{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T10:40:29Z","timestamp":1770288029879,"version":"3.49.0"},"reference-count":89,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:00:00Z","timestamp":1669161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2023,7]]},"abstract":"<jats:title>Abstract\n<\/jats:title><jats:p>Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (<jats:italic>n<\/jats:italic>\u2009=\u200930), healthy older adults (<jats:italic>n<\/jats:italic>\u2009=\u200939) and older adults with MCI (<jats:italic>n<\/jats:italic>\u2009=\u200927) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation.\n<\/jats:p>","DOI":"10.1007\/s12559-022-10075-7","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T19:01:25Z","timestamp":1669230085000},"page":"1273-1299","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3031-3502","authenticated-orcid":false,"given":"Mark","family":"Crook-Rumsey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina J.","family":"Howard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zohreh","family":"Doborjeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maryam","family":"Doborjeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josafath Israel Espinosa","family":"Ramos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikola","family":"Kasabov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Sumich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,23]]},"reference":[{"key":"10075_CR1","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1001\/archneur.56.3.303","volume":"56","author":"RC Petersen","year":"1999","unstructured":"Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol American Medical Association. 1999;56:303\u20138.","journal-title":"Arch Neurol American Medical Association"},{"key":"10075_CR2","first-page":"1447","volume":"66","author":"RC Petersen","year":"2009","unstructured":"Petersen RC, Roberts RO, Knopman DS, Boeve BF, Geda YE, Ivnik RJ, et al. Mild cognitive impairment: ten years later. Arch Neurol American Medical Association. 2009;66:1447\u201355.","journal-title":"Arch Neurol American Medical Association"},{"key":"10075_CR3","doi-asserted-by":"crossref","unstructured":"Saunders NLJ, Summers MJ. Longitudinal deficits to attention, executive, and working memory in subtypes of mild cognitive impairment. Neuropsychology. Am Psychol Assoc. 2011;25:237.","DOI":"10.1037\/a0021134"},{"key":"10075_CR4","doi-asserted-by":"crossref","unstructured":"Brandt J, Aretouli E, Neijstrom E, Samek J, Manning K, Albert MS, et al. Selectivity of executive function deficits in mild cognitive impairment. Neuropsychology. Am Psychol Assoc.\u00a02009;23:607.","DOI":"10.1037\/a0015851"},{"key":"10075_CR5","doi-asserted-by":"crossref","unstructured":"Blanco-Campal A, Coen RF, Lawlor BA, Walsh JB, Burke TE. Detection of prospective memory deficits in mild cognitive impairment of suspected Alzheimer\u2019s disease etiology using a novel event-based prospective memory task. Cambridge University Press.\u00a0J Int Neuropsychol Soc.\u00a02009;15:154\u20139.","DOI":"10.1017\/S1355617708090127"},{"key":"10075_CR6","unstructured":"Einstein GO, McDaniel MA, Anderson FT. Multiple processes in prospective memory: exploring the nature of spontaneous retrieval. Guilford Press. 2018."},{"key":"10075_CR7","first-page":"73","volume":"5","author":"DHE Boelen","year":"2011","unstructured":"Boelen DHE, Spikman JM, Fasotti L. Rehabilitation of executive disorders after brain injury: are interventions effective? J Neuropsychol Wiley Online Library. 2011;5:73\u2013113.","journal-title":"J Neuropsychol Wiley Online Library"},{"key":"10075_CR8","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1080\/00207590344000114","volume":"38","author":"M Kliegel","year":"2003","unstructured":"Kliegel M, Martin M. Prospective memory research: Why is it relevant? Int J Psychol Taylor & Francis. 2003;38:193\u20134.","journal-title":"Int J Psychol Taylor & Francis"},{"key":"10075_CR9","doi-asserted-by":"crossref","unstructured":"Brandimonte MA, Einstein GO, McDaniel MA. Prospective memory: theory and applications. Psychol Press. 