1
|
Ross G, Radtke-Schuller S, Frohlich F. Ferret as a model system for studying the anatomy and function of the prefrontal cortex: A systematic review. Neurosci Biobehav Rev 2024; 162:105701. [PMID: 38718987 PMCID: PMC11162921 DOI: 10.1016/j.neubiorev.2024.105701] [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: 10/30/2023] [Revised: 04/12/2024] [Accepted: 05/01/2024] [Indexed: 05/19/2024]
Abstract
There is a lack of consensus on anatomical nomenclature, standards of documentation, and functional equivalence of the frontal cortex between species. There remains a major gap between human prefrontal function and interpretation of findings in the mouse brain that appears to lack several key prefrontal areas involved in cognition and psychiatric illnesses. The ferret is an emerging model organism that has gained traction as an intermediate model species for the study of top-down cognitive control and other higher-order brain functions. However, this research has yet to benefit from synthesis. Here, we provide a summary of all published research pertaining to the frontal and/or prefrontal cortex of the ferret across research scales. The targeted location within the ferret brain is summarized visually for each experiment, and the anatomical terminology used at time of publishing is compared to what would be the appropriate term to use presently. By doing so, we hope to improve clarity in the interpretation of both previous and future publications on the comparative study of frontal cortex.
Collapse
Affiliation(s)
- Grace Ross
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA
| | - Susanne Radtke-Schuller
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Flavio Frohlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Neuroscience Center, University of North Carolina, Chapel Hill, NC, USA; Department of Cell Biology and Physiology, University of North Carolina, Chapel Hill, NC, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA.
| |
Collapse
|
2
|
Voges N, Lima V, Hausmann J, Brovelli A, Battaglia D. Decomposing Neural Circuit Function into Information Processing Primitives. J Neurosci 2024; 44:e0157232023. [PMID: 38050070 PMCID: PMC10866194 DOI: 10.1523/jneurosci.0157-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Revised: 09/01/2023] [Accepted: 09/19/2023] [Indexed: 12/06/2023] Open
Abstract
It is challenging to measure how specific aspects of coordinated neural dynamics translate into operations of information processing and, ultimately, cognitive functions. An obstacle is that simple circuit mechanisms-such as self-sustained or propagating activity and nonlinear summation of inputs-do not directly give rise to high-level functions. Nevertheless, they already implement simple the information carried by neural activity. Here, we propose that distinct functions, such as stimulus representation, working memory, or selective attention, stem from different combinations and types of low-level manipulations of information or information processing primitives. To test this hypothesis, we combine approaches from information theory with simulations of multi-scale neural circuits involving interacting brain regions that emulate well-defined cognitive functions. Specifically, we track the information dynamics emergent from patterns of neural dynamics, using quantitative metrics to detect where and when information is actively buffered, transferred or nonlinearly merged, as possible modes of low-level processing (storage, transfer and modification). We find that neuronal subsets maintaining representations in working memory or performing attentional gain modulation are signaled by their boosted involvement in operations of information storage or modification, respectively. Thus, information dynamic metrics, beyond detecting which network units participate in cognitive processing, also promise to specify how and when they do it, that is, through which type of primitive computation, a capability that may be exploited for the analysis of experimental recordings.
