Applying fMRI complexity analyses to the single subject: a case study for proposed neurodiagnostics

References

• Allen, G., Buxton, R. B., Wong, E. C., & Courchesne, E. (1997). Attentional activation of the cerebellum independent of motor involvement. Science, 275, 1940–1943. doi:10.1126/science.275.5308.1940
   PubMed Web of Science ®Google Scholar
• Anderson, J. S., Zielinski, B. A., Nielsen, J. A., & Ferguson, M. A. (2014). Complexity of low-frequency blood oxygen level-dependent fluctuations covaries with local connectivity. Human Brain Mapping, 35, 1273–1283. doi:10.1002/hbm.22251
   PubMed Web of Science ®Google Scholar
• Bandettini, P. A., et al. (2008). Endogenous oscillations and networks in functional magnetic resonance imaging. Human Brain Mapping, 29, 737. doi:10.1002/hbm.20607
   PubMed Web of Science ®Google Scholar
• Banks, S. J., Eddy, K. T., Angstadt, M., Nathan, P. J., & Phan, K. L. (2007). Amygdala–frontal connectivity during emotion regulation. Social Cognitive and Affective Neuroscience, 2, 303–312. doi:10.1093/scan/nsm029
   PubMed Web of Science ®Google Scholar
• Barnes, A., Bullmore, E. T., & Suckling, J. (2009). Endogenous human brain dynamics recover slowly following cognitive effort. Plos One, 4, e6626. doi:10.1371/journal.pone.0006626
   PubMed Web of Science ®Google Scholar
• Bassett, D. S., & Gazzaniga, M. S. (2011). Understanding complexity in the human brain. Trends in Cognitive Sciences, 15, 200–209. doi:10.1016/j.tics.2011.03.006
   PubMed Web of Science ®Google Scholar
• Bédard, C., & Destexhe, A. (2009). Macroscopic models of local field potentials and the apparent 1/f noise in brain activity. Biophysical Journal, 96, 2589–2603. doi:10.1016/j.bpj.2008.12.3951
   PubMed Web of Science ®Google Scholar
• Bianciardi, M., Fukunaga, M., Van Gelderen, P., Horovitz, S. G., De Zwart, J. A., & Duyn, J. H. (2009). Modulation of spontaneous fmri activity in human visual cortex by behavioral state. Neuroimage, 450, 160–168. doi:10.1016/j.neuroimage.2008.10.034
   Web of Science ®Google Scholar
• Boustani, S. E., Marre, O., Béhuret, S., Baudot, P., Yger, P., Bal, T., … Yves, F. (2009). Network-state modulation of power-law frequency-scaling in visual cortical neurons. Plos Comput Biol, 5, e1000519. doi:10.1371/journal.pcbi.1000519
   PubMed Web of Science ®Google Scholar
• Büchel, C., & Friston, K. J. (1997). Modulation of connectivity in visual pathways by attention: Cortical interactions evaluated with structural equation modelling and fmri. Cerebral Cortex, 7, 768–778. doi:10.1093/cercor/7.8.768
   PubMed Web of Science ®Google Scholar
• Bullmore, E., Barnes, A., Bassett, D. S., Fornito, A., Kitzbichler, M., Meunier, D., & Suckling, J. (2009). Generic aspects of complexity in brain imaging data and other biological systems. Neuroimage, 47, 1125–1134. doi:10.1016/j.neuroimage.2009.05.032
   PubMed Web of Science ®Google Scholar
• Bullmore, E., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., … Brammer, M. (2001). Colored noise and computational inference in neurophysiological (fmri) time series analysis: Resampling methods in time and wavelet domains. Human Brain Mapping, 12, 61–78. doi:10.1002/(ISSN)1097-0193
   PubMed Web of Science ®Google Scholar
• Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186–198. doi:10.1038/nrn2575
   PubMed Web of Science ®Google Scholar
• Buzsaki, G. (2006). Rhythms of the Brain. New York, NY: Oxford University Press.
