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Koslov SR, Kable JW, Foster BL. Dissociable Contributions of the Medial Parietal Cortex to Recognition Memory. J Neurosci 2024; 44:e2220232024. [PMID: 38527809 PMCID: PMC11063824 DOI: 10.1523/jneurosci.2220-23.2024] [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: 11/28/2023] [Revised: 03/04/2024] [Accepted: 03/15/2024] [Indexed: 03/27/2024] Open
Abstract
Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its distinct functional characteristics during different retrieval tasks. Specifically, while recognition and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct spatial patterns of response within MPC. However, other studies have emphasized alternate MPC functional dissociations in terms of brain network connectivity profiles or stimulus category selectivity. As the unique contributions of MPC to episodic memory remain unclear, adjudicating between these different accounts can provide better consensus regarding MPC function. Therefore, we used a precision-neuroimaging dataset (7T functional magnetic resonance imaging) to examine how MPC regions are differentially engaged during recognition memory and how these task-related dissociations may also reflect distinct connectivity and stimulus category functional profiles. We observed interleaved, though spatially distinct, subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli. In addition, this dissociation was further accentuated by functional subregions displaying distinct profiles of connectivity with the hippocampus during task and rest. Finally, we show that recent observations of dissociable person and place selectivity within the MPC reflect category-specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, by examining precision functional mapping within individuals, these data suggest that previously distinct observations of functional dissociation within MPC conform to a common principle of organization throughout hippocampal-neocortical memory systems.
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Affiliation(s)
- Seth R Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | - Brett L Foster
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104
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2
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Koslov SR, Kable JW, Foster BL. Dissociable contributions of the medial parietal cortex to recognition memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.12.557048. [PMID: 37745317 PMCID: PMC10515876 DOI: 10.1101/2023.09.12.557048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its ubiquitous, and yet distinct, functional characteristics during different types of retrieval tasks. Specifically, while recognition memory and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct patterns of response within MPC. This dissociation adds to growing evidence suggesting a common principle of functional organization across memory related brain structures, specifically regarding the control or content demands of memory-based decisions. To carefully examine this putative organization, we used a high-resolution fMRI dataset collected at ultra-high field (7T) while subjects performed thousands of recognition-memory trials to identify MPC regions responsive to recognition-decisions or semantic content of stimuli within and across individuals. We observed interleaving, though distinct, functional subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli, but rarely both. In addition, this functional dissociation within MPC was further accentuated by distinct profiles of connectivity bias with the hippocampus during task and rest. Finally, we show that recent observations of person and place selectivity within MPC reflect category specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, these data better account for how distinct patterns of MPC responses can occur as a result of task demands during episodic retrieval and may reflect a common principle of organization throughout hippocampal-neocortical memory systems.
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Affiliation(s)
- Seth R. Koslov
- Department of Neurosurgery, Perelman School of Medicine; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Joseph W. Kable
- Department of Psychology; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Brett L. Foster
- Department of Neurosurgery, Perelman School of Medicine; University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
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3
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Vaz AP, Wittig JH, Inati SK, Zaghloul KA. Backbone spiking sequence as a basis for preplay, replay, and default states in human cortex. Nat Commun 2023; 14:4723. [PMID: 37550285 PMCID: PMC10406814 DOI: 10.1038/s41467-023-40440-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 07/27/2023] [Indexed: 08/09/2023] Open
Abstract
Sequences of spiking activity have been heavily implicated as potential substrates of memory formation and retrieval across many species. A parallel line of recent evidence also asserts that sequential activity may arise from and be constrained by pre-existing network structure. Here we reconcile these two lines of research in the human brain by measuring single unit spiking sequences in the temporal lobe cortex as participants perform an episodic memory task. We find the presence of an average backbone spiking sequence identified during pre-task rest that is stable over time and different cognitive states. We further demonstrate that these backbone sequences are composed of both rigid and flexible sequence elements, and that flexible elements within these sequences serve to promote memory specificity when forming and retrieving new memories. These results support the hypothesis that pre-existing network dynamics serve as a scaffold for ongoing neural activity in the human cortex.
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Affiliation(s)
- Alex P Vaz
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - John H Wittig
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sara K Inati
- Office of the Clinical Director, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Kareem A Zaghloul
- Surgical Neurology Branch, NINDS, National Institutes of Health, Bethesda, MD, 20892, USA.
