1
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Ye C, Zhang Y, Ran C, Ma T. Recent Progress in Brain Network Models for Medical Applications: A Review. HEALTH DATA SCIENCE 2024; 4:0157. [PMID: 38979037 PMCID: PMC11227951 DOI: 10.34133/hds.0157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024]
Abstract
Importance: Pathological perturbations of the brain often spread via connectome to fundamentally alter functional consequences. By integrating multimodal neuroimaging data with mathematical neural mass modeling, brain network models (BNMs) enable to quantitatively characterize aberrant network dynamics underlying multiple neurological and psychiatric disorders. We delved into the advancements of BNM-based medical applications, discussed the prevalent challenges within this field, and provided possible solutions and future directions. Highlights: This paper reviewed the theoretical foundations and current medical applications of computational BNMs. Composed of neural mass models, the BNM framework allows to investigate large-scale brain dynamics behind brain diseases by linking the simulated functional signals to the empirical neurophysiological data, and has shown promise in exploring neuropathological mechanisms, elucidating therapeutic effects, and predicting disease outcome. Despite that several limitations existed, one promising trend of this research field is to precisely guide clinical neuromodulation treatment based on individual BNM simulation. Conclusion: BNM carries the potential to help understand the mechanism underlying how neuropathology affects brain network dynamics, further contributing to decision-making in clinical diagnosis and treatment. Several constraints must be addressed and surmounted to pave the way for its utilization in the clinic.
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Affiliation(s)
- Chenfei Ye
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Yixuan Zhang
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Chen Ran
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
| | - Ting Ma
- International Research Institute for Artificial Intelligence,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Department of Electronic and Information Engineering,
Harbin Institute of Technology at Shenzhen, Shenzhen, China
- Peng Cheng Laboratory, Shenzhen, China
- Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology,
Harbin Institute of Technology at Shenzhen, China
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2
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Wang HE, Triebkorn P, Breyton M, Dollomaja B, Lemarechal JD, Petkoski S, Sorrentino P, Depannemaecker D, Hashemi M, Jirsa VK. Virtual brain twins: from basic neuroscience to clinical use. Natl Sci Rev 2024; 11:nwae079. [PMID: 38698901 PMCID: PMC11065363 DOI: 10.1093/nsr/nwae079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 02/05/2024] [Accepted: 02/20/2024] [Indexed: 05/05/2024] Open
Abstract
Virtual brain twins are personalized, generative and adaptive brain models based on data from an individual's brain for scientific and clinical use. After a description of the key elements of virtual brain twins, we present the standard model for personalized whole-brain network models. The personalization is accomplished using a subject's brain imaging data by three means: (1) assemble cortical and subcortical areas in the subject-specific brain space; (2) directly map connectivity into the brain models, which can be generalized to other parameters; and (3) estimate relevant parameters through model inversion, typically using probabilistic machine learning. We present the use of personalized whole-brain network models in healthy ageing and five clinical diseases: epilepsy, Alzheimer's disease, multiple sclerosis, Parkinson's disease and psychiatric disorders. Specifically, we introduce spatial masks for relevant parameters and demonstrate their use based on the physiological and pathophysiological hypotheses. Finally, we pinpoint the key challenges and future directions.
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Affiliation(s)
- Huifang E Wang
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Paul Triebkorn
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Martin Breyton
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
- Service de Pharmacologie Clinique et Pharmacosurveillance, AP–HM, Marseille, 13005, France
| | - Borana Dollomaja
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Jean-Didier Lemarechal
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Spase Petkoski
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Pierpaolo Sorrentino
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Damien Depannemaecker
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Meysam Hashemi
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
| | - Viktor K Jirsa
- Aix Marseille Université, Institut National de la Santé et de la Recherche Médicale, Institut de Neurosciences des Systèmes (INS) UMR1106; Marseille 13005, France
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3
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Powers A, Angelos P, Bond A, Farina E, Fredericks C, Gandhi J, Greenwald M, Hernandez-Busot G, Hosein G, Kelley M, Mourgues C, Palmer W, Rodriguez-Sanchez J, Seabury R, Toribio S, Vin R, Weleff J, Benrimoh D. A computational account of the development and evolution of psychotic symptoms. ARXIV 2024:arXiv:2404.10954v1. [PMID: 38699166 PMCID: PMC11065053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
The mechanisms of psychotic symptoms like hallucinations and delusions are often investigated in fully-formed illness, well after symptoms emerge. These investigations have yielded key insights, but are not well-positioned to reveal the dynamic forces underlying symptom formation itself. Understanding symptom development over time would allow us to identify steps in the pathophysiological process leading to psychosis, shifting the focus of psychiatric intervention from symptom alleviation to prevention. We propose a model for understanding the emergence of psychotic symptoms within the context of an adaptive, developing neural system. We will make the case for a pathophysiological process that begins with cortical hyperexcitability and bottom-up noise transmission, which engenders inappropriate belief formation via aberrant prediction error signaling. We will argue that this bottom-up noise drives learning about the (im)precision of new incoming sensory information because of diminished signal-to-noise ratio, causing an adaptive relative over-reliance on prior beliefs. This over-reliance on priors predisposes to hallucinations and covaries with hallucination severity. An over-reliance on priors may also lead to increased conviction in the beliefs generated by bottom-up noise and drive movement toward conversion to psychosis. We will identify predictions of our model at each stage, examine evidence to support or refute those predictions, and propose experiments that could falsify or help select between alternative elements of the overall model. Nesting computational abnormalities within longitudinal development allows us to account for hidden dynamics among the mechanisms driving symptom formation and to view established symptomatology as a point of equilibrium among competing biological forces.
