Skip to main content
Erschienen in:

12.11.2022 | Research Article

Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression

verfasst von: Afshin Shoeibi, Navid Ghassemi, Marjane Khodatars, Parisa Moridian, Abbas Khosravi, Assef Zare, Juan M. Gorriz, Amir Hossein Chale-Chale, Ali Khadem, U. Rajendra Acharya

Erschienen in: Cognitive Neurodynamics | Ausgabe 6/2023

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Nowadays, many people worldwide suffer from brain disorders, and their health is in danger. So far, numerous methods have been proposed for the diagnosis of Schizophrenia (SZ) and attention deficit hyperactivity disorder (ADHD), among which functional magnetic resonance imaging (fMRI) modalities are known as a popular method among physicians. This paper presents an SZ and ADHD intelligent detection method of resting-state fMRI (rs-fMRI) modality using a new deep learning method. The University of California Los Angeles dataset, which contains the rs-fMRI modalities of SZ and ADHD patients, has been used for experiments. The FMRIB software library toolbox first performed preprocessing on rs-fMRI data. Then, a convolutional Autoencoder model with the proposed number of layers is used to extract features from rs-fMRI data. In the classification step, a new fuzzy method called interval type-2 fuzzy regression (IT2FR) is introduced and then optimized by genetic algorithm, particle swarm optimization, and gray wolf optimization (GWO) techniques. Also, the results of IT2FR methods are compared with multilayer perceptron, k-nearest neighbors, support vector machine, random forest, and decision tree, and adaptive neuro-fuzzy inference system methods. The experiment results show that the IT2FR method with the GWO optimization algorithm has achieved satisfactory results compared to other classifier methods. Finally, the proposed classification technique was able to provide 72.71% accuracy.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
Zurück zum Zitat Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S (2021) ANFIS-Net for automatic detection of COVID-19. Sci Rep 11(1):1–13CrossRef Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S (2021) ANFIS-Net for automatic detection of COVID-19. Sci Rep 11(1):1–13CrossRef
Zurück zum Zitat Alizadehsani R, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Gorriz JM, Acharya UR (2021) Uncertainty-Aware Semi-supervised method using large unlabeled and limited labeled COVID-19 Data. ACM Trans Multimed Comput Commun Appl (TOMM) 17(3):1–24 Alizadehsani R, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Gorriz JM, Acharya UR (2021) Uncertainty-Aware Semi-supervised method using large unlabeled and limited labeled COVID-19 Data. ACM Trans Multimed Comput Commun Appl (TOMM) 17(3):1–24
Zurück zum Zitat Anter AM, Abd Elaziz M, Zhang Z (2022) Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning. Futur Gener Comput Syst 127:426–434CrossRef Anter AM, Abd Elaziz M, Zhang Z (2022) Real-time epileptic seizure recognition using Bayesian genetic whale optimizer and adaptive machine learning. Futur Gener Comput Syst 127:426–434CrossRef
Zurück zum Zitat Aoe J, Fukuma R, Yanagisawa T, Harada T, Tanaka M, Kobayashi M, Kishima H (2019) Automatic diagnosis of neurological diseases using MEG signals with a deep neural network. Sci Rep 9(1):1–9CrossRef Aoe J, Fukuma R, Yanagisawa T, Harada T, Tanaka M, Kobayashi M, Kishima H (2019) Automatic diagnosis of neurological diseases using MEG signals with a deep neural network. Sci Rep 9(1):1–9CrossRef
Zurück zum Zitat Appaji A, Harish V, Korann V, Devi P, Jacob A, Padmanabha A, Rao NP (2022) Deep learning model using retinal vascular images for classifying schizophrenia. Schizophr Res 241:238–243PubMedCrossRef Appaji A, Harish V, Korann V, Devi P, Jacob A, Padmanabha A, Rao NP (2022) Deep learning model using retinal vascular images for classifying schizophrenia. Schizophr Res 241:238–243PubMedCrossRef
Zurück zum Zitat Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA, Fallani FDV, Babiloni F (2006) Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data. IEEE Trans Biomed Eng 53(9):1802–1812PubMedCrossRef Astolfi L, Cincotti F, Mattia D, Marciani MG, Baccala LA, Fallani FDV, Babiloni F (2006) Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data. IEEE Trans Biomed Eng 53(9):1802–1812PubMedCrossRef
Zurück zum Zitat Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Mosavi A (2021) Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 27:104495PubMedPubMedCentralCrossRef Ayoobi N, Sharifrazi D, Alizadehsani R, Shoeibi A, Gorriz JM, Moosaei H, Mosavi A (2021) Time series forecasting of new cases and new deaths rate for COVID-19 using deep learning methods. Results Phys 27:104495PubMedPubMedCentralCrossRef
Zurück zum Zitat Bajestani NS, Kamyad AV, Esfahani EN, Zare A (2017) Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model. Biocybern Biomed Eng 37(2):281–289CrossRef Bajestani NS, Kamyad AV, Esfahani EN, Zare A (2017) Nephropathy forecasting in diabetic patients using a GA-based type-2 fuzzy regression model. Biocybern Biomed Eng 37(2):281–289CrossRef
Zurück zum Zitat Bastos AM, Schoffelen JM (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 9:175PubMedPubMedCentralCrossRef Bastos AM, Schoffelen JM (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 9:175PubMedPubMedCentralCrossRef
Zurück zum Zitat Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31CrossRef Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31CrossRef
Zurück zum Zitat Bitsch F, Berger P, Fink A, Nagels A, Straube B, Falkenberg I (2021) Antagonism between brain regions relevant for cognitive control and emotional memory facilitates the generation of humorous ideas. Sci Rep 11(1):1–12 Bitsch F, Berger P, Fink A, Nagels A, Straube B, Falkenberg I (2021) Antagonism between brain regions relevant for cognitive control and emotional memory facilitates the generation of humorous ideas. Sci Rep 11(1):1–12
