Building better biomarkers: brain models in translational neuroimaging
Choong-Wan Woo, Luke J Chang, Martin A Lindquist, Tor D Wager, Choong-Wan Woo, Luke J Chang, Martin A Lindquist, Tor D Wager
Abstract
Despite its great promise, neuroimaging has yet to substantially impact clinical practice and public health. However, a developing synergy between emerging analysis techniques and data-sharing initiatives has the potential to transform the role of neuroimaging in clinical applications. We review the state of translational neuroimaging and outline an approach to developing brain signatures that can be shared, tested in multiple contexts and applied in clinical settings. The approach rests on three pillars: (i) the use of multivariate pattern-recognition techniques to develop brain signatures for clinical outcomes and relevant mental processes; (ii) assessment and optimization of their diagnostic value; and (iii) a program of broad exploration followed by increasingly rigorous assessment of generalizability across samples, research contexts and populations. Increasingly sophisticated models based on these principles will help to overcome some of the obstacles on the road from basic neuroscience to better health and will ultimately serve both basic and applied goals.
Figures
References
- Mather M, Cacioppo JT, Kanwisher N. Introduction to the special section: 20 years of fMRI-what has it done for understanding cognition? Perspect Psychol Sci. 2013;8:41–43.
- Kapur S, Phillips AG, Insel TR. Why has it taken so long for biological psychiatry to develop clinical tests and what to do about it? Mol Psychiatry. 2012;17:1174–1179.
- Mayberg HS, et al. Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156:675–682.
- Keedwell PA, Andrew C, Williams SC, Brammer MJ, Phillips ML. The neural correlates of anhedonia in major depressive disorder. Biol Psychiatry. 2005;58:843–853.
- Tom SM, Fox CR, Trepel C, Poldrack RA. The neural basis of loss aversion in decision-making under risk. Science. 2007;315:515–518.
- Rosenberg MD, et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat Neurosci. 2016;19:165–171.
- Sanislow CA, et al. Developing constructs for psychopathology research: research domain criteria. J Abnorm Psychol. 2010;119:631–639.
- Scoville WB, Milner B. Loss of recent memory after bilateral hippocampal lesions. J Neurol Neurosurg Psychiatry. 1957;20:11–21.
- Fodor JA. The Modularity of Mind. MIT Press; 1983.
- Hamani C, et al. Deep brain stimulation for chronic neuropathic pain: long-term outcome and the incidence of insertional effect. Pain. 2006;125:188–196.
- Welter ML, et al. Basal ganglia dysfunction in OCD: subthalamic neuronal activity correlates with symptoms severity and predicts high-frequency stimulation efficacy. Transl Psychiatry. 2011;1:e5.
- Krack P, et al. Five-year follow-up of bilateral stimulation of the subthalamic nucleus in advanced Parkinson’s disease. N Engl J Med. 2003;349:1925–1934.
- Swartz JR, Knodt AR, Radtke SR, Hariri AR. A neural biomarker of psychological vulnerability to future life stress. Neuron. 2015;85:505–511.
- Dougherty DD, 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.
- Morishita T, Fayad SM, Higuchi MA, Nestor KA, Foote KD. Deep brain stimulation for treatment-resistant depression: systematic review of clinical outcomes. Neurotherapeutics. 2014;11:475–484.
- Reddan MC, Lindquist MA, Wager TD. Effect size estimation in neuroimaging. JAMA Psychiatry. 2017:3356. .
- Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008;453:869–878.
- Kvitsiani D, et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature. 2013;498:363–366.
- Price JL, Drevets WC. Neural circuits underlying the pathophysiology of mood disorders. Trends Cogn Sci. 2012;16:61–71.
- Roy M, Shohamy D, Wager TD. Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends Cogn Sci. 2012;16:147–156.
- Wager TD, et al. Pain in the ACC? Proc Natl Acad Sci USA. 2016;113:E2474–E2475.
- Poldrack RA. Can cognitive processes be inferred from neuroimaging data? Trends Cogn Sci. 2006;10:59–63.
- Wager TD, et al. An fMRI-based neurologic signature of physical pain. N Engl J Med. 2013;368:1388–1397.
- Chang LJ, Gianaros PJ, Manuck SB, Krishnan A, Wager TD. A sensitive and specific neural signature for picture-induced negative affect. PLoS Biol. 2015;13:e1002180.
- Doyle OM, Mehta MA, Brammer MJ. The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology (Berl ) 2015;232:4179–4189.