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Affiliation(s)
- Albert Powers
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Philip Angelos
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Alexandria Bond
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Emily Farina
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Carolyn Fredericks
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jay Gandhi
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Maximillian Greenwald
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | | | - Gabriel Hosein
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Megan Kelley
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Catalina Mourgues
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - William Palmer
- Yale University Department of Psychology, New Haven, CT USA
| | | | - Rashina Seabury
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Silmilly Toribio
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Raina Vin
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - Jeremy Weleff
- Yale University School of Medicine and the Connecticut Mental Health Center, New Haven, CT, USA
| | - David Benrimoh
- Department of Psychiatry, McGill University, Montreal, Canada
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Miedema R, Strydis C. ExaFlexHH: an exascale-ready, flexible multi-FPGA library for biologically plausible brain simulations. Front Neuroinform 2024; 18:1330875. [PMID: 38680548 PMCID: PMC11045893 DOI: 10.3389/fninf.2024.1330875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 02/05/2024] [Indexed: 05/01/2024] Open
Abstract
Introduction In-silico simulations are a powerful tool in modern neuroscience for enhancing our understanding of complex brain systems at various physiological levels. To model biologically realistic and detailed systems, an ideal simulation platform must possess: (1) high performance and performance scalability, (2) flexibility, and (3) ease of use for non-technical users. However, most existing platforms and libraries do not meet all three criteria, particularly for complex models such as the Hodgkin-Huxley (HH) model or for complex neuron-connectivity modeling such as gap junctions. Methods This work introduces ExaFlexHH, an exascale-ready, flexible library for simulating HH models on multi-FPGA platforms. Utilizing FPGA-based Data-Flow Engines (DFEs) and the dataflow programming paradigm, ExaFlexHH addresses all three requirements. The library is also parameterizable and compliant with NeuroML, a prominent brain-description language in computational neuroscience. We demonstrate the performance scalability of the platform by implementing a highly demanding extended-Hodgkin-Huxley (eHH) model of the Inferior Olive using ExaFlexHH. Results Model simulation results show linear scalability for unconnected networks and near-linear scalability for networks with complex synaptic plasticity, with a 1.99 × performance increase using two FPGAs compared to a single FPGA simulation, and 7.96 × when using eight FPGAs in a scalable ring topology. Notably, our results also reveal consistent performance efficiency in GFLOPS per watt, further facilitating exascale-ready computing speeds and pushing the boundaries of future brain-simulation platforms. Discussion The ExaFlexHH library shows superior resource efficiency, quantified in FLOPS per hardware resources, benchmarked against other competitive FPGA-based brain simulation implementations.
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Affiliation(s)
- Rene Miedema
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
| | - Christos Strydis
- Department of Neuroscience, Erasmus Medical Center, Rotterdam, Netherlands
- Quantum and Computer Engineering Department, Delft University of Technology, Delft, Netherlands
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5
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Gu YW, Fan JW, Zhào H, Zhao SW, Liu XF, Yu R, Ren L, Wang X, Yin H, Cui LB. Imaging Transcriptomics of the Brain for Schizophrenia. ALPHA PSYCHIATRY 2024; 25:9-14. [PMID: 38799487 PMCID: PMC11114239 DOI: 10.5152/alphapsychiatry.2024.231369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/04/2023] [Indexed: 05/29/2024]
Abstract
Schizophrenia is a severe mental disorder with a neurodevelopmental origin. Although schizophrenia results from changes in the brain, the underlying biological mechanisms are unknown. Transcriptomics studies quantitative expression changes or qualitative changes of all genes and isoforms, providing a more meaningful biological insight. Magnetic resonance imaging (MRI) techniques play roles in revealing brain structure and function. We give a narrative focused review on the current transcriptome combined with MRI studies related to schizophrenia and summarize the research methodology and content of these studies to identify the research commonalities as well as the implications for future research, in an attempt to provide new insights into the mechanism, clinical diagnosis, and treatments of schizophrenia.
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Affiliation(s)
- Yue-Wen Gu
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Jing-Wen Fan
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Hóngyi Zhào
- Department of Neurology, NO. 984 Hospital of PLA, Beijing, China
| | - Shu-Wan Zhao
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
| | - Xiao-Fan Liu
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
| | - Renqiang Yu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lei Ren
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
| | - Xinjiang Wang
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
| | - Hong Yin
- Department of Radiology, Xijing Hospital, Air Force Medical University, Xi’an, China
- Department of Radiology, Xi’an People’s Hospital (Xi’an Fourth Hospital), Xi’an, China
| | - Long-Biao Cui
- Department of Clinical Psychology, Air Force Medical University, Xi’an, China
- Department of Radiology, The Second Medical Center, Chinese PLA General Hospital, Beijing, China
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6
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Manos T, Diaz-Pier S, Fortel I, Driscoll I, Zhan L, Leow A. Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes. Front Comput Neurosci 2023; 17:1295395. [PMID: 38188355 PMCID: PMC10770256 DOI: 10.3389/fncom.2023.1295395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 12/05/2023] [Indexed: 01/09/2024] Open
Abstract
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome.
