Abstract
Neuropathological conditions often result in abnormal functional relationship between different regions in the brain and are specific to certain spectral bands that are not known in advance. Typically, these abnormalities are spatially and temporally very localized in nature, and detecting these changes can be clinically very useful. In this article, a novel evolutionary computation-based procedure is introduced to discover such localized changes in a data-driven manner. Given a predefined set of regions of interest (ROIs), the procedure automatically detects a subset of ROIs, a time window, and a frequency band, such that the functional relationship among the ROIs significantly differ between controls and neuropathological cases; the procedure makes no prior assumptions regarding the spectral characteristics of the data. To demonstrate the effectiveness of this procedure, a publicly available EEG dataset of 46 alcoholics and 31 controls is used. In all, 100 cross-validation runs are performed. Using the procedure, many weakened inter-hemispheric functional connections, primarily between the left and the right parietal lobe sensors, are detected in chronic alcoholics. For these functional connections, gamma band (35–50 Hz) activity in 200–400 ms window was found to be significantly different between alcoholics and controls. These results are consistent with the existing literature and helps to validate the procedure. In addition, the procedure is also tested via simulation using a graph generation model with known characteristics, and its general utility to brain imaging literature is discussed.
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The term ROI is more commonly used in fMRI research.
Other authors who have used this dataset for classification purposes have also not removed these artifacts.
The details regarding the training protocol is discussed in Sect. 2.5.
MSC spectrum is estimated using R function spec.pgram.
Good solutions have lower penalty.
To generate a range of networks between random to lattice, R igraph function watts.strogatz.game() is used. In the initial lattice network, each node is connected to 3 nearest neighbors.
References
Allen EA, Damaraju E, Plis SM, Erhardt EB, Eichele T, Calhoun VD (2012) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24(3):663–676. doi:10.1093/cercor/bhs352
Allgaier N (2015) Reverse engineering the human brain: an evolutionary computation approach to the analysis of fMRI. The University of Vermont, Burlington
Alstott J, Breakspear M, Hagmann P, Cammoun L, Sporns O (2009) Modeling the impact of lesions in the human brain. PLoS Comput Biol 5(6):e1000408
Anscombe FJ (1963) Tests of goodness of fit. J R Stat Soc Ser B (Methodol) 25:81–94
Ashburner J, Friston KJ (2001) Why voxel-based morphometry should be used. Neuroimage 14(6):1238–1243
Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15(7–8):456–467
Bassett DS, Bullmore ET, Meyer-Lindenberg A, Apud JA, Weinberger DR, Coppola R (2009) Cognitive fitness of cost-efficient brain functional networks. Proc Natl Acad Sci 106(28):11747–11752
Bassett DS, Meyer-Lindenberg A, Achard S, Duke T, Bullmore E (2006) Adaptive reconfiguration of fractal small-world human brain functional networks. Proc Natl Acad Sci 103(51):19518–19523
Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST (2011) Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci 108(18):7641–7646
Begleiter H (1999). http://archive.ics.uci.edu/ml/datasets/eeg+database. Accessed Oct 1999
Bluhm RL, Miller J, Lanius RA, Osuch EA, Boksman K, Neufeld RWJ, Williamson P (2007) Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull 33(4):1004–1012
Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Johnson KA (2009) Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer’s disease. J Neurosci 29(6):1860–1873
Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14(3):140–151
Calhoun VD, Liu J, Adalı T (2009) A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage 45(1):S163–S172
Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Meyerand ME (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am J Neuroradiol 22(7):1326–1333
Cribben I, Haraldsdottir R, Atlas LY, Wager TD, Lindquist MA (2012) Dynamic connectivity regression: determining state-related changes in brain connectivity. Neuroimage 61(4):907–920
Csardi G, Nepusz T (2006) The igraph software package for complex network research. InterJ Complex Syst 1695(5):1–9
