Al-Ali A, Elharrouss O, Qidwai U, Al-Maaddeed S (2021) ANFIS-Net for automatic detection of COVID-19. Sci Rep 11(1):1–13
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
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 127:426–434
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):1–9
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 241:238–243
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):1802–1812
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 27:104495
Article PubMed PubMed Central Google Scholar
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):281–289
Bastos AM, Schoffelen JM (2016) A tutorial review of functional connectivity analysis methods and their interpretational pitfalls. Front Syst Neurosci 9:175
Article PubMed PubMed Central Google Scholar
Belgiu M, Drăguţ L (2016) Random forest in remote sensing: a review of applications and future directions. ISPRS J Photogramm Remote Sens 114:24–31
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
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):827–853
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):279–296
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 99:217–223
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):172–184
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 58:153–167
Castle L, Aubert RE, Verbrugge RR, Khalid M, Epstein RS (2007) Trends in medication treatment for ADHD. J Atten Disord 10(4):335–342
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):282–293
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 43:447–453
Article PubMed PubMed Central Google Scholar
Cisler JM, Bush K, Steele JS (2014) A comparison of statistical methods for detecting context-modulated functional connectivity in fMRI. Neuroimage 84:1042–1052
Coupland S, John R (2007) Geometric type-1 and type-2 fuzzy logic systems. IEEE Trans Fuzzy Syst 15(1):3–15
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 29:102531
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):259–263
Article PubMed CAS Google Scholar
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):1120–1132
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 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 275:14–20
Dillon JV, Langmore I, Tran D, Brevdo E, Vasudevan S, Moore D, Saurous RA (2017) Tensorflow distributions. http://arxiv.org/abs/arXiv:1711.10604
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
Do Austerman J (2015) ADHD and behavioral disorders: Assessment, management, and an update from DSM-5. Cleveland Clin J Med 82:S3
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 111:101834
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 13:70
Article PubMed PubMed Central Google Scholar
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
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
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):1–12
Georgousis S, Kenning MP, Xie X (2021) Graph deep learning: State of the art and challenges. IEEE Access 9:22106–22140
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
Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press
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):1–9
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):85–95
Article PubMed CAS Google Scholar
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 66:198–212
Hao AJ, He BL, Yin CH (2015) Discrimination of ADHD children based on Deep Bayesian Network
Hashimoto Y, Ogata Y, Honda M, Yamashita Y (2021) Deep feature extraction for resting-state functional mri by self-supervised learning and application to schizophrenia diagnosis. Front Neurosci. https://doi.org/10.3389/fnins.2021.696853
Article PubMed PubMed Central Google Scholar
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):65–77
Highland D, Zhou G (2022) A review of detection techniques for depression and bipolar disorder. Smart Health. https://doi.org/10.1016/j.smhl.2022.100282
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 33:102881
https://legacy.openfmri.org/dataset/ds000030/
https://www.wjgnet.com/2220-3206/full/v5/i1/47.htm
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
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) Fsl Neuroimage 62(2):782–790
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):604–616
Article PubMed CAS Google Scholar
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 11:632
留言 (0)