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Geerligs L, Tsvetanov KA. The use of resting state data in an integrative approach to studying neurocognitive ageing - Commentary on Campbell and Schacter (2016). LANGUAGE, COGNITION AND NEUROSCIENCE 2017; 32:684-691. [PMID: 36381062 PMCID: PMC7613799 DOI: 10.1080/23273798.2016.1251600] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
This is a commentary on Campbell and Schacter (2016), 'Ageing and the Resting State: Is Cognition Obsolete?'. Campbell and Schacter argue that resting state data have a limited ability to contribute to the study of neurocognitive ageing and that the field should focus more on results from carefully controlled experimental designs. In this commentary, we argue for a different perspective on future research directions in neurocognitive ageing. Specifically for the need to use a more integrative approach; combining rest and task data as well as information from different modalities to obtain a better understanding of the neural mechanisms that underlie healthy cognitive ageing. Potential benefits of this integrative approach are illustrated with a number of examples. In addition, we discuss some of the advantages of using resting state data as part of this integrative approach.
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Affiliation(s)
| | - Kamen A. Tsvetanov
- Centre for Speech, Language and the Brain, Department of Psychology, University of Cambridge
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2
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Thilaga M, Vijayalakshmi R, Nadarajan R, Nandagopal D. A novel pattern mining approach for identifying cognitive activity in EEG based functional brain networks. J Integr Neurosci 2016; 15:223-45. [PMID: 27401999 DOI: 10.1142/s0219635216500151] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The complex nature of neuronal interactions of the human brain has posed many challenges to the research community. To explore the underlying mechanisms of neuronal activity of cohesive brain regions during different cognitive activities, many innovative mathematical and computational models are required. This paper presents a novel Common Functional Pattern Mining approach to demonstrate the similar patterns of interactions due to common behavior of certain brain regions. The electrode sites of EEG-based functional brain network are modeled as a set of transactions and node-based complex network measures as itemsets. These itemsets are transformed into a graph data structure called Functional Pattern Graph. By mining this Functional Pattern Graph, the common functional patterns due to specific brain functioning can be identified. The empirical analyses show the efficiency of the proposed approach in identifying the extent to which the electrode sites (transactions) are similar during various cognitive load states.
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Affiliation(s)
- M Thilaga
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - R Vijayalakshmi
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - R Nadarajan
- * Department of Applied Mathematics and Computational Sciences, Computational Neuroscience Laboratory, PSG College of Technology, Coimbatore 641004, Tamil Nadu, India
| | - D Nandagopal
- † Cognitive NeuroEngineering Laboratory, Division of Information Technology, Engineering and the Environment, University of South Australia, Adelaide, South Australia 5001, Australia
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Dasari NM, Nandagopal ND, Ramasamy V, Cocks B, Thomas BH, Dahal N, Gaertner P. Moment to moment variability in functional brain networks during cognitive activity in EEG data. J Integr Neurosci 2015; 14:383-402. [PMID: 26365114 DOI: 10.1142/s0219635215500211] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Functional brain networks (FBNs) are gaining increasing attention in computational neuroscience due to their ability to reveal dynamic interdependencies between brain regions. The dynamics of such networks during cognitive activity between stimulus and response using multi-channel electroencephalogram (EEG), recorded from 16 healthy human participants are explored in this research. Successive EEG segments of 500[Formula: see text]ms duration starting from the onset of cognitive stimulation have been used to analyze and understand the cognitive dynamics. The approach employs a combination of signal processing techniques, nonlinear statistical measures and graph-theoretical analysis. The efficacy of this approach in detecting and tracking cognitive load induced changes in EEG data is clearly demonstrated using graph metrics. It is revealed that most cognitive activity occurs within approximately 500[Formula: see text]ms of the stimulus presentation in addition to temporal variability in the FBNs. It is shown that mutual information (MI), a nonlinear measure, produces good correlations between the EEG channels thus enabling the construction of FBNs which are sensitive to cognitive load induced changes in EEG. Analyses of the dynamics of FBNs and the visualization approach reveal hard to detect subtle changes in cognitive function and hence may lead to a better understanding of cognitive processing in the brain. The techniques exploited have the potential to detect human cognitive dysfunction (impairments).