- Haynes JD. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron. 2015;87:257–270.
- Orrù G, Pettersson-Yeo W, Marquand AF, Sartori G, Mechelli A. Using Support Vector Machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci Biobehav Rev. 2012;36:1140–1152.
- Hackmack K, Paul F, Weygandt M, Allefeld C, Haynes JD. Multi-scale classification of disease using structural MRI and wavelet transform. Neuroimage. 2012;62:48–58.
- Miyawaki Y, et al. Visual image reconstruction from human brain activity using a combination of multiscale local image decoders. Neuron. 2008;60:915–929.
- Kamitani Y, Tong F. Decoding the visual and subjective contents of the human brain. Nat Neurosci. 2005;8:679–685.
- Kriegeskorte N, Cusack R, Bandettini P. How does an fMRI voxel sample the neuronal activity pattern: compact-kernel or complex spatiotemporal filter? Neuroimage. 2010;49:1965–1976.
- Poldrack RA, Gorgolewski KJ. Making big data open: data sharing in neuroimaging. Nat Neurosci. 2014;17:1510–1517.
- Abi-Dargham A, Horga G. The search for imaging biomarkers in psychiatric disorders. Nat Med. 2016;22:1248–1255.
- Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2. Springer; 2009.
- Mohri M, Rostamizadeh A, Talwalkar A. Foundations of Machine Learning. MIT Press; 2012.
- de Leon MJ, et al. Positron emission tomographic studies of aging and Alzheimer disease. AJNR Am J Neuroradiol. 1983;4:568–571.
- Kippenhan JS, Barker WW, Pascal S, Nagel J, Duara R. Evaluation of a neural-network classifier for PET scans of normal and Alzheimer’s disease subjects. J Nucl Med. 1992;33:1459–1467.
- Doyle OM, et al. Predicting progression of Alzheimer’s disease using ordinal regression. PLoS One. 2014;9:e105542.
- Singh G, Samavedham L. Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: A case study on early-stage diagnosis of Parkinson disease. J Neurosci Methods. 2015;256:30–40.
- Koutsouleris N, et al. Use of neuroanatomical pattern classification to identify subjects in at-risk mental states of psychosis and predict disease transition. Arch Gen Psychiatry. 2009;66:700–712.
- Sørensen L, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp. 2016;37:1148–1161.
- Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage. 2015;104:398–412.
- Beardslee WR, et al. Prevention of depression in at-risk adolescents: longer-term effects. JAMA Psychiatry. 2013;70:1161–1170.
- Addington J, Heinssen R. Prediction and prevention of psychosis in youth at clinical high risk. Annu Rev Clin Psychol. 2012;8:269–289.
- Davatzikos C, Xu F, An Y, Fan Y, Resnick SM. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain. 2009;132:2026–2035.
- Fan Y, Batmanghelich N, Clark CM, Davatzikos C Alzheimer’s Disease Neuroimaging Initiative\par. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline. Neuroimage. 2008;39:1731–1743.
- Misra C, Fan Y, Davatzikos C. Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage. 2009;44:1415–1422.
- Tang CC, et al. Differential diagnosis of Parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 2010;9:149–158.
- Pantazatos SP, Talati A, Schneier FR, Hirsch J. Reduced anterior temporal and hippocampal functional connectivity during face processing discriminates individuals with social anxiety disorder from healthy controls and panic disorder, and increases following treatment. Neuropsychopharmacology. 2014;39:425–434.
- Anticevic A, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24:3116–3130.
- Calhoun VD, Maciejewski PK, Pearlson GD, Kiehl KA. Temporal lobe and “default” hemodynamic brain modes discriminate between schizophrenia and bipolar disorder. Hum Brain Mapp. 2008;29:1265–1275.
- Insel TR, Cuthbert BN. Medicine. Brain disorders? Precisely. Science. 2015;348:499–500.
- Clementz BA, et al. Identification of distinct psychosis biotypes using brain-based biomarkers. Am J Psychiatry. 2016;173:373–384.
- Price RB, et al. Parsing heterogeneity in the brain connectivity of depressed and healthy adults during positive mood. Biol Psychiatry. 2016 S0006-3223(16)32540-9.
- Drysdale AT, et al. Resting-state connectivity biomarkers define neurophysiological subtypes of depression. Nat Med. 2016
- Weinstein JN, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45:1113–1120.