2014.","DOI":"10.4324\/9781315806488"},{"key":"10075_CR10","doi-asserted-by":"publisher","first-page":"2692","DOI":"10.1016\/j.neuropsychologia.2012.07.033","volume":"50","author":"G Cona","year":"2012","unstructured":"Cona G, Arcara G, Tarantino V, Bisiacchi PS. Age-related differences in the neural correlates of remembering time-based intentions. Neuropsychologia Elsevier. 2012;50:2692\u2013704.","journal-title":"Neuropsychologia Elsevier"},{"key":"10075_CR11","doi-asserted-by":"publisher","first-page":"3494","DOI":"10.1016\/j.neuropsychologia.2011.08.026","volume":"49","author":"F Mattli","year":"2011","unstructured":"Mattli F, Z\u00f6llig J, West R. Age-related differences in the temporal dynamics of prospective memory retrieval: a lifespan approach. Neuropsychologia Elsevier. 2011;49:3494\u2013504.","journal-title":"Neuropsychologia Elsevier"},{"key":"10075_CR12","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neubiorev.2015.02.007","volume":"52","author":"G Cona","year":"2015","unstructured":"Cona G, Scarpazza C, Sartori G, Moscovitch M, Bisiacchi PS. Neural bases of prospective memory: a meta-analysis and the \u201cAttention to Delayed Intention\u201d(AtoDI) model. Neurosci Biobehav Rev Elsevier. 2015;52:21\u201337.","journal-title":"Neurosci Biobehav Rev Elsevier"},{"key":"10075_CR13","doi-asserted-by":"publisher","first-page":"2257","DOI":"10.3174\/ajnr.A4055","volume":"35","author":"E Mak","year":"2014","unstructured":"Mak E, Bergsland N, Dwyer MG, Zivadinov R, Kandiah N. Subcortical atrophy is associated with cognitive impairment in mild Parkinson disease: a combined investigation of volumetric changes, cortical thickness, and vertex-based shape analysis. Am J Neuroradiol Am Soc Neuroradiology. 2014;35:2257\u201364.","journal-title":"Am J Neuroradiol Am Soc Neuroradiology"},{"key":"10075_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s00401-011-0884-1","volume":"123","author":"EJ Mufson","year":"2012","unstructured":"Mufson EJ, Binder L, Counts SE, DeKosky ST, deToledo-Morrell L, Ginsberg SD, et al. Mild cognitive impairment: pathology and mechanisms. Acta Neuropathol Springer. 2012;123:13\u201330.","journal-title":"Acta Neuropathol Springer"},{"key":"10075_CR15","unstructured":"Otten LJ, Rugg MD. Interpreting event-related brain potentials. Event-related potentials A methods Handb. 2005;3\u201316."},{"key":"10075_CR16","doi-asserted-by":"publisher","first-page":"2233","DOI":"10.1016\/j.neuropsychologia.2010.12.028","volume":"49","author":"R West","year":"2011","unstructured":"West R. The temporal dynamics of prospective memory: a review of the ERP and prospective memory literature. Neuropsychologia Elsevier. 2011;49:2233\u201345.","journal-title":"Neuropsychologia Elsevier"},{"key":"10075_CR17","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.ijpsycho.2019.11.003","volume":"147","author":"A Hering","year":"2020","unstructured":"Hering A, Wild-Wall N, Falkenstein M, Gajewski PD, Zinke K, Altgassen M, et al. Beyond prospective memory retrieval: encoding and remembering of intentions across the lifespan. Int J Psychophysiol Elsevier. 2020;147:44\u201359.","journal-title":"Int J Psychophysiol Elsevier"},{"key":"10075_CR18","doi-asserted-by":"publisher","first-page":"3299","DOI":"10.1016\/j.neuropsychologia.2007.06.010","volume":"45","author":"J Z\u00f6llig","year":"2007","unstructured":"Z\u00f6llig J, West R, Martin M, Altgassen M, Lemke U, Kliegel M. Neural correlates of prospective memory across the lifespan. Neuropsychologia Elsevier. 2007;45:3299\u2013314.","journal-title":"Neuropsychologia Elsevier"},{"key":"10075_CR19","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.clinph.2021.09.019","volume":"133","author":"M Crook-Rumsey","year":"2022","unstructured":"Crook-Rumsey M, Howard CJ, Hadjiefthyvoulou F, Sumich A. Neurophysiological markers of prospective memory and working memory in typical ageing and mild cognitive impairment. Clin Neurophysiol Elsevier. 