Cuadra MB, Pollo C, Bardera A, Cuisenaire O, Villemure JG, Thiran JP (2004) Atlas-based segmentation of pathological MR brain images using a model of lesion growth. IEEE Trans Med Imaging 23(10):1301–1314
Daniell PJ (1946) Discussion on symposium on autocorrelation in time series. Suppl J R Stat Soc 8:88–90
De Jong KA (2006) Evolutionary computation: a unified approach. MIT press, Cambridge
Eshelman LJ (1991) The CHC adaptive search algorithm: how to have safe search when engaging. Found Genet Algorithms 1991 (FOGA 1) 1:265
Eshelman LJ, Schaffer JD (1991) Spurious correlations and premature convergence in genetic algorithms. In: Rawlins GJE (ed) Foundations of genetic algorithms. Morgan Kaufmann Publishers, San Mateo, pp 102–112
Eshelman LJ, Schaffer JD (1992) Real-coded genetic algorithms and interval-schemata. Foundations of genetic algorithms. Morgan Kaufmann, Burlington
Eshelman LJ, Mathias KE, Schaffer JD (1997) Convergence controlled variation. Found of Genet Algorithms 4:203–224
Fay MP, Shaw PA (2010) Exact and asymptotic weighted logrank tests for interval censored data: the interval R package. J Stati Softw 36(2)
Fridriksson J, Morrow-Odom L, Moser D, Fridriksson A, Baylis G (2006) Neural recruitment associated with anomia treatment in aphasia. Neuroimage 32(3):1403–1412
Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13(1):5–14. doi:10.1038/jcbfm.1993.4
GeethaRamani R, Sivaselvi K (2014) Human brain hubs (provincial and connector) identification using centrality measures. In: 2014 international conference on recent Trends in information technology (ICRTIT). IEEE, pp 1–6
Gonzalez R, Berman MG (2010) The value of brain imaging in psychological research. 42(1): 111–119
Greenough WT, Chang FF (1988) Plasticity of synapse structure and pattern in the cerebral cortex. In: Peters A, Jones EG (eds) Cerebral cortex. Plenum, New York, pp 391–440
Hagmann P, Kurant M, Gigandet X, Thiran P, Wedeen VJ, Meuli R, Thiran JP (2007) Mapping human whole-brain structural networks with diffusion MRI. PLoS ONE 2(7):e597
He Y, Chen ZJ, Evans AC (2007) Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb Cortex 17(10):2407–2419
Hebb DO (1947) The effects of early experience on problem solving at maturity. Am Psychol 2:306–307
Heeger DJ, Ress D (2002) What does fMRI tell us about neuronal activity? Nat Rev Neurosci 3(2):142–151
Hillary FG, Rajtmajer SM, Roman CA, Medaglia JD, Slocomb-Dluzen JE, Calhoun VD, Wylie GR (2014) The rich get richer: brain injury elicits hyperconnectivity in core subnetworks. PLoS ONE 9(8):e104021
Honea R, Crow TJ, Passingham D, Mackay CE (2005) Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 162(12):2233–2245
Hoppenbrouwers SS, Hofman D, Schutter DJ (2010) Alcohol breaks down interhemispheric inhibition in females but not in males. Psychopharmacology 208(3):469–474
Huettel SA, Song AW, McCarthy G (2009) Functional magnetic resonance imaging, 2nd edn. Sinauer, Massachusetts ISBN 978-0-87893-286-3
Hwang K, Hallquist MN, Luna B (2013) The development of hub architecture in the human functional brain network. Cereb Cortex 23(10):2380–2393
Irimia A, Chambers MC, Torgerson CM, Filippou M, Hovda DA, Alger JR, Van Horn JD (2012) Patient-tailored connectomics visualization for the assessment of white matter atrophy in traumatic brain injury. Front Neurol 3:10
Jirapech-Umpai T, Aitken S (2005) Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes. BMC Bioinform 6(1):1
Kaiser J, Lutzenberger W (2005) Human gamma-band activity: a window to cognitive processing. Neuroreport 16(3):207–211
Kudo M, Sklansky J (2000) Comparison of algorithms that select features for pattern classifiers. Pattern Recognit 33(1):25–41
Land WH, Qiao X, Margolis DE, Ford WS, Paquette CT, Perez-Rogers JF, Deng Y (2011) Kernelized partial least squares for feature reduction and classification of gene microarray data. BMC Syst Biol 5(3):1
Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemom 6(5):267–281
Liu H, Yu L (2005) Toward integrating feature selection algorithms for classification and clustering. IEEE Trans Knowl Data Eng 17(4):491–502
Lucasius CB, Kateman G (1992) Towards solving subset selection problems with the aid of the genetic algorithm. In: Manner R, Mandrick B (eds) Parallel problem solving from nature 2. Amsterdam, North-Holland, pp 239–247
Luque-Baena RM, Urda D, Claros MG, Franco L, Jerez JM (2014) Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords. J Biomed Inform 49:32–44
Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, Bullmore E (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci 30(28):9477–9487