Zurück zum Zitat Bracha HS (2006) Human brain evolution and the “Neuroevolutionary Time-depth Principle:” Implications for the Reclassification of fear-circuitry-related traits in DSM-V and for studying resilience to warzone-related posttraumatic stress disorder. Prog Neuropsychopharmacol Biol Psychiatr 30(5):827–853CrossRef Bracha HS (2006) Human brain evolution and the “Neuroevolutionary Time-depth Principle:” Implications for the Reclassification of fear-circuitry-related traits in DSM-V and for studying resilience to warzone-related posttraumatic stress disorder. Prog Neuropsychopharmacol Biol Psychiatr 30(5):827–853CrossRef
Zurück zum Zitat Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ (2009) Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 33(3):279–296PubMedCrossRef Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga-Barke EJ (2009) Default-mode brain dysfunction in mental disorders: a systematic review. Neurosci Biobehav Rev 33(3):279–296PubMedCrossRef
Zurück zum Zitat Buchlak QD, Milne MR, Seah J, Johnson A, Samarasinghe G, Hachey B, Brotchie P (2022) Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 99:217–223PubMedCrossRef Buchlak QD, Milne MR, Seah J, Johnson A, Samarasinghe G, Hachey B, Brotchie P (2022) Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 99:217–223PubMedCrossRef
Zurück zum Zitat Cai XL, Xie DJ, Madsen KH, Wang YM, Bögemann SA, Cheung EF, Chan RC (2020) Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data. Hum Brain Mapp 41(1):172–184PubMedCrossRef Cai XL, Xie DJ, Madsen KH, Wang YM, Bögemann SA, Cheung EF, Chan RC (2020) Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data. Hum Brain Mapp 41(1):172–184PubMedCrossRef
Zurück zum Zitat Castillo-Barnes D, Su L, Ramírez J, Salas-Gonzalez D, Martinez-Murcia FJ, Illan IA, Network DIA (2020) Autosomal dominantly inherited alzheimer disease: analysis of genetic subgroups by machine learning. Inform Fusion 58:153–167CrossRef Castillo-Barnes D, Su L, Ramírez J, Salas-Gonzalez D, Martinez-Murcia FJ, Illan IA, Network DIA (2020) Autosomal dominantly inherited alzheimer disease: analysis of genetic subgroups by machine learning. Inform Fusion 58:153–167CrossRef
Zurück zum Zitat Castle L, Aubert RE, Verbrugge RR, Khalid M, Epstein RS (2007) Trends in medication treatment for ADHD. J Atten Disord 10(4):335–342PubMedCrossRef Castle L, Aubert RE, Verbrugge RR, Khalid M, Epstein RS (2007) Trends in medication treatment for ADHD. J Atten Disord 10(4):335–342PubMedCrossRef
Zurück zum Zitat Chen J, Patil KR, Weis S, Sim K, Nickl-Jockschat T, Zhou J, Visser E (2020) Neurobiological divergence of the positive and negative schizophrenia subtypes identified on a new factor structure of psychopathology using non-negative factorization: an international machine learning study. Biol Psychiat 87(3):282–293PubMedCrossRef Chen J, Patil KR, Weis S, Sim K, Nickl-Jockschat T, Zhou J, Visser E (2020) Neurobiological divergence of the positive and negative schizophrenia subtypes identified on a new factor structure of psychopathology using non-negative factorization: an international machine learning study. Biol Psychiat 87(3):282–293PubMedCrossRef
Zurück zum Zitat Choi H, Ha S, Kang H, Lee H, Lee DS, Initiative ADN (2019) Deep learning only by normal brain PET identify unheralded brain anomalies. EBioMedicine 43:447–453PubMedPubMedCentralCrossRef Choi H, Ha S, Kang H, Lee H, Lee DS, Initiative ADN (2019) Deep learning only by normal brain PET identify unheralded brain anomalies. EBioMedicine 43:447–453PubMedPubMedCentralCrossRef
Zurück zum Zitat Cisler JM, Bush K, Steele JS (2014) A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI. Neuroimage 84:1042–1052PubMedCrossRef Cisler JM, Bush K, Steele JS (2014) A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI. Neuroimage 84:1042–1052PubMedCrossRef
Zurück zum Zitat Coupland S, John R (2007) Geometric type-1 and type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 15(1):3–15CrossRef Coupland S, John R (2007) Geometric type-1 and type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 15(1):3–15CrossRef
Zurück zum Zitat Culbreth AJ, Wu Q, Chen S, Adhikari BM, Hong LE, Gold JM, Waltz JA (2021) Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia. NeuroImage Clin 29:102531PubMedCrossRef Culbreth AJ, Wu Q, Chen S, Adhikari BM, Hong LE, Gold JM, Waltz JA (2021) Temporal-thalamic and cingulo-opercular connectivity in people with schizophrenia. NeuroImage Clin 29:102531PubMedCrossRef
Zurück zum Zitat Dalsgaard S, Mortensen PB, Frydenberg M, Maibing CM, Nordentoft M, Thomsen PH (2014) Association between attention-deficit hyperactivity disorder in childhood and schizophrenia later in adulthood. Eur Psychiatry 29(4):259–263PubMedCrossRef Dalsgaard S, Mortensen PB, Frydenberg M, Maibing CM, Nordentoft M, Thomsen PH (2014) Association between attention-deficit hyperactivity disorder in childhood and schizophrenia later in adulthood. Eur Psychiatry 29(4):259–263PubMedCrossRef
Zurück zum Zitat De Oca MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13(5):1120–1132CrossRef De Oca MAM, Stutzle T, Birattari M, Dorigo M (2009) Frankenstein’s PSO: a composite particle swarm optimization algorithm. IEEE Trans Evol Comput 13(5):1120–1132CrossRef
Zurück zum Zitat de Pierrefeu A, Löfstedt T, Laidi C, Hadj-Selem F, Leboyer M, Ciuciu P, Duchesnay E (2018) Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. In: 2018 international workshop on pattern recognition in neuroimaging (PRNI) (pp. 1–4). IEEE de Pierrefeu A, Löfstedt T, Laidi C, Hadj-Selem F, Leboyer M, Ciuciu P, Duchesnay E (2018) Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. In: 2018 international workshop on pattern recognition in neuroimaging (PRNI) (pp. 1–4). IEEE
