Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Datasets
2.1.1. Experimental Conditions
2.1.2. NREM2 Sleep
2.1.3. Propofol Deep Sedation
2.1.4. Propofol General Anesthesia
2.1.5. Lysergic Acid Diethylamide (LSD)
2.1.6. Ketamine
2.1.7. Nitrous Oxide (N2O)
2.1.8. Neuropsychiatric Disorders
2.2. Data Preprocessing
2.3. Feature Extraction
2.3.1. Connectivity Measures
2.3.2. Graph Measures
2.3.3. Cortical Gradient Analysis
2.4. Classification
2.4.1. Base Models
2.4.2. Ensemble Models
2.4.3. Model Evaluation
2.4.4. Feature Importance Evaluation
3. Results
3.1. General Model Performance
3.2. Condition-Specific Model Performance
3.3. Cross-Dataset Performance
3.4. Feature Importance
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Du, Y.; Fu, Z.; Calhoun, V.D. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front. Neurosci. 2018, 12, 525. [Google Scholar] [CrossRef] [PubMed]
- Stephan, K.E.; Iglesias, S.; Heinzle, J.; Diaconescu, A.O. Translational Perspectives for Computational Neuroimaging. Neuron 2015, 87, 716–732. [Google Scholar] [CrossRef]
- Pereira-Sanchez, V.; Castellanos, F.X. Neuroimaging in Attention-Deficit/Hyperactivity Disorder. Curr. Opin. Psychiatry 2021, 34, 105. [Google Scholar] [CrossRef] [PubMed]
- Cohen, J.R.; Asarnow, R.F.; Sabb, F.W.; Bilder, R.M.; Bookheimer, S.Y.; Knowlton, B.J.; Poldrack, R.A. Decoding Continuous Variables from Neuroimaging Data: Basic and Clinical Applications. Front. Neurosci. 2011, 5, 75. [Google Scholar] [CrossRef]
- Huys, Q.J.M.; Maia, T.V.; Frank, M.J. Computational Psychiatry as a Bridge from Neuroscience to Clinical Applications. Nat. Neurosci. 2016, 19, 404–413. [Google Scholar] [CrossRef] [PubMed]
- Sperling, R.; Mormino, E.; Johnson, K. The Evolution of Preclinical Alzheimer’s Disease: Implications for Prevention Trials. Neuron 2014, 84, 608–622. [Google Scholar] [CrossRef]
- Drenthen, G.S.; Jansen, J.F.A.; Gommer, E.; Gupta, L.; Hofman, P.A.M.; van Kranen-Mastenbroek, V.H.; Hilkman, D.M.; Vlooswijk, M.C.G.; Rouhl, R.P.W.; Backes, W.H. Predictive Value of Functional MRI and EEG in Epilepsy Diagnosis after a First Seizure. Epilepsy Behav. 2021, 115, 107651. [Google Scholar] [CrossRef]
- He, D.; Ren, D.; Guo, Z.; Jiang, B. Insomnia Disorder Diagnosed by Resting-State fMRI-Based SVM Classifier. Sleep. Med. 2022, 95, 126–129. [Google Scholar] [CrossRef]
- Canario, E.; Chen, D.; Biswal, B. A Review of Resting-State fMRI and Its Use to Examine Psychiatric Disorders. Psychoradiology 2021, 1, 42–53. [Google Scholar] [CrossRef]
- Arbabshirani, M.R.; Plis, S.; Sui, J.; Calhoun, V.D. Single Subject Prediction of Brain Disorders in Neuroimaging: Promises and Pitfalls. NeuroImage 2017, 145, 137–165. [Google Scholar] [CrossRef]
- Van Montfort, S.J.T.; Slooter, A.J.C.; Kant, I.M.J.; Van Der Leur, R.R.; Spies, C.; De Bresser, J.; Witkamp, T.D.; Hendrikse, J.; Van Dellen, E. fMRI Network Correlates of Predisposing Risk Factors for Delirium: A Cross-Sectional Study. NeuroImage Clin. 2020, 27, 102347. [Google Scholar] [CrossRef] [PubMed]