   Google Scholar
• Carlson, J. M., Greenberg, T., Rubin, D., & Mujica-Parodi, L. R. (2011). Feeling anxious: Anticipatory amygdalo-insular response predicts the feeling of anxious anticipation. Social Cognitive and Affective Neuroscience, 6, 74–81. doi:10.1093/scan/nsq017
   PubMed Web of Science ®Google Scholar
• Chatterjee, A. (2005). A madness to the methods in cognitive neuroscience?. Journal of Cognitive Neuroscience, 17, 847–849. doi:10.1162/0898929054021085
   PubMed Web of Science ®Google Scholar
• Chouinard, P. A., & Paus, T. (2006). The primary motor and premotor areas of the human cerebral cortex. The Neuroscientist, 12, 143–152. doi:10.1177/1073858405284255
   PubMed Web of Science ®Google Scholar
• Ciuciu, P., Abry, P., & He, B. J. (2014). Interplay between functional connectivity and scale-free dynamics in intrinsic fmri networks. Neuroimage, 95, 248–263. doi:10.1016/j.neuroimage.2014.03.047
   PubMed Web of Science ®Google Scholar
• Ciuciu, P., Varoquaux, G., Abry, P., Sadaghiani, S., & Kleinschmidt, A. (2012). Scale-free and multifractal time dynamics of fMRI signals during rest and task. Frontiers in Physiology, 3, 186–186. doi:10.3389/fphys.2012.00186
   PubMed Web of Science ®Google Scholar
• Courtney, S. M., Petit, L., Maisog, J. M., Ungerleider, L. G., & Haxby, J. V. (1998). An area specialized for spatial working memory in human frontal cortex. Science, 2790, 1347–1351. doi:10.1126/science.279.5355.1347
   Web of Science ®Google Scholar
• Culham, J. C. (2006). Functional neuroimaging: Experimental design and analysis. Handbook of Functional Neuroimaging of Cognition, 2, 53–82.
   Google Scholar
• Danckert, J., Mirsattarri, S. M., & Bright, P. (2012). Neuroimaging of single cases: Benefits and pitfalls. InTech Europe Open Access Publisher.
   Google Scholar
• Daneshyari, M., Lily Kamkar, L., & Daneshyari, M. (2010). Epileptic EEG: A comprehensive study of nonlinear behavior. In: Arabnia H. (eds) Advances in Computational Biology. Advances in Experimental Medicine and Biology, 680: 677-683. New York, NY: Springer.
   Google Scholar
• Doya, K. (2000). Complementary roles of basal ganglia and cerebellum in learning and motor control. Current Opinion in Neurobiology, 10, 732–739. doi:10.1016/S0959-4388(00)00153-7
   PubMed Web of Science ®Google Scholar
• Dresler M, Wehrle R, Spoormaker VI, Steiger A, Holsboer F, Czisch M, Hobson JA (2015). Neural correlates of insight in dreaming and psychosis.In Sleep Medicine Reviews, 20, 92-99. Elsevier.
   Google Scholar
• Engoren, M. (1998). Approximate entropy of respiratory rate and tidal volume during weaning from mechanical ventilation. Critical Care Medicine, 26, 1817–1823. doi:10.1097/00003246-199811000-00021
   PubMed Web of Science ®Google Scholar
• Ernst, M., Nelson, E.E., McClure, E.B., Monk, C.S., Munson, S., Eshel, N., Zarahn, E., Leibenluft, E., Zametkin, A., Towbin, K. and Blair, J. (2004). Choice selection and reward anticipation: An fMRI study. Neuropsychologia, 42, 1585–1597. doi:10.1016/j.neuropsychologia.2004.05.011
   PubMed Web of Science ®Google Scholar
• Etkin, A., Egner, T., & Kalisch, R. (2011). Emotional processing in anterior cingulate and medial prefrontal cortex. Trends in Cognitive Sciences, 15, 85–93. doi:10.1016/j.tics.2010.11.004
   PubMed Web of Science ®Google Scholar
• Fine EJ, Ionita CC, Lohr L (2002). The history of the development of the cerebellar examination. In Seminars in Neurology, 22 (4) 375-384. New York, NY: Thieme Medical Publishers.