- Roychowdhury S, Chinnaiyan AM. Translating genomics for precision cancer medicine. Annu Rev Genomics Hum Genet. 2014;15:395–415.
- Hahn T, et al. Predicting treatment response to cognitive behavioral therapy in panic disorder with agoraphobia by integrating local neural information. JAMA Psychiatry. 2015;72:68–74.
- Doehrmann O, et al. Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging. JAMA Psychiatry. 2013;70:87–97.
- Whitfield-Gabrieli S, et al. Brain connectomics predict response to treatment in social anxiety disorder. Mol Psychiatry. 2016;21:680–685.
- van Waarde JA, et al. A functional MRI marker may predict the outcome of electroconvulsive therapy in severe and treatment-resistant depression. Mol Psychiatry. 2015;20:609–614.
- Widge AS, Avery DH, Zarkowski P. Baseline and treatment-emergent EEG biomarkers of antidepressant medication response do not predict response to repetitive transcranial magnetic stimulation. Brain Stimul. 2013;6:929–931.
- Sarpal DK, et al. Baseline striatal functional connectivity as a predictor of response to antipsychotic drug treatment. Am J Psychiatry. 2016;173:69–77.
- Ye Z, et al. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson’s disease with clinical and neuroimaging measures. Hum Brain Mapp. 2016;37:1026–1037.
- Woo CW, Wager TD. Neuroimaging-based biomarker discovery and validation. Pain. 2015;156:1379–1381.
- Robinson M, Boissoneault J, Sevel L, Letzen J, Staud R. The effect of base rate on the predictive value of brain biomarkers. J Pain. 2016;17:637–641.
- Cronbach LJ, Meehl PE. Construct validity in psychological tests. Psychol Bull. 1955;52:281–302.
- Freedman R, et al. The initial field trials of DSM-5: new blooms and old thorns. Am J Psychiatry. 2013;170:1–5.
- Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex. 2015;63:55–67.
- Duffy FH, Als H. A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study. BMC Med. 2012;10:64.
- Deshpande G, Wang P, Rangaprakash D, Wilamowski B. Fully connected cascade artificial neural network architecture for attention deficit hyperactivity disorder classification from functional magnetic resonance imaging data. IEEE Trans Cybern. 2015;45:2668–2679.
- Whelan R, Garavan H. When optimism hurts: inflated predictions in psychiatric neuroimaging. Biol Psychiatry. 2014;75:746–748.
- Zaki J, Wager TD, Singer T, Keysers C, Gazzola V. The anatomy of suffering: understanding the relationship between nociceptive and empathic pain. Trends Cogn Sci. 2016;20:249–259.
- Olivetti E, Sona D, Veeramachaneni S. Gaussian process regression and recurrent neural networks for fmri image classification. Proc. 12th Meeting Org. for Human Brain Mapping; Florence, Italy. 2006.
- Ribeiro MT, Singh S, Guestrin C. “Why should I trust you?”: Explaining the predictions of any classifier. 2016 Preprint at arXiv .
- HD-200 Consortium. The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front Syst Neurosci. 2012;6:62.
- Eloyan A, et al. Automated diagnoses of attention deficit hyperactive disorder using magnetic resonance imaging. Front Syst Neurosci. 2012;6:61.
- Eldridge J, Lane AE, Belkin M, Dennis S. Robust features for the automatic identification of autism spectrum disorder in children. J Neurodev Disord. 2014;6:12.
- Geurts JJ, Calabrese M, Fisher E, Rudick RA. Measurement and clinical effect of grey matter pathology in multiple sclerosis. Lancet Neurol. 2012;11:1082–1092.
- van den Heuvel MP, et al. Abnormal rich club organization and functional brain dynamics in schizophrenia. JAMA Psychiatry. 2013;70:783–792.
- Yahata N, et al. A small number of abnormal brain connections predicts adult autism spectrum disorder. Nat Commun. 2016;7:11254.
- Huth AG, de Heer WA, Griffiths TL, Theunissen FE, Gallant JL. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature. 2016;532:453–458.
- Vemuri P, et al. Antemortem MRI based STructural Abnormality iNDex (STAND)-scores correlate with postmortem Braak neurofibrillary tangle stage. Neuroimage. 2008;42:559–567.
- Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. Large-scale automated synthesis of human functional neuroimaging data. Nat Methods. 2011;8:665–670.
- Yeo BT, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106:1125–1165.
- Glasser MF, et al. A multi-modal parcellation of human cerebral cortex. Nature. 2016;536:171–178.