2022;133:111\u201325.","journal-title":"Clin Neurophysiol Elsevier"},{"key":"10075_CR20","doi-asserted-by":"crossref","unstructured":"Doborjeh Z, Doborjeh M, Crook-Rumsey M, Taylor T, Wang GY, Moreau D, et al. Interpretability of spatiotemporal dynamics of the brain processes followed by mindfulness intervention in a brain-inspired spiking neural network architecture. Sensors. Multidisciplinary Digital Publishing Institute; 2020;20:7354.","DOI":"10.3390\/s20247354"},{"key":"10075_CR21","doi-asserted-by":"crossref","unstructured":"Kasabov N. Time-space, spiking neural networks and brain-inspired artificial intelligence. Springer; 2019.","DOI":"10.1007\/978-3-662-57715-8"},{"key":"10075_CR22","first-page":"73","volume":"2014","author":"MG Doborjeh","year":"2014","unstructured":"Doborjeh MG, Capecci E, Classification KN, segmentation of fMRI spatio-temporal brain data with a NeuCube evolving spiking neural network model. IEEE Symp Evol Auton Learn Syst. IEEE. 2014;2014:73\u201380.","journal-title":"IEEE"},{"key":"10075_CR23","first-page":"1360","volume":"2016","author":"E Capecci","year":"2016","unstructured":"Capecci E, Doborjeh ZG, Mammone N, La Foresta F, Morabito FC, Kasabov N, Longitudinal study of Alzheimer\u2019s disease degeneration through EEG data analysis with a NeuCube spiking neural network model. Int Jt Conf Neural Networks. IEEE. 2016;2016:1360\u20136.","journal-title":"IEEE"},{"key":"10075_CR24","first-page":"1","volume":"8","author":"ZG Doborjeh","year":"2018","unstructured":"Doborjeh ZG, Kasabov N, Doborjeh MG, Sumich A. Modelling peri-perceptual brain processes in a deep learning spiking neural network architecture. Sci Rep Nature Publishing Group. 2018;8:1\u201313.","journal-title":"Sci Rep Nature Publishing Group"},{"key":"10075_CR25","doi-asserted-by":"publisher","first-page":"2924","DOI":"10.1109\/TBME.2019.2898871","volume":"66","author":"S Yang","year":"2019","unstructured":"Yang S, Bornot JMS, Wong-Lin K, Prasad G. M\/EEG-based bio-markers to predict the MCI and Alzheimer\u2019s disease: a review from the ML perspective. IEEE Trans Biomed Eng IEEE. 2019;66:2924\u201335.","journal-title":"IEEE Trans Biomed Eng IEEE"},{"key":"10075_CR26","doi-asserted-by":"crossref","unstructured":"Farina FR, Emek-Sava\u015f DD, Rueda-Delgado L, Boyle R, Kiiski H, Yener G, et al. A comparison of resting state EEG and structural MRI for classifying Alzheimer\u2019s disease and mild cognitive impairment. Elsevier. Neuroimag.\u00a02020;116795.","DOI":"10.1016\/j.neuroimage.2020.116795"},{"key":"10075_CR27","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1080\/01616412.2020.1726586","volume":"42","author":"T Wadhera","year":"2020","unstructured":"Wadhera T, Kakkar D. Multiplex temporal measures reflecting neural underpinnings of brain functional connectivity under cognitive load in autism spectrum disorder. Neurol Res Taylor & Francis. 2020;42:327\u201337.","journal-title":"Neurol Res Taylor & Francis"},{"key":"10075_CR28","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1076\/clin.12.1.43.1726","volume":"12","author":"RHB Benedict","year":"1998","unstructured":"Benedict RHB, Schretlen D, Groninger L, Brandt J. Hopkins verbal learning test\u2013revised: normative data and analysis of inter-form and test-retest reliability. Clin Neuropsychol Taylor & Francis. 1998;12:43\u201355.","journal-title":"Clin Neuropsychol Taylor & Francis"},{"key":"10075_CR29","volume-title":"Geriatric Depression Scale (GDS): recent evidence and development of a shorter version","author":"JI Sheikh","year":"1986","unstructured":"Sheikh JI, Yesavage JA. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version. Clin Gerontol J Aging Ment Heal: Haworth Press; 1986."},{"key":"10075_CR30","doi-asserted-by":"publisher","unstructured":"Cruz G, Miyakoshi M, Makeig S, Kilborn K, Evans JJ. ERPs and their brain sources in perceptual and conceptual prospective memory tasks: commonalities and differences between the two tasks. Elsevier.\u00a0Neuropsychol [Internet].\u00a02016;91:173\u201385. Available from: https:\/\/doi.org\/10.1016\/j.neuropsychologia.2016.08.005.","DOI":"10.1016\/j.neuropsychologia.2016.08.005"},{"key":"10075_CR31","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1016\/j.jml.2003.10.003","volume":"50","author":"JP Van Overschelde","year":"2004","unstructured":"Van Overschelde JP, Rawson KA, Dunlosky J. Category norms: an updated and expanded version of the norms. J Mem Lang Elsevier. 