Zurück zum Zitat de Moura AM, Pinaya WHL, Gadelha A, Zugman A, Noto C, Cordeiro Q, Sato JR (2018) Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatr Res Neuroimaging 275:14–20CrossRef de Moura AM, Pinaya WHL, Gadelha A, Zugman A, Noto C, Cordeiro Q, Sato JR (2018) Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach. Psychiatr Res Neuroimaging 275:14–20CrossRef
Zurück zum Zitat Dillon K, Wang YP (2016) An image resolution perspective on functional activity mapping. In: 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 1139–1142). IEEE Dillon K, Wang YP (2016) An image resolution perspective on functional activity mapping. In: 2016 38th Annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 1139–1142). IEEE
Zurück zum Zitat Do Austerman J (2015) ADHD and behavioral disorders: Assessment, management, and an update from DSM-5. Cleveland Clin J Med 82:S3CrossRef Do Austerman J (2015) ADHD and behavioral disorders: Assessment, management, and an update from DSM-5. Cleveland Clin J Med 82:S3CrossRef
Zurück zum Zitat Dou C, Zhang S, Wang H, Sun L, Huang Y, Yue W (2020) ADHD fMRI short-time analysis method for edge computing based on multi-instance learning. J Syst Architect 111:101834CrossRef Dou C, Zhang S, Wang H, Sun L, Huang Y, Yue W (2020) ADHD fMRI short-time analysis method for edge computing based on multi-instance learning. J Syst Architect 111:101834CrossRef
Zurück zum Zitat Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F (2019) ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform 13:70PubMedPubMedCentralCrossRef Eslami T, Mirjalili V, Fong A, Laird AR, Saeed F (2019) ASD-DiagNet: a hybrid learning approach for detection of autism spectrum disorder using fMRI data. Front Neuroinform 13:70PubMedPubMedCentralCrossRef
Zurück zum Zitat Farzi S, Kianian S, Rastkhadive I (2017) Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach. In: 2017 5th International symposium on computational and business intelligence (ISCBI) (pp. 96–99). IEEE Farzi S, Kianian S, Rastkhadive I (2017) Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach. In: 2017 5th International symposium on computational and business intelligence (ISCBI) (pp. 96–99). IEEE
Zurück zum Zitat Feng W, Liu G, Zeng K, Zeng M, Liu Y (2021) A review of methods for classification and recognition of ASD using fMRI data. J Neurosci Methods, 109456 Feng W, Liu G, Zeng K, Zeng M, Liu Y (2021) A review of methods for classification and recognition of ASD using fMRI data. J Neurosci Methods, 109456
Zurück zum Zitat Fernandez Rojas R, Huang X, Ou KL (2019) A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci Rep 9(1):1–12CrossRef Fernandez Rojas R, Huang X, Ou KL (2019) A machine learning approach for the identification of a biomarker of human pain using fNIRS. Sci Rep 9(1):1–12CrossRef
Zurück zum Zitat Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: State of the art and challenges. IEEE Access 9:22106–22140CrossRef Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: State of the art and challenges. IEEE Access 9:22106–22140CrossRef
Zurück zum Zitat Ghassemi N, Shoeibi A, Rouhani M, Hosseini-Nejad H (2019) Epileptic seizures detection in EEG signals using TQWT and ensemble learning. In: 2019 9th International conference on computer and knowledge engineering (ICCKE) (pp 403–408). IEEE Ghassemi N, Shoeibi A, Rouhani M, Hosseini-Nejad H (2019) Epileptic seizures detection in EEG signals using TQWT and ensemble learning. In: 2019 9th International conference on computer and knowledge engineering (ICCKE) (pp 403–408). IEEE
Zurück zum Zitat Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press
Zurück zum Zitat Grimm O, Thomä L, Kranz TM, Reif A (2022) Is genetic risk of ADHD mediated via dopaminergic mechanism? A study of functional connectivity in ADHD and pharmacologically challenged healthy volunteers with a genetic risk profile. Transl Psychiatr 12(1):1–9CrossRef Grimm O, Thomä L, Kranz TM, Reif A (2022) Is genetic risk of ADHD mediated via dopaminergic mechanism? A study of functional connectivity in ADHD and pharmacologically challenged healthy volunteers with a genetic risk profile. Transl Psychiatr 12(1):1–9CrossRef
Zurück zum Zitat Groom MJ, Jackson GM, Calton TG, Andrews HK, Bates AT, Liddle PF, Hollis C (2008) Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD. Schizophr Res 99(1–3):85–95PubMedCrossRef Groom MJ, Jackson GM, Calton TG, Andrews HK, Bates AT, Liddle PF, Hollis C (2008) Cognitive deficits in early-onset schizophrenia spectrum patients and their non-psychotic siblings: a comparison with ADHD. Schizophr Res 99(1–3):85–95PubMedCrossRef
Zurück zum Zitat Górriz JM, Jimenez-Mesa C, Romero-Garcia R, Segovia F, Ramirez J, Castillo-Barnes D, Suckling J (2021) Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities. Information Fusion 66:198–212CrossRef Górriz JM, Jimenez-Mesa C, Romero-Garcia R, Segovia F, Ramirez J, Castillo-Barnes D, Suckling J (2021) Statistical agnostic mapping: a framework in neuroimaging based on concentration inequalities. Information Fusion 66:198–212CrossRef
Zurück zum Zitat Hao AJ, He BL, Yin CH (2015) Discrimination of ADHD children based on Deep Bayesian Network Hao AJ, He BL, Yin CH (2015) Discrimination of ADHD children based on Deep Bayesian Network
Zurück zum Zitat Havlicek M, Jan J, Brazdil M, Calhoun VD (2010) Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. Neuroimage 53(1):65–77PubMedCrossRef Havlicek M, Jan J, Brazdil M, Calhoun VD (2010) Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data. Neuroimage 53(1):65–77PubMedCrossRef
Zurück zum Zitat Hilland E, Johannessen C, Jonassen R, Alnæs D, Jørgensen KN, Barth C, Agartz I (2022) Aberrant default mode connectivity in adolescents with early-onset psychosis: a resting state fMRI study. NeuroImage Clin 33:102881PubMedCrossRef Hilland E, Johannessen C, Jonassen R, Alnæs D, Jørgensen KN, Barth C, Agartz I (2022) Aberrant default mode connectivity in adolescents with early-onset psychosis: a resting state fMRI study. NeuroImage Clin 33:102881PubMedCrossRef