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Affiliation(s)
- Thanos Manos
- ETIS, ENSEA, CNRS, UMR8051, CY Cergy-Paris University, Cergy, France
- Laboratoire de Physique Théorique et Modélisation, UMR 8089, CNRS, Cergy-Pontoise, CY Cergy Paris Université, Cergy, France
| | - Sandra Diaz-Pier
- Simulation and Data Lab Neuroscience, Institute for Advanced Simulation, Jülich Supercomputing Centre (JSC), JARA, Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Igor Fortel
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Ira Driscoll
- Department of Psychology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States
| | - Liang Zhan
- Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alex Leow
- Department of Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, United States
- Department of Psychiatry, University of Illinois at Chicago, Chicago, IL, United States
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7
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Dickie EW, Shahab S, Hawco C, Miranda D, Herman G, Argyelan M, Ji JL, Jeyachandra J, Anticevic A, Malhotra AK, Voineskos AN. Robust hierarchically organized whole-brain patterns of dysconnectivity in schizophrenia spectrum disorders observed after personalized intrinsic network topography. Hum Brain Mapp 2023; 44:5153-5166. [PMID: 37605827 PMCID: PMC10502662 DOI: 10.1002/hbm.26453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 07/05/2023] [Accepted: 08/01/2023] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Spatial patterns of brain functional connectivity can vary substantially at the individual level. Applying cortical surface-based approaches with individualized rather than group templates may accelerate the discovery of biological markers related to psychiatric disorders. We investigated cortico-subcortical networks from multi-cohort data in people with schizophrenia spectrum disorders (SSDs) and healthy controls (HC) using individualized connectivity profiles. METHODS We utilized resting-state and anatomical MRI data from n = 406 participants (n = 203 SSD, n = 203 HC) from four cohorts. Functional timeseries were extracted from previously defined intrinsic network subregions of the striatum, thalamus, and cerebellum as well as 80 cortical regions of interest, representing six intrinsic networks using (1) volume-based approaches, (2) a surface-based group atlas approaches, and (3) Personalized Intrinsic Network Topography (PINT). RESULTS The correlations between all cortical networks and the expected subregions of the striatum, cerebellum, and thalamus were increased using a surface-based approach (Cohen's D volume vs. surface 0.27-1.00, all p < 10-6 ) and further increased after PINT (Cohen's D surface vs. PINT 0.18-0.96, all p < 10-4 ). In SSD versus HC comparisons, we observed robust patterns of dysconnectivity that were strengthened using a surface-based approach and PINT (Number of differing pairwise-correlations: volume: 404, surface: 570, PINT: 628, FDR corrected). CONCLUSION Surface-based and individualized approaches can more sensitively delineate cortical network dysconnectivity differences in people with SSDs. These robust patterns of dysconnectivity were visibly organized in accordance with the cortical hierarchy, as predicted by computational models.
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Affiliation(s)
- Erin W. Dickie
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
| | - Saba Shahab
- Department of MedicineUniversity of OttawaOttawaOntarioCanada
| | - Colin Hawco
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
| | - Dayton Miranda
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Gabrielle Herman
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Miklos Argyelan
- Psychiatry Research, The Zucker Hillside HospitalGlen CoveNew YorkUSA
- Institute of Behavioral Science, Feinstein Institutes for Medical ResearchManhassetNew YorkUSA
- Donald and Barbara Zucker School of Medicine at Hofstra/NorthwellHempsteadNew YorkUSA
| | - Jie Lisa Ji
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - Jerrold Jeyachandra
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
| | - Alan Anticevic
- Department of PsychiatryYale UniversityNew HavenConnecticutUSA
| | - Anil K. Malhotra
- Psychiatry Research, The Zucker Hillside HospitalGlen CoveNew YorkUSA
- Institute of Behavioral Science, Feinstein Institutes for Medical ResearchManhassetNew YorkUSA
- Donald and Barbara Zucker School of Medicine at Hofstra/NorthwellHempsteadNew YorkUSA
| | - Aristotle N. Voineskos
- Center for Addiction and Mental HealthCampbell Family Mental Health ResearchTorontoOntarioCanada
- Department of PsychiatryUniversity of TorontoTorontoOntarioUSA
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Manos T, Diaz-Pier S, Fortel I, Driscoll I, Zhan L, Leow A. Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.16.528836. [PMID: 36824821 PMCID: PMC9948985 DOI: 10.1101/2023.02.16.528836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activity between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC well aligns with the observed FC when compared to that simulated with traditional structural connectome. Simulations were performed using the open source framework The Virtual Brain on High Performance Computing infrastructure.
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9
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Castro Martínez JC, Santamaría-García H. Understanding mental health through computers: An introduction to computational psychiatry. Front Psychiatry 2023; 14:1092471. [PMID: 36824671 PMCID: PMC9941647 DOI: 10.3389/fpsyt.2023.1092471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Accepted: 01/16/2023] [Indexed: 02/10/2023] Open
Abstract
Computational psychiatry recently established itself as a new tool in the study of mental disorders and problems. Integration of different levels of analysis is creating computational phenotypes with clinical and research values, and constructing a way to arrive at precision psychiatry are part of this new branch. It conceptualizes the brain as a computational organ that receives from the environment parameters to respond to challenges through calculations and algorithms in continuous feedback and feedforward loops with a permanent degree of uncertainty. Through this conception, one can seize an understanding of the cerebral and mental processes in the form of theories or hypotheses based on data. Using these approximations, a better understanding of the disorder and its different determinant factors facilitates the diagnostics and treatment by having an individual, ecologic, and holistic approach. It is a tool that can be used to homologate and integrate multiple sources of information given by several theoretical models. In conclusion, it helps psychiatry achieve precision and reproducibility, which can help the mental health field achieve significant advancement. This article is a narrative review of the basis of the functioning of computational psychiatry with a critical analysis of its concepts.
