A systematic review and methodological analysis of EEG-based biomarkers of Alzheimer's disease

Crous-Bou, 2017, Alzheimer’s disease prevention: from risk factors to early intervention, Alzheimers Res. Ther., 9, 1, 10.1186/s13195-017-0297-z Matthews, 2019, Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015–2060) in adults aged≥ 65 years, Alzheimers Dement., 15, 17, 10.1016/j.jalz.2018.06.3063 Kaufmann, 2019, Common brain disorders are associated with heritable patterns of apparent aging of the brain, Nat. Neurosci., 22, 1617, 10.1038/s41593-019-0471-7 Lane, 2018, Alzheimer's disease, Eur. J. Neurol., 10.1111/ene.13439 Dubois, 2016, Preclinical Alzheimer's disease: definition, natural history, and diagnostic criteria, Alzheimers Dement., 12, 292, 10.1016/j.jalz.2016.02.002 Association, A.s. ‘2019 Alzheimer's disease facts and figures’, Alzheimer's & dementia, 2019, 15, (3), pp. 321-387. ‘2021 Alzheimer's disease facts and figures’, Alzheimer's & Dementia, 2021, 17, (3), pp. 327-406. Nordlund, 2005, The Goteborg MCI study: mild cognitive impairment is a heterogeneous condition, J. Neurol. Neurosurg. Psychiatry, 76, 1485, 10.1136/jnnp.2004.050385 Sabbagh, 2020, Early detection of mild cognitive impairment (MCI) in primary care, J. Prev. Alzheimers Dis., 7, 165 ‘2023 Alzheimer's disease facts and figures’, Alzheimer's & Dementia, 2023, 19, (4), pp. 1598-1695. Csukly, 2016, The differentiation of amnestic type MCI from the non-amnestic types by structural MRI, Front. Aging. Neurosci., 8, 52, 10.3389/fnagi.2016.00052 Vyas, 2020, Hippocampal deficits in amyloid-β-related rodent models of Alzheimer’s disease, Front. Neurosci., 14, 266, 10.3389/fnins.2020.00266 Seaman, 2020, “Like He’sa Kid”: relationality, family caregiving, and alzheimer’s disease, Med. Anthropol., 39, 29, 10.1080/01459740.2019.1667344 Monica Moore, M., Díaz-Santos, M., and Vossel, K.: ‘Alzheimer’s Association 2021 Facts and Figures Report’. Atri, 2019, The Alzheimer’s disease clinical spectrum: diagnosis and management, Med. Clinics, 103, 263 Mohankumar, 2021, Recent developments in biosensors for healthcare and biomedical applications: a review, Measurement, 167, 10.1016/j.measurement.2020.108293 Ashrafian, 2021, Review on Alzheimer's disease: inhibition of amyloid beta and tau tangle formation, Int. J. Biol. Macromol., 167, 382, 10.1016/j.ijbiomac.2020.11.192 DeTure, 2019, The neuropathological diagnosis of Alzheimer’s disease, Mol. Neurodegener., 14, 1, 10.1186/s13024-019-0333-5 Harrington, 2012, The molecular pathology of Alzheimer's disease, Neuroimaging Clin., 22, 11, 10.1016/j.nic.2011.11.003 Hebert, 2013, Alzheimer disease in the United States (2010–2050) estimated using the 2010 census, Neurology, 80, 1778, 10.1212/WNL.0b013e31828726f5 Agrawal, 2018, Nose-to-brain drug delivery: An update on clinical challenges and progress towards approval of anti-Alzheimer drugs, J. Control. Release, 281, 139, 10.1016/j.jconrel.2018.05.011 Salloway, 2021, Amyloid-related imaging abnormalities in 2 phase 3 studies evaluating aducanumab in patients with early Alzheimer disease, JAMA Neurol. Naz, 2022, Transfer learning using freeze features for Alzheimer neurological disorder detection using ADNI dataset, Multimedia Syst., 28, 85, 10.1007/s00530-021-00797-3 Lopez-Martin, 2020, Detection of early stages of Alzheimer’s disease based on MEG activity with a randomized convolutional neural network, Artif. Intell. Med., 107, 10.1016/j.artmed.2020.101924 Mokhber, 2021, Cerebral blood