Zurück zum Zitat Hu M, Sim K, Zhou JH, Jiang X, Guan C (2020) Brain MRI-based 3D convolutional neural networks for classification of schizophrenia and controls. In: 2020 42nd annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 1742–1745). IEEE Hu M, Sim K, Zhou JH, Jiang X, Guan C (2020) Brain MRI-based 3D convolutional neural networks for classification of schizophrenia and controls. In: 2020 42nd annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp 1742–1745). IEEE
Zurück zum Zitat Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl Neuroimage 62(2):782–790PubMedCrossRef Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl Neuroimage 62(2):782–790PubMedCrossRef
Zurück zum Zitat Jepsen JRM, Rydkjaer J, Fagerlund B, Pagsberg AK, Glenthøj BY, Oranje B (2018) Overlapping and disease specific trait, response, and reflection impulsivity in adolescents with first-episode schizophrenia spectrum disorders or attention-deficit/hyperactivity disorder. Psychol Med 48(4):604–616PubMedCrossRef Jepsen JRM, Rydkjaer J, Fagerlund B, Pagsberg AK, Glenthøj BY, Oranje B (2018) Overlapping and disease specific trait, response, and reflection impulsivity in adolescents with first-episode schizophrenia spectrum disorders or attention-deficit/hyperactivity disorder. Psychol Med 48(4):604–616PubMedCrossRef
Zurück zum Zitat Johnsen LK, Loren V, van Themaat AH, Larsen KM, Burton BK, Baare WFC, Madsen KS, Plessen KJ (2020) Alterations in task-related brain activation in children, adolescents and young adults at familial high-risk for schizophrenia or bipolar disorder-a systematic review. Front Psych 11:632CrossRef Johnsen LK, Loren V, van Themaat AH, Larsen KM, Burton BK, Baare WFC, Madsen KS, Plessen KJ (2020) Alterations in task-related brain activation in children, adolescents and young adults at familial high-risk for schizophrenia or bipolar disorder-a systematic review. Front Psych 11:632CrossRef
Zurück zum Zitat Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Berk M (2021) Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput Biol Med 139:104949PubMedCrossRef Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Berk M (2021) Deep learning for neuroimaging-based diagnosis and rehabilitation of autism spectrum disorder: a review. Comput Biol Med 139:104949PubMedCrossRef
Zurück zum Zitat Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Islam SMS (2021) Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 11(1):1–18CrossRef Khozeimeh F, Sharifrazi D, Izadi NH, Joloudari JH, Shoeibi A, Alizadehsani R, Islam SMS (2021) Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Sci Rep 11(1):1–18CrossRef
Zurück zum Zitat Konrad K, Eickhoff SB (2010) Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum Brain Mapp 31(6):904–916PubMedPubMedCentralCrossRef Konrad K, Eickhoff SB (2010) Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder. Hum Brain Mapp 31(6):904–916PubMedPubMedCentralCrossRef
Zurück zum Zitat Kuang D, Guo X, An X, Zhao Y, He L (2014) Discrimination of ADHD based on fMRI data with deep belief network. In: International conference on intelligent computing (pp 225–232). Springer, Cham Kuang D, Guo X, An X, Zhao Y, He L (2014) Discrimination of ADHD based on fMRI data with deep belief network. In: International conference on intelligent computing (pp 225–232). Springer, Cham
Zurück zum Zitat Langberg JM, Epstein JN, Graham AJ (2008) Organizational-skills interventions in the treatment of ADHD. Expert Rev Neurother 8(10):1549–1561PubMedCrossRef Langberg JM, Epstein JN, Graham AJ (2008) Organizational-skills interventions in the treatment of ADHD. Expert Rev Neurother 8(10):1549–1561PubMedCrossRef
Zurück zum Zitat Li J, Sun Y, Huang Y, Bezerianos A, Yu R (2019) Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method. Brain Imaging Behav 13(5):1386–1396PubMedCrossRef Li J, Sun Y, Huang Y, Bezerianos A, Yu R (2019) Machine learning technique reveals intrinsic characteristics of schizophrenia: an alternative method. Brain Imaging Behav 13(5):1386–1396PubMedCrossRef
Zurück zum Zitat Li C, Zhang S, Qin Y, Estupinan E (2020) A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing 407:121–135CrossRef Li C, Zhang S, Qin Y, Estupinan E (2020) A systematic review of deep transfer learning for machinery fault diagnosis. Neurocomputing 407:121–135CrossRef
Zurück zum Zitat Liang S, Deng W, Li X, Wang Q, Greenshaw AJ, Guo W, Li T (2020) Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: a machine learning study. Schizophr Res 220:187–193PubMedCrossRef Liang S, Deng W, Li X, Wang Q, Greenshaw AJ, Guo W, Li T (2020) Aberrant posterior cingulate connectivity classify first-episode schizophrenia from controls: a machine learning study. Schizophr Res 220:187–193PubMedCrossRef
Zurück zum Zitat Liao Y, Vemuri VR (2002) Use of k-nearest neighbor classifier for intrusion detection. Comput Secur 21(5):439–448CrossRef Liao Y, Vemuri VR (2002) Use of k-nearest neighbor classifier for intrusion detection. Comput Secur 21(5):439–448CrossRef
Zurück zum Zitat Liu X, Milanova M (2018) Visual attention in deep learning: a review. Int Rob Auto J 4(3):154–155 Liu X, Milanova M (2018) Visual attention in deep learning: a review. Int Rob Auto J 4(3):154–155
Zurück zum Zitat Ma G, Ahmed NK, Willke TL, Yu PS (2021) Deep graph similarity learning: a survey. Data Min Knowl Disc 35(3):688–725CrossRef Ma G, Ahmed NK, Willke TL, Yu PS (2021) Deep graph similarity learning: a survey. Data Min Knowl Disc 35(3):688–725CrossRef
Zurück zum Zitat Mao Z, Su Y, Xu G, Wang X, Huang Y, Yue W, Xiong N (2019) Spatio-temporal deep learning method for ADHD fMRI classification. Inf Sci 499:1–11CrossRef Mao Z, Su Y, Xu G, Wang X, Huang Y, Yue W, Xiong N (2019) Spatio-temporal deep learning method for ADHD fMRI classification. Inf Sci 499:1–11CrossRef