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4
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Messé A, Hollensteiner KJ, Delettre C, Dell-Brown LA, Pieper F, Nentwig LJ, Galindo-Leon EE, Larrat B, Mériaux S, Mangin JF, Reillo I, de Juan Romero C, Borrell V, Engler G, Toro R, Engel AK, Hilgetag CC. Structural basis of envelope and phase intrinsic coupling modes in the cerebral cortex. Neuroimage 2023; 276:120212. [PMID: 37269959 PMCID: PMC10300241 DOI: 10.1016/j.neuroimage.2023.120212] [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: 12/09/2022] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/05/2023] Open
Abstract
Intrinsic coupling modes (ICMs) can be observed in ongoing brain activity at multiple spatial and temporal scales. Two families of ICMs can be distinguished: phase and envelope ICMs. The principles that shape these ICMs remain partly elusive, in particular their relation to the underlying brain structure. Here we explored structure-function relationships in the ferret brain between ICMs quantified from ongoing brain activity recorded with chronically implanted micro-ECoG arrays and structural connectivity (SC) obtained from high-resolution diffusion MRI tractography. Large-scale computational models were used to explore the ability to predict both types of ICMs. Importantly, all investigations were conducted with ICM measures that are sensitive or insensitive to volume conduction effects. The results show that both types of ICMs are significantly related to SC, except for phase ICMs when using measures removing zero-lag coupling. The correlation between SC and ICMs increases with increasing frequency which is accompanied by reduced delays. Computational models produced results that were highly dependent on the specific parameter settings. The most consistent predictions were derived from measures solely based on SC. Overall, the results demonstrate that patterns of cortical functional coupling as reflected in both phase and envelope ICMs are both related, albeit to different degrees, to the underlying structural connectivity in the cerebral cortex.
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Affiliation(s)
- Arnaud Messé
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany.
| | - Karl J Hollensteiner
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Céline Delettre
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France
| | - Leigh-Anne Dell-Brown
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Florian Pieper
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Lena J Nentwig
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Edgar E Galindo-Leon
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Benoît Larrat
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Sébastien Mériaux
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Jean-François Mangin
- NeuroSpin, CEA, Paris-Saclay University, Centre d'études de Saclay, Bâtiment 145, Gif-sur-Yvette 91191, France
| | - Isabel Reillo
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Camino de Juan Romero
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Víctor Borrell
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas - Universidad Miguel Hernández, Sant Joan d'Alacant, Av. Santiago Ramón y Cajal s/n, Sant Joan d'Alacant 03550, Spain
| | - Gerhard Engler
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Roberto Toro
- Unité de Neuroanatomie Appliquée et Théorique, Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, Université Paris Cité, 25-28 rue du Dr Roux, Paris 75015, France; Center for Research and Interdisciplinarity, Paris Descartes University, 24, rue du Faubourg Saint Jacques, Paris 75014, France
| | - Andreas K Engel
- Department of Neurophysiology and Pathophysiology, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany
| | - Claus C Hilgetag
- Institute of Computational Neuroscience, Hamburg Center of Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Martinistraße 52, Hamburg 20246, Germany; Department of Health Sciences, Boston University, 635 Commonwealth Avenue, Boston, Massachusetts 02215, USA
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5
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Fuhrer J, Glette K, Llorens A, Endestad T, Solbakk AK, Blenkmann AO. Quantifying evoked responses through information-theoretical measures. Front Neuroinform 2023; 17:1128866. [PMID: 37287586 PMCID: PMC10242156 DOI: 10.3389/fninf.2023.1128866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 05/04/2023] [Indexed: 06/09/2023] Open
Abstract
Information theory is a viable candidate to advance our understanding of how the brain processes information generated in the internal or external environment. With its universal applicability, information theory enables the analysis of complex data sets, is free of requirements about the data structure, and can help infer the underlying brain mechanisms. Information-theoretical metrics such as Entropy or Mutual Information have been highly beneficial for analyzing neurophysiological recordings. However, a direct comparison of the performance of these methods with well-established metrics, such as the t-test, is rare. Here, such a comparison is carried out by evaluating the novel method of Encoded Information with Mutual Information, Gaussian Copula Mutual Information, Neural Frequency Tagging, and t-test. We do so by applying each method to event-related potentials and event-related activity in different frequency bands originating from intracranial electroencephalography recordings of humans and marmoset monkeys. Encoded Information is a novel procedure that assesses the similarity of brain responses across experimental conditions by compressing the respective signals. Such an information-based encoding is attractive whenever one is interested in detecting where in the brain condition effects are present.