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Affiliation(s)
- Juan Camilo Castro Martínez
- Departamento de Psiquiatría y Salud Mental, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Hernando Santamaría-García
- Ph.D. Programa de Neurociencias, Departamento de Psiquiatría y Salud Mental, Pontificia Universidad Javeriana, Bogotá, Colombia
- Centro de Memoria y Cognición Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia
- Global Brain Health Institute, University of California, San Francisco – Trinity College Dublin, San Francisco, CA, United States
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10
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Anticevic A, Halassa MM. The thalamus in psychosis spectrum disorder. Front Neurosci 2023; 17:1163600. [PMID: 37123374 PMCID: PMC10133512 DOI: 10.3389/fnins.2023.1163600] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Psychosis spectrum disorder (PSD) affects 1% of the world population and results in a lifetime of chronic disability, causing devastating personal and economic consequences. Developing new treatments for PSD remains a challenge, particularly those that target its core cognitive deficits. A key barrier to progress is the tenuous link between the basic neurobiological understanding of PSD and its clinical phenomenology. In this perspective, we focus on a key opportunity that combines innovations in non-invasive human neuroimaging with basic insights into thalamic regulation of functional cortical connectivity. The thalamus is an evolutionary conserved region that forms forebrain-wide functional loops critical for the transmission of external inputs as well as the construction and update of internal models. We discuss our perspective across four lines of evidence: First, we articulate how PSD symptomatology may arise from a faulty network organization at the macroscopic circuit level with the thalamus playing a central coordinating role. Second, we discuss how recent animal work has mechanistically clarified the properties of thalamic circuits relevant to regulating cortical dynamics and cognitive function more generally. Third, we present human neuroimaging evidence in support of thalamic alterations in PSD, and propose that a similar "thalamocortical dysconnectivity" seen in pharmacological imaging (under ketamine, LSD and THC) in healthy individuals may link this circuit phenotype to the common set of symptoms in idiopathic and drug-induced psychosis. Lastly, we synthesize animal and human work, and lay out a translational path for biomarker and therapeutic development.
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Affiliation(s)
- Alan Anticevic
- School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Alan Anticevic,
| | - Michael M. Halassa
- Department of Neuroscience, Tufts University School of Medicine, Boston, MA, United States
- Michael M. Halassa,
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11
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Luo L, You W, DelBello MP, Gong Q, Li F. Recent advances in psychoradiology. Phys Med Biol 2022; 67. [PMID: 36279868 DOI: 10.1088/1361-6560/ac9d1e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 10/24/2022] [Indexed: 11/24/2022]
Abstract
Abstract
Psychiatry, as a field, lacks objective markers for diagnosis, progression, treatment planning, and prognosis, in part due to difficulties studying the brain in vivo, and diagnoses are based on self-reported symptoms and observation of patient behavior and cognition. Rapid advances in brain imaging techniques allow clinical investigators to noninvasively quantify brain features at the structural, functional, and molecular levels. Psychoradiology is an emerging discipline at the intersection of psychiatry and radiology. Psychoradiology applies medical imaging technologies to psychiatry and promises not only to improve insight into structural and functional brain abnormalities in patients with psychiatric disorders but also to have potential clinical utility. We searched for representative studies related to recent advances in psychoradiology through May 1, 2022, and conducted a selective review of 165 references, including 75 research articles. We summarize the novel dynamic imaging processing methods to model brain networks and present imaging genetics studies that reveal the relationship between various neuroimaging endophenotypes and genetic markers in psychiatric disorders. Furthermore, we survey recent advances in psychoradiology, with a focus on future psychiatric diagnostic approaches with dimensional analysis and a shift from group-level to individualized analysis. Finally, we examine the application of machine learning in psychoradiology studies and the potential of a novel option for brain stimulation treatment based on psychoradiological findings in precision medicine. Here, we provide a summary of recent advances in psychoradiology research, and we hope this review will help guide the practice of psychoradiology in the scientific and clinical fields.
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12
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Kobeleva X, Varoquaux G, Dagher A, Adhikari M, Grefkes C, Gilson M. Advancing brain network models to reconcile functional neuroimaging and clinical research. Neuroimage Clin 2022; 36:103262. [PMID: 36451365 PMCID: PMC9723311 DOI: 10.1016/j.nicl.2022.103262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/26/2022] [Accepted: 11/06/2022] [Indexed: 11/09/2022]
Abstract
Functional magnetic resonance imaging (fMRI) captures information on brain function beyond the anatomical alterations that are traditionally visually examined by neuroradiologists. However, the fMRI signals are complex in addition to being noisy, so fMRI still faces limitations for clinical applications. Here we review methods that have been proposed as potential solutions so far, namely statistical, biophysical and decoding models, with their strengths and weaknesses. We especially evaluate the ability of these models to directly predict clinical variables from their parameters (predictability) and to extract clinically relevant information regarding biological mechanisms and relevant features for classification and prediction (interpretability). We then provide guidelines for useful applications and pitfalls of such fMRI-based models in a clinical research context, looking beyond the current state of the art. In particular, we argue that the clinical relevance of fMRI calls for a new generation of models for fMRI data, which combine the strengths of both biophysical and decoding models. This leads to reliable and biologically meaningful model parameters, which thus fulfills the need for simultaneous interpretability and predictability. In our view, this synergy is fundamental for the discovery of new pharmacological and interventional targets, as well as the use of models as biomarkers in neurology and psychiatry.
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Affiliation(s)
- Xenia Kobeleva
- Department of Neurology, University of Bonn, Bonn, Germany; German Center for Neurodegenerative Diseases (DZNE) Bonn, Bonn, Germany
| | | | - Alain Dagher
- Montreal Neurological Institute, McGill University, Montréal, Canada
| | - Mohit Adhikari
- Bio-imaging Lab, University of Antwerp, Antwerp, Belgium
| | - Christian Grefkes
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany; Institute of Neuroscience and Medicine (INM-1, INM-3), Research Centre Juelich, Juelich, Germany; Department of Neurology, Goethe University Frankfurt, Frankfurt, Germany
| | - Matthieu Gilson
- Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany; Center for Brain and Cognition, Department of Information and Telecommunication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut de Neurosciences des Systèmes, Aix-Marseille University, Marseille, France.