2004;50:289\u2013335.","journal-title":"J Mem Lang Elsevier"},{"key":"10075_CR32","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.neunet.2014.01.006","volume":"52","author":"N Kasabov","year":"2014","unstructured":"Kasabov N. NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Networks Elsevier. 2014;52:62\u201376.","journal-title":"Neural Networks Elsevier"},{"key":"10075_CR33","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neunet.2015.09.011","volume":"78","author":"N Kasabov","year":"2016","unstructured":"Kasabov N, Scott NM, Tu E, Marks S, Sengupta N, Capecci E, et al. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Networks Elsevier. 2016;78:1\u201314.","journal-title":"Neural Networks Elsevier"},{"key":"10075_CR34","doi-asserted-by":"crossref","unstructured":"Petro B, Kasabov N, Kiss RM. Selection and optimization of temporal spike encoding methods for spiking neural networks.\u00a0IEEE Trans neural networks Learn Syst. 2019;31:358\u201370.","DOI":"10.1109\/TNNLS.2019.2906158"},{"issue":"270","key":"10075_CR35","first-page":"90125","volume":"1988","author":"Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain","year":"1988","unstructured":"Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain. Theime. Stuttgart, Ger. 1988;1988(270):90125\u20138.","journal-title":"Stuttgart, Ger"},{"key":"10075_CR36","unstructured":"Braitenberg V, Sch\u00fcz A. Cortex: statistics and geometry of neuronal connectivity. Springer Sci Bus Med. 2013."},{"key":"10075_CR37","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1038\/78829","volume":"3","author":"S Song","year":"2000","unstructured":"Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci Nature Publishing Group. 2000;3:919\u201326.","journal-title":"Nat Neurosci Nature Publishing Group"},{"key":"10075_CR38","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neunet.2012.11.014","volume":"41","author":"N Kasabov","year":"2013","unstructured":"Kasabov N, Dhoble K, Nuntalid N, Indiveri G. Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition. Neural Networks Elsevier. 2013;41:188\u2013201.","journal-title":"Neural Networks Elsevier"},{"key":"10075_CR39","first-page":"2","volume":"2015","author":"G Indiveri","year":"2015","unstructured":"Indiveri G, Corradi F, Qiao N, Neuromorphic architectures for spiking deep neural networks. IEEE Int Electron Devices Meet. IEEE. 2015;2015:2\u20134.","journal-title":"IEEE"},{"key":"10075_CR40","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.jneumeth.2014.04.020","volume":"229","author":"P Giacometti","year":"2014","unstructured":"Giacometti P, Perdue KL, Diamond SG. Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain. J Neurosci Methods Elsevier. 2014;229:84\u201396.","journal-title":"J Neurosci Methods Elsevier"},{"key":"10075_CR41","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/j.neuroimage.2009.02.006","volume":"46","author":"L Koessler","year":"2009","unstructured":"Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, et al. Automated cortical projection of EEG sensors: anatomical correlation via the international 10\u201310 system. Neuroimage Elsevier. 2009;46:64\u201372.","journal-title":"Neuroimage Elsevier"},{"key":"10075_CR42","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1085\/jgp.59.6.734","volume":"59","author":"BW Knight","year":"1972","unstructured":"Knight BW. Dynamics of encoding in a population of neurons. J Gen Physiol Rockefeller University Press. 1972;59:734\u201366.","journal-title":"J Gen Physiol Rockefeller University Press"},{"key":"10075_CR43","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1016\/j.neubiorev.2017.03.018","volume":"77","author":"X Liao","year":"2017","unstructured":"Liao X, Vasilakos AV, He Y. Small-world human brain networks: perspectives and challenges. Neurosci Biobehav Rev Elsevier. 2017;77:286\u2013300.","journal-title":"Neurosci Biobehav Rev Elsevier"},{"key":"10075_CR44","doi-asserted-by":"crossref","unstructured":"Thorpe S, Gautrais J. Rank order coding. Springer.\u00a0Comput Neurosci.