- Boutet, A.; Madhavan, R.; Elias, G.J.B.; Joel, S.E.; Gramer, R.; Ranjan, M.; Paramanandam, V.; Xu, D.; Germann, J.; Loh, A.; et al. Predicting Optimal Deep Brain Stimulation Parameters for Parkinson’s Disease Using Functional MRI and Machine Learning. Nat. Commun. 2021, 12, 3043. [Google Scholar] [CrossRef]
- Wandschneider, B.; Koepp, M.J. Pharmaco fMRI: Determining the Functional Anatomy of the Effects of Medication. NeuroImage Clin. 2016, 12, 691–697. [Google Scholar] [CrossRef]
- Demertzi, A.; Tagliazucchi, E.; Dehaene, S.; Deco, G.; Barttfeld, P.; Raimondo, F.; Martial, C.; Fernández-Espejo, D.; Rohaut, B.; Voss, H.U.; et al. Human Consciousness Is Supported by Dynamic Complex Patterns of Brain Signal Coordination. Sci. Adv. 2019, 5, eaat7603. [Google Scholar] [CrossRef] [PubMed]
- Huang, Z.; Zhang, J.; Wu, J.; Mashour, G.A.; Hudetz, A.G. Temporal Circuit of Macroscale Dynamic Brain Activity Supports Human Consciousness. Sci. Adv. 2020, 6, eaaz0087. [Google Scholar] [CrossRef]
- Shine, J.M.; Bissett, P.G.; Bell, P.T.; Koyejo, O.; Balsters, J.H.; Gorgolewski, K.J.; Moodie, C.A.; Poldrack, R.A. The Dynamics of Functional Brain Networks: Integrated Network States during Cognitive Task Performance. Neuron 2016, 92, 544–554. [Google Scholar] [CrossRef]
- Poldrack, R.A.; Halchenko, Y.O.; Hanson, S.J. Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals. Psychol. Sci. 2009, 20, 1364–1372. [Google Scholar] [CrossRef]
- Shirer, W.R.; Ryali, S.; Rykhlevskaia, E.; Menon, V.; Greicius, M.D. Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns. Cereb. Cortex 2012, 22, 158–165. [Google Scholar] [CrossRef] [PubMed]
- Azmi, M.H.; Saripan, M.I.; Nordin, A.J.; Ahmad Saad, F.F.; Abdul Aziz, S.A.; Wan Adnan, W.A. 18F-FDG PET Brain Images as Features for Alzheimer Classification. Radiat. Phys. Chem. 2017, 137, 135–143. [Google Scholar] [CrossRef]
- Jeong, J. EEG Dynamics in Patients with Alzheimer’s Disease. Clin. Neurophysiol. 2004, 115, 1490–1505. [Google Scholar] [CrossRef]
- Guo, H.; Zhang, F.; Chen, J.; Xu, Y.; Xiang, J. Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer’s Disease. Front. Neurosci. 2017, 11, 615. [Google Scholar] [CrossRef] [PubMed]
- Campbell, J.M.; Huang, Z.; Zhang, J.; Wu, X.; Qin, P.; Northoff, G.; Mashour, G.A.; Hudetz, A.G. Pharmacologically Informed Machine Learning Approach for Identifying Pathological States of Unconsciousness via Resting-State fMRI. NeuroImage 2020, 206, 116316. [Google Scholar] [CrossRef]
- Mantini, D.; Marzetti, L.; Corbetta, M.; Romani, G.L.; Del Gratta, C. Multimodal Integration of fMRI and EEG Data for High Spatial and Temporal Resolution Analysis of Brain Networks. Brain Topogr. 2010, 23, 150–158. [Google Scholar] [CrossRef] [PubMed]
- Smith, S.M.; Vidaurre, D.; Beckmann, C.F.; Glasser, M.F.; Jenkinson, M.; Miller, K.L.; Nichols, T.E.; Robinson, E.C.; Salimi-Khorshidi, G.; Woolrich, M.W.; et al. Functional Connectomics from Resting-State fMRI. Trends Cogn. Sci. 2013, 17, 666–682. [Google Scholar] [CrossRef] [PubMed]