flow changes during aging process and in cognitive disorders: a review, Neuroradiol. J., 34, 300, 10.1177/19714009211002778 Jia, 2022, Detection of plasma Aβ seeding activity by a newly developed analyzer for diagnosis of Alzheimer’s disease, Alzheimers Res. Ther., 14, 1, 10.1186/s13195-022-00964-2 Chiarelli, 2021, Evidence of neurovascular un-coupling in mild Alzheimer’s disease through multimodal EEG-fNIRS and multivariate analysis of resting-state data, Biomedicines, 9, 337, 10.3390/biomedicines9040337 Ingram, 2022, Spatial covariance analysis of FDG-PET and HMPAO-SPECT for the differential diagnosis of dementia with Lewy bodies and Alzheimer's disease, Psychiatry Res. Neuroimaging, 322, 10.1016/j.pscychresns.2022.111460 Diekämper, 2021, Neurofilament levels are reflecting the loss of presynaptic dopamine receptors in movement disorders, Front. Neurosci., 15, 10.3389/fnins.2021.690013 Han, H., Li, X., Gan, J.Q., Yu, H., Wang, H., and Initiative, A.s.D.N.: ‘Biomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer’s Disease’, Neuroscience, 2022, 484, pp. 38-52. Yang, 2021, Quantitative assessment of resting-state for mild cognitive impairment detection: a functional near-infrared spectroscopy and deep learning approach, J. Alzheimers Dis., 80, 647, 10.3233/JAD-201163 Plant, 2010, Automated detection of brain atrophy patterns based on MRI for the prediction of Alzheimer's disease, Neuroimage, 50, 162, 10.1016/j.neuroimage.2009.11.046 Kido, 1989, Temporal lobe atrophy in patients with Alzheimer disease: a CT study, Am. J. Neuroradiol., 10, 551 Sperling, 2013, Preclinical Alzheimer disease—the challenges ahead, Nat. Rev. Neurol., 9, 54, 10.1038/nrneurol.2012.241 Chan, 2021, Diagnostic performance of digital cognitive tests for the identification of MCI and dementia: a systematic review, Ageing Res. Rev., 72, 10.1016/j.arr.2021.101506 Aihara, 2020, Resting-state functional connectivity estimated with hierarchical bayesian diffuse optical tomography, Front. Neurosci., 14, 32, 10.3389/fnins.2020.00032 Gossé, 2021, Functional near-infrared spectroscopy in developmental psychiatry: a review of attention deficit hyperactivity disorder, Eur. Arch. Psychiatry Clin. Neurosci., 1 Mattsson, 2011, Radiation dose management in CT, SPECT/CT and PET/CT techniques, Radiat. Prot. Dosim., 147, 13, 10.1093/rpd/ncr261 Gloebel, B., Andres, C., and Lehnen, H.: ‘Radiation exposition by nuclear medicine’: ‘Radioactive isotopes in clinic and research’ (1984). Bennett, 2006, Neuropathology of older persons without cognitive impairment from two community-based studies, Neurology, 66, 1837, 10.1212/01.wnl.0000219668.47116.e6 Amezquita-Sanchez, 2021, A new dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms, Clin. Neurol. Neurosurg., 201, 10.1016/j.clineuro.2020.106446 Ando, 2021, Identification of Electroencephalogram signals in Alzheimer's disease by multifractal and multiscale entropy analysis, Front. Neurosci., 772 Cai, 2020, Functional integration and segregation in multiplex brain networks for Alzheimer's disease, Front. Neurosci., 14, 51, 10.3389/fnins.2020.00051 Del Val, 2016, Atrophy of amygdala and abnormal memory-related alpha oscillations over posterior cingulate predict conversion to Alzheimer’s disease, Sci. Rep., 6, 1 Tait, 2020, EEG microstate complexity for aiding early diagnosis of Alzheimer’s disease, Sci. Rep., 10, 1, 10.1038/s41598-020-74790-7 Khosla, 2020, A comparative analysis of signal processing and classification methods for