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Hudetz AG. Microstimulation reveals anesthetic state-dependent effective connectivity of neurons in cerebral cortex. Front Neurosci 2024; 18:1387098. [PMID: 39035779 PMCID: PMC11258030 DOI: 10.3389/fnins.2024.1387098] [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: 02/16/2024] [Accepted: 06/07/2024] [Indexed: 07/23/2024] Open
Abstract
Introduction Complex neuronal interactions underlie cortical information processing that can be compromised in altered states of consciousness. Here intracortical microstimulation was applied to investigate anesthetic state-dependent effective connectivity of neurons in rat visual cortex in vivo. Methods Extracellular activity was recorded at 32 sites in layers 5/6 while stimulating with charge-balanced discrete pulses at each electrode in random order. The same stimulation pattern was applied at three levels of anesthesia with desflurane and in wakefulness. Spikes were sorted and classified by their waveform features as putative excitatory and inhibitory neurons. Network motifs were identified in graphs of effective connectivity constructed from monosynaptic cross-correlograms. Results Microstimulation caused early (<10 ms) increase followed by prolonged (11-100 ms) decrease in spiking of all neurons throughout the electrode array. The early response of excitatory but not inhibitory neurons decayed rapidly with distance from the stimulation site over 1 mm. Effective connectivity of neurons with significant stimulus response was dense in wakefulness and sparse under anesthesia. The number of network motifs, especially those of higher order, increased rapidly as the anesthesia was withdrawn indicating a substantial increase in network connectivity as the animals woke up. Conclusion The results illuminate the impact of anesthesia on functional integrity of local cortical circuits affecting the state of consciousness.
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Affiliation(s)
- Anthony G Hudetz
- Department of Anesthesiology, Center for Consciousness Science, University of Michigan, Ann Arbor, MI, United States
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2
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Hudetz AG. Microstimulation reveals anesthetic state-dependent effective connectivity of neurons in cerebral cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.29.591664. [PMID: 38746366 PMCID: PMC11092428 DOI: 10.1101/2024.04.29.591664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Complex neuronal interactions underlie cortical information processing that can be compromised in altered states of consciousness. Here intracortical microstimulation was applied to investigate the state-dependent effective connectivity of neurons in rat visual cortex in vivo. Extracellular activity was recorded at 32 sites in layers 5/6 while stimulating with charge-balanced discrete pulses at each electrode in random order. The same stimulation pattern was applied at three levels of anesthesia with desflurane and in wakefulness. Spikes were sorted and classified by their waveform features as putative excitatory and inhibitory neurons. Microstimulation caused early (<10ms) increase followed by prolonged (11-100ms) decrease in spiking of all neurons throughout the electrode array. The early response of excitatory but not inhibitory neurons decayed rapidly with distance from the stimulation site over 1mm. Effective connectivity of neurons with significant stimulus response was dense in wakefulness and sparse under anesthesia. Network motifs were identified in graphs of effective connectivity constructed from monosynaptic cross-correlograms. The number of motifs, especially those of higher order, increased rapidly as the anesthesia was withdrawn indicating a substantial increase in network connectivity as the animals woke up. The results illuminate the impact of anesthesia on functional integrity of local circuits affecting the state of consciousness.
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3
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Baker CM, Gong Y. Identifying properties of pattern completion neurons in a computational model of the visual cortex. PLoS Comput Biol 2023; 19:e1011167. [PMID: 37279242 DOI: 10.1371/journal.pcbi.1011167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 05/09/2023] [Indexed: 06/08/2023] Open
Abstract
Neural ensembles are found throughout the brain and are believed to underlie diverse cognitive functions including memory and perception. Methods to activate ensembles precisely, reliably, and quickly are needed to further study the ensembles' role in cognitive processes. Previous work has found that ensembles in layer 2/3 of the visual cortex (V1) exhibited pattern completion properties: ensembles containing tens of neurons were activated by stimulation of just two neurons. However, methods that identify pattern completion neurons are underdeveloped. In this study, we optimized the selection of pattern completion neurons in simulated ensembles. We developed a computational model that replicated the connectivity patterns and electrophysiological properties of layer 2/3 of mouse V1. We identified ensembles of excitatory model neurons using K-means clustering. We then stimulated pairs of neurons in identified ensembles while tracking the activity of the entire ensemble. Our analysis of ensemble activity quantified a neuron pair's power to activate an ensemble using a novel metric called pattern completion capability (PCC) based on the mean pre-stimulation voltage across the ensemble. We found that PCC was directly correlated with multiple graph theory parameters, such as degree and closeness centrality. To improve selection of pattern completion neurons in vivo, we computed a novel latency metric that was correlated with PCC and could potentially be estimated from modern physiological recordings. Lastly, we found that stimulation of five neurons could reliably activate ensembles. These findings can help researchers identify pattern completion neurons to stimulate in vivo during behavioral studies to control ensemble activation.
