad Sci U S A, 109, 3095, 10.1073/pnas.1121461109
Roberts, 2014, Critical role for resource constraints in neural models, Front Syst Neurosci, 8, 154, 10.3389/fnsys.2014.00154
Steyn-Ross, 1999, Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: Evidence for a general anesthetic-induced phase transition, Phys Rev E, 60, 7299, 10.1103/PhysRevE.60.7299
Bojak, 2005, Modeling the effects of anesthesia on the electroencephalogram, Phys Rev E, 71, 041902, 10.1103/PhysRevE.71.041902
Hindriks, 2012, Meanfield modeling of propofol-induced changes in spontaneous EEG rhythms, Neuroimage, 60, 2323, 10.1016/j.neuroimage.2012.02.042
Boly, 2012, Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness, J Neurosci, 32, 7082, 10.1523/JNEUROSCI.3769-11.2012
Kuhlmann, 2016, Neural mass model-based tracking of anesthetic brain states, Neuroimage, 133, 438, 10.1016/j.neuroimage.2016.03.039
Bonsall, 2012, Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder, Proc Biol Sci, 279, 916, 10.1098/rspb.2011.1246
Moore, 2014, Mood dynamics in bipolar disorder, Int J Bipolar Disord, 2, 11, 10.1186/s40345-014-0011-z
Bonsall, 2015, Bipolar disorder dynamics: Affective instabilities, relaxation oscillations and noise, J R Soc Interface, 12, 20150670, 10.1098/rsif.2015.0670
Daugherty, 2009, Mathematical models of bipolar disorder, Commun Nonlinear Sci Num Simul, 14, 2897, 10.1016/j.cnsns.2008.10.027
Steinacher, 2013, Relating the bipolar spectrum to dysregulation of behavioural activation: A perspective from dynamical modelling, PLoS One, 8, e63345, 10.1371/journal.pone.0063345
Goldbeter, 2013, Origin of cyclicity in bipolar disorders: A computational approach, Pharmacopsychiatry, 46, S44, 10.1055/s-0033-1341502
Frank, 2013, A limit cycle oscillator model for cycling mood variations of bipolar disorder patients derived from cellular biochemical reaction equations, Commun Nonlinear Sci Num Simul, 18, 2107, 10.1016/j.cnsns.2012.12.037
Holmes, 2016, Applications of time-series analysis to mood fluctuations in bipolar disorder to promote treatment innovation: A case series, Transl Psychiatry, 6, e720, 10.1038/tp.2015.207
Critchley, 2005, Neural mechanisms of autonomic, affective, and cognitive integration, J Comp Neurol, 493, 154, 10.1002/cne.20749
Critchley, 2004, Neural systems supporting interoceptive awareness, Nat Neurosci, 7, 189, 10.1038/nn1176
Paulus, 2006, An insular view of anxiety, Biol Psychiatry, 60, 383, 10.1016/j.biopsych.2006.03.042
Huys, 2016, Computational psychiatry as a bridge from neuroscience to clinical applications, Nat Neurosci, 19, 404, 10.1038/nn.4238
van de Leemput, 2014, Critical slowing down as early warning for the onset and termination of depression, Proc Natl Acad Sci U S A, 111, 87, 10.1073/pnas.1312114110
Hyett, 2015, Disrupted effective connectivity of cortical systems supporting attention and interoception in melancholia, JAMA Psychiatry, 72, 350, 10.1001/jamapsychiatry.2014.2490
Hyett, 2015, Scene unseen: Disrupted neuronal adaptation in melancholia during emotional film viewing, Neuroimage Clin, 9, 660, 10.1016/j.nicl.2015.10.011
Parker, 1994, Defining melancholia: Properties of a refined sign-based measure, Br J Psychiatry, 164, 316, 10.1192/bjp.164.3.316
Friston, 2011, Network discovery with DCM, Neuroimage, 56, 1202, 10.1016/j.neuroimage.2010.12.039
Phillips, 2007, A quantitative model of sleep-wake dynamics based on the physiology of the brainstem ascending arousal system, J Biol Rhythms, 22, 167, 10.1177/0748730406297512
Fulcher, 2014, A physiologically based model of orexinergic stabilization of sleep and wake, PLoS One, 9, e91982, 10.1371/journal.pone.0091982
Yang, 2016, Wake-sleep transition as a noisy bifurcation, Phys Rev E, 94, 022412, 10.1103/PhysRevE.94.022412
Robinson, 2015, A multiscale “working brain” model, 107
Abeysuriya, 2015, Physiologically based arousal state estimation and dynamics, J Neurosci Methods, 253, 55, 10.1016/j.jneumeth.2015.06.002