Zurück zum Zitat Marill KA (2004) Advanced statistics: linear regression, part II: multiple linear regression. Acad Emerg Med 11(1):94–102PubMedCrossRef Marill KA (2004) Advanced statistics: linear regression, part II: multiple linear regression. Acad Emerg Med 11(1):94–102PubMedCrossRef
Zurück zum Zitat Marsh PJ, Williams LM (2006) ADHD and schizophrenia phenomenology: visual scanpaths to emotional faces as a potential psychophysiological marker? Neurosci Biobehav Rev 30(5):651–665PubMedCrossRef Marsh PJ, Williams LM (2006) ADHD and schizophrenia phenomenology: visual scanpaths to emotional faces as a potential psychophysiological marker? Neurosci Biobehav Rev 30(5):651–665PubMedCrossRef
Zurück zum Zitat Matsubara T, Tashiro T, Uehara K (2019) Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans Biomed Eng 66(10):2768–2779PubMedCrossRef Matsubara T, Tashiro T, Uehara K (2019) Deep neural generative model of functional MRI images for psychiatric disorder diagnosis. IEEE Trans Biomed Eng 66(10):2768–2779PubMedCrossRef
Zurück zum Zitat Maulud D, Abdulazeez AM (2020) A review on linear regression comprehensive in machine learning. J Appl Sci Technol Trends 1(4):140–147CrossRef Maulud D, Abdulazeez AM (2020) A review on linear regression comprehensive in machine learning. J Appl Sci Technol Trends 1(4):140–147CrossRef
Zurück zum Zitat Mittal K, Jain A, Vaisla KS, Castillo O, Kacprzyk J (2020) A comprehensive review on type 2 fuzzy logic applications: past, present and future. Eng Appl Artif Intell 95:103916CrossRef Mittal K, Jain A, Vaisla KS, Castillo O, Kacprzyk J (2020) A comprehensive review on type 2 fuzzy logic applications: past, present and future. Eng Appl Artif Intell 95:103916CrossRef
Zurück zum Zitat Mohammed F, He X, Lin Y (2021) Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images Mohammed F, He X, Lin Y (2021) Retracted: An easy-to-use deep-learning model for highly accurate diagnosis of Parkinson's disease using SPECT images
Zurück zum Zitat Mäki-Marttunen V, Diez I, Cortes JM, Chialvo DR, Villarreal M (2013) Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness. Front Neuroinform 7:24PubMedPubMedCentralCrossRef Mäki-Marttunen V, Diez I, Cortes JM, Chialvo DR, Villarreal M (2013) Disruption of transfer entropy and inter-hemispheric brain functional connectivity in patients with disorder of consciousness. Front Neuroinform 7:24PubMedPubMedCentralCrossRef
Zurück zum Zitat Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48–62CrossRef Niu Z, Zhong G, Yu H (2021) A review on the attention mechanism of deep learning. Neurocomputing 452:48–62CrossRef
Zurück zum Zitat O’Driscoll C, Laing J, Mason O (2014) Cognitive emotion regulation strategies, alexithymia and dissociation in schizophrenia, a review and meta-analysis. Clin Psychol Rev 34(6):482–495PubMedCrossRef O’Driscoll C, Laing J, Mason O (2014) Cognitive emotion regulation strategies, alexithymia and dissociation in schizophrenia, a review and meta-analysis. Clin Psychol Rev 34(6):482–495PubMedCrossRef
Zurück zum Zitat Oh J, Oh BL, Lee KU, Chae JH, Yun K (2020) Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psych 11:16CrossRef Oh J, Oh BL, Lee KU, Chae JH, Yun K (2020) Identifying schizophrenia using structural MRI with a deep learning algorithm. Front Psych 11:16CrossRef
Zurück zum Zitat Pagano G, Niccolini F, Politis M (2016) Imaging in Parkinson’s disease. Clin Med 16(4):371CrossRef Pagano G, Niccolini F, Politis M (2016) Imaging in Parkinson’s disease. Clin Med 16(4):371CrossRef
Zurück zum Zitat Pan X, Wang Y, He S (2021) The evidential reasoning approach for renewable energy resources evaluation under interval type-2 fuzzy uncertainty. Inf Sci 576:432–453CrossRef Pan X, Wang Y, He S (2021) The evidential reasoning approach for renewable energy resources evaluation under interval type-2 fuzzy uncertainty. Inf Sci 576:432–453CrossRef
Zurück zum Zitat Park MTM, Raznahan A, Shaw P, Gogtay N, Lerch JP, Chakravarty MM (2018) Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J Psychiatr Neurosci 43(3):201–212CrossRef Park MTM, Raznahan A, Shaw P, Gogtay N, Lerch JP, Chakravarty MM (2018) Neuroanatomical phenotypes in mental illness: identifying convergent and divergent cortical phenotypes across autism, ADHD and schizophrenia. J Psychiatr Neurosci 43(3):201–212CrossRef
Zurück zum Zitat Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830 Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Duchesnay E (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825–2830
Zurück zum Zitat Peralta V, de Jalón EG, Campos MS, Zandio M, Sanchez-Torres A, Cuesta MJ (2011) The meaning of childhood attention-deficit hyperactivity symptoms in patients with a first-episode of schizophrenia-spectrum psychosis. Schizophr Res 126(1–3):28–35PubMedCrossRef Peralta V, de Jalón EG, Campos MS, Zandio M, Sanchez-Torres A, Cuesta MJ (2011) The meaning of childhood attention-deficit hyperactivity symptoms in patients with a first-episode of schizophrenia-spectrum psychosis. Schizophr Res 126(1–3):28–35PubMedCrossRef
Zurück zum Zitat Poldrack RA, Congdon E, Triplett W, Gorgolewski KJ, Karlsgodt KH, Mumford JA, Sabb FW, Freimer NB, London ED, Cannon TD, Bilder RM (2016) A phenome-wide examination of neural and cognitive function. Sci Data 3(1):1–12CrossRef Poldrack RA, Congdon E, Triplett W, Gorgolewski KJ, Karlsgodt KH, Mumford JA, Sabb FW, Freimer NB, London ED, Cannon TD, Bilder RM (2016) A phenome-wide examination of neural and cognitive function. Sci Data 3(1):1–12CrossRef
Zurück zum Zitat Ramkiran S, Sharma A, Rao NP (2019) Resting-state anticorrelated networks in schizophrenia. Psychiatr Res Neuroimaging 284:1–8CrossRef Ramkiran S, Sharma A, Rao NP (2019) Resting-state anticorrelated networks in schizophrenia. Psychiatr Res Neuroimaging 284:1–8CrossRef