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Affiliation(s)
- Naga M Dasari
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Nanda D Nandagopal
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Vijayalaxmi Ramasamy
- † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India
| | - Bernadine Cocks
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Bruce H Thomas
- † Department of Applied Mathematics and Computational Sciences, PSG College of Technology, Tamil Nadu, India
| | - Nabaraj Dahal
- * Cognitive Neuro-Engineering & Computational Neuroscience Laboratory, School of Information Technology & Mathematical Sciences, University of South Australia, Mawson Lakes Campus, Adelaide, Australia
| | - Paul Gaertner
- ‡ Defence Science and Technology Group, Edinburgh, South Australia, Australia
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Abstract
The issue of integration in neural networks is intimately connected with that of consciousness. In this paper, integration as an effective level of physical organization is contrasted with a methodological integrative approach. Understanding how consciousness arises out of neural processes requires a model of integration in just causal physical terms. Based on a set of feasible criteria (physical grounding, causal efficacy, no circularity and scaling), a causal account of physical integration for consciousness centered on joint causation is outlined.
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Affiliation(s)
- Riccardo Manzotti
- Institute "GP Fabris", IULM University, via Carlo Bo, 8, 20143 Milano, Italy
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Abstract
Human behavior emerges from a complex dynamic interaction between graded and context-sensitive neural processes, the biomechanics of our bodies, and the vicissitudes of our environments. These coupled processes bear little resemblance to the iterated application of simple symbolic rules. Still, there are circumstances under which our behavior appears to be guided by explicit mental rules. A prototypical case is when succinct verbal instructions are communicated and are promptly followed by another. How does the brain support such rule-guided behavior? How are explicit rules represented in the brain? How are rule representations shaped by experience? What neural processes form the foundation of our ability to systematically represent and apply rules from the vast range of possible rules? This article reviews a line of research that has sought a computational cognitive neuroscience account of rule-guided behavior in terms of the functioning of the prefrontal cortex, the basal ganglia, and related brain systems.
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Affiliation(s)
- David C Noelle
- University of California, Merced, Merced, CA 95343, USA.
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Aur D, Jog M, Poznanski RR. Computing by physical interaction in neurons. J Integr Neurosci 2012; 10:413-22. [PMID: 22262533 DOI: 10.1142/s0219635211002865] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2011] [Accepted: 11/08/2011] [Indexed: 11/18/2022] Open
Abstract
The electrodynamics of action potentials represents the fundamental level where information is integrated and processed in neurons. The Hodgkin-Huxley model cannot explain the non-stereotyped spatial charge density dynamics that occur during action potential propagation. Revealed in experiments as spike directivity, the non-uniform charge density dynamics within neurons carry meaningful information and suggest that fragments of information regarding our memories are endogenously stored in structural patterns at a molecular level and are revealed only during spiking activity. The main conceptual idea is that under the influence of electric fields, efficient computation by interaction occurs between charge densities embedded within molecular structures and the transient developed flow of electrical charges. This process of computation underlying electrical interactions and molecular mechanisms at the subcellular level is dissimilar from spiking neuron models that are completely devoid of physical interactions. Computation by interaction describes a more powerful continuous model of computation than the one that consists of discrete steps as represented in Turing machines.
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Affiliation(s)
- Dorian Aur
- Department of Comparative Medicine, Stanford University, Palo Alto, CA 94305, USA.