different applications based on EEG signals, Biocybernetics and Biomed. Eng., 40, 649, 10.1016/j.bbe.2020.02.002 Burle, 2015, Spatial and temporal resolutions of EEG: Is it really black and white? A scalp current density view, Int. J. Psychophysiol., 97, 210, 10.1016/j.ijpsycho.2015.05.004 Lee, 2018, Dry electrode-based fully isolated EEG/fNIRS hybrid brain-monitoring system, IEEE Trans. Biomed. Eng., 66, 1055, 10.1109/TBME.2018.2866550 Cecchetti, 2021, Resting-state electroencephalographic biomarkers of Alzheimer’s disease, NeuroImage: Clin., 31 De Jesus Junior, 2021, Multimodal prediction of Alzheimer's disease severity level based on resting-state EEG and structural MRI, Front. Hum. Neurosci., 495 Farina, 2020, A comparison of resting state EEG and structural MRI for classifying Alzheimer’s disease and mild cognitive impairment, Neuroimage, 215, 10.1016/j.neuroimage.2020.116795 Khatun, 2019, A single-channel EEG-based approach to detect mild cognitive impairment via speech-evoked brain responses, IEEE Trans. Neural Syst. Rehabil. Eng., 27, 1063, 10.1109/TNSRE.2019.2911970 Bruña, 2018, Phase locking value revisited: teaching new tricks to an old dog, J. Neural Eng., 15, 10.1088/1741-2552/aacfe4 Morabito, F.C., Campolo, M., Ieracitano, C., Ebadi, J.M., Bonanno, L., Bramanti, A., Desalvo, S., Mammone, N., and Bramanti, P.: ‘Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings’, in Editor (Ed.)^(Eds.): ‘Book Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings’ (IEEE, 2016, edn.), pp. 1-6. Heron, M.P.: ‘Deaths: leading causes for 2017’, 2019. Huang, 2020, Clinical trials of new drugs for Alzheimer disease, J. Biomed. Sci., 27, 1, 10.1186/s12929-019-0609-7 Liberati, 2009, The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration, J. Clin. Epidemiol., 62, e1, 10.1016/j.jclinepi.2009.06.006 Hussen, 2021, Combined markers for predicting cognitive deficit in patients with Alzheimer’s disease, Egyptian J. Med. Human Genetics, 22, 1, 10.1186/s43042-021-00184-7 Jiang, 2019, A novel detection tool for mild cognitive impairment patients based on eye movement and electroencephalogram, J. Alzheimers Dis., 72, 389, 10.3233/JAD-190628 Fiscon, 2018, Combining EEG signal processing with supervised methods for Alzheimer’s patients classification, BMC Med. Inf. Decis. Making, 18, 1 Chiang, 2016, An EEG-based fuzzy probability model for early diagnosis of Alzheimer’s disease, J. Med. Syst., 40, 1, 10.1007/s10916-016-0476-7 McBride, J.C., Zhao, X., Munro, N.B., Jicha, G.A., Schmitt, F.A., Kryscio, R.J., Smith, C.D., and Jiang, Y.: ‘Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease’, NeuroImage: Clinical, 2015, 7, pp. 258-265. Zhang, 2021, The significance of EEG alpha oscillation spectral power and beta oscillation phase synchronization for diagnosing probable alzheimer disease, Front. Aging Neurosci., 13, 291 Moghadami, 2021, The investigation of simultaneous eeg and eye tracking characteristics during fixation task in mild alzheimer’s disease, Clin. EEG Neurosci., 52, 211, 10.1177/1550059420932752 Gaubert, 2021, A machine learning approach to screen for preclinical Alzheimer's disease, Neurobiol. Aging, 105, 205, 10.1016/j.neurobiolaging.2021.04.024 Ferri, 2021, Stacked autoencoders as new models for an accurate Alzheimer’s disease classification support using resting-state EEG and MRI measurements, Clin. Neurophysiol., 132, 232, 10.1016/j.clinph.2020.09.015 You, 2020, ‘Alzheimer's disease classification with a cascade neural