Zurück zum Zitat Riaz A, Asad M, Al Arif SMR, Alonso E, Dima D, Corr P, Slabaugh G (2018) Deep fMRI: An end-to-end deep network for classification of fMRI data. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp 1419–1422). IEEE Riaz A, Asad M, Al Arif SMR, Alonso E, Dima D, Corr P, Slabaugh G (2018) Deep fMRI: An end-to-end deep network for classification of fMRI data. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp 1419–1422). IEEE
Zurück zum Zitat Rivera MJ, Teruel MA, Maté A, Trujillo J (2021) Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev, 1–43 Rivera MJ, Teruel MA, Maté A, Trujillo J (2021) Diagnosis and prognosis of mental disorders by means of EEG and deep learning: a systematic mapping study. Artif Intell Rev, 1–43
Zurück zum Zitat Rodriguez-Rivero J, Ramirez J, Martínez-Murcia FJ, Segovia F, Ortiz A, Salas D, Górriz JM (2020) Granger causality-based information fusion applied to electrical measurements from power transformers. Inf Fusion 57:59–70CrossRef Rodriguez-Rivero J, Ramirez J, Martínez-Murcia FJ, Segovia F, Ortiz A, Salas D, Górriz JM (2020) Granger causality-based information fusion applied to electrical measurements from power transformers. Inf Fusion 57:59–70CrossRef
Zurück zum Zitat Rogers GB, Keating DJ, Young RL, Wong ML, Licinio J, Wesselingh S (2016) From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways. Mol Psychiatry 21(6):738–748PubMedPubMedCentralCrossRef Rogers GB, Keating DJ, Young RL, Wong ML, Licinio J, Wesselingh S (2016) From gut dysbiosis to altered brain function and mental illness: mechanisms and pathways. Mol Psychiatry 21(6):738–748PubMedPubMedCentralCrossRef
Zurück zum Zitat Ross RG, Olincy A, Harris JG, Sullivan B, Radant A (2000) Smooth pursuit eye movements in schizophrenia and attentional dysfunction: adults with schizophrenia, ADHD, and a normal comparison group. Biol Psychiatr 48(3):197–203CrossRef Ross RG, Olincy A, Harris JG, Sullivan B, Radant A (2000) Smooth pursuit eye movements in schizophrenia and attentional dysfunction: adults with schizophrenia, ADHD, and a normal comparison group. Biol Psychiatr 48(3):197–203CrossRef
Zurück zum Zitat Runkler TA, Chen C, John R (2018) Type reduction operators for interval type–2 defuzzification. Inf Sci 467:464–476CrossRef Runkler TA, Chen C, John R (2018) Type reduction operators for interval type–2 defuzzification. Inf Sci 467:464–476CrossRef
Zurück zum Zitat Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674CrossRef Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674CrossRef
Zurück zum Zitat Salmi J, Metwaly M, Tohka J, Alho K, Leppämäki S, Tani P, Laine M (2020) ADHD desynchronizes brain activity during watching a distracted multi-talker conversation. Neuroimage 216:116352PubMedCrossRef Salmi J, Metwaly M, Tohka J, Alho K, Leppämäki S, Tani P, Laine M (2020) ADHD desynchronizes brain activity during watching a distracted multi-talker conversation. Neuroimage 216:116352PubMedCrossRef
Zurück zum Zitat Sanei S, Chambers JA (2013) EEG signal processing. Wiley Sanei S, Chambers JA (2013) EEG signal processing. Wiley
Zurück zum Zitat Sendi MS, Zendehrouh E, Fu Z, Mahmoudi B, Miller RL, Calhoun VD (2020) A machine learning model for exploring aberrant functional network connectivity transition in schizophrenia. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp 112–115). IEEE Sendi MS, Zendehrouh E, Fu Z, Mahmoudi B, Miller RL, Calhoun VD (2020) A machine learning model for exploring aberrant functional network connectivity transition in schizophrenia. In: 2020 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI) (pp 112–115). IEEE
Zurück zum Zitat Sharifrazi D, Alizadehsani R, Joloudari JH, Shamshirband S, Hussain S, Sani ZA, Alinejad-Rokny H (2020) CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering Sharifrazi D, Alizadehsani R, Joloudari JH, Shamshirband S, Hussain S, Sani ZA, Alinejad-Rokny H (2020) CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering
Zurück zum Zitat Shoeibi A, Ghassemi N, Alizadehsani R, Rouhani M, Hosseini-Nejad H, Khosravi A, Nahavandi S (2021c) A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals. Expert Syst Appl 163:113788CrossRef Shoeibi A, Ghassemi N, Alizadehsani R, Rouhani M, Hosseini-Nejad H, Khosravi A, Nahavandi S (2021c) A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals. Expert Syst Appl 163:113788CrossRef
Zurück zum Zitat Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Gorriz JM (2022c) Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 73:103417CrossRef Shoeibi A, Ghassemi N, Khodatars M, Moridian P, Alizadehsani R, Zare A, Gorriz JM (2022c) Detection of epileptic seizures on EEG signals using ANFIS classifier, autoencoders and fuzzy entropies. Biomed Signal Process Control 73:103417CrossRef
Zurück zum Zitat Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Acharya UR (2021e) Epileptic seizures detection using deep learning techniques: a review. Int J Environ Res Public Health 18(11):5780PubMedPubMedCentralCrossRef Shoeibi A, Khodatars M, Ghassemi N, Jafari M, Moridian P, Alizadehsani R, Acharya UR (2021e) Epileptic seizures detection using deep learning techniques: a review. Int J Environ Res Public Health 18(11):5780PubMedPubMedCentralCrossRef
Zurück zum Zitat Shoeibi A, Khodatars M, Jafari M, Moridian P, Rezaei M, Alizadehsani R, Acharya UR (2021d) Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: a review. Comput Biol Med 136:104697PubMedCrossRef Shoeibi A, Khodatars M, Jafari M, Moridian P, Rezaei M, Alizadehsani R, Acharya UR (2021d) Applications of deep learning techniques for automated multiple sclerosis detection using magnetic resonance imaging: a review. Comput Biol Med 136:104697PubMedCrossRef