\u00a01998;113\u20138.","DOI":"10.1007\/978-1-4615-4831-7_19"},{"key":"10075_CR45","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.ijpsycho.2014.06.008","volume":"93","author":"A Scolaro","year":"2014","unstructured":"Scolaro A, West R, Cohen A-L. The ERP correlates of target checking are dependent upon the defining features of the prospective memory cues. Int J Psychophysiol Elsevier. 2014;93:298\u2013304.","journal-title":"Int J Psychophysiol Elsevier"},{"key":"10075_CR46","doi-asserted-by":"crossref","unstructured":"Z\u00f6llig J, Mattli F, Sutter C, Aurelio A, Martin M. Plasticity of prospective memory through a familiarization intervention in old adults. Taylor & Francis.\u00a0Aging Neuropsychol Cogn.\u00a02012;19:168\u201394.","DOI":"10.1080\/13825585.2011.633160"},{"key":"10075_CR47","doi-asserted-by":"crossref","unstructured":"Rousselet GA, Gaspar CM, Pernet CR, Husk JS, Bennett PJ, Sekuler AB. Healthy aging delays scalp EEG sensitivity to noise in a face discrimination task. Frontiers.\u00a0Front Psychol.\u00a02010;1:19.","DOI":"10.3389\/fpsyg.2010.00019"},{"key":"10075_CR48","doi-asserted-by":"crossref","unstructured":"Baker J, Castro A, Dunn AK, Mitra S. Asymmetric interference between cognitive task components and concurrent sensorimotor coordination. American Physiological Society Bethesda, MD.\u00a0J Neurophysiol.\u00a02018;120:330\u201342.","DOI":"10.1152\/jn.00073.2018"},{"key":"10075_CR49","doi-asserted-by":"publisher","first-page":"379","DOI":"10.3758\/BF03192707","volume":"37","author":"R Bakeman","year":"2005","unstructured":"Bakeman R. Recommended effect size statistics for repeated measures designs. Behav Res Methods Springer. 2005;37:379\u201384.","journal-title":"Behav Res Methods Springer"},{"key":"10075_CR50","doi-asserted-by":"crossref","unstructured":"Wadhera T, Kakkar D. Drift-diffusion model parameters underlying cognitive mechanism and perceptual learning in autism spectrum disorder. 2020;847\u201357.","DOI":"10.1007\/978-981-15-0751-9_77"},{"key":"10075_CR51","doi-asserted-by":"crossref","unstructured":"Tibshirani R. Regression shrinkage and selection via the lasso. Wiley Online Library.\u00a0J R Stat Soc Ser B.\u00a01996;58:267\u201388.","DOI":"10.1111\/j.2517-6161.1996.tb02080.x"},{"key":"10075_CR52","first-page":"246","volume":"37","author":"N Meinshausen","year":"2009","unstructured":"Meinshausen N, Yu B. Lasso-type recovery of sparse representations for high-dimensional data. Ann Stat Institute of Mathematical Statistics. 2009;37:246\u201370.","journal-title":"Ann Stat Institute of Mathematical Statistics"},{"key":"10075_CR53","first-page":"2541","volume":"7","author":"P Zhao","year":"2006","unstructured":"Zhao P, Yu B. On model selection consistency of Lasso. J Mach Learn Res. 2006;7:2541\u201363.","journal-title":"J Mach Learn Res"},{"key":"10075_CR54","doi-asserted-by":"crossref","unstructured":"Zou H. The adaptive lasso and its oracle properties. Taylor & Francis.\u00a0J Am Stat Assoc.\u00a02006;101:1418\u201329.","DOI":"10.1198\/016214506000000735"},{"key":"10075_CR55","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1140\/epjb\/e2004-00111-4","volume":"38","author":"M Barthelemy","year":"2004","unstructured":"Barthelemy M. Betweenness centrality in large complex networks. Eur Phys J B Springer. 2004;38:163\u20138.","journal-title":"Eur Phys J B Springer"},{"key":"10075_CR56","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.socnet.2004.11.008","volume":"27","author":"SP Borgatti","year":"2005","unstructured":"Borgatti SP. Centrality and network flow. Soc Networks Elsevier. 2005;27:55\u201371.","journal-title":"Soc Networks Elsevier"},{"key":"10075_CR57","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/j.neuroimage.2014.11.021","volume":"105","author":"MJ Rosa","year":"2015","unstructured":"Rosa MJ, Portugal L, Hahn T, Fallgatter AJ, Garrido MI, Shawe-Taylor J, et al. Sparse network-based models for patient classification using fMRI. Neuroimage Elsevier. 2015;105:493\u2013506.","journal-title":"Neuroimage Elsevier"},{"key":"10075_CR58","doi-asserted-by":"crossref","unstructured":"Kemmer PB, Guo Y, Wang Y, Pagnoni G. Network-based characterization of brain functional connectivity in Zen practitioners. Frontiers.