- Mourão-Miranda, J.; Bokde, A.L.W.; Born, C.; Hampel, H.; Stetter, M. Classifying Brain States and Determining the Discriminating Activation Patterns: Support Vector Machine on Functional MRI Data. NeuroImage 2005, 28, 980–995. [Google Scholar] [CrossRef]
- Zhu, X.; Du, X.; Kerich, M.; Lohoff, F.W.; Momenan, R. Random Forest Based Classification of Alcohol Dependence Patients and Healthy Controls Using Resting State MRI. Neurosci. Lett. 2018, 676, 27–33. [Google Scholar] [CrossRef]
- Qureshi, M.N.I.; Oh, J.; Lee, B. 3D-CNN Based Discrimination of Schizophrenia Using Resting-State fMRI. Artif. Intell. Med. 2019, 98, 10–17. [Google Scholar] [CrossRef] [PubMed]
- Shahzad, M.N.; Ali, H.; Saba, T.; Rehman, A.; Kolivand, H.; Bahaj, S.A. Identifying Patients with PTSD Utilizing Resting-State fMRI Data and Neural Network Approach. IEEE Access 2021, 9, 107941–107954. [Google Scholar] [CrossRef]
- Garner, R.; Rocca, M.L.; Barisano, G.; Toga, A.W.; Duncan, D.; Vespa, P. A Machine Learning Model to Predict Seizure Susceptibility from Resting-State fMRI Connectivity. In Proceedings of the 2019 Spring Simulation Conference (SpringSim), Tucson, AZ, USA, 29 April–2 May 2019; pp. 1–11. [Google Scholar]
- Tagliazucchi, E.; von Wegner, F.; Morzelewski, A.; Borisov, S.; Jahnke, K.; Laufs, H. Automatic Sleep Staging Using fMRI Functional Connectivity Data. NeuroImage 2012, 63, 63–72. [Google Scholar] [CrossRef]
- Costafreda, S.G.; Fu, C.H.; Picchioni, M.; Toulopoulou, T.; McDonald, C.; Kravariti, E.; Walshe, M.; Prata, D.; Murray, R.M.; McGuire, P.K. Pattern of Neural Responses to Verbal Fluency Shows Diagnostic Specificity for Schizophrenia and Bipolar Disorder. BMC Psychiatry 2011, 11, 18. [Google Scholar] [CrossRef]
- Steardo, L., Jr.; Carbone, E.A.; De Filippis, R.; Pisanu, C.; Segura-Garcia, C.; Squassina, A.; De Fazio, P.; Steardo, L. Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review. Front. Psychiatry 2020, 11, 588. [Google Scholar] [CrossRef] [PubMed]
- Orrù, G.; Pettersson-Yeo, W.; Marquand, A.F.; Sartori, G.; Mechelli, A. Using Support Vector Machine to Identify Imaging Biomarkers of Neurological and Psychiatric Disease: A Critical Review. Neurosci. Biobehav. Rev. 2012, 36, 1140–1152. [Google Scholar] [CrossRef] [PubMed]
- Bondi, E.; Maggioni, E.; Brambilla, P.; Delvecchio, G. A Systematic Review on the Potential Use of Machine Learning to Classify Major Depressive Disorder from Healthy Controls Using Resting State fMRI Measures. Neurosci. Biobehav. Rev. 2023, 144, 104972. [Google Scholar] [CrossRef]
- Fu, C.H.Y.; Mourao-Miranda, J.; Costafreda, S.G.; Khanna, A.; Marquand, A.F.; Williams, S.C.R.; Brammer, M.J. Pattern Classification of Sad Facial Processing: Toward the Development of Neurobiological Markers in Depression. Biol. Psychiatry 2008, 63, 656–662. [Google Scholar] [CrossRef] [PubMed]