Zurück zum Zitat Shoeibi A, Rezaei M, Ghassemi N, Namadchian Z, Zare A, Gorriz JM (2022a) Automatic diagnosis of schizophrenia in EEG signals using functional connectivity features and CNN-LSTM model. In: International work-conference on the interplay between natural and artificial computation (pp 63–73). Springer, Cham Shoeibi A, Rezaei M, Ghassemi N, Namadchian Z, Zare A, Gorriz JM (2022a) Automatic diagnosis of schizophrenia in EEG signals using functional connectivity features and CNN-LSTM model. In: International work-conference on the interplay between natural and artificial computation (pp 63–73). Springer, Cham
Zurück zum Zitat Shoeibi A, Ghassemi N, Heras J, Rezaei M, Gorriz JM (2022b). Automatic diagnosis of myocarditis in cardiac magnetic images using CycleGAN and deep PreTrained models. In: International work-conference on the interplay between natural and artificial computation (pp 145–155). Springer, Cham Shoeibi A, Ghassemi N, Heras J, Rezaei M, Gorriz JM (2022b). Automatic diagnosis of myocarditis in cardiac magnetic images using CycleGAN and deep PreTrained models. In: International work-conference on the interplay between natural and artificial computation (pp 145–155). Springer, Cham
Zurück zum Zitat Shoeibi A, Ghassemi N, Khodatars M, Jafari M, Moridian P, Alizadehsani R, Nahavandi S (2021b) Applications of epileptic seizures detection in neuroimaging modalities using deep learning techniques: methods, challenges, and future works. http://arxiv.org/abs/arXiv:2105.14278 Shoeibi A, Ghassemi N, Khodatars M, Jafari M, Moridian P, Alizadehsani R, Nahavandi S (2021b) Applications of epileptic seizures detection in neuroimaging modalities using deep learning techniques: methods, challenges, and future works. http://​arxiv.​org/​abs/​arXiv:​2105.​14278
Zurück zum Zitat Singh M, Badhwar R, Bagler G (2016) Graph theoretical biomarkers for schizophrenic brain functional networks. In: 2016 International conference on signal processing and communication (ICSC) (pp 289–294). IEEE Singh M, Badhwar R, Bagler G (2016) Graph theoretical biomarkers for schizophrenic brain functional networks. In: 2016 International conference on signal processing and communication (ICSC) (pp 289–294). IEEE
Zurück zum Zitat Suk HI, Wee CY, Lee SW, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292–307PubMedCrossRef Suk HI, Wee CY, Lee SW, Shen D (2016) State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage 129:292–307PubMedCrossRef
Zurück zum Zitat Suleymani M, Bemani A (2018) Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass. Energy Sour Part A Recover Util Environ Eff 40(3):288–293 Suleymani M, Bemani A (2018) Application of ANFIS-PSO algorithm as a novel method for estimation of higher heating value of biomass. Energy Sour Part A Recover Util Environ Eff 40(3):288–293
Zurück zum Zitat Sun FT, Miller LM, D’esposito M (2004) Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage 21(2):647–658PubMedCrossRef Sun FT, Miller LM, D’esposito M (2004) Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. Neuroimage 21(2):647–658PubMedCrossRef
Zurück zum Zitat Sun X, Zhang Y, Tian X, Cao J, Zhu J (2021) Speed sensorless control for IPMSMs using a modified MRAS with grey wolf optimization algorithm. IEEE Trans Transp Electrif Sun X, Zhang Y, Tian X, Cao J, Zhu J (2021) Speed sensorless control for IPMSMs using a modified MRAS with grey wolf optimization algorithm. IEEE Trans Transp Electrif
Zurück zum Zitat Tandon R, Gaebel W, Barch DM, Bustillo J, Gur RE, Heckers S, Carpenter W (2013) Definition and description of schizophrenia in the DSM-5. Schizophr Res 150(1):3–10PubMedCrossRef Tandon R, Gaebel W, Barch DM, Bustillo J, Gur RE, Heckers S, Carpenter W (2013) Definition and description of schizophrenia in the DSM-5. Schizophr Res 150(1):3–10PubMedCrossRef
Zurück zum Zitat Thirumurugan P, Shanthakumar P (2016) Brain tumor detection and diagnosis using ANFIS classifier. Int J Imaging Syst Technol 26(2):157–162CrossRef Thirumurugan P, Shanthakumar P (2016) Brain tumor detection and diagnosis using ANFIS classifier. Int J Imaging Syst Technol 26(2):157–162CrossRef
Zurück zum Zitat Tien Bui D, Khosravi K, Li S, Shahabi H, Panahi M, Singh VP, Bin Ahmad B (2018) New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water 10(9):1210CrossRef Tien Bui D, Khosravi K, Li S, Shahabi H, Panahi M, Singh VP, Bin Ahmad B (2018) New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water 10(9):1210CrossRef
Zurück zum Zitat Verdoux H, Sutter AL (2002) Perinatal risk factors for schizophrenia: diagnostic specificity and relationships with maternal psychopathology. Am J Med Genet 114(8):898–905PubMedCrossRef Verdoux H, Sutter AL (2002) Perinatal risk factors for schizophrenia: diagnostic specificity and relationships with maternal psychopathology. Am J Med Genet 114(8):898–905PubMedCrossRef
Zurück zum Zitat Vieira S, Pinaya WH, Mechelli A (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 74:58–75PubMedCrossRef Vieira S, Pinaya WH, Mechelli A (2017) Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev 74:58–75PubMedCrossRef
Zurück zum Zitat Villringer A, Chance B (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci 20(10):435–442PubMedCrossRef Villringer A, Chance B (1997) Non-invasive optical spectroscopy and imaging of human brain function. Trends Neurosci 20(10):435–442PubMedCrossRef
Zurück zum Zitat Volkow ND, O’Brien CP (2007) Issues for DSM-V: should obesity be included as a brain disorder? Am J Psychiatry 164(5):708–710PubMedCrossRef Volkow ND, O’Brien CP (2007) Issues for DSM-V: should obesity be included as a brain disorder? Am J Psychiatry 164(5):708–710PubMedCrossRef
Zurück zum Zitat Vovou F, Hull L, Petrides KV (2021) Mental health literacy of ADHD, autism, schizophrenia, and bipolar disorder: a cross-cultural investigation. J Ment Health 30(4):470–480PubMedCrossRef Vovou F, Hull L, Petrides KV (2021) Mental health literacy of ADHD, autism, schizophrenia, and bipolar disorder: a cross-cultural investigation. J Ment Health 30(4):470–480PubMedCrossRef