\u00a0Front Psychol.\u00a02015;6:603.","DOI":"10.3389\/fpsyg.2015.00603"},{"key":"10075_CR59","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.schres.2006.06.028","volume":"87","author":"S Micheloyannis","year":"2006","unstructured":"Micheloyannis S, Pachou E, Stam CJ, Breakspear M, Bitsios P, Vourkas M, et al. Small-world networks and disturbed functional connectivity in schizophrenia. Schizophr Res Elsevier. 2006;87:60\u20136.","journal-title":"Schizophr Res Elsevier"},{"key":"10075_CR60","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.neulet.2003.10.063","volume":"355","author":"CJ Stam","year":"2004","unstructured":"Stam CJ. Functional connectivity patterns of human magnetoencephalographic recordings: a \u2018small-world\u2019network? Neurosci Lett Elsevier. 2004;355:25\u20138.","journal-title":"Neurosci Lett Elsevier"},{"key":"10075_CR61","doi-asserted-by":"publisher","first-page":"524","DOI":"10.1093\/cercor\/bhn102","volume":"19","author":"G Gong","year":"2009","unstructured":"Gong G, He Y, Concha L, Lebel C, Gross DW, Evans AC, et al. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb cortex Oxford University Press. 2009;19:524\u201336.","journal-title":"Cereb cortex Oxford University Press"},{"key":"10075_CR62","doi-asserted-by":"crossref","unstructured":"Dauwels J, Yu H, Wang X, Vialatte F, Latchoumane C, Jeong J, et al. Inferring brain networks through graphical models with hidden variables. Mach Learn Interpret Neuroimaging. Springer. 2012;194\u2013201.","DOI":"10.1007\/978-3-642-34713-9_25"},{"key":"10075_CR63","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1007\/s12559-017-9517-x","volume":"10","author":"ZG Doborjeh","year":"2018","unstructured":"Doborjeh ZG, Doborjeh MG, Kasabov N. Attentional bias pattern recognition in spiking neural networks from spatio-temporal EEG data. Cognit Comput Springer. 2018;10:35\u201348.","journal-title":"Cognit Comput Springer"},{"key":"10075_CR64","doi-asserted-by":"crossref","unstructured":"Capano V, Herrmann HJ, De Arcangelis L. Optimal percentage of inhibitory synapses in multi-task learning. Nature Publishing Group.\u00a0Sci Rep.\u00a02015;5:9895.","DOI":"10.1038\/srep09895"},{"key":"10075_CR65","doi-asserted-by":"crossref","unstructured":"Sultan KT, Shi S. Generation of diverse cortical inhibitory interneurons. Wiley Online Library.\u00a0Wiley Interdiscip Rev Dev Biol.\u00a02018;7:e306.","DOI":"10.1002\/wdev.306"},{"key":"10075_CR66","unstructured":"LeCun Y, Denker JS, Solla SA. Optimal brain damage. Adv Neural Inf Process Syst. 1990;598\u2013605."},{"key":"10075_CR67","doi-asserted-by":"publisher","first-page":"354","DOI":"10.3758\/CABN.4.3.354","volume":"4","author":"R West","year":"2004","unstructured":"West R, Wymbs N. Is detecting prospective cues the same as selecting targets? An ERP study. Cogn Affect Behav Neurosci Springer. 2004;4:354\u201363.","journal-title":"Cogn Affect Behav Neurosci Springer"},{"key":"10075_CR68","doi-asserted-by":"crossref","unstructured":"Wahid A, Khan DM, Hussain I. Robust Adaptive Lasso method for parameter\u2019s estimation and variable selection in high-dimensional sparse models. PLoS One. Pub Lib Sci. 2017;12.","DOI":"10.1371\/journal.pone.0183518"},{"key":"10075_CR69","unstructured":"Kliegel M, Martin M, McDaniel MA, Einstein GO. Complex prospective memory and executive control of working memory: A process model. Psychol Test Assess Model. PABST Science Publishers. 2002;44:303."},{"key":"10075_CR70","doi-asserted-by":"crossref","unstructured":"Vijayakumari AA, Menon RN, Thomas B, Arun TM, Nandini M, Kesavadas C. Glutamatergic response to a low load working memory paradigm in the left dorsolateral prefrontal cortex in patients with mild cognitive impairment: a functional magnetic resonance spectroscopy study. Springer.\u00a0Brain Imaging Behav.\u00a02019;1\u20139.","DOI":"10.1007\/s11682-019-00122-7"},{"key":"10075_CR71","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1159\/000450993","volume":"42","author":"MK Yeung","year":"2016","unstructured":"Yeung MK, Sze SL, Woo J, Kwok T, Shum DHK, Yu R, et al. Reduced frontal activations at high working memory load in mild cognitive impairment: near-infrared spectroscopy. Dement Geriatr Cogn Disord Karger Publishers. 