network’, frontiers, Public Health, 665 Cicalese, 2020, An EEG-fNIRS hybridization technique in the four-class classification of alzheimer’s disease, J. Neurosci. Methods, 336, 10.1016/j.jneumeth.2020.108618 Miao, 2021, Dynamic theta/beta ratio of clinical EEG in Alzheimer's disease, J. Neurosci. Methods, 359, 10.1016/j.jneumeth.2021.109219 Trinh, 2021, Identifying individuals with mild cognitive impairment using working memory-induced intra-subject variability of resting-State EEGs, Front. Comput. Neurosci., 15, 10.3389/fncom.2021.700467 Buvaneswari, 2019, High performance hybrid cognitive framework for bio-facial signal fusion processing for the disease diagnosis, Measurement, 140, 89, 10.1016/j.measurement.2019.02.041 Doan, 2021, Predicting dementia with prefrontal electroencephalography and event-related potential, Front. Aging Neurosci., 13, 180, 10.3389/fnagi.2021.659817 Sedghizadeh, 2020, Olfactory response as a marker for Alzheimer’s disease: evidence from perceptual and frontal lobe oscillation coherence deficit, PLoS One, 15, e0243535, 10.1371/journal.pone.0243535 Rad, 2021, Diagnosis of mild Alzheimer's disease by EEG and ERP signals using linear and nonlinear classifiers, Biomed. Signal Process. Control, 70 Morabito, F.C., Ieracitano, C., and Mammone, N.: ‘An explainable Artificial Intelligence approach to study MCI to AD conversion via HD-EEG processing’, Clinical EEG and Neuroscience, 2021, pp. 15500594211063662. Dattola, 2021, Testing graph robustness indexes for EEG analysis in alzheimer’s disease diagnosis, Electronics, 10, 1440, 10.3390/electronics10121440 Hsiao, 2021, EEG-based classification between individuals with mild cognitive impairment and healthy controls using conformal kernel-based fuzzy support vector machine, Int. J. Fuzzy Syst., 23, 2432, 10.1007/s40815-021-01186-8 Zhang, 2021, ‘Classification of cognitive impairment and healthy controls based on transcranial magnetic stimulation evoked potentials’, frontiers in aging, Neuroscience, 13 San-Martin, 2021, A method for diagnosis support of mild cognitive impairment through EEG rhythms source location during working memory tasks, Biomed. Signal Process. Control, 66, 10.1016/j.bspc.2021.102499 Tülay, 2020, Evoked and induced EEG oscillations to visual targets reveal a differential pattern of change along the spectrum of cognitive decline in Alzheimer's Disease, Int. J. Psychophysiol., 155, 41, 10.1016/j.ijpsycho.2020.06.001 Abazid, 2021, A comparative study of functional connectivity measures for brain network analysis in the context of AD detection with EEG, Entropy, 23, 1553, 10.3390/e23111553 Oltu, 2021, A novel electroencephalography based approach for Alzheimer’s disease and mild cognitive impairment detection, Biomed. Signal Process. Control, 63, 10.1016/j.bspc.2020.102223 Meghdadi, 2021, Resting state EEG biomarkers of cognitive decline associated with Alzheimer’s disease and mild cognitive impairment, PLoS One, 16, e0244180, 10.1371/journal.pone.0244180 Al-Nuaimi, 2021, Robust EEG-based biomarkers to detect alzheimer’s disease, Brain Sci., 11, 1026, 10.3390/brainsci11081026 Miltiadous, 2021, Alzheimer’s disease and frontotemporal dementia: a robust classification method of EEG signals and a comparison of validation methods, Diagnostics, 11, 1437, 10.3390/diagnostics11081437 Yu, 2020, Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach, Front. Neurosci., 14, 641, 10.3389/fnins.2020.00641 Li, 2021, Feature extraction and identification of Alzheimer’s disease based on latent factor of multi-channel EEG, IEEE Trans. Neural Syst. Rehabil. Eng., 29, 1557, 10.1109/TNSRE.2021.3101240 Ge, Q., Lin, Z.