Zurück zum Zitat Wang HE, Bénar CG, Quilichini PP, Friston KJ, Jirsa VK, Bernard C (2014) A systematic framework for functional connectivity measures. Front Neurosci 8:405PubMedPubMedCentralCrossRef Wang HE, Bénar CG, Quilichini PP, Friston KJ, Jirsa VK, Bernard C (2014) A systematic framework for functional connectivity measures. Front Neurosci 8:405PubMedPubMedCentralCrossRef
Zurück zum Zitat Wang S, Celebi ME, Zhang YD, Yu X, Lu S, Yao X, Tyukin I (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Inf Fusion 76:376–421CrossRef Wang S, Celebi ME, Zhang YD, Yu X, Lu S, Yao X, Tyukin I (2021) Advances in data preprocessing for biomedical data fusion: an overview of the methods, challenges, and prospects. Inf Fusion 76:376–421CrossRef
Zurück zum Zitat Wang Z, Alahmadi A, Zhu D, Li T (2015) Brain functional connectivity analysis using mutual information. In: 2015 IEEE global conference on signal and information processing (GlobalSIP) (pp 542–546). IEEE Wang Z, Alahmadi A, Zhu D, Li T (2015) Brain functional connectivity analysis using mutual information. In: 2015 IEEE global conference on signal and information processing (GlobalSIP) (pp 542–546). IEEE
Zurück zum Zitat Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, Vannucci M (2018) A Bayesian approach for estimating dynamic functional network connectivity in fMRI data. J Am Stat Assoc 113(521):134–151PubMedPubMedCentralCrossRef Warnick R, Guindani M, Erhardt E, Allen E, Calhoun V, Vannucci M (2018) A Bayesian approach for estimating dynamic functional network connectivity in fMRI data. J Am Stat Assoc 113(521):134–151PubMedPubMedCentralCrossRef
Zurück zum Zitat Wei LY (2013) A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Econ Model 33:893–899CrossRef Wei LY (2013) A hybrid model based on ANFIS and adaptive expectation genetic algorithm to forecast TAIEX. Econ Model 33:893–899CrossRef
Zurück zum Zitat Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85CrossRef Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4(2):65–85CrossRef
Zurück zum Zitat Windeatt T (2006) Accuracy/diversity and ensemble MLP classifier design. IEEE Trans Neural Networks 17(5):1194–1211PubMedCrossRef Windeatt T (2006) Accuracy/diversity and ensemble MLP classifier design. IEEE Trans Neural Networks 17(5):1194–1211PubMedCrossRef
Zurück zum Zitat Wortzel HS, Arciniegas DB (2014) The DSM-5 approach to the evaluation of traumatic brain injury and its neuropsychiatric sequelae. NeuroRehabilitation 34(4):613–623PubMedCrossRef Wortzel HS, Arciniegas DB (2014) The DSM-5 approach to the evaluation of traumatic brain injury and its neuropsychiatric sequelae. NeuroRehabilitation 34(4):613–623PubMedCrossRef
Zurück zum Zitat Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L (2020) Connectomic underpinnings of working memory deficits in schizophrenia: evidence from a replication fMRI study. Schizophr Bull 46(4):916–926PubMedPubMedCentralCrossRef Yang J, Pu W, Wu G, Chen E, Lee E, Liu Z, Palaniyappan L (2020) Connectomic underpinnings of working memory deficits in schizophrenia: evidence from a replication fMRI study. Schizophr Bull 46(4):916–926PubMedPubMedCentralCrossRef
Zurück zum Zitat Zhang T, Li C, Li P, Peng Y, Kang X, Jiang C, Xu P (2020) Separated channel attention convolutional neural network (SC-CNN-attention) to identify ADHD in multi-site Rs-fMRI dataset. Entropy 22(8):893PubMedPubMedCentralCrossRef Zhang T, Li C, Li P, Peng Y, Kang X, Jiang C, Xu P (2020) Separated channel attention convolutional neural network (SC-CNN-attention) to identify ADHD in multi-site Rs-fMRI dataset. Entropy 22(8):893PubMedPubMedCentralCrossRef
Zurück zum Zitat Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y (2022) A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 246:118774PubMedCrossRef Zhao K, Duka B, Xie H, Oathes DJ, Calhoun V, Zhang Y (2022) A dynamic graph convolutional neural network framework reveals new insights into connectome dysfunctions in ADHD. Neuroimage 246:118774PubMedCrossRef
Zurück zum Zitat Zhu Y, Fu S, Yang S, Liang P, Tan Y (2020) Weighted deep forest for schizophrenia data classification. IEEE Access 8:62698–62705CrossRef Zhu Y, Fu S, Yang S, Liang P, Tan Y (2020) Weighted deep forest for schizophrenia data classification. IEEE Access 8:62698–62705CrossRef
Zurück zum Zitat Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ (2017b) 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access 5:23626–23636CrossRef Zou L, Zheng J, Miao C, Mckeown MJ, Wang ZJ (2017b) 3D CNN based automatic diagnosis of attention deficit hyperactivity disorder using functional and structural MRI. IEEE Access 5:23626–23636CrossRef
Zurück zum Zitat Zou L, Zheng J, McKeown MJ (2017a) Deep learning based automatic diagnoses of attention deficit hyperactive disorder. In: 2017a IEEE global conference on signal and information processing (GlobalSIP) (pp 962–966). IEEE Zou L, Zheng J, McKeown MJ (2017a) Deep learning based automatic diagnoses of attention deficit hyperactive disorder. In: 2017a IEEE global conference on signal and information processing (GlobalSIP) (pp 962–966). IEEE
Metadaten
Titel
Automatic diagnosis of schizophrenia and attention deficit hyperactivity disorder in rs-fMRI modality using convolutional autoencoder model and interval type-2 fuzzy regression
verfasst von
Afshin Shoeibi
Navid Ghassemi
Marjane Khodatars
Parisa Moridian
Abbas Khosravi
Assef Zare
Juan M. Gorriz
Amir Hossein Chale-Chale
Ali Khadem
U. Rajendra Acharya
Publikationsdatum
12.11.2022
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 6/2023
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-022-09897-w

Weitere Artikel der Ausgabe 6/2023

Cognitive Neurodynamics 6/2023 Zur Ausgabe