2016;42:278\u201396.","journal-title":"Dement Geriatr Cogn Disord Karger Publishers"},{"key":"10075_CR72","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1034\/j.1600-0447.2002.01417.x","volume":"106","author":"J Bischkopf","year":"2002","unstructured":"Bischkopf J, Busse A, Angermeyer MC. Mild cognitive impairment 1\u2013a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr Scand Wiley Online Library. 2002;106:403\u201314.","journal-title":"Acta Psychiatr Scand Wiley Online Library"},{"key":"10075_CR73","doi-asserted-by":"crossref","unstructured":"Burgess PW, Gilbert SJ, Dumontheil I. Function and localization within rostral prefrontal cortex (area 10). The Royal Society London.\u00a0Philos Trans R Soc B Biol Sci.\u00a02007;362:887\u201399.","DOI":"10.1098\/rstb.2007.2095"},{"key":"10075_CR74","doi-asserted-by":"crossref","unstructured":"Franzmeier N, G\u00f6ttler J, Grimmer T, Drzezga A, \u00c1raque-Caballero MA, Simon-Vermot L, et al. Resting-state connectivity of the left frontal cortex to the default mode and dorsal attention network supports reserve in mild cognitive impairment. Frontiers.\u00a0Front Aging Neurosci.\u00a02017;9:264.","DOI":"10.3389\/fnagi.2017.00264"},{"key":"10075_CR75","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1007\/s11682-018-9860-x","volume":"13","author":"K Yamashita","year":"2019","unstructured":"Yamashita K, Uehara T, Prawiroharjo P, Yamashita K, Togao O, Hiwatashi A, et al. Functional connectivity change between posterior cingulate cortex and ventral attention network relates to the impairment of orientation for time in Alzheimer\u2019s disease patients. Brain Imaging Behav Springer. 2019;13:154\u201361.","journal-title":"Brain Imaging Behav Springer"},{"key":"10075_CR76","doi-asserted-by":"crossref","unstructured":"Zhang L, Sun W, Xing M, Wang Y, Zhang Y, Sun Q, et al. Medial temporal lobe atrophy is related to learning strategy changes in amnestic mild cognitive impairment. Cambridge University Press.\u00a0J Int Neuropsychol Soc.\u00a02019;25:706\u201317.","DOI":"10.1017\/S1355617719000353"},{"key":"10075_CR77","doi-asserted-by":"crossref","unstructured":"Salami A, Pudas S, Nyberg L. Elevated hippocampal resting-state connectivity underlies deficient neurocognitive function in aging. National Acad Sciences.\u00a0Proc Natl Acad Sci.\u00a02014;111:17654\u20139.","DOI":"10.1073\/pnas.1410233111"},{"key":"10075_CR78","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1016\/j.neurobiolaging.2004.03.008","volume":"26","author":"T K\u00f6nig","year":"2005","unstructured":"K\u00f6nig T, Prichep L, Dierks T, Hubl D, Wahlund LO, John ER, et al. Decreased EEG synchronization in Alzheimer\u2019s disease and mild cognitive impairment. Neurobiol Aging Elsevier. 2005;26:165\u201371.","journal-title":"Neurobiol Aging Elsevier"},{"key":"10075_CR79","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Sanz D, Garc\u00e9s P, \u00c1lvarez B, Delgado-Losada ML, L\u00f3pez-Higes R, Maest\u00fa F. Network disruption in the preclinical stages of Alzheimer\u2019s disease: from subjective cognitive decline to mild cognitive impairment. World Scientific.\u00a0Int J Neural Syst.\u00a02017;27:1750041.","DOI":"10.1142\/S0129065717500411"},{"key":"10075_CR80","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijpsycho.2014.02.001","volume":"92","author":"B T\u00f3th","year":"2014","unstructured":"T\u00f3th B, File B, Boha R, Kardos Z, Hidasi Z, Ga\u00e1l ZA, et al. EEG network connectivity changes in mild cognitive impairment\u2014Preliminary results. Int J Psychophysiol Elsevier. 2014;92:1\u20137.","journal-title":"Int J Psychophysiol Elsevier"},{"key":"10075_CR81","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.neuroscience.2015.12.036","volume":"316","author":"F Vecchio","year":"2016","unstructured":"Vecchio F, Miraglia F, Quaranta D, Granata G, Romanello R, Marra C, et al. Cortical connectivity and memory performance in cognitive decline: a study via graph theory from EEG data. Neuroscience Elsevier. 