- Bota M, Dong HW, Swanson LW. Brain architecture management system. Neuroinformatics. 2005;3:15–48.
- Stephan KE. The history of CoCoMac. Neuroimage. 2013;80:46–52.
- Power JD, Schlaggar BL, Petersen SE. Recent progress and outstanding issues in motion correction in resting state fMRI. Neuroimage. 2015;105:536–551.
- Gorgolewski KJ, Poldrack RA. A practical guide for improving transparency and reproducibility in neuroimaging research. PLoS Biol. 2016;14:e1002506.
- Davatzikos C, Bhatt P, Shaw LM, Batmanghelich KN, Trojanowski JQ. Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol Aging. 2011;32:2322.e19–2322.e27.
- Weintraub D, et al. Alzheimer’s disease pattern of brain atrophy predicts cognitive decline in Parkinson’s disease. Brain. 2012;135:170–180.
- Toledo JB, et al. Memory, executive, and multidomain subtle cognitive impairment: clinical and biomarker findings. Neurology. 2015;85:144–153.
- Habes M, et al. White matter hyperintensities and imaging patterns of brain ageing in the general population. Brain. 2016;139:1164–1179.
- Asanuma K, et al. Network modulation in the treatment of Parkinson’s disease. Brain. 2006;129:2667–2678.
- Eidelberg D. Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 2009;32:548–557.
- Wu P, et al. Metabolic brain network in the Chinese patients with Parkinson’s disease based on 18F-FDG PET imaging. Parkinsonism Relat Disord. 2013;19:622–627.
- Teune LK, et al. Validation of parkinsonian disease-related metabolic brain patterns. Mov Disord. 2013;28:547–551.
- Westfall J, Judd CM, Kenny DA. Replicating studies in which samples of participants respond to samples of stimuli. Perspect Psychol Sci. 2015;10:390–399.
- Hashmi JA, et al. Shape shifting pain: chronification of back pain shifts brain representation from nociceptive to emotional circuits. Brain. 2013;136:2751–2768.
- Petersen SE, et al. Imaging in population science: cardiovascular magnetic resonance in 100,000 participants of UK Biobank - rationale, challenges and approaches. J Cardiovasc Magn Reson. 2013;15:46.
- Weiner MW, et al. Impact of the Alzheimer’s Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement. 2015;11:865–884.
- Tagliazucchi E, Laufs H. Decoding wakefulness levels from typical fMRI resting-state data reveals reliable drifts between wakefulness and sleep. Neuron. 2014;82:695–708.
- Buckner RL, Krienen FM, Yeo BTT. Opportunities and limitations of intrinsic functional connectivity MRI. Nat Neurosci. 2013;16:832–837.
- Glover GH, et al. Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J Magn Reson Imaging. 2012;36:39–54.
- Landis JR, et al. The MAPP research network: design, patient characterization and operations. BMC Urol. 2014;14:58.
- Thompson PM, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 2014;8:153–182.
- Borsook D, Becerra L, Hargreaves R. Biomarkers for chronic pain and analgesia. Part 1: the need, reality, challenges, and solutions. Discov Med. 2011;11:197–207.
- Hargreaves RJ, et al. Optimizing central nervous system drug development using molecular imaging. Clin Pharmacol Ther. 2015;98:47–60.
- López-Solà M, et al. Towards a neurophysiological signature for fibromyalgia. Pain. 2016
- Lombardo MV, et al. Different functional neural substrates for good and poor language outcome in autism. Neuron. 2015;86:567–577.
- Woolf CJ, Salter MW. Neuronal plasticity: increasing the gain in pain. Science. 2000;288:1765–1769.
- Diatchenko L, Nackley AG, Slade GD, Fillingim RB, Maixner W. Idiopathic pain disorders--pathways of vulnerability. Pain. 2006;123:226–230.
- Adler G, Gattaz WF. Pain perception threshold in major depression. Biol Psychiatry. 1993;34:687–689.
- Krishnan A, et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife. 2016;5:e15166.
- Woo CW, Roy M, Buhle JT, Wager TD. Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain. PLoS Biol. 2015;13:e1002036.
- Ma Y, et al. Serotonin transporter polymorphism alters citalopram effects on human pain responses to physical pain. Neuroimage. 2016;135:186–196.
- Bräscher AK, Becker S, Hoeppli ME, Schweinhardt P. Different brain circuitries mediating controllable and uncontrollable pain. J Neurosci. 2016;36:5013–5025.