Collapse
Affiliation(s)
- Nicole Voges
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Vinicius Lima
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
| | - Johannes Hausmann
- R&D Department, Hyland Switzerland Sarl, Corcelles NE 2035, Switzerland
| | - Andrea Brovelli
- Institut de Neurosciences de La Timone, UMR 7289, CNRS, Aix-Marseille Université, Marseille 13005, France
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
| | - Demian Battaglia
- Institute for Language, Communication and the Brain (ILCB), Aix-Marseille Université, Marseille 13005, France
- Institut de Neurosciences des Systèmes (INS), UMR 1106, Aix-Marseille Université, Marseille 13005, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Strasbourg 67000, France
| |
Collapse
|
3
|
Toker D, Müller E, Miyamoto H, Riga MS, Lladó-Pelfort L, Yamakawa K, Artigas F, Shine JM, Hudson AE, Pouratian N, Monti MM. Criticality supports cross-frequency cortical-thalamic information transfer during conscious states. eLife 2024; 13:e86547. [PMID: 38180472 PMCID: PMC10805384 DOI: 10.7554/elife.86547] [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: 01/31/2023] [Accepted: 11/27/2023] [Indexed: 01/06/2024] Open
Abstract
Consciousness is thought to be regulated by bidirectional information transfer between the cortex and thalamus, but the nature of this bidirectional communication - and its possible disruption in unconsciousness - remains poorly understood. Here, we present two main findings elucidating mechanisms of corticothalamic information transfer during conscious states. First, we identify a highly preserved spectral channel of cortical-thalamic communication that is present during conscious states, but which is diminished during the loss of consciousness and enhanced during psychedelic states. Specifically, we show that in humans, mice, and rats, information sent from either the cortex or thalamus via δ/θ/α waves (∼1-13 Hz) is consistently encoded by the other brain region by high γ waves (52-104 Hz); moreover, unconsciousness induced by propofol anesthesia or generalized spike-and-wave seizures diminishes this cross-frequency communication, whereas the psychedelic 5-methoxy-N,N-dimethyltryptamine (5-MeO-DMT) enhances this low-to-high frequency interregional communication. Second, we leverage numerical simulations and neural electrophysiology recordings from the thalamus and cortex of human patients, rats, and mice to show that these changes in cross-frequency cortical-thalamic information transfer may be mediated by excursions of low-frequency thalamocortical electrodynamics toward/away from edge-of-chaos criticality, or the phase transition from stability to chaos. Overall, our findings link thalamic-cortical communication to consciousness, and further offer a novel, mathematically well-defined framework to explain the disruption to thalamic-cortical information transfer during unconscious states.
Collapse
Affiliation(s)
- Daniel Toker
- Department of Neurology, University of California, Los AngelesLos AngelesUnited States
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
| | - Eli Müller
- Brain and Mind Centre, University of SydneySydneyAustralia
| | - Hiroyuki Miyamoto
- Laboratory for Neurogenetics, RIKEN Center for Brain ScienceSaitamaJapan
- PRESTO, Japan Science and Technology AgencySaitamaJapan
- International Research Center for Neurointelligence, University of TokyoNagoyaJapan
| | - Maurizio S Riga
- Andalusian Center for Molecular Biology and Regenerative MedicineSevilleSpain
| | - Laia Lladó-Pelfort
- Departament de Ciències Bàsiques, Universitat de Vic-Universitat Central de CatalunyaBarcelonaSpain
| | - Kazuhiro Yamakawa
- Laboratory for Neurogenetics, RIKEN Center for Brain ScienceSaitamaJapan
- Department of Neurodevelopmental Disorder Genetics, Institute of Brain Science, Nagoya City University Graduate School of Medical ScienceNagoyaJapan
| | - Francesc Artigas
- Departament de Neurociències i Terapèutica Experimental, CSIC-Institut d’Investigacions Biomèdiques de BarcelonaBarcelonaSpain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos IIIMadridSpain
| | - James M Shine
- Brain and Mind Centre, University of SydneySydneyAustralia
| | - Andrew E Hudson
- Department of Anesthesiology, Veterans Affairs Greater Los Angeles Healthcare SystemLos AngelesUnited States
- Department of Anesthesiology and Perioperative Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Nader Pouratian
- Department of Neurological Surgery, UT Southwestern Medical CenterDallasUnited States
| | - Martin M Monti
- Department of Psychology, University of California, Los AngelesLos AngelesUnited States
- Department of Neurosurgery, University of California, Los AngelesLos AngelesUnited States
| |
Collapse
|
4
|
Wollstadt P, Rathbun DL, Usrey WM, Bastos AM, Lindner M, Priesemann V, Wibral M. Information-theoretic analyses of neural data to minimize the effect of researchers' assumptions in predictive coding studies. PLoS Comput Biol 2023; 19:e1011567. [PMID: 37976328 PMCID: PMC10703417 DOI: 10.1371/journal.pcbi.1011567] [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: 03/21/2023] [Revised: 12/07/2023] [Accepted: 10/02/2023] [Indexed: 11/19/2023] Open
Abstract
Studies investigating neural information processing often implicitly ask both, which processing strategy out of several alternatives is used and how this strategy is implemented in neural dynamics. A prime example are studies on predictive coding. These often ask whether confirmed predictions about inputs or prediction errors between internal predictions and inputs are passed on in a hierarchical neural system-while at the same time looking for the neural correlates of coding for errors and predictions. If we do not know exactly what a neural system predicts at any given moment, this results in a circular analysis-as has been criticized correctly. To circumvent such circular analysis, we propose to express information processing strategies (such as predictive coding) by local information-theoretic quantities, such that they can be estimated directly from neural data. We demonstrate our approach by investigating two opposing accounts of predictive coding-like processing strategies, where we quantify the building blocks of predictive coding, namely predictability of inputs and transfer of information, by local active information storage and local transfer entropy. We define testable hypotheses on the relationship of both quantities, allowing us to identify which of the assumed strategies was used. We demonstrate our approach on spiking data collected from the retinogeniculate synapse of the cat (N = 16). Applying our local information dynamics framework, we are able to show that the synapse codes for predictable rather than surprising input. To support our findings, we estimate quantities applied in the partial information decomposition framework, which allow to differentiate whether the transferred information is primarily bottom-up sensory input or information transferred conditionally on the current state of the synapse. Supporting our local information-theoretic results, we find that the synapse preferentially transfers bottom-up information.