Marple SL Jr (1987) Digital spectral analysis with applications, vol 512. Prentice-Hall, Inc, Englewood Cliffs, p 1
Mathias KE, Eshelman LJ, Schaffer JD, Augusteijn L, Hoogendijk PF, Wiel R (2000) Code compaction using genetic algorithms. In: GECO, pp 710–717
Mechelli A, Price CJ, Friston KJ, Ashburner J (2005) Voxel-based morphometry of the human brain: methods and applications. Curr Med Imaging Rev 1(2):105–113
Moselhy HF, Georgiou G, Kahn A (2001) Frontal lobe changes in alcoholism: a review of the literature. Alcohol and Alcohol 36(5):357–368
Nakamura T, Hillary FG, Biswal BB (2009) Resting network plasticity following brain injury. PLoS ONE 4(12):e8220
Nakatomi H, Kuriu T, Okabe S, Yamamoto SI, Hatano O, Kawahara N, Nakafuku M (2002) Regeneration of hippocampal pyramidal neurons after ischemic brain injury by recruitment of endogenous neural progenitors. Cell 110(4):429–441
Narendra PM, Fukunaga K (1977) A branch and bound algorithm for feature subset selection. IEEE Trans Comput 100(9):917–922
Newman M (2010) Networks: an introduction. OUP Oxford, Oxford
Oh IS, Lee JS, Moon BR (2004) Hybrid genetic algorithms for feature selection. IEEE Trans Pattern Anal Mach Intell 26(11):1424–1437
Ong KM, Thung KH, Wee CY, Paramesranle R (2005) Selection of a subset of EEG channels using PCA to classify alcoholics and non-alcoholics. In: Proceedings of the 2005 IEEE engineering in medicine and biology 27th annual conference, Shanghai, China
Palaniappan R (2005) Discrimination of alcoholic subjects using second order autoregressive modelling of brain signals evoked during visual stimulus perception. In: Proceedings of world academy of science, engineering and technology, Vol 7 (Prague), pp 282–287
Palaniappan R (2006) Improved automated classification of alcoholics and non-alcoholics. Inf Technol 2:182–186
Palaniappan R (2007) Screening for chronic alcoholic subjects using multiple gamma band EEG: a pilot study. J Comput Sci Technol 7:182–185
Palaniappan R, Paramesran R (2002) Using genetic algorithm to identify the discriminatory subset of multi-channel spectral bands for visual response. Appl Soft Comput 2(1):48–60
Peralta D, del Río S, Ramírez-Gallego S, Triguero I, Benitez JM, Herrera F (2015) Evolutionary feature selection for big data classification: a mapreduce approach. Math Probl Eng 501:246139
Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Petersen SE (2011) Functional network organization of the human brain. Neuron 72(4):665–678
Radcliffe NJ (1990) Genetic neural networks on MIMD computers (compressed edition). Doctoral dissertation, Ph. D. dissertation, Dep. Theoretical Phys., Univ. Edinburgh, UK
Radcliffe NJ (1992) Genetic set recombination. Found Genet Algorithms 2:203–220
Reuter-Lorenz PA, Stanczak L, Miller AC (1999) Neural recruitment and cognitive aging: two hemispheres are better than one, especially as you age. Psychol Sci 10(6):494–500
Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12(1):1
Romer M (2016) Applied time series analysis. https://onlinecourses.science.psu.edu/stat510/?q=book/export/html/57
Roy A (2014) Evolving spike neural network based spatio-temporal pattern classifiers with an application to identifying the alcoholic brain. State University of New York at Binghamton, Vestal
Roy A, Schaffer JD, Laramee CB (2013) Evolving spike neural network sensors to characterize the alcoholic brain using visually evoked response potential. Procedia Comput Sci 20:27–32
Roy A, Schaffer JD, Laramee CB (2015) New crossover operators for multiple subset selection tasks. Comput Commun Collab 3(1)
Roy A, Campbell C, Bernier RA, Hillary FG (2016a) An evolutionary computation approach to examine functional brain plasticity. Front Neurosci 10
Roy A, Schaffer JD, Laramee CB (2016b) A novel approach to signal classification with an application to identifying the alcoholic brain. Appl Soft Comput 43:406–414
Rutter L, Nadar SR, Holroyd T, Carver FW, Apud J, Weinberger DR, Coppola R (2013) Graph theoretical analysis of resting magnetoencephalographic functional connectivity networks. Front Comput Neurosci 7:93
Schaffer JD, Janevski A, Simpson MR (2005) A genetic algorithm approach for discovering diagnostic patterns in molecular measurement data. In: Proceedings of the 2005 IEEE symposium on computational intelligence in bioinformatics and computational biology, 2005. CIBCB’05. IEEE, pp 1–8
Shri TP, Sriraam N (2012a) EEG based detection of alcoholics: a selective review. Int J Biomed Clin Eng (IJBCE) 1(1):59–76