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Affiliation(s)
- Julian Fuhrer
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Kyrre Glette
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
| | - Anaïs Llorens
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, Berkeley, CA, United States
| | - Tor Endestad
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Anne-Kristin Solbakk
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- Department of Neurosurgery, Oslo University Hospital, Rikshospitalet, Oslo, Norway
- Department of Neuropsychology, Helgeland Hospital, Mosjøen, Norway
| | - Alejandro Omar Blenkmann
- RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
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6
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Foster BL, Koslov SR, Aponik-Gremillion L, Monko ME, Hayden BY, Heilbronner SR. A tripartite view of the posterior cingulate cortex. Nat Rev Neurosci 2023; 24:173-189. [PMID: 36456807 PMCID: PMC10041987 DOI: 10.1038/s41583-022-00661-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/03/2022]
Abstract
The posterior cingulate cortex (PCC) is one of the least understood regions of the cerebral cortex. By contrast, the anterior cingulate cortex has been the subject of intensive investigation in humans and model animal systems, leading to detailed behavioural and computational theoretical accounts of its function. The time is right for similar progress to be made in the PCC given its unique anatomical and physiological properties and demonstrably important contributions to higher cognitive functions and brain diseases. Here, we describe recent progress in understanding the PCC, with a focus on convergent findings across species and techniques that lay a foundation for establishing a formal theoretical account of its functions. Based on this converging evidence, we propose that the broader PCC region contains three major subregions - the dorsal PCC, ventral PCC and retrosplenial cortex - that respectively support the integration of executive, mnemonic and spatial processing systems. This tripartite subregional view reconciles inconsistencies in prior unitary theories of PCC function and offers promising new avenues for progress.
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Affiliation(s)
- Brett L Foster
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Seth R Koslov
- Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lyndsey Aponik-Gremillion
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA.,Department of Health Sciences, Dumke College for Health Professionals, Weber State University, Ogden, UT, USA
| | - Megan E Monko
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Benjamin Y Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA.,Center for Magnetic Resonance Research and Center for Neural Engineering, University of Minnesota, Minneapolis, MN, USA
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7
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Simola J, Siebenhühner F, Myrov V, Kantojärvi K, Paunio T, Palva JM, Brattico E, Palva S. Genetic polymorphisms in COMT and BDNF influence synchronization dynamics of human neuronal oscillations. iScience 2022; 25:104985. [PMID: 36093050 PMCID: PMC9460523 DOI: 10.1016/j.isci.2022.104985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 06/15/2022] [Accepted: 08/16/2022] [Indexed: 11/01/2022] Open
Abstract
Neuronal oscillations, their inter-areal synchronization, and scale-free dynamics constitute fundamental mechanisms for cognition by regulating communication in neuronal networks. These oscillatory dynamics have large inter-individual variability that is partly heritable. We hypothesized that this variability could be partially explained by genetic polymorphisms in neuromodulatory genes. We recorded resting-state magnetoencephalography (MEG) from 82 healthy participants and investigated whether oscillation dynamics were influenced by genetic polymorphisms in catechol-O-methyltransferase (COMT) Val158Met and brain-derived neurotrophic factor (BDNF) Val66Met. Both COMT and BDNF polymorphisms influenced local oscillation amplitudes and their long-range temporal correlations (LRTCs), while only BDNF polymorphism affected the strength of large-scale synchronization. Our findings demonstrate that COMT and BDNF genetic polymorphisms contribute to inter-individual variability in neuronal oscillation dynamics. Comparison of these results to computational modeling of near-critical synchronization dynamics further suggested that COMT and BDNF polymorphisms influenced local oscillations by modulating the excitation-inhibition balance according to the brain criticality framework. Human local oscillation dynamics is influenced by polymorphisms in COMT and BNDF COMT and BDNF influence oscillation amplitudes and long-range temporal correlations BDNF polymorphism affected the strength of large-scale synchronization Framework of brain criticality links COMT and BDNF with local E/I-balance
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8