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13
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Yurchenko SB. From the origins to the stream of consciousness and its neural correlates. Front Integr Neurosci 2022; 16:928978. [PMID: 36407293 PMCID: PMC9672924 DOI: 10.3389/fnint.2022.928978] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 10/12/2022] [Indexed: 09/22/2023] Open
Abstract
There are now dozens of very different theories of consciousness, each somehow contributing to our understanding of its nature. The science of consciousness needs therefore not new theories but a general framework integrating insights from those, yet not making it a still-born "Frankenstein" theory. First, the framework must operate explicitly on the stream of consciousness, not on its static description. Second, this dynamical account must also be put on the evolutionary timeline to explain the origins of consciousness. The Cognitive Evolution Theory (CET), outlined here, proposes such a framework. This starts with the assumption that brains have primarily evolved as volitional subsystems of organisms, inherited from primitive (fast and random) reflexes of simplest neural networks, only then resembling error-minimizing prediction machines. CET adopts the tools of critical dynamics to account for metastability, scale-free avalanches, and self-organization which are all intrinsic to brain dynamics. This formalizes the stream of consciousness as a discrete (transitive, irreflexive) chain of momentary states derived from critical brain dynamics at points of phase transitions and mapped then onto a state space as neural correlates of a particular conscious state. The continuous/discrete dichotomy appears naturally between the brain dynamics at the causal level and conscious states at the phenomenal level, each volitionally triggered from arousal centers of the brainstem and cognitively modulated by thalamocortical systems. Their objective observables can be entropy-based complexity measures, reflecting the transient level or quantity of consciousness at that moment.
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14
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Piccinini J, Deco G, Kringelbach M, Laufs H, Sanz Perl Y, Tagliazucchi E. Data-driven discovery of canonical large-scale brain dynamics. Cereb Cortex Commun 2022; 3:tgac045. [PMID: 36479448 PMCID: PMC9721525 DOI: 10.1093/texcom/tgac045] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/28/2022] [Accepted: 10/30/2022] [Indexed: 09/23/2023] Open
Abstract
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, whereas deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.
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Affiliation(s)
- Juan Piccinini
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
| | - Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Department of Information and Communication Technologies, Universitat Pompeu Fabra Plaça de la Mercè, 10-12, 08002 Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avancats (ICREA), Passeig Lluís Companys 23 08010 Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
- School of Psychological Sciences, Monash University, Wellington Rd, Clayton VIC 3800, Australia
| | - Morten Kringelbach
- Department of Psychiatry, University of Oxford, Warneford Hospital, Warneford Ln, Headington, Oxford OX3 7JX, United Kingdom
- Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Building 1710, Universitetsbyen 3, 8000 Aarhus C, Denmark
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Campus de Gualtar 4710-057 Braga, Portugal
- Centre for Eudaimonia and Human Flourishing, University of Oxford, 7 Stoke Pl, Headington, Oxford OX3 9BX, United Kingdom
| | - Helmut Laufs
- Neurology Department, Schleswig-Holstein University Hospital, University of Kiel, Haus D, Arnold-Heller-Straße 3, 24105 Kiel, Germany
| | - Yonatan Sanz Perl
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Vito Dumas 284 Victoria, Buenos Aires, Argentina
- Paris Brain Institute (ICM), Hôpital Pitié, 47 Bd de l'Hôpital, 75013 Paris, France
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Intendente Guiraldes 2160 Ciudad Universitaria, CABA, Argentina
- National Scientific and Technical Research Council (CONICET), Godoy Cruz 2290, CABA, Argentina
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibañez, Diagonal las Torres 2640, Peñalolén Av. Presidente Errázuriz 3485, Las Condes, Santiago, Chile
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15
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Burt JB, Preller KH, Demirtas M, Ji JL, Krystal JH, Vollenweider FX, Anticevic A, Murray JD. Transcriptomics-informed large-scale cortical model captures topography of pharmacological neuroimaging effects of LSD. eLife 2021; 10:e69320. [PMID: 34313217 PMCID: PMC8315798 DOI: 10.7554/elife.69320] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 06/08/2021] [Indexed: 11/13/2022] Open
Abstract
Psychoactive drugs can transiently perturb brain physiology while preserving brain structure. The role of physiological state in shaping neural function can therefore be investigated through neuroimaging of pharmacologically induced effects. Previously, using pharmacological neuroimaging, we found that neural and experiential effects of lysergic acid diethylamide (LSD) are attributable to agonism of the serotonin-2A receptor (Preller et al., 2018). Here, we integrate brain-wide transcriptomics with biophysically based circuit modeling to simulate acute neuromodulatory effects of LSD on human cortical large-scale spatiotemporal dynamics. Our model captures the inter-areal topography of LSD-induced changes in cortical blood oxygen level-dependent (BOLD) functional connectivity. These findings suggest that serotonin-2A-mediated modulation of pyramidal-neuronal gain is a circuit mechanism through which LSD alters cortical functional topography. Individual-subject model fitting captures patterns of individual neural differences in pharmacological response related to altered states of consciousness. This work establishes a framework for linking molecular-level manipulations to systems-level functional alterations, with implications for precision medicine.