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Lewis ER, Macgregor RJ. A natural science approach to consciousness. J Integr Neurosci 2011; 9:153-91. [PMID: 20589952 DOI: 10.1142/s0219635210002202] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2009] [Accepted: 04/30/2010] [Indexed: 01/23/2023] Open
Abstract
We begin with premises about natural science, its fundamental protocols and its limitations. With those in mind, we construct alternative descriptive models of consciousness, each comprising a synthesis of recent literature in cognitive science. Presuming that consciousness arose through natural selection, we eliminate the subset of alternatives that lack selectable physical phenotypes, leaving the subset with limited free will (mostly in the form of free won't). We argue that membership in this subset implies a two-way exchange of energy between the conscious mental realm and the physical realm of the brain. We propose an analogy between the mental and physical phases of energy and the phases (e.g., gas/liquid) of matter, and a possible realization in the form of a generic resonator. As candidate undergirdings of such a system, we propose astroglial-pyramidal cell and electromagnetic-field models. Finally, we consider the problem of identification of the presence of consciousness in other beings or in machines.
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Affiliation(s)
- Edwin R Lewis
- Department of Electrical Engineering and Computer Science, University of California at Berkeley, CA 94720-1770, USA.
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Vimal RLP. MATCHING AND SELECTION OF A SPECIFIC SUBJECTIVE EXPERIENCE: CONJUGATE MATCHING AND EXPERIENCE. J Integr Neurosci 2010; 9:193-251. [DOI: 10.1142/s0219635210002214] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2008] [Accepted: 09/07/2009] [Indexed: 11/18/2022] Open
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POZNANSKI ROMANR. MODEL-BASED NEUROIMAGING FOR COGNITIVE COMPUTING. J Integr Neurosci 2009; 8:345-69. [DOI: 10.1142/s021963520900223x] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2009] [Indexed: 11/18/2022] Open
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VIMAL RAMLAKHANPANDEY. PROTO-EXPERIENCES AND SUBJECTIVE EXPERIENCES: CLASSICAL AND QUANTUM CONCEPTS. J Integr Neurosci 2008; 7:49-73. [DOI: 10.1142/s0219635208001757] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2008] [Revised: 02/20/2008] [Indexed: 11/18/2022] Open
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MACGREGOR RONALDJ, VIMAL RAMLAKHANPANDEY. CONSCIOUSNESS AND THE STRUCTURE OF MATTER. J Integr Neurosci 2008; 7:75-116. [DOI: 10.1142/s0219635208001733] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2007] [Accepted: 02/12/2008] [Indexed: 11/18/2022] Open
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BRUZZO ANGELAALESSIA, VIMAL RAMLAKHANPANDEY. SELF: AN ADAPTIVE PRESSURE ARISING FROM SELF-ORGANIZATION, CHAOTIC DYNAMICS, AND NEURAL DARWINISM. J Integr Neurosci 2007; 6:541-66. [DOI: 10.1142/s0219635207001659] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2007] [Accepted: 10/15/2007] [Indexed: 11/18/2022] Open
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ORPWOOD ROGER. NEUROBIOLOGICAL MECHANISMS UNDERLYING QUALIA. J Integr Neurosci 2007; 6:523-40. [DOI: 10.1142/s0219635207001696] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2007] [Accepted: 11/04/2007] [Indexed: 11/18/2022] Open
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Macgregor RJ. QUANTUM MECHANICS AND BRAIN UNCERTAINTY. J Integr Neurosci 2006; 5:373-80. [PMID: 17125159 DOI: 10.1142/s0219635206001215] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2006] [Accepted: 08/11/2006] [Indexed: 11/18/2022] Open
Abstract
This paper argues that molecular governing structures (such as receptors, gating molecules, or ionic channels) which operate pervasively in the brain, often with small number particle systems (as, for example, at the surfaces of membranes, synaptic clefts, or macromolecules), may plausibly be vehicles for the transmutation of quantum mechanical fluctuations to normal-level neural signaling.
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Affiliation(s)
- Ronald J Macgregor
- Department of Aerospace Engineering Sciences, University of Colorado, 38 Rock Ridge Dr. NE, Albuquerque, NM 87122-2007, USA.