- Khazaee, A.; Ebrahimzadeh, A.; Babajani-Feremi, A. Alzheimer’s Disease Neuroimaging Initiative Classification of Patients with MCI and AD from Healthy Controls Using Directed Graph Measures of Resting-State fMRI. Behav. Brain Res. 2017, 322, 339–350. [Google Scholar] [CrossRef]
- Rashid, B.; Arbabshirani, M.R.; Damaraju, E.; Cetin, M.S.; Miller, R.; Pearlson, G.D.; Calhoun, V.D. Classification of Schizophrenia and Bipolar Patients Using Static and Dynamic Resting-State fMRI Brain Connectivity. NeuroImage 2016, 134, 645–657. [Google Scholar] [CrossRef]
- Sitaram, R.; Lee, S.; Ruiz, S.; Rana, M.; Veit, R.; Birbaumer, N. Real-Time Support Vector Classification and Feedback of Multiple Emotional Brain States. NeuroImage 2011, 56, 753–765. [Google Scholar] [CrossRef]
- Chen, B.; Xu, T.; Zhou, C.; Wang, L.; Yang, N.; Wang, Z.; Dong, H.-M.; Yang, Z.; Zang, Y.-F.; Zuo, X.-N.; et al. Individual Variability and Test-Retest Reliability Revealed by Ten Repeated Resting-State Brain Scans over One Month. PLoS ONE 2015, 10, e0144963. [Google Scholar] [CrossRef]
- Wei, C.-S.; Keller, C.J.; Li, J.; Lin, Y.-P.; Nakanishi, M.; Wagner, J.; Wu, W.; Zhang, Y.; Jung, T.-P. Editorial: Inter- and Intra-Subject Variability in Brain Imaging and Decoding. Front. Comput. Neurosci. 2021, 15, 791129. [Google Scholar] [CrossRef]
- Murphy, K.; Fox, M.D. Towards a Consensus Regarding Global Signal Regression for Resting State Functional Connectivity MRI. NeuroImage 2017, 154, 169–173. [Google Scholar] [CrossRef]
- de Vos, F.; Koini, M.; Schouten, T.M.; Seiler, S.; van der Grond, J.; Lechner, A.; Schmidt, R.; de Rooij, M.; Rombouts, S.A.R.B. A Comprehensive Analysis of Resting State fMRI Measures to Classify Individual Patients with Alzheimer’s Disease. NeuroImage 2018, 167, 62–72. [Google Scholar] [CrossRef] [PubMed]
- Zhang, T.; Zhao, Z.; Zhang, C.; Zhang, J.; Jin, Z.; Li, L. Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI. Front. Psychiatry 2019, 10, 572. [Google Scholar] [CrossRef] [PubMed]
- Rubinov, M.; Sporns, O. Complex Network Measures of Brain Connectivity: Uses and Interpretations. NeuroImage 2010, 52, 1059–1069. [Google Scholar] [CrossRef] [PubMed]
- Margulies, D.S.; Ghosh, S.S.; Goulas, A.; Falkiewicz, M.; Huntenburg, J.M.; Langs, G.; Bezgin, G.; Eickhoff, S.B.; Castellanos, F.X.; Petrides, M.; et al. Situating the Default-Mode Network along a Principal Gradient of Macroscale Cortical Organization. Proc. Natl. Acad. Sci. USA 2016, 113, 12574–12579. [Google Scholar] [CrossRef]
- Huang, Z.; Mashour, G.A.; Hudetz, A.G. Functional Geometry of the Cortex Encodes Dimensions of Consciousness. Nat. Commun. 2023, 14, 72. [Google Scholar] [CrossRef]
- Huntenburg, J.M.; Bazin, P.-L.; Margulies, D.S. Large-Scale Gradients in Human Cortical Organization. Trends Cogn. Sci. 2018, 22, 21–31. [Google Scholar] [CrossRef]
- De Rosa, A.P.; d’Ambrosio, A.; Bisecco, A.; Altieri, M.; Cirillo, M.; Gallo, A.; Esposito, F. Functional Gradients Reveal Cortical Hierarchy Changes in Multiple Sclerosis. Human. Brain Mapp. 2024, 45, e26678. [Google Scholar] [CrossRef]