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Affiliation(s)
- Joshua B Burt
- Department of Psychiatry, Yale UniversityNew HavenUnited States
| | - Katrin H Preller
- Pharmaco-Neuroimaging and Cognitive-Emotional Processing, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Murat Demirtas
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Jie Lisa Ji
- Department of Psychiatry, Yale UniversityNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - John H Krystal
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
| | - Franz X Vollenweider
- Neuropsychopharmacology and Brain Imaging, Department of Psychiatry, Psychotherapy and Psychosomatics, University Hospital for Psychiatry ZurichZurichSwitzerland
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
| | - John D Murray
- Department of Psychiatry, Yale UniversityNew HavenUnited States
- Department of Psychiatry, Yale University School of MedicineNew HavenUnited States
- Interdepartmental Neuroscience Program, Yale UniversityNew HavenUnited States
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16
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Cofré R, Herzog R, Mediano PA, Piccinini J, Rosas FE, Sanz Perl Y, Tagliazucchi E. Whole-Brain Models to Explore Altered States of Consciousness from the Bottom Up. Brain Sci 2020; 10:E626. [PMID: 32927678 PMCID: PMC7565030 DOI: 10.3390/brainsci10090626] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 09/03/2020] [Accepted: 09/07/2020] [Indexed: 01/16/2023] Open
Abstract
The scope of human consciousness includes states departing from what most of us experience as ordinary wakefulness. These altered states of consciousness constitute a prime opportunity to study how global changes in brain activity relate to different varieties of subjective experience. We consider the problem of explaining how global signatures of altered consciousness arise from the interplay between large-scale connectivity and local dynamical rules that can be traced to known properties of neural tissue. For this purpose, we advocate a research program aimed at bridging the gap between bottom-up generative models of whole-brain activity and the top-down signatures proposed by theories of consciousness. Throughout this paper, we define altered states of consciousness, discuss relevant signatures of consciousness observed in brain activity, and introduce whole-brain models to explore the biophysics of altered consciousness from the bottom-up. We discuss the potential of our proposal in view of the current state of the art, give specific examples of how this research agenda might play out, and emphasize how a systematic investigation of altered states of consciousness via bottom-up modeling may help us better understand the biophysical, informational, and dynamical underpinnings of consciousness.
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Affiliation(s)
- Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso 2340000, Chile
| | - Rubén Herzog
- Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso 2360103, Chile;
| | - Pedro A.M. Mediano
- Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK;
| | - Juan Piccinini
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires C1428EGA, Argentina
| | - Fernando E. Rosas
- Centre for Psychedelic Research, Department of Brain Science, Imperial College London, London SW7 2DD, UK;
- Data Science Institute, Imperial College London, London SW7 2AZ, UK
- Centre for Complexity Science, Imperial College London, London SW7 2AZ, UK
| | - Yonatan Sanz Perl
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Departamento de Matemáticas y Ciencias, Universidad de San Andrés, Buenos Aires B1644BID, Argentina
| | - Enzo Tagliazucchi
- National Scientific and Technical Research Council, Buenos Aires C1033AAJ, Argentina; (J.P.); (Y.S.P.); (E.T.)
- Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires C1428EGA, Argentina
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17
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Papadopoulos L, Lynn CW, Battaglia D, Bassett DS. Relations between large-scale brain connectivity and effects of regional stimulation depend on collective dynamical state. PLoS Comput Biol 2020; 16:e1008144. [PMID: 32886673 PMCID: PMC7537889 DOI: 10.1371/journal.pcbi.1008144] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2020] [Revised: 10/06/2020] [Accepted: 07/12/2020] [Indexed: 01/09/2023] Open
Abstract
At the macroscale, the brain operates as a network of interconnected neuronal populations, which display coordinated rhythmic dynamics that support interareal communication. Understanding how stimulation of different brain areas impacts such activity is important for gaining basic insights into brain function and for further developing therapeutic neurmodulation. However, the complexity of brain structure and dynamics hinders predictions regarding the downstream effects of focal stimulation. More specifically, little is known about how the collective oscillatory regime of brain network activity—in concert with network structure—affects the outcomes of perturbations. Here, we combine human connectome data and biophysical modeling to begin filling these gaps. By tuning parameters that control collective system dynamics, we identify distinct states of simulated brain activity and investigate how the distributed effects of stimulation manifest at different dynamical working points. When baseline oscillations are weak, the stimulated area exhibits enhanced power and frequency, and due to network interactions, activity in this excited frequency band propagates to nearby regions. Notably, beyond these linear effects, we further find that focal stimulation causes more distributed modifications to interareal coherence in a band containing regions’ baseline oscillation frequencies. Importantly, depending on the dynamical state of the system, these broadband effects can be better predicted by functional rather than structural connectivity, emphasizing a complex interplay between anatomical organization, dynamics, and response to perturbation. In contrast, when the network operates in a regime of strong regional oscillations, stimulation causes only slight shifts in power and frequency, and structural connectivity becomes most predictive of stimulation-induced changes in network activity patterns. In sum, this work builds upon and extends previous computational studies investigating the impacts of stimulation, and underscores the fact that both the stimulation site, and, crucially, the regime of brain network dynamics, can influence the network-wide responses to local perturbations. Stimulation can be used to alter brain activity and is a therapeutic option for certain neurological conditions. However, predicting the distributed effects of local perturbations is difficult. Previous studies show that responses to stimulation depend on anatomical (or structural) coupling. In addition to structure, here we consider how stimulation effects also depend on the brain’s collective dynamical (or functional) state, arising from the coordination of rhythmic activity across large-scale networks. In a whole-brain computational model, we show that global responses to regional stimulation can indeed be contingent upon and differ across various dynamical working points. Notably, depending on the network’s oscillatory regime, stimulation can accelerate the activity of the stimulated site, and lead to widespread effects at both the new, excited frequency, as well as in a much broader frequency range including areas’ baseline frequencies. While structural connectivity is a good predictor of “excited band” changes, in some states “baseline band” effects can be better predicted by functional connectivity, which depends upon the system’s oscillatory regime. By integrating and extending past efforts, our results thus indicate that dynamical—in additional to structural—brain organization plays a role in governing how focal stimulation modulates interactions between distributed network elements.