Collapse
Affiliation(s)
- Patricia Wollstadt
- MEG Unit, Brain Imaging Center, Goethe University, Frankfurt/Main, Germany
| | - Daniel L. Rathbun
- Center for Neuroscience, University of California, Davis, California, United States of America
- Center for Ophthalmology, University of Tübingen, Tübingen, Germany
| | - W. Martin Usrey
- Center for Neuroscience, University of California, Davis, California, United States of America
- Department of Neurobiology, Physiology, and Behavior, University of California, Davis, California, United States of America
| | - André Moraes Bastos
- Department of Psychology and Vanderbilt Brain Institute, Vanderbilt University, Nashville, Tennessee, United States of America
| | - Michael Lindner
- Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
| | - Viola Priesemann
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
| | - Michael Wibral
- Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
| |
Collapse
|
5
|
Martínez-Cancino R, Delorme A, Wagner J, Kreutz-Delgado K, Sotero RC, Makeig S. What Can Local Transfer Entropy Tell Us about Phase-Amplitude Coupling in Electrophysiological Signals? ENTROPY 2020; 22:e22111262. [PMID: 33287030 PMCID: PMC7712258 DOI: 10.3390/e22111262] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/03/2020] [Accepted: 11/04/2020] [Indexed: 12/18/2022]
Abstract
Modulation of the amplitude of high-frequency cortical field activity locked to changes in the phase of a slower brain rhythm is known as phase-amplitude coupling (PAC). The study of this phenomenon has been gaining traction in neuroscience because of several reports on its appearance in normal and pathological brain processes in humans as well as across different mammalian species. This has led to the suggestion that PAC may be an intrinsic brain process that facilitates brain inter-area communication across different spatiotemporal scales. Several methods have been proposed to measure the PAC process, but few of these enable detailed study of its time course. It appears that no studies have reported details of PAC dynamics including its possible directional delay characteristic. Here, we study and characterize the use of a novel information theoretic measure that may address this limitation: local transfer entropy. We use both simulated and actual intracranial electroencephalographic data. In both cases, we observe initial indications that local transfer entropy can be used to detect the onset and offset of modulation process periods revealed by mutual information estimated phase-amplitude coupling (MIPAC). We review our results in the context of current theories about PAC in brain electrical activity, and discuss technical issues that must be addressed to see local transfer entropy more widely applied to PAC analysis. The current work sets the foundations for further use of local transfer entropy for estimating PAC process dynamics, and extends and complements our previous work on using local mutual information to compute PAC (MIPAC).
Collapse
Affiliation(s)
- Ramón Martínez-Cancino
- Swartz Center for Computational Neurosciences, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; (A.D.); (J.W.); (S.M.)
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA;
- Correspondence:
| | - Arnaud Delorme
- Swartz Center for Computational Neurosciences, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; (A.D.); (J.W.); (S.M.)
- Centre de Recherche Cerveau et Cognition (CerCo), Université Paul Sabatier, 31059 Toulouse, France
- CNRS, UMR 5549, 31052 Toulouse, France
| | - Johanna Wagner
- Swartz Center for Computational Neurosciences, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; (A.D.); (J.W.); (S.M.)
| | - Kenneth Kreutz-Delgado
- Jacobs School of Engineering, University of California San Diego, La Jolla, CA 92093, USA;
| | - Roberto C. Sotero
- Computational Neurophysics Lab, University of Calgary, Calgary, AB T2N 4N1, Canada;
| | - Scott Makeig
- Swartz Center for Computational Neurosciences, Institute for Neural Computation, University of California San Diego, La Jolla, CA 92093, USA; (A.D.); (J.W.); (S.M.)
| |
Collapse
|