-C., Gao, Y.-X., and Zhang, J.-X.: ‘A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer’s Disease’, in Editor (Ed.)^(Eds.): ‘Book A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer’s Disease’ (Multidisciplinary Digital Publishing Institute, 2020, edn.), pp. 476. Puri, D., Nalbalwar, S., Nandgaonkar, A., and Wagh, A.: ‘EEG-based diagnosis of alzheimer's disease using kolmogorov complexity’, in Editor (Ed.)^(Eds.): ‘Book EEG-based diagnosis of alzheimer's disease using kolmogorov complexity’ (Springer, 2022, edn.), pp. 157-165. Al-Nuaimi, 2018, Complexity measures for quantifying changes in electroencephalogram in Alzheimer’s disease, Complexity, 10.1155/2018/8915079 Safi, 2021, Early detection of Alzheimer’s disease from EEG signals using Hjorth parameters, Biomed. Signal Process. Control, 65, 10.1016/j.bspc.2020.102338 Li, 2021, The diagnosis of amnestic mild cognitive impairment by combining the characteristics of brain functional network and support vector machine classifier, J. Neurosci. Methods, 363, 10.1016/j.jneumeth.2021.109334 Siuly, 2020, A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 1966, 10.1109/TNSRE.2020.3013429 Das, 2020, Complex network analysis of MCI-AD EEG signals under cognitive and resting state, Brain Res., 1735, 10.1016/j.brainres.2020.146743 Huggins, 2021, Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer’s disease, mild cognitive impairment and healthy ageing, J. Neural Eng., 18, 10.1088/1741-2552/ac05d8 Santos Toural, 2021, Classification among healthy, mild cognitive impairment and Alzheimer’s disease subjects based on wavelet entropy and relative beta and theta power, Pattern Anal. Appl., 24, 413, 10.1007/s10044-020-00910-8 Toural, 2021, A new method for classification of subjects with major cognitive disorder, Inf. Med. Unlocked, 23 Kim, 2021, Machine learning to predict brain amyloid pathology in pre-dementia alzheimer’s disease using QEEG features and genetic algorithm heuristic, Front. Comput. Neurosci., 15, 10.3389/fncom.2021.755499 Cejnek, 2021, Novelty detection-based approach for Alzheimer’s disease and mild cognitive impairment diagnosis from EEG, Med. Biol. Eng. Compu., 59, 2287, 10.1007/s11517-021-02427-6 Cassani, 2019, Alzheimer's disease diagnosis and severity level detection based on electroencephalography modulation spectral “patch” features, IEEE J. Biomed. Health Inform., 24, 1982 Tzimourta, K.D., Giannakeas, N., Tzallas, A.T., Astrakas, L.G., Afrantou, T., Ioannidis, P., Grigoriadis, N., Angelidis, P., Tsalikakis, D.G., and Tsipouras, M.G.: ‘EEG window length evaluation for the detection of Alzheimer’s disease over different brain regions’, Brain sciences, 2019, 9, (4), pp. 81. Rodrigues, 2021, Lacsogram: a new EEG tool to diagnose alzheimer's disease, IEEE J. Biomed. Health Inform., 25, 3384, 10.1109/JBHI.2021.3069789 Jiang, 2021, Memory-Related frontal brainwaves predict transition to mild cognitive impairment in healthy older individuals five years before diagnosis, J. Alzheimers Dis., 79, 531, 10.3233/JAD-200931 Şeker, 2021, Complexity of EEG dynamics for early diagnosis of alzheimer's disease using permutation entropy neuromarker, Comput. Methods Programs Biomed., 206, 10.1016/j.cmpb.2021.106116 Núñez, 2021, Abnormal meta-state activation of dynamic brain networks across the Alzheimer spectrum, Neuroimage, 232, 