- Wang, M.; Li, A.; Liu, Y.; Yan, H.; Sun, Y.; Song, M.; Chen, J.; Chen, Y.; Wang, H.; Guo, H.; et al. Reproducible Abnormalities of Functional Gradient Reliably Predict Clinical and Cognitive Symptoms in Schizophrenia 2020. BioRxiv 2020, 2020, 395251. [Google Scholar]
- Lee, A.Y. Degrees of Consciousness. Nous 2023, 57, 553–575. [Google Scholar] [CrossRef]
- Gu, Y.; Han, F.; Sainburg, L.E.; Schade, M.M.; Buxton, O.M.; Duyn, J.H.; Liu, X. An Orderly Sequence of Autonomic and Neural Events at Transient Arousal Changes. NeuroImage 2022, 264, 119720. [Google Scholar] [CrossRef]
- Gu, Y.; Sainburg, L.E.; Han, F.; Liu, X. Simultaneous EEG and Functional MRI Data during Rest and Sleep from Humans. Data Brief. 2023, 48, 109059. [Google Scholar] [CrossRef] [PubMed]
- Huang, Z.; Tarnal, V.; Vlisides, P.E.; Janke, E.L.; McKinney, A.M.; Picton, P.; Mashour, G.A.; Hudetz, A.G. Asymmetric Neural Dynamics Characterize Loss and Recovery of Consciousness. NeuroImage 2021, 236, 118042. [Google Scholar] [CrossRef] [PubMed]
- Huang, Z.; Tarnal, V.; Vlisides, P.E.; Janke, E.L.; McKinney, A.M.; Picton, P.; Mashour, G.A.; Hudetz, A.G. Anterior Insula Regulates Brain Network Transitions that Gate Conscious Access. Cell Rep. 2021, 35, 109081. [Google Scholar] [CrossRef]
- Huang, Z.; Zhang, J.; Wu, J.; Qin, P.; Wu, X.; Wang, Z.; Dai, R.; Li, Y.; Liang, W.; Mao, Y.; et al. Decoupled Temporal Variability and Signal Synchronization of Spontaneous Brain Activity in Loss of Consciousness: An fMRI Study in Anesthesia. NeuroImage 2016, 124, 693–703. [Google Scholar] [CrossRef] [PubMed]
- Carhart-Harris, R.L.; Muthukumaraswamy, S.; Roseman, L.; Kaelen, M.; Droog, W.; Murphy, K.; Tagliazucchi, E.; Schenberg, E.E.; Nest, T.; Orban, C.; et al. Neural Correlates of the LSD Experience Revealed by Multimodal Neuroimaging. Proc. Natl. Acad. Sci. USA 2016, 113, 4853–4858. [Google Scholar] [CrossRef]
- Dai, R.; Larkin, T.E.; Huang, Z.; Tarnal, V.; Picton, P.; Vlisides, P.E.; Janke, E.; McKinney, A.; Hudetz, A.G.; Harris, R.E.; et al. Classical and Non-Classical Psychedelic Drugs Induce Common Network Changes in Human Cortex. NeuroImage 2023, 273, 120097. [Google Scholar] [CrossRef]
- Dai, R.; Huang, Z.; Larkin, T.E.; Tarnal, V.; Picton, P.; Vlisides, P.E.; Janke, E.; McKinney, A.; Hudetz, A.G.; Harris, R.E.; et al. Psychedelic Concentrations of Nitrous Oxide Reduce Functional Differentiation in Frontoparietal and Somatomotor Cortical Networks. Commun. Biol. 2023, 6, 1284. [Google Scholar] [CrossRef] [PubMed]
- Poldrack, R.A.; Congdon, E.; Triplett, W.; Gorgolewski, K.J.; Karlsgodt, K.H.; Mumford, J.A.; Sabb, F.W.; Freimer, N.B.; London, E.D.; Cannon, T.D.; et al. A Phenome-Wide Examination of Neural and Cognitive Function. Sci. Data 2016, 3, 160110. [Google Scholar] [CrossRef]