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Affiliation(s)
- Casey M Baker
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
| | - Yiyang Gong
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America
- Department of Neurobiology, Duke University, Durham, North Carolina, United States of America
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4
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Evaluating the statistical similarity of neural network activity and connectivity via eigenvector angles. Biosystems 2023; 223:104813. [PMID: 36460172 DOI: 10.1016/j.biosystems.2022.104813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 11/15/2022] [Accepted: 11/15/2022] [Indexed: 12/02/2022]
Abstract
Neural systems are networks, and strategic comparisons between multiple networks are a prevalent task in many research scenarios. In this study, we construct a statistical test for the comparison of matrices representing pairwise aspects of neural networks, in particular, the correlation between spiking activity and connectivity. The "eigenangle test" quantifies the similarity of two matrices by the angles between their ranked eigenvectors. We calibrate the behavior of the test for use with correlation matrices using stochastic models of correlated spiking activity and demonstrate how it compares to classical two-sample tests, such as the Kolmogorov-Smirnov distance, in the sense that it is able to evaluate also structural aspects of pairwise measures. Furthermore, the principle of the eigenangle test can be applied to compare the similarity of adjacency matrices of certain types of networks. Thus, the approach can be used to quantitatively explore the relationship between connectivity and activity with the same metric. By applying the eigenangle test to the comparison of connectivity matrices and correlation matrices of a random balanced network model before and after a specific synaptic rewiring intervention, we gauge the influence of connectivity features on the correlated activity. Potential applications of the eigenangle test include simulation experiments, model validation, and data analysis.
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5
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Ryan JD, Shen K, Kacollja A, Tian H, Griffiths J, Bezgin G, McIntosh AR. Modeling the influence of the hippocampal memory system on the oculomotor system. Netw Neurosci 2020; 4:217-233. [PMID: 32166209 PMCID: PMC7055646 DOI: 10.1162/netn_a_00120] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Accepted: 12/04/2019] [Indexed: 01/12/2023] Open
Abstract
Visual exploration is related to activity in the hippocampus (HC) and/or extended medial temporal lobe system (MTL), is influenced by stored memories, and is altered in amnesic cases. An extensive set of polysynaptic connections exists both within and between the HC and oculomotor systems such that investigating how HC responses ultimately influence neural activity in the oculomotor system, and the timing by which such neural modulation could occur, is not trivial. We leveraged TheVirtualBrain, a software platform for large-scale network simulations, to model the functional dynamics that govern the interactions between the two systems in the macaque cortex. Evoked responses following the stimulation of the MTL and some, but not all, subfields of the HC resulted in observable responses in oculomotor regions, including the frontal eye fields, within the time of a gaze fixation. Modeled lesions to some MTL regions slowed the dissipation of HC signal to oculomotor regions, whereas HC lesions generally did not affect the rapid MTL activity propagation to oculomotor regions. These findings provide a framework for investigating how information represented by the HC/MTL may influence the oculomotor system during a fixation and predict how HC lesions may affect visual exploration.
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Affiliation(s)
- Jennifer D Ryan
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Kelly Shen
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Arber Kacollja
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Heather Tian
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - John Griffiths
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Gleb Bezgin
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
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6
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Zimmermann J, Perry A, Breakspear M, Schirner M, Sachdev P, Wen W, Kochan NA, Mapstone M, Ritter P, McIntosh AR, Solodkin A. Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models. NEUROIMAGE-CLINICAL 2018; 19:240-251. [PMID: 30035018 PMCID: PMC6051478 DOI: 10.1016/j.nicl.2018.04.017] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Revised: 04/05/2018] [Accepted: 04/14/2018] [Indexed: 01/09/2023]
Abstract
Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes. Modeled local and global dynamics correlate with individual cognition in Alzheimer's. Proof of concept of The Virtual Brain to characterize individual dynamics Brain-behaviour relations depend on the network modeled (whole brain or limbic). Model parameters predict cognition better than metrics of neuroimaging data.