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Changes in intrinsic functional brain connectivity related to occupational stress of firefighters. Psychiatry Res 2022; 314:114688. [PMID: 35777276 DOI: 10.1016/j.psychres.2022.114688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 06/13/2022] [Accepted: 06/18/2022] [Indexed: 11/21/2022]
Abstract
Firefighters are exposed to physical and mental threats in the working environment that put them at risk for occupational stress and burnout. Stress responses can impact the functional interactions between brain regions involved in emotional and cognitive regulation. The objective of this study was to investigate brain functional connectivity (FC) related to occupational stress in firefighters. Male firefighters (n = 77) completed the Korean Occupational Stress Scale questionnaire on occupational stress and underwent brain magnetic resonance imaging. Seed-based FC analyses were conducted by setting core regions of the large-scale functional networks as seeds. Subsequent correlational analyses detected relationships between occupational stress scale scores and brain FC. The results showed that occupational stress was negatively correlated with FC between the central executive network (CEN)-related brain regions and seed regions of other networks. Additionally, occupational stress was negatively correlated with FC within the default mode network (DMN), but positively correlated with FC between the salience network (SN) and the DMN. Changes in FC in large-scale neural networks are likely involved in occupational stress responses. Taken together, these results suggest that proper management of occupational stress may help prevent the occurrence of clinical problems caused by changes in brain functional networks.
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9
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Walter Y, Koenig T. Neural network involvement for religious experiences in worship measured by EEG microstate analysis. Soc Neurosci 2022; 17:258-275. [PMID: 35613474 DOI: 10.1080/17470919.2022.2083228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
To date, not much is known about large-scale brain activation patterns in religious states of mind and previous studies have not set an emphasis on experience. The present study investigated the phenomenon of religious experiences through microstate analysis, and it was the first neurocognitive research to tackle the dimension of experience directly. Hence, a total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. With a bar slider, people were able to continuously rate how strongly they sensed God's presence at any given moment. A selection of songs was used to help in the induction of the desired experience. With 64 electroencephalography (EEG) electrodes, the brain activity was assessed and analyzed with five clusters of microstate classes. First, we hypothesized that the neural network for multisensory integration was involved in the religious experience. Second, we hypothesized that the same was true for the Default Mode Network (DMN). Our results suggested an association between the auditory network and the religious experience, and an association with the salience network as well as with the DMN. No associations with the network thought to be involved with multisensory integration was detected.
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Affiliation(s)
- Yoshija Walter
- Laboratory for Cognitive Neurosciences, University of Fribourg, Switzerland.,University Hospital of Psychiatry and Psychotherapy, Translational Research Center, University of Bern, Switzerland
| | - Thomas Koenig
- University Hospital of Psychiatry and Psychotherapy, Translational Research Center, University of Bern, Switzerland
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10
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Aykan S, Puglia MH, Kalaycıoğlu C, Pelphrey KA, Tuncalı T, Nalçacı E. Right Anterior Theta Hypersynchrony as a Quantitative Measure Associated with Autistic Traits and K-Cl Cotransporter KCC2 Polymorphism. J Autism Dev Disord 2022; 52:61-72. [PMID: 33635423 DOI: 10.1007/s10803-021-04924-x] [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] [Accepted: 02/10/2021] [Indexed: 10/22/2022]
Abstract
Our aim was to use theta coherence as a quantitative trait to investigate the relation of the polymorphisms in NKCC1 (rs3087889) and KCC2 (rs9074) channel protein genes to autistic traits (AQ) in neurotypicals. Coherence values for candidate connection regions were calculated from eyes-closed resting EEGs in two independent groups. Hypersynchrony within the right anterior region was related to AQ in both groups (p < 0.05), and variability in this hypersynchrony was related to the rs9074 polymorphism in the total group (p < 0.05). In conclusion, theta hypersynchrony within the right anterior region during eyes-closed rest can be considered a quantitative measure for autistic traits. Replicating our findings in two independent populations with different backgrounds strengthens the validity of the current study.