   Google Scholar
• Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D., & Alvaro, P.-L. (2012). Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate. Biological Psychiatry, 72, 595–603. doi:10.1016/j.biopsych.2012.04.028
   PubMed Web of Science ®Google Scholar
• Fransson, P. (2006). How default is the default mode of brain function?: Further evidence from intrinsic bold signal fluctuations. Neuropsychologia, 44, 2836–2845. doi:10.1016/j.neuropsychologia.2006.06.017
   PubMed Web of Science ®Google Scholar
• Fransson, P., Metsäranta, M., Blennow, M., Åden, U., Lagercrantz, H., & Vanhatalo, S. (2013). Early development of spatial patterns of power-law frequency scaling in fmri resting-state and eeg data in the newborn brain. Cerebral Cortex, 23, 638–646. doi:10.1093/cercor/bhs047
   PubMed Web of Science ®Google Scholar
• Freyer, F., Aquino, K., Robinson, P. A., Ritter, P., & Breakspear, M. (2009). Bistability and non-gaussian fluctuations in spontaneous cortical activity. The Journal of Neuroscience, 29, 8512–8524. doi:10.1523/JNEUROSCI.0754-09.2009
   PubMed Web of Science ®Google Scholar
• Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. Neuroimage, 19, 1273–1302. doi:10.1016/S1053-8119(03)00202-7
   PubMed Web of Science ®Google Scholar
• Friston, K. J., & Price, C. J. (2003). Degeneracy and redundancy in cognitive anatomy. Trends in Cognitive Sciences, 7, 151–152. doi:10.1016/S1364-6613(03)00054-8
   PubMed Web of Science ®Google Scholar
• Glenn, T., Whybrow, P. C., Rasgon, N., Grof, P., Alda, M., Baethge, C., & Bauer, M. (2006). Approximate entropy of self-reported mood prior to episodes in bipolar disorder. Bipolar Disorders, 8, 424–429. doi:10.1111/j.1399-5618.2006.00373.x
   PubMed Web of Science ®Google Scholar
• Goldin, P. R., McRae, K., Ramel, W., & Gross, J. J. (2008). The neural bases of emotion regulation: Reappraisal and suppression of negative emotion. Biological Psychiatry, 63, 577–586. doi:10.1016/j.biopsych.2007.05.031
   PubMed Web of Science ®Google Scholar
• He, B. J. (2011). Scale-free properties of the functional magnetic resonance imaging signal during rest and task. The Journal of Neuroscience, 31, 13786–13795. doi:10.1523/JNEUROSCI.2111-11.2011
   PubMed Web of Science ®Google Scholar
• He, B. J. (2014). Scale-free brain activity: Past, present, and future. Trends in Cognitive Sciences, 18, 480–487. doi:10.1016/j.tics.2014.04.003
   PubMed Web of Science ®Google Scholar
• He, B. J., & Raichle, M. E. (2009). The fMRI signal, slow cortical potential and consciousness. Trends in Cognitive Sciences, 13, 302–309. doi:10.1016/j.tics.2009.04.004
   PubMed Web of Science ®Google Scholar
• He, B. J., Zempel, J. M., Snyder, A. Z., & Raichle, M. E. (2010). The temporal structures and functional significance of scale-free brain activity. Neuron, 66, 353–369. doi:10.1016/j.neuron.2010.04.020
   PubMed Web of Science ®Google Scholar
• Hölzel, B. K., Ott, U., Hempel, H., Hackl, A., Wolf, K., Stark, R., & Vaitl, D. (2007). Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and non-meditators. Neuroscience Letters, 421, 16–21. doi:10.1016/j.neulet.2007.04.074
   PubMed Web of Science ®Google Scholar
• Houk, J. C., Buckingham, J. T., & Barto, A. G. (1996). Models of the cerebellum and motor learning. Behavioral and Brain Sciences, 19, 368–383. doi:10.1017/S0140525X00081474
   Web of Science ®Google Scholar
• Huff, D. (1954). How to lie with statistics. New York, NY: W.W. Norton & Company.