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Affiliation(s)
- J Zimmermann
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada.
| | - A Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max Planck Institute for Human Development, Lentzeallee 94, 14195 Berlin, Germany
| | - M Breakspear
- Program of Mental Health Research, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Service, Royal Brisbane and Women's Hospital, Herston, QLD 4029, Australia
| | - M Schirner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - P Sachdev
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - W Wen
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - N A Kochan
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia
| | - M Mapstone
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
| | - P Ritter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Dept. of Neurology, Chariteplatz 1, Berlin 13353, Germany; Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - A R McIntosh
- Baycrest Health Sciences, Rotman Research Institute, 3560 Bathurst St, Toronto, Ontario M6A 2E1, Canada
| | - A Solodkin
- UC Irvine Health School of Medicine, Irvine Hall, 1001 Health Sciences Road, Irvine, CA 92697-3950, USA
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7
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Zippo AG, Della Rosa PA, Castiglioni I, Biella GEM. Alternating Dynamics of Segregation and Integration in Human EEG Functional Networks During Working-memory Task. Neuroscience 2017; 371:191-206. [PMID: 29246785 DOI: 10.1016/j.neuroscience.2017.12.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Revised: 12/02/2017] [Accepted: 12/04/2017] [Indexed: 01/28/2023]
Abstract
Brain functional networks show high variability in short time windows but mechanisms governing these transient dynamics remain unknown. In this work, we studied the temporal evolution of functional brain networks involved in a working memory (WM) task while recording high-density electroencephalography (EEG) in human normal subjects. We found that functional brain networks showed an initial phase characterized by an increase of the functional segregation index followed by a second phase where the functional segregation faded after the prevailing the functional integration. Notably, wrong trials were associated with different or disrupted sequences of the segregation-integration profiles and measures of network centrality and modularity were able to identify crucial aspects of the oscillatory network dynamics. Additionally, computational investigations further supported the experimental results. The brain functional organization may respond to the information processing demand of a WM task following a 2-step atomic scheme wherein segregation and integration alternately dominate the functional configurations.
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Affiliation(s)
- Antonio G Zippo
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche, Segrate, Milan, Italy.
| | | | - Isabella Castiglioni
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche, Segrate, Milan, Italy
| | - Gabriele E M Biella
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche, Segrate, Milan, Italy
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8
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Shen K, Bezgin G, Selvam R, McIntosh AR, Ryan JD. An Anatomical Interface between Memory and Oculomotor Systems. J Cogn Neurosci 2016; 28:1772-1783. [DOI: 10.1162/jocn_a_01007] [Citation(s) in RCA: 43] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Abstract
Visual behavior is guided by memories from prior experience and knowledge of the visual scene. The hippocampal system (HC), in particular, has been implicated in the guidance of saccades: Amnesic patients, following damage to the HC, exhibit selective deficits in their gaze patterns. However, the neural circuitry by which mnemonic representations influence the oculomotor system remains unknown. We used a data-driven, network-based approach on directed anatomical connectivity from the macaque brain to reveal an extensive set of polysnaptic pathways spanning the extrastriate, posterior parietal and prefrontal cortices that potentially mediate the exchange of information between the memory and visuo-oculomotor systems. We additionally show how the potential for directed information flow from the hippocampus to oculomotor control areas is exceptionally high. In particular, the dorsolateral pFC and FEF—regions known to be responsible for the cognitive control of saccades—are topologically well positioned to receive information from the hippocampus. Together with neuropsychological evidence of altered gaze patterns following damage to the hippocampus, our findings suggest that a reconsideration of hippocampal involvement in oculomotor guidance is needed.
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9
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Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity. Sci Rep 2016; 6:26029. [PMID: 27212008 PMCID: PMC4876512 DOI: 10.1038/srep26029] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2015] [Accepted: 04/08/2016] [Indexed: 11/26/2022] Open
Abstract
Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.