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Affiliation(s)
- Simge Aykan
- Department of Physiology, Ankara University School of Medicine, Ankara, Turkey.
| | - Meghan H Puglia
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Psychology, University of Virginia, Charlottesville, VA, USA
| | - Canan Kalaycıoğlu
- Department of Physiology, Ankara University School of Medicine, Ankara, Turkey
| | - Kevin A Pelphrey
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
| | - Timur Tuncalı
- Department of Medical Genetics, Ankara University School of Medicine, Ankara, Turkey
| | - Erhan Nalçacı
- Department of Physiology, Ankara University School of Medicine, Ankara, Turkey
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11
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Jacob MS, Roach BJ, Sargent KS, Mathalon DH, Ford JM. Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study. Neuroimage 2021; 245:118705. [PMID: 34798229 DOI: 10.1016/j.neuroimage.2021.118705] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 10/11/2021] [Accepted: 11/01/2021] [Indexed: 11/24/2022] Open
Abstract
The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.
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Affiliation(s)
- Michael S Jacob
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Brian J Roach
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Kaia S Sargent
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States.
| | - Daniel H Mathalon
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
| | - Judith M Ford
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, 4150 Clement St, San Francisco, CA 94121 United States; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, 505 Parnassus Ave, San Francisco, CA 94143 United States.
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12
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Lee KM, Ferreira-Santos F, Satpute AB. Predictive processing models and affective neuroscience. Neurosci Biobehav Rev 2021; 131:211-228. [PMID: 34517035 DOI: 10.1016/j.neubiorev.2021.09.009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 02/10/2021] [Accepted: 09/07/2021] [Indexed: 01/17/2023]
Abstract
The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.
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Affiliation(s)
- Kent M Lee
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA.
| | - Fernando Ferreira-Santos
- Laboratory of Neuropsychophysiology, Faculty of Psychology and Education Sciences, University of Porto, Portugal
| | - Ajay B Satpute
- Northeastern University, 360 Huntington Ave, 125 NI, Boston, MA 02118, USA
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13
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Gschwandtner U, Bogaarts G, Chaturvedi M, Hatz F, Meyer A, Fuhr P, Roth V. Dynamic Functional Connectivity of EEG: From Identifying Fingerprints to Gender Differences to a General Blueprint for the Brain's Functional Organization. Front Neurosci 2021; 15:683633. [PMID: 34456669 PMCID: PMC8385669 DOI: 10.3389/fnins.2021.683633] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Accepted: 06/23/2021] [Indexed: 11/13/2022] Open
Abstract
An individual's brain functional organization is unique and can reliably be observed using modalities such as functional magnetic resonance imaging (fMRI). Here we demonstrate that a quantification of the dynamics of functional connectivity (FC) as measured using electroencephalography (EEG) offers an alternative means of observing an individual's brain functional organization. Using data from both healthy individuals as well as from patients with Parkinson's disease (PD) (n = 103 healthy individuals, n = 57 PD patients), we show that “dynamic FC” (DFC) profiles can be used to identify individuals in a large group. Furthermore, we show that DFC profiles predict gender and exhibit characteristics shared both among individuals as well as between both hemispheres. Furthermore, DFC profile characteristics are frequency band specific, indicating that they reflect distinct processes in the brain. Our empirically derived method of DFC demonstrates the potential of studying the dynamics of the functional organization of the brain using EEG.
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Affiliation(s)
- Ute Gschwandtner
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Guy Bogaarts
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland.,Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
| | - Menorca Chaturvedi
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Florian Hatz
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Antonia Meyer
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Peter Fuhr
- Department of Neurology and Neurophysiology, University Hospital of Basel, Basel, Switzerland
| | - Volker Roth
- Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland
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14
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Coronel-Oliveros C, Castro S, Cofré R, Orio P. Structural Features of the Human Connectome That Facilitate the Switching of Brain Dynamics via Noradrenergic Neuromodulation. Front Comput Neurosci 2021; 15:687075. [PMID: 34335217 PMCID: PMC8316621 DOI: 10.3389/fncom.2021.687075] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/11/2021] [Indexed: 11/27/2022] Open
Abstract
The structural connectivity of human brain allows the coexistence of segregated and integrated states of activity. Neuromodulatory systems facilitate the transition between these functional states and recent computational studies have shown how an interplay between the noradrenergic and cholinergic systems define these transitions. However, there is still much to be known about the interaction between the structural connectivity and the effect of neuromodulation, and to what extent the connectome facilitates dynamic transitions. In this work, we use a whole brain model, based on the Jasen and Rit equations plus a human structural connectivity matrix, to find out which structural features of the human connectome network define the optimal neuromodulatory effects. We simulated the effect of the noradrenergic system as changes in filter gain, and studied its effects related to the global-, local-, and meso-scale features of the connectome. At the global-scale, we found that the ability of the network of transiting through a variety of dynamical states is disrupted by randomization of the connection weights. By simulating neuromodulation of partial subsets of nodes, we found that transitions between integrated and segregated states are more easily achieved when targeting nodes with greater connection strengths-local feature-or belonging to the rich club-meso-scale feature. Overall, our findings clarify how the network spatial features, at different levels, interact with neuromodulation to facilitate the switching between segregated and integrated brain states and to sustain a richer brain dynamics.