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Lewis ER, MacGregor RJ. ON INDETERMINISM, CHAOS, AND SMALL NUMBER PARTICLE SYSTEMS IN THE BRAIN. J Integr Neurosci 2006; 5:223-47. [PMID: 16783870 DOI: 10.1142/s0219635206001112] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2006] [Accepted: 04/14/2006] [Indexed: 11/18/2022] Open
Abstract
This paper presents rational, theoretical, and empirical grounds for doubting the principle of determinism in nature and in the brain, and discusses implications of this for free will and the chaos model of the brain. Small number particle systems are practically indeterministic and may be intrinsically indeterministic. Determinism in nature has often been taken to preclude free will. Strict determinism is a concept frequently applied to systems theory, establishing, e.g., the uniqueness of state-space trajectories. In order to consider determinism as a law of nature, however, one must be able to subject it to empirical tests. Presently, one is not able to and whether this can be shown to enable free will or not is not clear. It does remove, at least for the present, determinism itself as a rationale for precluding free will. The work partially supports the chaos model, but weakens the computational computer metaphor of brain function.
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Affiliation(s)
- Edwin R Lewis
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720-1770, USA.
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Poznanski RR, Riera JJ. fMRI MODELS OF DENDRITIC AND ASTROCYTIC NETWORKS. J Integr Neurosci 2006; 5:273-326. [PMID: 16783872 DOI: 10.1142/s0219635206001173] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2005] [Accepted: 02/06/2006] [Indexed: 11/18/2022] Open
Abstract
In order to elucidate the relationships between hierarchical structures within the neocortical neuropil and the information carried by an ensemble of neurons encompassing a single voxel, it is essential to predict through volume conductor modeling LFPs representing average extracellular potentials, which are expressed in terms of interstitial potentials of individual cells in networks of gap-junctionally connected astrocytes and synaptically connected neurons. These relationships have been provided and can then be used to investigate how the underlying neuronal population activity can be inferred from the measurement of the BOLD signal through electrovascular coupling mechanisms across the blood-brain barrier. The importance of both synaptic and extrasynaptic transmission as the basis of electrophysiological indices triggering vascular responses between dendritic and astrocytic networks, and sequential configurations of firing patterns in composite neural networks is emphasized. The purpose of this review is to show how fMRI data may be used to draw conclusions about the information transmitted by individual neurons in populations generating the BOLD signal.
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Affiliation(s)
- Roman R Poznanski
- CRIAMS, Claremont Graduate University, Claremont CA 91711-3988, USA.
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MacGregor RJ. A functional view of consciousness and its relations in brain. J Integr Neurosci 2004; 3:253-66. [PMID: 15366096 DOI: 10.1142/s0219635204000531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2003] [Accepted: 05/12/2004] [Indexed: 11/18/2022] Open
Abstract
Consciousness is seen as evolving jointly with plasticity, self-direction, and autonomy. Its main function is interpreted as the partially autonomous direction of behavioral living in accordance with a vast system of plastic patterns of inner construction, and the development of these--all in the service of a central concern with widely-perceived global well-being. Its central constituents are: conscious awareness itself; sensations and images of sensory bombardment; active development and utilization of inner constructions; and active direction of attention, actions, and behavior. Consciousness operates by the partially autonomous feedback redirection of activation-energization in response to a global distribution of neurobiological flags and states. Central qualities of conscious experience are addressed from this view. Consciousness may act towards an overall principle of minimization. Contrasting its existentially observable manifestations and its neurobiological correspondences provides foundations for five basic views of the fundamental nature of consciousness and its relation to brain.
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Werner G. SIREN CALL OF METAPHOR: SUBVERTING THE PROPER TASK OF NEUROSCIENCE. J Integr Neurosci 2004; 3:245-52. [PMID: 15366095 DOI: 10.1142/s0219635204000543] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2004] [Revised: 07/07/2004] [Indexed: 11/18/2022] Open
Abstract
Under the assumption that nervous systems form a distinct category among the objects in Nature, applying metaphors of psychological and behavioral science disciplines is flawed and invites confusion. Moreover, such practices obscure and detract from the primary task of Neurophysiology: to investigate the intrinsic properties of nervous systems, uncontaminated with concepts borrowed from other disciplines.
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Affiliation(s)
- Gerhard Werner
- Department of Biomedical Engineering, University of Texas at Austin, 1 University Station C0800, Austin TX, 78712-0238, USA.
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