   Google Scholar
• Huikuri, H. V., Mäkikallio, T. H., Peng, C.-K., Goldberger, A. L., Hintze, U., Møller, M., et al. (2000). Fractal correlation properties of R-R interval dynamics and mortality in patients with depressed left ventricular function after an acute myocardial infarction. Circulation, 101, 47–53. doi:10.1161/01.CIR.101.1.47
   PubMed Web of Science ®Google Scholar
• Ide, J. S., Sien, H., Zhang, S., Mujica-Parodi, L. R., & Li Chiang-Shan, R. (2016). Power spectrum scale invariance as a neural marker of cocaine misuse and altered cognitive control. Neuroimage: Clinical, 11, 349–356. doi:10.1016/j.nicl.2016.03.004
   PubMed Web of Science ®Google Scholar
• Kastner, S., Pinsk, M. A., De Weerd, P., Desimone, R., & Ungerleider, L. G. (1999). Increased activity in human visual cortex during directed attention in the absence of visual stimulation. Neuron, 22, 751–761. doi:10.1016/S0896-6273(00)80734-5
   PubMed Web of Science ®Google Scholar
• Kim, J., Zhu, W., Chang, L., Bentler, P. M., & Ernst, T. (2007). Unified structural equation modeling approach for the analysis of multisubject, multivariate functional mri data. Human Brain Mapping, 28, 85–93. doi:10.1002/hbm.20259
   PubMed Web of Science ®Google Scholar
• Lai M-C, Lombardo MV, Chakrabarti B, Sadek SA, Pasco G, Wheelwright SJ, Bullmore ET, Baron-Cohen S, Suckling J, MRC AIMS Consortium. (2010). A shift to randomness of brain oscillations in people with autism. Biological Psychiatry, 68, 1092–1099. doi:10.1016/j.biopsych.2010.06.027
   PubMed Web of Science ®Google Scholar
• Levina, A., Michael Herrmann, J., & Geisel, T. (2007). Dynamical synapses causing self-organized criticality in neural networks. Nature Physics, 3, 857–860. doi:10.1038/nphys758
   Web of Science ®Google Scholar
• Likhtik, E., Pelletier, J. G., Paz, R., & Denis, P. (2005). Prefrontal control of the amygdala. The Journal of Neuroscience, 25, 7429–7437. doi:10.1523/JNEUROSCI.2314-05.2005
   PubMed Web of Science ®Google Scholar
• Lowen, S. B., Cash, S. S., Poo, M.-M., & Teich, M. C. (1997). Quantal neurotransmitter secretion rate exhibits fractal behavior. The Journal of Neuroscience, 17, 5666–5677.
   PubMed Web of Science ®Google Scholar
• Macaluso, E., Frith, C. D., & Driver, J. (2000). Modulation of human visual cortex by crossmodal spatial attention. Science, 289, 1206–1208. doi:10.1126/science.289.5482.1206
   PubMed Web of Science ®Google Scholar
• Maxim, V., Şendur, L., Fadili, J., Suckling, J., Gould, R., Howard, R., & Bullmore, E. D. (2005). Fractional Gaussian noise, functional MRI and Alzheimer’s disease. Neuroimage, 25, 141–158. doi:10.1016/j.neuroimage.2004.10.044
   PubMed Web of Science ®Google Scholar
• Montano, N., Gnecchi Ruscone, T., Porta, A., Lombardi, F., Pagani, M., & Malliani, A. (1994). Power spectrum analysis of heart rate variability to assess the changes in sympathovagal balance during graded orthostatic tilt. Circulation, 90, 1826–1831. doi:10.1161/01.CIR.90.4.1826
   PubMed Web of Science ®Google Scholar
• Mujica-Parodi, L. R., Carlson, J. M., Cha, J., & Rubin, D. (2014). The fine line between braveand reckless: Amygdala reactivity and regulation predict recognition of risk. NeuroImage. doi:10.1016/j.neuroimage.2014.08.038
   Web of Science ®Google Scholar
• Mujica-Parodi, L. R., Korgaonkar, M., Ravindranath, B., Greenberg, T., Tomasi, D., Wagshul, M., … Zhong, Y., et al. (2009). Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults. Human Brain Mapping, 30, 47–58. doi:10.1002/hbm.20483
   PubMed Web of Science ®Google Scholar
• Nedic, S., Stufflebeam, S. M., Rondinoni, C., Velasco, T. R., Antonio C, D. S., Leite, J. P., … Ide, J. S. (2015). Using network dynamic fMRI for detection of epileptogenic foci. BMC Neurology, 15, 1. doi:10.1186/s12883-015-0514-y
   PubMed Web of Science ®Google Scholar
• Ochsner, K. N., Ray, R. D., Cooper, J. C., Robertson, E. R., Chopra, S., Gabrieli, J. D. E., & Gross, J. J. (2004). For better or for worse: Neural systems supporting the cognitive down-and up-regulation of negative emotion. Neuroimage, 23, 483–499. doi:10.1016/j.neuroimage.2004.06.030
   PubMed Web of Science ®Google Scholar
• Paulus, M. P., Geyer, M. A., & Braff, D. L. (1996). Use of methods from chaos theory to quantify a fundamental dysfunction in the behavioral organization of schizophrenic patients. The American Journal of Psychiatry, 153, 714. doi:10.1176/ajp.153.5.714
   PubMed Web of Science ®Google Scholar
• Pellé, H., Ciuciu, P., Rahim, M., Dohmatob, E., Abry, P., & Wassenhove, V. V. Multivariate hurst exponent estimation in fMRI. Application to brain decoding of perceptual learning. In Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, pages 996–1000. IEEE, 2016.