- Thomas Yeo, B.T.; Krienen, F.M.; Sepulcre, J.; Sabuncu, M.R.; Lashkari, D.; Hollinshead, M.; Roffman, J.L.; Smoller, J.W.; Zöllei, L.; Polimeni, J.R.; et al. The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity. J. Neurophysiol. 2011, 106, 1125–1165. [Google Scholar] [CrossRef]
- Schaefer, A.; Kong, R.; Gordon, E.M.; Laumann, T.O.; Zuo, X.-N.; Holmes, A.J.; Eickhoff, S.B.; Yeo, B.T.T. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb. Cortex 2018, 28, 3095–3114. [Google Scholar] [CrossRef]
- Hallquist, M.N.; Hillary, F.G. Graph Theory Approaches to Functional Network Organization in Brain Disorders: A Critique for a Brave New Small-World. Netw. Neurosci. 2018, 3, 1–26. [Google Scholar] [CrossRef]
- Timmermann, C.; Roseman, L.; Haridas, S.; Rosas, F.E.; Luan, L.; Kettner, H.; Martell, J.; Erritzoe, D.; Tagliazucchi, E.; Pallavicini, C.; et al. Human Brain Effects of DMT Assessed via EEG-fMRI. Proc. Natl. Acad. Sci. USA 2023, 120, e2218949120. [Google Scholar] [CrossRef]
- Vos de Wael, R.; Benkarim, O.; Paquola, C.; Lariviere, S.; Royer, J.; Tavakol, S.; Xu, T.; Hong, S.-J.; Langs, G.; Valk, S.; et al. BrainSpace: A Toolbox for the Analysis of Macroscale Gradients in Neuroimaging and Connectomics Datasets. Commun. Biol. 2020, 3, 103. [Google Scholar] [CrossRef]
- Mckeown, B.; Strawson, W.H.; Wang, H.-T.; Karapanagiotidis, T.; Vos de Wael, R.; Benkarim, O.; Turnbull, A.; Margulies, D.; Jefferies, E.; McCall, C.; et al. The Relationship between Individual Variation in Macroscale Functional Gradients and Distinct Aspects of Ongoing Thought. NeuroImage 2020, 220, 117072. [Google Scholar] [CrossRef]
- Hong, S.-J.; Vos de Wael, R.; Bethlehem, R.A.I.; Lariviere, S.; Paquola, C.; Valk, S.L.; Milham, M.P.; Di Martino, A.; Margulies, D.S.; Smallwood, J.; et al. Atypical Functional Connectome Hierarchy in Autism. Nat. Commun. 2019, 10, 1022. [Google Scholar] [CrossRef]
- Bethlehem, R.A.I.; Paquola, C.; Seidlitz, J.; Ronan, L.; Bernhardt, B.; Consortium, C.-C.; Tsvetanov, K.A. Dispersion of Functional Gradients across the Adult Lifespan. NeuroImage 2020, 222, 117299. [Google Scholar] [CrossRef]
- Song, C.; Boly, M.; Tagliazucchi, E.; Laufs, H.; Tononi, G. fMRI Spectral Signatures of Sleep. Proc. Natl. Acad. Sci. USA 2022, 119, e2016732119. [Google Scholar] [CrossRef]
- Stevner, A.B.A.; Vidaurre, D.; Cabral, J.; Rapuano, K.; Nielsen, S.F.V.; Tagliazucchi, E.; Laufs, H.; Vuust, P.; Deco, G.; Woolrich, M.W.; et al. Discovery of Key Whole-Brain Transitions and Dynamics during Human Wakefulness and Non-REM Sleep. Nat. Commun. 2019, 10, 1035. [Google Scholar] [CrossRef]
- Zhang, J.; Wamsley, E.J. EEG Predictors of Dreaming Outside of REM Sleep. Psychophysiology 2019, 56, e13368. [Google Scholar] [CrossRef]
- Tagliazucchi, E.; Roseman, L.; Kaelen, M.; Orban, C.; Muthukumaraswamy, S.D.; Murphy, K.; Laufs, H.; Leech, R.; McGonigle, J.; Crossley, N.; et al. Increased Global Functional Connectivity Correlates with LSD-Induced Ego Dissolution. Curr. Biol. 2016, 26, 1043–1050. [Google Scholar] [CrossRef]