Shri TP, Sriraam N (2012b) EEG based detection of alcoholics using spectral entropy with neural network classifiers. In: 2012 international conference on biomedical engineering (ICoBE). IEEE, pp 89–93
Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recognit Lett 10(5):335–347
Snodgrass JG, Vanderwart M (1980) A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity. J Exp Psychol Hum Learn Mem 6(2):174
Sörnmo L, Laguna P (2005) Bioelectrical signal processing in cardiac and neurological applications. Academic Press, Cambridge
Sporns O, Zwi JD (2004) The small world of the cerebral cortex. Neuroinformatics 2(2):145–162
Sporns O, Honey CJ, Kötter R (2007) Identification and classification of hubs in brain networks. PLoS ONE 2(10):e1049
Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224(1):109–125
Sporns O (2013) Structure and function of complex brain networks. Dialogues Clin Neurosci 15(3):247–262
Stam CJ (2004) Functional connectivity patterns of human magnetoencephalographic recordings: a ‘small-world’network? Neurosci Lett 355(1):25–28
Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P (2007) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17(1):92–99
Stoean R, Stoean C (2013) Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection. Expert Syst Appl 40(7):2677–2686
Sullivan TJ, Deiss SR, Jung TP, Cauwenberghs G (2008) A brain-machine interface using dry-contact, low-noise EEG sensors. In: IEEE international symposium on circuits and systems, (2008) ISCAS 2008. IEEE, pp 1986–1989
Swets JA (2014) Signal detection theory and ROC analysis in psychology and diagnostics: collected papers. Psychology Press, Oxfordshire
Thirion B, Varoquaux G, Dohmatob E, Poline JB (2014) Which fMRI clustering gives good brain parcellations? Front Neurosci 8(167):13
Troy ML, Joseph TG, Daniel PF (2012) How many electrodes are really needed for EEG-based mobile brain imaging? J Behav Brain Sci 2:387–393
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289
Wang L (2012) Feature selection in bioinformatics. Proc. SPIE 8401, Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 840113 (May 1, 2012). doi:10.1117/12.921417
Wang L, Zang Y, He Y, Liang M, Zhang X, Tian L, Li K (2006) Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. Neuroimage 31(2):496–504
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’networks. Nature 393(6684):440–442
Wheeler B (2010) lmPerm: permutation tests for linear models. R Package Version 1:1–2
Xu Y, Lindquist MA (2015) Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data. Front Neurosci 9
Xue B, Zhang M, Browne W, Yao X (2015) A survey on evolutionary computation approaches to feature selection. IEEE Trans Evol Comput 20(4):606–626
Yamada K, Sakai K, Akazawa K, Yuen S, Nishimura T (2009) MR tractography: a review of its clinical applications. Magn Reson Med Sci 8(4):165–174
Yang J, Honavar V (1998) Feature subset selection using a genetic algorithm. IEEE Intell Syst 13(2):44–49
Zalesky A, Fornito A, Egan GF, Pantelis C, Bullmore ET (2012) The relationship between regional and inter-regional functional connectivity deficits in schizophrenia. Hum Brain Mapp 33(11):2535–2549
Zhang XL, Begleiter H, Porjesz B (1997) Do chronic alcoholics have intact implicit memory? An ERP study. Electroencephalogr Clin Neurophysiol 103(4):457–473
Zhang XL, Begleiter H, Porjesz B, Wang W, Litke A (1995) Event related potentials during object recognition tasks. Brain Res Bull 38(6):531–538
Zhang H, Sun HG (2002) Feature selection using tabu search method. Pattern Recognit 35(3):701–711
Zhao M, Fu C, Ji L, Tang K, Zhou M (2011) Feature selection and parameter optimization for support vector machines: a new approach based on genetic algorithm with feature chromosomes. Expert Syst Appl 38(5):5197–5204
Acknowledgements
The data for this research was made available on the web by Dr. Henri Begleiter, Neurodynamics Laboratory, State University of New York Health Center at Brooklyn. I would also like to thank Dr. J. David Schaffer, Institute for Multi-Generational Studies, Binghamton University, Binghamton, NY and Dr. Bharath Sriperumbudur, Department of Statistics, Pennsylvania State University, State College, PA for their valuable comments that greatly helped to improve the manuscript. Finally, I would like to thank Ms. Rachel A. Bernier, Department of Psychology, Pennsylvania State University, State College, PA for her comments regarding clinical utility of this work.