   Google Scholar
• Petrides, M. (2000). The role of the mid-dorsolateral prefrontal cortex in working memory. In Experimental Brain Research, 133, 44-54. Springer.
   Google Scholar
• Pezard, L., Nandrino, J.-L., Renault, B., Massioui, F. E., Allilaire, J.-F., Müller, J., … Martinerie, J. (1996). Depression as a dynamical disease. Biological Psychiatry, 39, 991–999. doi:10.1016/0006-3223(95)00307-X
   PubMed Web of Science ®Google Scholar
• Phelps, E. A. (2004). Human emotion and memory: Interactions of the amygdala and hippocampal complex. Current Opinion in Neurobiology, 14, 198–202. doi:10.1016/j.conb.2004.03.015
   PubMed Web of Science ®Google Scholar
• Phelps, E. A., & LeDoux, J. E. (2005). Contributions of the amygdala to emotion processing: From animal models to human behavior. Neuron, 48, 175–187. doi:10.1016/j.neuron.2005.09.025
   PubMed Web of Science ®Google Scholar
• Pincus, S., & Singer, B. H. (1996). Randomness and degrees of irregularity. Proceedings of the National Academy of Sciences, 93, 2083–2088. doi:10.1073/pnas.93.5.2083
   PubMed Web of Science ®Google Scholar
• Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88, 2297–2301. doi:10.1073/pnas.88.6.2297
   PubMed Web of Science ®Google Scholar
• Pincus, S. M. (2003). Quantitative assessment strategies and issues for mood and other psychiatric serial study data. Bipolar Disorders, 5, 287–294. doi:10.1034/j.1399-5618.2003.00036.x
   PubMed Web of Science ®Google Scholar
• Pincus, S. M. (2006). Approximate entropy as a measure of irregularity for psychiatric serial metrics. Bipolar Disorders, 8, 430–440. doi:10.1111/j.1399-5618.2006.00375.x
   PubMed Web of Science ®Google Scholar
• Pincus, S. M., & Goldberger, A. L. (1994). Physiological time-series analysis: What does regularity quantify?. American Journal of Physiology-Heart and Circulatory Physiology, 266, H1643–H1656.
   PubMed Web of Science ®Google Scholar
• Pizzagalli, D. A., Greischar, L. L., & Davidson, R. J. (2003). Spatio-temporal dynamics of brain mechanisms in aversive classical conditioning: High-density event-related potential and brain electrical tomography analyses. Neuropsychologia, 41, 184–194. doi:10.1016/S0028-3932(02)00148-3
   PubMed Web of Science ®Google Scholar
• Price, C. J. (2000). The anatomy of language: Contributions from functional neuroimaging. Journal of Anatomy, 197, 335–359. doi:10.1046/j.1469-7580.2000.19730335.x
   PubMed Web of Science ®Google Scholar
• Radhakrishnan, N., & Gangadhar, B. N. (1998). Estimating regularity in epileptic seizure time-series data. IEEE Engineering in Medicine and Biology Magazine, 17, 89–94. doi:10.1109/51.677174
   PubMedGoogle Scholar
• Rǎdulescu, A, & Mujica-Parodi, LR. (2014). Network connectivity modulates power spectrum scale invariance. Neuroimage, 90(436–448). doi: 10.1016/j.neuroimage.2013.12.001
   PubMed Web of Science ®Google Scholar
• Rădulescu, A. R., & Mujica-Parodi, L. R. (2008). A systems approach to prefrontal-limbic dysregulation in schizophrenia. Neuropsychobiology, 57, 206–216. doi:10.1159/000151731
   PubMed Web of Science ®Google Scholar
• Rădulescu, A. R., & Mujica-Parodi, L. R. (2009). A principal component network analysis of prefrontal-limbic functional magnetic resonance imaging time series in schizophrenia patients and healthy controls. Psychiatry Research: Neuroimaging, 174, 184–194. doi:10.1016/j.pscychresns.2009.04.017
   PubMed Web of Science ®Google Scholar
• Rădulescu, A. R., Rubin, D., Strey, H. H., & Mujica-Parodi, L. R. (2012). Power spectrum scale invariance identifies prefrontal dysregulation in paranoid schizophrenia. Human Brain Mapping, 33, 1582–1593. doi:10.1002/hbm.21309
   PubMed Web of Science ®Google Scholar
• Rapp, B. (2001). The handbook of cognitive neuropsychology: What deficits reveal about the human mind. Psychology Press.