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Affiliation(s)
- Lia Papadopoulos
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Christopher W. Lynn
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Demian Battaglia
- Université Aix-Marseille, INSERM UMR 1106, Institut de Neurosciences des Systèmes, F-13005, Marseille, France
| | - Danielle S. Bassett
- Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
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18
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Mujica-Parodi LR, Strey HH. Making Sense of Computational Psychiatry. Int J Neuropsychopharmacol 2020; 23:339-347. [PMID: 32219396 PMCID: PMC7251632 DOI: 10.1093/ijnp/pyaa013] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
In psychiatry we often speak of constructing "models." Here we try to make sense of what such a claim might mean, starting with the most fundamental question: "What is (and isn't) a model?" We then discuss, in a concrete measurable sense, what it means for a model to be useful. In so doing, we first identify the added value that a computational model can provide in the context of accuracy and power. We then present limitations of standard statistical methods and provide suggestions for how we can expand the explanatory power of our analyses by reconceptualizing statistical models as dynamical systems. Finally, we address the problem of model building-suggesting ways in which computational psychiatry can escape the potential for cognitive biases imposed by classical hypothesis-driven research, exploiting deep systems-level information contained within neuroimaging data to advance our understanding of psychiatric neuroscience.
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Affiliation(s)
- Lilianne R Mujica-Parodi
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York,Correspondence: Lilianne R. Mujica-Parodi, PhD, Director, Laboratory for Computational Neurodiagnostics, Professor, Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook, NY 11794-5281 () or Helmut H. Strey, PhD, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794-5281 ()
| | - Helmut H Strey
- Department of Biomedical Engineering, Stony Brook University, Stony Brook, New York,Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, New York,Correspondence: Lilianne R. Mujica-Parodi, PhD, Director, Laboratory for Computational Neurodiagnostics, Professor, Department of Biomedical Engineering, Renaissance School of Medicine, Stony Brook, NY 11794-5281 () or Helmut H. Strey, PhD, Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY 11794-5281 ()
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19
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Topographic Mapping as a Basic Principle of Functional Organization for Visual and Prefrontal Functional Connectivity. eNeuro 2020; 7:ENEURO.0532-19.2019. [PMID: 31988218 PMCID: PMC7029189 DOI: 10.1523/eneuro.0532-19.2019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Accepted: 12/18/2019] [Indexed: 02/06/2023] Open
Abstract
The organization of region-to-region functional connectivity has major implications for understanding information transfer and transformation between brain regions. We extended connective field mapping methodology to 3-D anatomic space to derive estimates of corticocortical functional organization. Using multiple publicly available human (both male and female) resting-state fMRI data samples for model testing and replication analysis, we have three main findings. First, we found that the functional connectivity between early visual regions maintained a topographic relationship along the anterior-posterior dimension, which corroborates previous research. Higher order visual regions showed a pattern of connectivity that supports convergence and biased sampling, which has implications for their receptive field properties. Second, we demonstrated that topographic organization is a fundamental aspect of functional connectivity across the entire cortex, with higher topographic connectivity between regions within a functional network than across networks. The principle gradient of topographic connectivity across the cortex resembled whole-brain gradients found in previous work. Last but not least, we showed that the organization of higher order regions such as the lateral prefrontal cortex demonstrate functional gradients of topographic connectivity and convergence. These organizational features of the lateral prefrontal cortex predict task-based activation patterns, particularly visual specialization and higher order rules. In sum, these findings suggest that topographic input is a fundamental motif of functional connectivity between cortical regions for information processing and transfer, with maintenance of topography potentially important for preserving the integrity of information from one region to another.
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20
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Geerts H, Wikswo J, van der Graaf PH, Bai JPF, Gaiteri C, Bennett D, Swalley SE, Schuck E, Kaddurah-Daouk R, Tsaioun K, Pelleymounter M. Quantitative Systems Pharmacology for Neuroscience Drug Discovery and Development: Current Status, Opportunities, and Challenges. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2019; 9:5-20. [PMID: 31674729 PMCID: PMC6966183 DOI: 10.1002/psp4.12478] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/09/2019] [Indexed: 12/18/2022]
Abstract
The substantial progress made in the basic sciences of the brain has yet to be adequately translated to successful clinical therapeutics to treat central nervous system (CNS) diseases. Possible explanations include the lack of quantitative and validated biomarkers, the subjective nature of many clinical endpoints, and complex pharmacokinetic/pharmacodynamic relationships, but also the possibility that highly selective drugs in the CNS do not reflect the complex interactions of different brain circuits. Although computational systems pharmacology modeling designed to capture essential components of complex biological systems has been increasingly accepted in pharmaceutical research and development for oncology, inflammation, and metabolic disorders, the uptake in the CNS field has been very modest. In this article, a cross-disciplinary group with representatives from academia, pharma, regulatory, and funding agencies make the case that the identification and exploitation of CNS therapeutic targets for drug discovery and development can benefit greatly from a system and network approach that can span the gap between molecular pathways and the neuronal circuits that ultimately regulate brain activity and behavior. The National Institute of Neurological Disorders and Stroke (NINDS), in collaboration with the National Institute on Aging (NIA), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), and National Center for Advancing Translational Sciences (NCATS), convened a workshop to explore and evaluate the potential of a quantitative systems pharmacology (QSP) approach to CNS drug discovery and development. The objective of the workshop was to identify the challenges and opportunities of QSP as an approach to accelerate drug discovery and development in the field of CNS disorders. In particular, the workshop examined the potential for computational neuroscience to perform QSP-based interrogation of the mechanism of action for CNS diseases, along with a more accurate and comprehensive method for evaluating drug effects and optimizing the design of clinical trials. Following up on an earlier white paper on the use of QSP in general disease mechanism of action and drug discovery, this report focuses on new applications, opportunities, and the accompanying limitations of QSP as an approach to drug development in the CNS therapeutic area based on the discussions in the workshop with various stakeholders.