Abeysuriya, 2016, Real-time automated EEG tracking of brain states using neural field theory, J Neurosci Methods, 258, 28, 10.1016/j.jneumeth.2015.09.026
Deco, 2003, Attention and working memory: A dynamical model of neuronal activity in the prefrontal cortex, Eur J Neurosci, 18, 2374, 10.1046/j.1460-9568.2003.02956.x
Deco, 2009, Stochastic dynamics as a principle of brain function, Prog Neurobiol, 88, 1, 10.1016/j.pneurobio.2009.01.006
Deco, 2004, A neurodynamical cortical model of visual attention and invariant object recognition, Vision Res, 44, 621, 10.1016/j.visres.2003.09.037
Wang, 2001, Synaptic reverberation underlying mnemonic persistent activity, Trends Neurosci, 24, 455, 10.1016/S0166-2236(00)01868-3
Compte, 2000, Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model, Cereb Cortex, 10, 910, 10.1093/cercor/10.9.910
Wang, 1999, Synaptic basis of cortical persistent activity: The importance of NMDA receptors to working memory, J Neurosci, 19, 9587, 10.1523/JNEUROSCI.19-21-09587.1999
Loh, 2007, A dynamical systems hypothesis of schizophrenia, PLoS Comput Biol, 3, e228, 10.1371/journal.pcbi.0030228
Durstewitz, 2008, The dual-state theory of prefrontal cortex dopamine function with relevance to catechol-o-methyltransferase genotypes and schizophrenia, Biol Psychiatry, 64, 739, 10.1016/j.biopsych.2008.05.015
Durstewitz, 2000, Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex, J Neurophysiol, 83, 1733, 10.1152/jn.2000.83.3.1733
Rolls, 2008, Computational models of schizophrenia and dopamine modulation in the prefrontal cortex, Nat Rev Neurosci, 9, 696, 10.1038/nrn2462
Yang, 2014, Altered global brain signal in schizophrenia, Proc Natl Acad Sci U S A, 111, 7438, 10.1073/pnas.1405289111
Anticevic, 2013, Connectivity, pharmacology, and computation: Toward a mechanistic understanding of neural system dysfunction in schizophrenia, Front Psychiatry, 4, 169, 10.3389/fpsyt.2013.00169
Anticevic, 2015, Bridging levels of understanding in schizophrenia through computational modeling, Clin Psychol Sci, 3, 433, 10.1177/2167702614562041
Ratcliff, 2016, Diffusion decision model: Current issues and history, Trends Cogn Sci, 20, 260, 10.1016/j.tics.2016.01.007
Bogacz, 2006, The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced-choice tasks, Psychol Rev, 113, 700, 10.1037/0033-295X.113.4.700
Moustafa, 2015, Drift diffusion model of reward and punishment learning in schizophrenia: Modeling and experimental data, Behav Brain Res, 291, 147, 10.1016/j.bbr.2015.05.024
Mulder, 2010, Basic impairments in regulating the speed-accuracy tradeoff predict symptoms of attention-deficit/hyperactivity disorder, Biol Psychiatry, 68, 1114, 10.1016/j.biopsych.2010.07.031
Banca, 2015, Evidence accumulation in obsessive-compulsive disorder: The role of uncertainty and monetary reward on perceptual decision-making thresholds, Neuropsychopharmacology, 40, 1192, 10.1038/npp.2014.303
Amit, 1997, Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex, Cereb Cortex, 7, 237, 10.1093/cercor/7.3.237
Brunel, 2001, Effects of neuromodulation in a cortical network model of object working memory dominated by recurrent inhibition, J Comput Neurosci, 11, 63, 10.1023/A:1011204814320
Deco, 2013, Brain mechanisms for perceptual and reward-related decision-making, Prog Neurobiol, 103, 194, 10.1016/j.pneurobio.2012.01.010
Wang, 2002, Probabilistic decision making by slow reverberation in cortical circuits, Neuron, 36, 955, 10.1016/S0896-6273(02)01092-9
Garrett, 2011, The importance of being variable, J Neurosci, 31, 4496, 10.1523/JNEUROSCI.5641-10.2011
McIntosh, 2010, The development of a noisy brain, Arch Ital Biol, 148, 323
McIntosh, 2008, Increased brain signal variability accompanies lower behavioral variability in development, PLoS Comput Biol, 4, e1000106, 10.1371/journal.pcbi.1000106
McIntosh, 2013, Spatiotemporal dependency of age-related changes in brain signal variability, Cereb Cortex, 24, 1806, 10.1093/cercor/bht030
Beharelle, 2012, Brain signal variability relates to stability of behavior after recovery from diffuse brain injury, Neuroimage, 60, 1528, 10.1016/j.neuroimage.2012.01.037