   Google Scholar
• Rockland, K. S., & Ojima, H. (2003). Multisensory convergence in calcarine visual areas in macaque monkey. International Journal of Psychophysiology, 50, 19–26. doi:10.1016/S0167-8760(03)00121-1
   PubMed Web of Science ®Google Scholar
• Roebroeck, A., Formisano, E., & Goebel, R. (2005). Mapping directed influence over the brain using Granger causality and fMRI. Neuroimage, 25, 230–242. doi:10.1016/j.neuroimage.2004.11.017
   PubMed Web of Science ®Google Scholar
• Roelfsema, F., Pincus, S. M., & Veldhuis, J. D. (1998). Patients with cushings disease secrete adrenocorticotropin and cortisol jointly more asynchronously than healthy subjects. The Journal of Clinical Endocrinology & Metabolism, 83, 688–692.
   PubMed Web of Science ®Google Scholar
• Rubin, D., Fekete, T., & Mujica-Parodi, L. R. (2013). Optimizing complexity measures for fMRI data: Algorithm, artifact, and sensitivity. Plos One, 8, e63448. doi:10.1371/journal.pone.0063448
   PubMed Web of Science ®Google Scholar
• Rubinov, M., Sporns, O., Thivierge, J.-P., & Breakspear, M. (2011). Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons. Plos Comput Biol, 7, e1002038. doi:10.1371/journal.pcbi.1002038
   PubMed Web of Science ®Google Scholar
• Ryan, S. M., Goldberger, A. L., Pincus, S. M., Mietus, J., & Lipsitz, L. A. (1994). Gender-and age-related differences in heart rate dynamics: Are women more complex than men?. Journal of the American College of Cardiology, 24, 1700–1707. doi:10.1016/0735-1097(94)90177-5
   PubMed Web of Science ®Google Scholar
• Schaefer, A., Brach, J. S., Perera, S., & Ervin, S. (2014). A comparative analysis of spectral exponent estimation techniques for 1/f β processes with applications to the analysis of stride interval time series. Journal of Neuroscience Methods, 222, 118–130. doi:10.1016/j.jneumeth.2013.10.017
   PubMed Web of Science ®Google Scholar
• Schmitz, O., Porksen, N., Nyholm, B., Skjærbæk, C., Butler, P. C., Veldhuis, J. D., & Pincus, S. M. (1997). Disorderly and nonstationary insulin secretion in relatives of patients with niddm. American Journal of Physiology-Endocrinology and Metabolism, 272, E218–E226.