- Wang, Z.; Zhou, X.; Gui, Y.; Liu, M.; Lu, H. Multiple Measurement Analysis of Resting-State fMRI for ADHD Classification in Adolescent Brain from the ABCD Study. Transl. Psychiatry 2023, 13, 45. [Google Scholar] [CrossRef]
- Cheng, W.; Ji, X.; Zhang, J.; Feng, J. Individual Classification of ADHD Patients by Integrating Multiscale Neuroimaging Markers and Advanced Pattern Recognition Techniques. Front. Syst. Neurosci. 2012, 6, 58. [Google Scholar] [CrossRef]
- Taspinar, G.; Ozkurt, N. A Review of ADHD Detection Studies with Machine Learning Methods Using rsfMRI Data. NMR Biomed. 2024, 37, e5138. [Google Scholar] [CrossRef]
- Campos-Ugaz, W.A.; Palacios Garay, J.P.; Rivera-Lozada, O.; Alarcón Diaz, M.A.; Fuster-Guillén, D.; Tejada Arana, A.A. An Overview of Bipolar Disorder Diagnosis Using Machine Learning Approaches: Clinical Opportunities and Challenges. Iran. J. Psychiatry 2023, 18, 237–247. [Google Scholar] [CrossRef]
- Kim, M.; Seo, J.W.; Yun, S.; Kim, M. Bidirectional Connectivity Alterations in Schizophrenia: A Multivariate, Machine-Learning Approach. Front. Psychiatry 2023, 14, 1232015. [Google Scholar] [CrossRef]
- Tavakoli, H.; Rostami, R.; Shalbaf, R.; Nazem-Zadeh, M.-R. Diagnosis of Schizophrenia and Its Subtypes Using MRI and Machine Learning. medRxiv 2024. [Google Scholar] [CrossRef]
- Blume, C.; del Giudice, R.; Wislowska, M.; Lechinger, J.; Schabus, M. Across the Consciousness Continuum—From Unresponsive Wakefulness to Sleep. Front. Hum. Neurosci. 2015, 9, 105. [Google Scholar] [CrossRef] [PubMed]
- Martial, C.; Cassol, H.; Laureys, S.; Gosseries, O. Near-Death Experience as a Probe to Explore (Disconnected) Consciousness. Trends Cogn. Sci. 2020, 24, 173–183. [Google Scholar] [CrossRef]
- Bayne, T.; Hohwy, J.; Owen, A.M. Are There Levels of Consciousness? Trends Cogn. Sci. 2016, 20, 405–413. [Google Scholar] [CrossRef]
- Schartner, M.M.; Carhart-Harris, R.L.; Barrett, A.B.; Seth, A.K.; Muthukumaraswamy, S.D. Increased Spontaneous MEG Signal Diversity for Psychoactive Doses of Ketamine, LSD and Psilocybin. Sci. Rep. 2017, 7, 46421. [Google Scholar] [CrossRef]
- Luppi, A.I.; Carhart-Harris, R.L.; Roseman, L.; Pappas, I.; Menon, D.K.; Stamatakis, E.A. LSD Alters Dynamic Integration and Segregation in the Human Brain. NeuroImage 2021, 227, 117653. [Google Scholar] [CrossRef] [PubMed]
- Lord, L.-D.; Carletti, T.; Fernandes, H.; Turkheimer, F.E.; Expert, P. Altered Dynamical Integration/Segregation Balance during Anesthesia-Induced Loss of Consciousness. Front. Netw. Physiol. 2023, 3, 1279646. [Google Scholar] [CrossRef] [PubMed]
- Bukhari, Q.; Schroeter, A.; Rudin, M. Increasing Isoflurane Dose Reduces Homotopic Correlation and Functional Segregation of Brain Networks in Mice as Revealed by Resting-State fMRI. Sci. Rep. 2018, 8, 10591. [Google Scholar] [CrossRef]
- Makris, N.; Biederman, J.; Valera, E.M.; Bush, G.; Kaiser, J.; Kennedy, D.N.; Caviness, V.S.; Faraone, S.V.; Seidman, L.J. Cortical Thinning of the Attention and Executive Function Networks in Adults with Attention-Deficit/Hyperactivity Disorder. Cereb. Cortex 2007, 17, 1364–1375. [Google Scholar] [CrossRef]