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10
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Effect of edge pruning on structural controllability and observability of complex networks. Sci Rep 2015; 5:18145. [PMID: 26674854 PMCID: PMC4682190 DOI: 10.1038/srep18145] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2015] [Accepted: 11/11/2015] [Indexed: 02/07/2023] Open
Abstract
Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex systems show dynamic changes in their network connectivity, it is important to understand how perturbations in the connectivity affect the controllability of the system. To this end, we studied the control structure of different types of artificial, social and biological neuronal networks (BNN) as their connections were progressively pruned using four different pruning strategies. We show that the BNNs are more similar to scale-free networks than to small-world networks, when comparing the robustness of their control structure to structural perturbations. We introduce a new graph descriptor, ‘the cardinality curve’, to quantify the robustness of the control structure of a network to progressive edge pruning. Knowing the susceptibility of control structures to different pruning methods could help design strategies to destroy the control structures of dangerous networks such as epidemic networks. On the other hand, it could help make useful networks more resistant to edge attacks.
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11
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Effenberger F, Jost J, Levina A. Self-organization in Balanced State Networks by STDP and Homeostatic Plasticity. PLoS Comput Biol 2015; 11:e1004420. [PMID: 26335425 PMCID: PMC4559467 DOI: 10.1371/journal.pcbi.1004420] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2014] [Accepted: 06/30/2015] [Indexed: 11/18/2022] Open
Abstract
Structural inhomogeneities in synaptic efficacies have a strong impact on population response dynamics of cortical networks and are believed to play an important role in their functioning. However, little is known about how such inhomogeneities could evolve by means of synaptic plasticity. Here we present an adaptive model of a balanced neuronal network that combines two different types of plasticity, STDP and synaptic scaling. The plasticity rules yield both long-tailed distributions of synaptic weights and firing rates. Simultaneously, a highly connected subnetwork of driver neurons with strong synapses emerges. Coincident spiking activity of several driver cells can evoke population bursts and driver cells have similar dynamical properties as leader neurons found experimentally. Our model allows us to observe the delicate interplay between structural and dynamical properties of the emergent inhomogeneities. It is simple, robust to parameter changes and able to explain a multitude of different experimental findings in one basic network. It is widely believed that the structure of neuronal circuits plays a major role in brain functioning. Although the full synaptic connectivity for larger populations is not yet assessable even by current experimental techniques, available data show that neither synaptic strengths nor the number of synapses per neuron are homogeneously distributed. Several studies have found long-tailed distributions of synaptic weights with many weak and a few exceptionally strong synaptic connections, as well as strongly connected cells and subnetworks that may play a decisive role for data processing in neural circuits. Little is known about how inhomogeneities could arise in the developing brain and we hypothesize that there is a self-organizing principle behind their appearance. In this study we show how structural inhomogeneities can emerge by simple synaptic plasticity mechanisms from an initially homogeneous network. We perform numerical simulations and show analytically how a small imbalance in the initial structure is amplified by the synaptic plasticities and their interplay. Our network can simultaneously explain several experimental observations that were previously not linked.
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Affiliation(s)
- Felix Effenberger
- Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany
- * E-mail:
| | - Jürgen Jost
- Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany
| | - Anna Levina
- Max-Planck-Institute for Mathematics in the Sciences, Leipzig, Germany
- Bernstein Center for Computational Neuroscience Göttingen, Göttingen, Germany
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12
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Abstract
What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal "true" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different "slices" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.
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Affiliation(s)
- Luiz Pessoa
- University of Maryland, College Park, United States
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13
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Abstract
AbstractIn The Cognitive-Emotional Brain (Pessoa 2013), I describe the many ways that emotion and cognition interact and are integrated in the brain. The book summarizes five areas of research that support this integrative view and makes four arguments to organize each area. (1) Based on rodent and human data, I propose that the amygdala's functions go beyond emotion as traditionally conceived. Furthermore, the processing of emotion-laden information is capacity limited, thus not independent of attention and awareness. (2) Cognitive-emotional interactions in the human prefrontal cortex (PFC) assume diverse forms and are not limited to mutual suppression. Particularly, the lateral PFC is a focal point for cognitive-emotional interactions. (3) Interactions between motivation and cognition can be seen across a range of perceptual and cognitive tasks. Motivation shapes behavior in specific ways – for example, by reducing response conflict or via selective effects on working memory. Traditional accounts, by contrast, typically describe motivation as a global activation independent of particular control demands. (4) Perception and cognition are directly influenced by information with affective or motivational content in powerful ways. A dual competition model outlines a framework for such interactions at the perceptual and executive levels. A specific neural architecture is proposed that embeds emotional and motivational signals into perception and cognition through multiple channels. (5) A network perspective should supplant the strategy of understanding the brain in terms of individual regions. More broadly, in a network view of brain architecture, “emotion” and “cognition” may be used as labels of certain behaviors, but will not map cleanly into compartmentalized pieces of the brain.