- Wiecki TV, Poland J, Frank MJ. Model-based cognitive neuroscience approaches to computational psychiatry: clustering and classification. Clin Psychol Sci. 2015;3:378–399.
- Huys QJ, Maia TV, Frank MJ. Computational psychiatry as a bridge from neuroscience to clinical applications. Nat Neurosci. 2016;19:404–413.
- Brodersen KH, et al. Generative embedding for model-based classification of fMRI data. PLOS Comput Biol. 2011;7:e1002079.
- Friston KJ, Harrison L, Penny W. Dynamic causal modelling. Neuroimage. 2003;19:1273–1302.
- Hein G, Morishima Y, Leiberg S, Sul S, Fehr E. The brain’s functional network architecture reveals human motives. Science. 2016;351:1074–1078.
- Fan Y, Resnick SM, Wu X, Davatzikos C. Structural and functional biomarkers of prodromal Alzheimer’s disease: a high-dimensional pattern classification study. Neuroimage. 2008;41:277–285.
- Casanova R, et al. Alzheimer’s disease risk assessment using large-scale machine learning methods. PLoS One. 2013;8:e77949.
- Tosun D, Joshi S, Weiner MW. Neuroimaging predictors of brain amyloidosis in mild cognitive impairment. Ann Neurol. 2013;74:188–198.
- Vemuri P, et al. Alzheimer’s disease diagnosis in individual subjects using structural MR images: validation studies. Neuroimage. 2008;39:1186–1197.
- Huang C, et al. Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage. 2007;34:714–723.
- Mure H, et al. Parkinson’s disease tremor-related metabolic network: characterization, progression, and treatment effects. Neuroimage. 2011;54:1244–1253.
- Eckert T, et al. Abnormal metabolic networks in atypical parkinsonism. Mov Disord. 2008;23:727–733.
- Niethammer M, et al. A disease-specific metabolic brain network associated with corticobasal degeneration. Brain. 2014;137:3036–3046.
- Geman S, Bienenstock E, Doursat R. Neural networks and the bias/variance dilemma. Neural Comput. 1992;4:1–58.
- Haxby JV, et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science. 2001;293:2425–2430.
- Kriegeskorte N, Goebel R, Bandettini P. Information-based functional brain mapping. Proc Natl Acad Sci USA. 2006;103:3863–3868.
- Sato JR, et al. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. 2015;233:289–291.
- Wager TD, Atlas LY, Leotti LA, Rilling JK. Predicting individual differences in placebo analgesia: contributions of brain activity during anticipation and pain experience. J Neurosci. 2011;31:439–452.
- Dukart J, Schroeter ML, Mueller K. Age correction in dementia--matching to a healthy brain. PLoS One. 2011;6:e22193.
- Naselaris T, Kay KN, Nishimoto S, Gallant JL. Encoding and decoding in fMRI. Neuroimage. 2011;56:400–410.
- Mitchell TM, et al. Predicting human brain activity associated with the meanings of nouns. Science. 2008;320:1191–1195.
- Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial Least Squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage. 2011;56:455–475.
- Nishimoto S, et al. Reconstructing visual experiences from brain activity evoked by natural movies. Curr Biol. 2011;21:1641–1646.
- Kay KN, Naselaris T, Prenger RJ, Gallant JL. Identifying natural images from human brain activity. Nature. 2008;452:352–355.
- Ketz N, O’Reilly RC, Curran T. Classification aided analysis of oscillatory signatures in controlled retrieval. Neuroimage. 2014;85:749–760.
- Kim J, Calhoun VD, Shim E, Lee JH. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124(Pt A):127–146.
- Kriegeskorte N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annual Review of Vision Science. 2015;1:417–446.
- O’Reilly RC. Biologically based computational models of high-level cognition. Science. 2006;314:91–94.
- Poldrack RA, Halchenko YO, Hanson SJ. Decoding the large-scale structure of brain function by classifying mental States across individuals. Psychol Sci. 2009;20:1364–1372.
- Todd MT, Nystrom LE, Cohen JD. Confounds in multivariate pattern analysis: Theory and rule representation case study. Neuroimage. 2013;77:157–165.
- Etzel JA, Zacks JM, Braver TS. Searchlight analysis: promise, pitfalls, and potential. Neuroimage. 2013;78:261–269.
- Haxby JV, et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron. 2011;72:404–416.
Source: PubMed