   Web of Science ®Google Scholar
• Schubotz, R. I., & Yves Von Cramon, D. (2001). Functional organization of the lateral premotor cortex: fMRI reveals different regions activated by anticipation of object properties, location and speed. Cognitive Brain Research, 11, 97–112. doi:10.1016/S0926-6410(00)00069-0
   PubMedGoogle Scholar
• Shimizu, Y., Barth, M., Windischberger, C., Moser, E., & Thurner, S. (2004). Wavelet-based multifractal analysis of fmri time series. NeuroImage, 22, 1195–1202. doi:10.1016/j.neuroimage.2004.03.007
   PubMed Web of Science ®Google Scholar
• Siegle, G. J., Thompson, W., Carter, C. S., Steinhauer, S. R., & Thase, M. E. (2007). Increased amygdala and decreased dorsolateral prefrontal bold responses in unipolar depression: Related and independent features. Biological Psychiatry, 61, 198–209. doi:10.1016/j.biopsych.2006.05.048
   PubMed Web of Science ®Google Scholar
• Sokunbi, M. O., Cameron, G. G., Ahearn, T. S., Murray, A. D., & Staff, R. T. (2015). Fuzzy approximate entropy analysis of resting state fMRI signal complexity across the adult life span. Medical Engineering & Physics, 37, 1082–1090. doi:10.1016/j.medengphy.2015.09.001
   PubMed Web of Science ®Google Scholar
• Sotres-Bayon, F., Bush, D. E. A., & LeDoux, J. E. (2004). Emotional perseveration: An update on prefrontal-amygdala interactions in fear extinction. Learning & Memory, 11, 525–535. doi:10.1101/lm.79504
   PubMed Web of Science ®Google Scholar
• Stam, C. J. (2005). Nonlinear dynamical analysis of eeg and meg: Review of an emerging field. Clinical Neurophysiology, 116, 2266–2301. doi:10.1016/j.clinph.2005.06.011
   PubMed Web of Science ®Google Scholar
• Stamkopoulos, T., Diamantaras, K., Maglaveras, N., & Strintzis, M. (1998). Ecg analysis using nonlinear pca neural networks for ischemia detection. Signal Processing, IEEE Transactions On, 46, 3058–3067. doi:10.1109/78.726818
   Web of Science ®Google Scholar
• Suckling, J., Wink, A. M., Bernard, F. A., Barnes, A., & Bullmore, E. (2008). Endogenous multifractal brain dynamics are modulated by age, cholinergic blockade and cognitive performance. Journal of Neuroscience Methods, 174, 292–300. doi:10.1016/j.jneumeth.2008.06.037
   PubMed Web of Science ®Google Scholar
• Thurner, S., Windischberger, C., Moser, E., & Barth, M. (2002). Fractal noise maps reveal human brain activity. Technical report.
   Google Scholar
• Tolkunov, D., Rubin, D., & Lilianne R, M.-P. (2010). Power spectrum scale invariance quantifies limbic dysregulation in trait anxious adults using fmri: Adapting methods optimized for characterizing autonomic dysregulation to neural dynamic time series. Neuroimage, 50, 72–80. doi:10.1016/j.neuroimage.2009.12.021
   PubMed Web of Science ®Google Scholar
• Tschacher, W., Scheier, C., & Hashimoto, Y. (1997). Dynamical analysis of schizophrenia courses. Biological Psychiatry, 41, 428–437. doi:10.1016/S0006-3223(96)00039-X
   PubMed Web of Science ®Google Scholar
• Turner, B. M., Paradiso, S., Marvel, C. L., Pierson, R., Boles Ponto, L. L., Hichwa, R. D., & Robinson, R. G. (2007). The cerebellum and emotional experience. Neuropsychologia, 45, 1331–1341. doi:10.1016/j.neuropsychologia.2006.09.023
   PubMed Web of Science ®Google Scholar
• Volz, K. G., Schubotz, R. I., & Cramon, D. (2005). Variants of uncertainty in decision-making and their neural correlates. Brain Research Bulletin, 67, 403–412. doi:10.1016/j.brainresbull.2005.06.011
   PubMed Web of Science ®Google Scholar
• Wang, Z., Yin, L., Childress, A. R., & Detre, J. A. (2014). Brain entropy mapping using fMRI. Plos One, 9, e89948. doi:10.1371/journal.pone.0089948
   PubMed Web of Science ®Google Scholar
• Wink, A.-M., Bullmore, E., Barnes, A., Bernard, F., & Suckling, J. (2008). Monofractal and multifractal dynamics of low frequency endogenous brain oscillations in functional MRI. Human Brain Mapping, 29, 791–801. doi:10.1002/hbm.20593
   PubMed Web of Science ®Google Scholar
• Xiao-Su, H., Hong, K.-S., & Ge, S. S. (2011). Recognition of stimulus-evoked neuronal optical response by identifying chaos levels of near-infrared spectroscopy time series. Neuroscience Letters, 504, 115–120. doi:10.1016/j.neulet.2011.09.011
   PubMed Web of Science ®Google Scholar