2016;316:143\u201350.","journal-title":"Neuroscience Elsevier"},{"key":"10075_CR82","first-page":"183","volume":"22","author":"R Bajo","year":"2010","unstructured":"Bajo R, Maest\u00fa F, Nevado A, Sancho M, Guti\u00e9rrez R, Campo P, et al. Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis. J Alzheimer\u2019s Dis IOS Press. 2010;22:183\u201393.","journal-title":"J Alzheimer\u2019s Dis IOS Press"},{"key":"10075_CR83","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.neurobiolaging.2018.10.001","volume":"73","author":"MD Sullivan","year":"2019","unstructured":"Sullivan MD, Anderson JAE, Turner GR, Spreng RN, Initiative ADN. Intrinsic neurocognitive network connectivity differences between normal aging and mild cognitive impairment are associated with cognitive status and age. Neurobiol Aging Elsevier. 2019;73:219\u201328.","journal-title":"Neurobiol Aging Elsevier"},{"key":"10075_CR84","doi-asserted-by":"publisher","first-page":"592","DOI":"10.1016\/j.bandc.2008.12.007","volume":"69","author":"JA Van Deursen","year":"2009","unstructured":"Van Deursen JA, Vuurman E, Smits LL, Verhey FRJ, Riedel WJ. Response speed, contingent negative variation and P300 in Alzheimer\u2019s disease and MCI. Brain Cogn Elsevier. 2009;69:592\u20139.","journal-title":"Brain Cogn Elsevier"},{"key":"10075_CR85","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.ejrad.2010.12.044","volume":"81","author":"Z Wang","year":"2012","unstructured":"Wang Z, Jia X, Liang P, Qi Z, Yang Y, Zhou W, et al. Changes in thalamus connectivity in mild cognitive impairment: evidence from resting state fMRI. Eur J Radiol Elsevier. 2012;81:277\u201385.","journal-title":"Eur J Radiol Elsevier"},{"key":"10075_CR86","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.neurobiolaging.2016.04.018","volume":"45","author":"Y Zhang","year":"2016","unstructured":"Zhang Y, Simon-Vermot L, Caballero M\u00c1A, Gesierich B, Taylor ANW, Duering M, et al. Enhanced resting-state functional connectivity between core memory-task activation peaks is associated with memory impairment in MCI. Neurobiol Aging Elsevier. 2016;45:43\u20139.","journal-title":"Neurobiol Aging Elsevier"},{"key":"10075_CR87","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1631\/jzus.2006.B0357","volume":"7","author":"Z Jiang","year":"2006","unstructured":"Jiang Z, Zheng L. Inter-and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task. J Zhejiang Univ Sci B Springer. 2006;7:357\u201364.","journal-title":"J Zhejiang Univ Sci B Springer"},{"key":"10075_CR88","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1631\/jzus.2007.B0584","volume":"8","author":"L Zheng","year":"2007","unstructured":"Zheng L, Jiang Z, Yu E. Alpha spectral power and coherence in the patients with mild cognitive impairment during a three-level working memory task. J Zhejiang Univ Sci B Springer. 2007;8:584\u201392.","journal-title":"J Zhejiang Univ Sci B Springer"},{"key":"10075_CR89","doi-asserted-by":"crossref","unstructured":"Engels MMA, Stam CJ, van der Flier WM, Scheltens P, de Waal H, van Straaten ECW. Declining functional connectivity and changing hub locations in Alzheimer\u2019s disease: an EEG study. Springer.\u00a0BMC Neurol.\u00a02015;15:145.","DOI":"10.1186\/s12883-015-0400-7"}],"container-title":["Cognitive Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10075-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12559-022-10075-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12559-022-10075-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T11:17:54Z","timestamp":1691061474000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12559-022-10075-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,23]]},"references-count":89,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["10075"],"URL":"https:\/\/doi.org\/10.1007\/s12559-022-10075-7","relation":{},"ISSN":["1866-9956","1866-9964"],"issn-type":[{"value":"1866-9956","type":"print"},{"value":"1866-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,23]]},"assertion":[{"value":"31 March 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}