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Dezhina Z, Smallwood J, Xu T, Turkheimer FE, Moran RJ, Friston KJ, Leech R, Fagerholm ED. Establishing brain states in neuroimaging data. PLoS Comput Biol 2023; 19:e1011571. [PMID: 37844124 PMCID: PMC10602380 DOI: 10.1371/journal.pcbi.1011571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 10/26/2023] [Accepted: 10/04/2023] [Indexed: 10/18/2023] Open
Abstract
The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience-from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the 'state' of a system-i.e., a specification of the system's future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets.
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Affiliation(s)
- Zalina Dezhina
- Department of Neuroimaging, King’s College London, United Kingdom
| | | | - Ting Xu
- Child Mind Institute, New York, United States of America
| | | | - Rosalyn J. Moran
- Department of Neuroimaging, King’s College London, United Kingdom
| | | | - Robert Leech
- Department of Neuroimaging, King’s College London, United Kingdom
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Blevins AS, Bassett DS, Scott EK, Vanwalleghem GC. From calcium imaging to graph topology. Netw Neurosci 2022; 6:1125-1147. [PMID: 38800465 PMCID: PMC11117109 DOI: 10.1162/netn_a_00262] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 06/13/2022] [Indexed: 05/29/2024] Open
Abstract
Systems neuroscience is facing an ever-growing mountain of data. Recent advances in protein engineering and microscopy have together led to a paradigm shift in neuroscience; using fluorescence, we can now image the activity of every neuron through the whole brain of behaving animals. Even in larger organisms, the number of neurons that we can record simultaneously is increasing exponentially with time. This increase in the dimensionality of the data is being met with an explosion of computational and mathematical methods, each using disparate terminology, distinct approaches, and diverse mathematical concepts. Here we collect, organize, and explain multiple data analysis techniques that have been, or could be, applied to whole-brain imaging, using larval zebrafish as an example model. We begin with methods such as linear regression that are designed to detect relations between two variables. Next, we progress through network science and applied topological methods, which focus on the patterns of relations among many variables. Finally, we highlight the potential of generative models that could provide testable hypotheses on wiring rules and network progression through time, or disease progression. While we use examples of imaging from larval zebrafish, these approaches are suitable for any population-scale neural network modeling, and indeed, to applications beyond systems neuroscience. Computational approaches from network science and applied topology are not limited to larval zebrafish, or even to systems neuroscience, and we therefore conclude with a discussion of how such methods can be applied to diverse problems across the biological sciences.
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Affiliation(s)
- Ann S. Blevins
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA
- Santa Fe Institute, Santa Fe, NM, USA
| | - Ethan K. Scott
- Queensland Brain Institute, University of Queensland, Brisbane, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, University of Melbourne, Parkville, Australia
| | - Gilles C. Vanwalleghem
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Molecular Biology and Genetics, Aarhus University, Aarhus, Denmark
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Cocina F, Vitalis A, Caflisch A. Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions. eNeuro 2021; 8:ENEURO.0484-20.2021. [PMID: 34544761 PMCID: PMC8577062 DOI: 10.1523/eneuro.0484-20.2021] [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: 11/13/2020] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/24/2022] Open
Abstract
The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.
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Affiliation(s)
- Francesco Cocina
- Biochemistry department, University of Zurich, Zurich, Switzerland CH-8057
| | - Andreas Vitalis
- Biochemistry department, University of Zurich, Zurich, Switzerland CH-8057
| | - Amedeo Caflisch
- Biochemistry department, University of Zurich, Zurich, Switzerland CH-8057
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Zendrikov D, Paraskevov A. Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2020.11.073] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Masumori A, Sinapayen L, Maruyama N, Mita T, Bakkum D, Frey U, Takahashi H, Ikegami T. Neural Autopoiesis: Organizing Self-Boundaries by Stimulus Avoidance in Biological and Artificial Neural Networks. ARTIFICIAL LIFE 2020; 26:130-151. [PMID: 32027532 DOI: 10.1162/artl_a_00314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Living organisms must actively maintain themselves in order to continue existing. Autopoiesis is a key concept in the study of living organisms, where the boundaries of the organism are not static but dynamically regulated by the system itself. To study the autonomous regulation of a self-boundary, we focus on neural homeodynamic responses to environmental changes using both biological and artificial neural networks. Previous studies showed that embodied cultured neural networks and spiking neural networks with spike-timing dependent plasticity (STDP) learn an action as they avoid stimulation from outside. In this article, as a result of our experiments using embodied cultured neurons, we find that there is also a second property allowing the network to avoid stimulation: If the agent cannot learn an action to avoid the external stimuli, it tends to decrease the stimulus-evoked spikes, as if to ignore the uncontrollable input. We also show such a behavior is reproduced by spiking neural networks with asymmetric STDP. We consider that these properties are to be regarded as autonomous regulation of self and nonself for the network, in which a controllable neuron is regarded as self, and an uncontrollable neuron is regarded as nonself. Finally, we introduce neural autopoiesis by proposing the principle of stimulus avoidance.
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Affiliation(s)
- Atsushi Masumori
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
| | - Lana Sinapayen
- Sony Computer Science Laboratories
- Tokyo Institute of Technology, Earth-Life Science Institute.
| | - Norihiro Maruyama
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
| | - Takeshi Mita
- University of Tokyo, Department of Mechano-Informatics, Graduate School of Information Science and Technology.
| | - Douglas Bakkum
- ETH Zurich, Department of Biosystems Science and Engineering.
| | | | - Hirokazu Takahashi
- University of Tokyo, Department of Mechano-Informatics, Graduate School of Information Science and Technology.
| | - Takashi Ikegami
- University of Tokyo, Department of General Systems Sciences, Graduate School of Arts and Sciences.
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Capone C, Gigante G, Del Giudice P. Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data. Sci Rep 2018; 8:17056. [PMID: 30451957 PMCID: PMC6242821 DOI: 10.1038/s41598-018-35433-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 10/28/2018] [Indexed: 11/09/2022] Open
Abstract
Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principled modification of a widely used generalized linear model (GLM), and learn its structural and dynamic parameters from in-vitro spike data. The spontaneous activity of the new model captures prominent features of the non-stationary and non-linear dynamics displayed by the biological network, where the reference GLM largely fails, and also reflects fine-grained spatio-temporal dynamical features. Two ingredients were key for success. The first is a saturating transfer function: beyond its biological plausibility, it limits the neuron's information transfer, improving robustness against endogenous and external noise. The second is a super-Poisson spikes generative mechanism; it accounts for the undersampling of the network, and allows the model neuron to flexibly incorporate the observed activity fluctuations.
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Affiliation(s)
- Cristiano Capone
- Physics department, "Sapienza" University, Rome, Italy
- INFN, Sezione di Roma, Rome, Italy
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