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14
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McDonnell MD, Ward LM. Small modifications to network topology can induce stochastic bistable spiking dynamics in a balanced cortical model. PLoS One 2014; 9:e88254. [PMID: 24743633 PMCID: PMC3990528 DOI: 10.1371/journal.pone.0088254] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 01/06/2014] [Indexed: 12/27/2022] Open
Abstract
Directed random graph models frequently are used successfully in modeling the population dynamics of networks of cortical neurons connected by chemical synapses. Experimental results consistently reveal that neuronal network topology is complex, however, in the sense that it differs statistically from a random network, and differs for classes of neurons that are physiologically different. This suggests that complex network models whose subnetworks have distinct topological structure may be a useful, and more biologically realistic, alternative to random networks. Here we demonstrate that the balanced excitation and inhibition frequently observed in small cortical regions can transiently disappear in otherwise standard neuronal-scale models of fluctuation-driven dynamics, solely because the random network topology was replaced by a complex clustered one, whilst not changing the in-degree of any neurons. In this network, a small subset of cells whose inhibition comes only from outside their local cluster are the cause of bistable population dynamics, where different clusters of these cells irregularly switch back and forth from a sparsely firing state to a highly active state. Transitions to the highly active state occur when a cluster of these cells spikes sufficiently often to cause strong unbalanced positive feedback to each other. Transitions back to the sparsely firing state rely on occasional large fluctuations in the amount of non-local inhibition received. Neurons in the model are homogeneous in their intrinsic dynamics and in-degrees, but differ in the abundance of various directed feedback motifs in which they participate. Our findings suggest that (i) models and simulations should take into account complex structure that varies for neuron and synapse classes; (ii) differences in the dynamics of neurons with similar intrinsic properties may be caused by their membership in distinctive local networks; (iii) it is important to identify neurons that share physiological properties and location, but differ in their connectivity.
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Affiliation(s)
- Mark D. McDonnell
- Computational and Theoretical Neuroscience Laboratory, Institute for Telecommunications Research, University of South Australia, Mawson Lakes, South Australia, Australia
- * E-mail:
| | - Lawrence M. Ward
- Department of Psychology and Brain Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
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15
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Sporns O. The human connectome: Origins and challenges. Neuroimage 2013; 80:53-61. [PMID: 23528922 DOI: 10.1016/j.neuroimage.2013.03.023] [Citation(s) in RCA: 243] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2013] [Revised: 03/11/2013] [Accepted: 03/12/2013] [Indexed: 12/20/2022] Open
Affiliation(s)
- Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
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16
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Mäki-Marttunen T, Aćimović J, Ruohonen K, Linne ML. Structure-dynamics relationships in bursting neuronal networks revealed using a prediction framework. PLoS One 2013; 8:e69373. [PMID: 23935998 PMCID: PMC3723901 DOI: 10.1371/journal.pone.0069373] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Accepted: 06/07/2013] [Indexed: 11/25/2022] Open
Abstract
The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.
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Affiliation(s)
- Tuomo Mäki-Marttunen
- Department of Signal Processing, Tampere University of Technology, Tampere, Finland.
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Kumar A, Vlachos I, Aertsen A, Boucsein C. Challenges of understanding brain function by selective modulation of neuronal subpopulations. Trends Neurosci 2013; 36:579-86. [PMID: 23876423 DOI: 10.1016/j.tins.2013.06.005] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Revised: 06/11/2013] [Accepted: 06/24/2013] [Indexed: 10/26/2022]
Abstract
Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function.
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Affiliation(s)
- Arvind Kumar
- Bernstein Center Freiburg (BCF), and Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany.
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