Mišić, 2010, Brain noise is task dependent and region specific, J Neurophysiol, 104, 2667, 10.1152/jn.00648.2010
Garrett, 2010, Blood oxygen level-dependent signal variability is more than just noise, J Neurosci, 30, 4914, 10.1523/JNEUROSCI.5166-09.2010
Garrett, 2013, Moment-to-moment brain signal variability: A next frontier in human brain mapping?, Neurosci Biobehav Rev, 37, 610, 10.1016/j.neubiorev.2013.02.015
Maia, 2012, A neurocomputational approach to obsessive-compulsive disorder, Trends Cogn Sci, 16, 14, 10.1016/j.tics.2011.11.011
Rolls, 2008, An attractor hypothesis of obsessive–compulsive disorder, Eur J Neurosci, 28, 782, 10.1111/j.1460-9568.2008.06379.x
Verduzco-Flores, 2012, Modeling neuropathologies as disruption of normal sequence generation in working memory networks, Neural Netw, 27, 21, 10.1016/j.neunet.2011.09.007
Maia, 2015, The role of serotonin in orbitofrontal function and obsessive-compulsive disorder, Clin Psychol Sci, 3, 460, 10.1177/2167702614566809
Heinzle, 2016, Computational models of eye movements and their application to schizophrenia, Curr Opin Behav Sci, 11, 21, 10.1016/j.cobeha.2016.03.008
Cutsuridis, 2014, Antisaccade performance in schizophrenia: A neural model of decision making in the superior colliculus, Front Neurosci, 8, 13, 10.3389/fnins.2014.00013
Adams, 2012, Smooth pursuit and visual occlusion: Active inference and oculomotor control in schizophrenia, PLoS One, 7, e47502, 10.1371/journal.pone.0047502
Friston, 2010, Action and behavior: A free-energy formulation, Biol Cybern, 102, 227, 10.1007/s00422-010-0364-z
Adams, 2015, Active inference and oculomotor pursuit: The dynamic causal modelling of eye movements, J Neurosci Methods, 242, 1, 10.1016/j.jneumeth.2015.01.003
Adams, 2016, Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG, Neuroimage, 132, 175, 10.1016/j.neuroimage.2016.02.055
Benson, 2012, Simple viewing tests can detect eye movement abnormalities that distinguish schizophrenia cases from controls with exceptional accuracy, Biol Psychiatry, 72, 716, 10.1016/j.biopsych.2012.04.019
Engbert, 2011, An integrated model of fixational eye movements and microsaccades, Proc Natl Acad Sci U S A, 108, E765, 10.1073/pnas.1102730108
Roberts, 2013, Fixational eye movements during viewing of dynamic natural scenes, Front Psychol, 4, 797, 10.3389/fpsyg.2013.00797
Seghier, 2012, Network discovery with large DCMs, Neuroimage, 68, 181, 10.1016/j.neuroimage.2012.12.005
Rinzel, 1987, A formal classification of bursting mechanisms in excitable systems, 267
Izhikevich, 2000, Neural excitability, spiking and bursting, Int J Bifurc Chaos, 10, 1171, 10.1142/S0218127400000840
Mathys, 2014, Uncertainty in perception and the hierarchical Gaussian filter, Front Hum Neurosci, 8, 825, 10.3389/fnhum.2014.00825
Breakspear, 2005, Dynamics of a neural system with a multiscale architecture, Philos Trans R Soc Lond B Biol Sci, 360, 1051, 10.1098/rstb.2005.1643
Stephan, 2015, Translational perspectives for computational neuroimaging, Neuron, 87, 716, 10.1016/j.neuron.2015.07.008
Brodersen, 2011, Generative embedding for model-based classification of fMRI data, PLoS Comput Biol, 7, e1002079, 10.1371/journal.pcbi.1002079
Eaton, 2015, Latent variable and network models of comorbidity: Toward an empirically derived nosology, Soc Psychiatry Psychiatr Epidemiol, 50, 845, 10.1007/s00127-015-1012-7
Stephan, 2016, Charting the landscape of priority problems in psychiatry, part 1: Classification and diagnosis, Lancet Psychiatry, 3, 77, 10.1016/S2215-0366(15)00361-2
Frank, 2007, Hold your horses: Impulsivity, deep brain stimulation, and medication in parkinsonism, Science, 318, 1309, 10.1126/science.1146157
Huys, 2015, Depression: A decision-theoretic analysis, Annu Rev Neurosci, 38, 1, 10.1146/annurev-neuro-071714-033928
Huys, 2013, Mapping anhedonia onto reinforcement learning: A behavioural meta-analysis, Biol Mood Anxiety Disord, 3, 12, 10.1186/2045-5380-3-12
Glimcher, 2014