Funding The research was not supported by any funding agency. All programs were written in R 3.2.3, and the experiments were run on a personal laptop computer. The data used in this work are part of a publicly available dataset currently hosted by UCI machine learning repository.
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Appendices
Appendix 1: Graph metrics
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1.
The global clustering coefficient, GCC, of an undirected graph is defined as follows:
$$\begin{aligned}&GCC = \frac{3\,{*}\,{\textit{Total number of triangles in the network}}}{{\textit{Total connected triples in the network}}}\nonumber \\ \end{aligned}$$(7)GCC is often used as a measure of network’s local communication efficiency. That is, if there are many triangles in a network then it would mean that the nodes in the network are locally very well connected.
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2.
The local clustering coefficient, LCC, of a node represents how well the neighbors of a node are connected to each other. The local clustering coefficient of a node for an undirected graph can be evaluated as:
$$\begin{aligned}&{} \textit{LCC} = \frac{{\textit{Number of direct connections between the neighbors of the node}}}{{\textit{Maximum possible direct connections that can exist among the neighbors}}}\nonumber \\ \end{aligned}$$(8) -
3.
Average shortest path length, L(G), of a undirected graph G(V, E) is defined as:
$$\begin{aligned} L\left( G \right) = \frac{1}{{n{*}\left( {n-1} \right) }}\,\, {*}\,\, {\sum \limits _{i\ne j} Dist\left( {v\_i, v\_j} \right) } \end{aligned}$$(9)where,
$$\begin{aligned} V \text { is a set of all nodes in } G \\ E \text { is a set of all edges in }G \end{aligned}$$\({{ Dist}(v\_i, v\_j)}\) is the shortest path length between nodes \({v\_i, v\_j}\) such that \({v\_i, v\_j \in V}\).
The average shortest path length (or its reciprocal value) is often used as a measure of network’s global communication efficiency. That is, smaller the average shortest path length, higher is the network’s efficiency.
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4.
Degree centrality of a node in an undirected graph is defined as the total number of edges that are incident on the node.
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5.
Betweenness centrality of a node in an undirected graph is based on the total number of shortest paths between other nodes in the network that passes through the given node.
Appendix 2: Encoding a small-world network as a function of time and frequency
To encode random and small-world (SW) networks as a function of time and frequency, the rewiring probability, p(t, f), was defined as follows:
where,
where,
The function \({ p\_}_{0}\) in Eq. 10 creates a 2D surface with a single peak. The parameter amplitude controls its height, the parameters \({ support\_}_{t }\) and \({ support\_}_{f}\) control the center point of the function in time and frequency axes, the parameters \({ cutoff\_}_{t}\) and \({ cutoff\_}_{f}\) control the width of the function in time and frequency axes, and the parameters \({ order\_}_{t}\) and \({ order\_}_{f}\) control how smoothly the function value declines to 0. That is, if the parameter order is set to a very high value, the function falls sharply, whereas if the order is set to a very low value, the function declines gradually. The function \({ p\_}_{1}(t,f)\) is created by adding ten \({ p\_}_{0}\) functions with amplitude varying from 0 to 1, and one \({ p\_}_{0}\) function with amplitude set to 10. The p(t, f) is then created by first normalizing \({ p\_}_{1}(t,f)\) and then converting all peaks to troughs. As a result of this, p(t, f) varies between 1 and 0.15. The max value of \({ p\_}_{1}\) function (i.e., where function \({ p\_}_{1}(t,f) = 10\) is situated) converts to a value of 0.15 in function p(t, f); the small-world class of networks exist in the range between 0.15 and 0.2 . The variable, order, while creating p(t, f) was set to 3. If the order is set to a very high number, then the 2D surface will contain 11 sharp falling troughs, hence producing a needle in a haystack type situation.
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Roy, A. Examining dynamic functional relationships in a pathological brain using evolutionary computation. Soft Comput 22, 2341–2368 (2018). https://doi.org/10.1007/s00500-017-2496-8
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DOI: https://doi.org/10.1007/s00500-017-2496-8