- Niesters, M.; Khalili-Mahani, N.; Martini, C.; Aarts, L.; Van Gerven, J.; Van Buchem, M.A.; Dahan, A.; Rombouts, S. Effect of Subanesthetic Ketamine on Intrinsic Functional Brain Connectivity. Anesthesiology 2012, 117, 868–877. [Google Scholar] [CrossRef]
- Eshaghzadeh Torbati, M.; Minhas, D.S.; Ahmad, G.; O’Connor, E.E.; Muschelli, J.; Laymon, C.M.; Yang, Z.; Cohen, A.D.; Aizenstein, H.J.; Klunk, W.E.; et al. A Multi-Scanner Neuroimaging Data Harmonization Using RAVEL and ComBat. NeuroImage 2021, 245, 118703. [Google Scholar] [CrossRef]
Dataset | Subjects (Male/Female) | Age (Year) | Scanner, TR (s) | Time Points |
---|---|---|---|---|
NREM2 sleep | 29 (17/16) | 22.1 ± 3.2 | 3 T, 2.1 | 2971 ± 833 |
Propofol deep sedation-1 | 26 (13/13) | 25.0 ± 4.1 | 3 T, 0.8 | 8370 |
Propofol deep sedation-2 | 26 (9/17) | 24.3 ± 5.2 | 3 T, 1.4 | 5576 |
Propofol general anesthesia | 26 (12/14) | 47.0 ± 11.4 | 3 T, 2.0 | 480 |
LSD | 15 (10/5) | 38.4 ± 8.6 | 3 T, 2.0 | 868 |
Ketamine | 12 (7/5) | 46.8 ± 13.4 | 3 T, 2.0 | 1628 ± 189 |
N2O | 16 (8/8) | 24.6 ± 3.7 | 3 T, 2.0 | 360 |
Neuropsychiatric disorders | 272 (155/117) (healthy: 130; ADHD: 43; bipolar: 49; schizophrenia: 50) | 33.0 ± 9.2 | 3 T, 2.0 | 150 |
Condition | Best Hyperparameter (Box Constraints/Kernel Scale) | |||||
noGSR Connectivity | GSR Connectivity | noGSR Graph | GSR Graph | Gradient | Feature Integration | |
NREM2 sleep | 10−5/10−3 | 10−5/10−3 | 10−5/10−3 | 10−6/10−3 | 10−5/10−3 | 10−6/10−3 |
Propofol deep sedation | 10−5/10−3 | 103/10−3 | 10−6/10−4 | 10−5/10−3 | 10−5/10−3 | 103/10−3 |
Propofol general anesthesia | 102/10−3 | 103/10−3 | 103/10−4 | 10−5/10−1 | 10−5/10−3 | 103/10−3 |
LSD | 10−2/10−1 | 10−6/10−4 | 103/10−2 | 10−6/102 | 100/100 | 10−6/10−3 |
Ketamine | 10−6/10−3 | 103/10−4 | 10−6/10−4 | 103/100 | 103/10−4 | 103/10−3 |
N2O | 10−6/10−3 | 10−6/10−4 | 10−6/10−4 | 10−6/10−4 | 103/10−4 | 10−6/10−3 |
ADHD | 10−6/102 | 10−5/10−3 | 10−6/10−3 | 10−6/102 | 10−6/102 | 10−6/10−3 |
Bipolar disorder | 10−6/10−3 | 10−5/10−3 | 10−5/10−3 | 10−6/10−3 | 10−6/10−3 | 10−6/10−3 |
Schizophrenia | 10−5/10−3 | 10−5/10−3 | 10−5/10−3 | 10−5/10−3 | 10−5/10−3 | 10−6/10−3 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Jang, H.; Dai, R.; Mashour, G.A.; Hudetz, A.G.; Huang, Z. Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis. Brain Sci. 2024, 14, 880. https://doi.org/10.3390/brainsci14090880
Jang H, Dai R, Mashour GA, Hudetz AG, Huang Z. Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis. Brain Sciences. 2024; 14(9):880. https://doi.org/10.3390/brainsci14090880
Chicago/Turabian StyleJang, Hyunwoo, Rui Dai, George A. Mashour, Anthony G. Hudetz, and Zirui Huang. 2024. "Classifying Unconscious, Psychedelic, and Neuropsychiatric Brain States with Functional Connectivity, Graph Theory, and Cortical Gradient Analysis" Brain Sciences 14, no. 9: 880. https://doi.org/10.3390/brainsci14090880