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Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, Pennsylvania, USA
| | - John Wikswo
- Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jane P F Bai
- Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Chris Gaiteri
- Rush Alzheimer's Disease Center, Rush University, Chicago, Illinois, USA
| | - David Bennett
- Rush Alzheimer's Disease Center, Rush University, Chicago, Illinois, USA
| | | | | | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, North Carolina, USA
| | - Katya Tsaioun
- Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Mary Pelleymounter
- Division of Translational Research, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland, USA
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21
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Saggar M, Uddin LQ. Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology. eNeuro 2019; 6:ENEURO.0384-19.2019. [PMID: 31685674 PMCID: PMC6860983 DOI: 10.1523/eneuro.0384-19.2019] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Revised: 10/22/2019] [Accepted: 10/23/2019] [Indexed: 12/22/2022] Open
Abstract
To accurately detect, track progression of, and develop novel treatments for mental illnesses, a diagnostic framework is needed that is grounded in biological features. Here, we present the case for utilizing personalized neuroimaging, computational modeling, standardized computing, and ecologically valid neuroimaging to anchor psychiatric nosology in biology.
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Affiliation(s)
- Manish Saggar
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA 94305
| | - Lucina Q Uddin
- Department of Psychology, University of Miami, Coral Gables, FL 33124
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22
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Oldham S, Fornito A. The development of brain network hubs. Dev Cogn Neurosci 2019; 36:100607. [PMID: 30579789 PMCID: PMC6969262 DOI: 10.1016/j.dcn.2018.12.005] [Citation(s) in RCA: 109] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 10/24/2018] [Accepted: 12/11/2018] [Indexed: 01/31/2023] Open
Abstract
Some brain regions have a central role in supporting integrated brain function, marking them as network hubs. Given the functional importance of hubs, it is natural to ask how they emerge during development and to consider how they shape the function of the maturing brain. Here, we review evidence examining how brain network hubs, both in structural and functional connectivity networks, develop over the prenatal, neonate, childhood, and adolescent periods. The available evidence suggests that structural hubs of the brain arise in the prenatal period and show a consistent spatial topography through development, but undergo a protracted period of consolidation that extends into late adolescence. In contrast, the hubs of brain functional networks show a more variable topography, being predominantly located in primary cortical areas in early development, before moving to association areas by late childhood. These findings suggest that while the basic anatomical infrastructure of hubs may be established early, the functional viability and integrative capacity of these areas undergoes extensive postnatal maturation. Not all findings are consistent with this view however. We consider methodological factors that might drive these inconsistencies, and which should be addressed to promote a more rigorous investigation of brain network development.
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Affiliation(s)
- Stuart Oldham
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia.
| | - Alex Fornito
- Brain and Mental Health Research Hub, School of Psychological Sciences and the Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Australia
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Demirtaş M, Burt JB, Helmer M, Ji JL, Adkinson BD, Glasser MF, Van Essen DC, Sotiropoulos SN, Anticevic A, Murray JD. Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics. Neuron 2019; 101:1181-1194.e13. [PMID: 30744986 PMCID: PMC6447428 DOI: 10.1016/j.neuron.2019.01.017] [Citation(s) in RCA: 195] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 12/04/2018] [Accepted: 01/10/2019] [Indexed: 01/20/2023]
Abstract
The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from magnetic resonance imaging (MRI)-derived T1- to T2-weighted (T1w/T2w) mapping and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to functional MRI (fMRI)-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized higher-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.
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Affiliation(s)
- Murat Demirtaş
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Joshua B. Burt
- Department of Physics, Yale University, New Haven, CT, USA,These authors contributed equally
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,These authors contributed equally
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brendan D. Adkinson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA,St. Luke’s Hospital, Saint Louis, MO, USA
| | - David C. Van Essen
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK,Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Department of Physics, Yale University, New Haven, CT, USA,Lead Contact,Correspondence:
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Abstract
The primate cerebral cortex displays a hierarchy that extends from primary sensorimotor to association areas, supporting increasingly integrated function underpinned by a gradient of heterogeneity in the brain's microcircuits. The extent to which these hierarchical gradients are unique to primate or may reflect a conserved mammalian principle of brain organization remains unknown. Here we report the topographic similarity of large-scale gradients in cytoarchitecture, gene expression, interneuron cell densities, and long-range axonal connectivity, which vary from primary sensory to prefrontal areas of mouse cortex, highlighting an underappreciated spatial dimension of mouse cortical specialization. Using the T1-weighted:T2-weighted (T1w:T2w) magnetic resonance imaging map as a common spatial reference for comparison across species, we report interspecies agreement in a range of large-scale cortical gradients, including a significant correspondence between gene transcriptional maps in mouse cortex with their human orthologs in human cortex, as well as notable interspecies differences. Our results support the view of systematic structural variation across cortical areas as a core organizational principle that may underlie hierarchical specialization in mammalian brains.
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Affiliation(s)
- Ben D Fulcher
- School of Physics, Sydney University, Sydney, NSW 2006, Australia;
| | - John D Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06511
| | - Valerio Zerbi
- Neural Control of Movement Laboratory, Department of Health Sciences and Technology, Eidgenössische Technische Hochschule Zürich, 8057 Zürich, Switzerland
| | - Xiao-Jing Wang
- Center for Neural Science, New York University, New York, NY 10003;
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
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25
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Abstract
A receptor map of serotonin distribution is integrated into a model of the dynamic activity of the brain under the effects of LSD. The approach opens new avenues to understand experimental manipulations of healthy brain activity and offers a novel drug-discovery platform.
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Affiliation(s)
- Johan van der Meer
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Michael Breakspear
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Metro North Mental Health Service, Brisbane, Queensland, Australia.
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26
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Fornito A, Zalesky A. Computational Approaches to Understanding Mental Dysfunction: Progress, Challenges, and New Frontiers. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:728-730. [PMID: 30170710 DOI: 10.1016/j.bpsc.2018.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 10/28/2022]
Affiliation(s)
- Alex Fornito
- Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neuroscience, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Australia.
| | - Andrew Zalesky
- Departments of Biomedical Engineering and Psychiatry, University of Melbourne, Melbourne, Australia
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