10.1016/j.neuroimage.2021.117898 Fröhlich, 2021, Characteristics of resting state EEG power in 80+-year-olds of different cognitive status, Front. Aging Neurosci., 469 AlSharabi, 2022, EEG signal processing for Alzheimer’s disorders using discrete wavelet transform and machine learning approaches, IEEE Access, 10, 89781, 10.1109/ACCESS.2022.3198988 Jutten, 1991, Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture, Signal Process., 24, 1, 10.1016/0165-1684(91)90079-X Delorme, 2007, Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis, Neuroimage, 34, 1443, 10.1016/j.neuroimage.2006.11.004 Rudin, 1992, Nonlinear total variation based noise removal algorithms, Physica D, 60, 259, 10.1016/0167-2789(92)90242-F Pascual-Marqui, 2002, Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details, Methods Find Exp Clin Pharmacol, 24, 5 He, 2011, eConnectome: A MATLAB toolbox for mapping and imaging of brain functional connectivity, J. Neurosci. Methods, 195, 261, 10.1016/j.jneumeth.2010.11.015 Jatoi, 2014, EEG based brain source localization comparison of sLORETA and eLORETA, Australas. Phys. Eng. Sci. Med., 37, 713, 10.1007/s13246-014-0308-3 Amini, 2021, Diagnosis of Alzheimer’s disease by time-dependent power spectrum descriptors and convolutional neural network using EEG Signal, Comput. Math. Methods Med., 2021, 10.1155/2021/5511922 Wu, 2020, Detecting Alzheimer’s dementia degree, IEEE Trans. Cognitive and Developmental Syst., 14, 116, 10.1109/TCDS.2020.3015131 Tewarie, 2019, Tracking dynamic brain networks using high temporal resolution MEG measures of functional connectivity, Neuroimage, 200, 38, 10.1016/j.neuroimage.2019.06.006 Barnes, 1983, Graph theory in network analysis, Soc. Networks, 5, 235, 10.1016/0378-8733(83)90026-6 Bishop, 2006 Chandrashekar, 2014, A survey on feature selection methods, Comput. Electr. Eng., 40, 16, 10.1016/j.compeleceng.2013.11.024 Chen, 2020, Ensemble feature selection in medical datasets: combining filter, wrapper, and embedded feature selection results, Expert. Syst., 37, e12553, 10.1111/exsy.12553 Rodriguez-Galiano, 2018, Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods, Sci. Total Environ., 624, 661, 10.1016/j.scitotenv.2017.12.152 Wainer, 2021, Nested cross-validation when selecting classifiers is overzealous for most practical applications, Expert Syst. Appl., 182, 10.1016/j.eswa.2021.115222 Abdi, 2010, Principal component analysis, Wiley Interdiscip. Rev. Comput. Stat., 2, 433, 10.1002/wics.101 Chen, 2021, Fault diagnosis of rolling bearing using marine predators algorithm-based support vector machine and topology learning and out-of-sample embedding, Measurement, 176, 10.1016/j.measurement.2021.109116 Goodfellow, 2016 Cui, 2011, A quantitative comparison of NIRS and fMRI across multiple cognitive tasks, Neuroimage, 54, 2808, 10.1016/j.neuroimage.2010.10.069 Knyazev, 2015, Age-related differences in electroencephalogram connectivity and network topology, Neurobiol. Aging, 36, 1849, 10.1016/j.neurobiolaging.2015.02.007 Dubois, 2007, Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS–ADRDA criteria, The Lancet Neurology, 6, 734, 10.1016/S1474-4422(07)70178-3 Nicholas, P.J., To, A., Tanglay, O., Young, I.M., Sughrue, M.E., and Doyen, S.: ‘Using a ResNet-18 Network to Detect Features of Alzheimer’s Disease on Functional Magnetic Resonance Imaging: A Failed Replication. Comment on Odusami et al. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics 2021, 11, 1071’, Diagnostics, 2022, 12, (5), pp. 1094.