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Affiliation(s)
- Carlos Coronel-Oliveros
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Programa de Doctorado en Ciencias, Mención Biofísica y Biología Computacional, Universidad de Valparaíso, Valparaíso, Chile
| | - Samy Castro
- Laboratoire de Neurosciences Cognitives et Adaptatives (LNCA), Faculté de Psychologie, Université de Strasbourg, Strasbourg, France
- University of Strasbourg Institute for Advanced Studies (USIAS), Université de Strasbourg, Strasbourg, France
| | - Rodrigo Cofré
- CIMFAV-Ingemat, Facultad de Ingeniería, Universidad de Valparaíso, Valparaíso, Chile
- Institute of Neuroscience (NeuroPSI), Paris-Saclay University, Centre National de la Recherche Scientifique (CNRS), Gif-sur-Yvette, France
| | - Patricio Orio
- Instituto Milenio Centro Interdisciplinario de Neurociencia de Valparaíso, Universidad de Valparaíso, Valparaíso, Chile
- Facultad de Ciencias, Instituto de Neurociencias, Universidad de Valparaíso, Valparaíso, Chile
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15
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Parbat D, Chakraborty M. A Novel Methodology to study the Cognitive Load Induced EEG Complexity Changes: Chaos, Fractal and Entropy based approach. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2020.102277] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
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16
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Alamian G, Pascarella A, Lajnef T, Knight L, Walters J, Singh KD, Jerbi K. Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia. Neuroimage Clin 2020; 28:102485. [PMID: 33395976 PMCID: PMC7691748 DOI: 10.1016/j.nicl.2020.102485] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/22/2020] [Accepted: 10/24/2020] [Indexed: 12/19/2022]
Abstract
Current theories of schizophrenia emphasize the role of altered information integration as the core dysfunction of this illness. While ample neuroimaging evidence for such accounts comes from investigations of spatial connectivity, understanding temporal disruptions is important to fully capture the essence of dysconnectivity in schizophrenia. Recent electrophysiology studies suggest that long-range temporal correlation (LRTC) in the amplitude dynamics of neural oscillations captures the integrity of transferred information in the healthy brain. Thus, in this study, 25 schizophrenia patients and 25 controls (8 females/group) were recorded during two five-minutes of resting-state magnetoencephalography (once with eyes-open and once with eyes-closed). We used source-level analyses to investigate temporal dysconnectivity in patients by characterizing LRTCs across cortical and sub-cortical brain regions. In addition to standard statistical assessments, we applied a machine learning framework using support vector machine to evaluate the discriminative power of LRTCs in identifying patients from healthy controls. We found that neural oscillations in schizophrenia patients were characterized by reduced signal memory and higher variability across time, as evidenced by cortical and subcortical attenuations of LRTCs in the alpha and beta frequency bands. Support vector machine significantly classified participants using LRTCs in key limbic and paralimbic brain areas, with decoding accuracy reaching 82%. Importantly, these brain regions belong to networks that are highly relevant to the symptomology of schizophrenia. These findings thus posit temporal dysconnectivity as a hallmark of altered information processing in schizophrenia, and help advance our understanding of this pathology.
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Affiliation(s)
- Golnoush Alamian
- CoCo Lab, Department of Psychology, Université de Montréal, Canada.
| | | | - Tarek Lajnef
- CoCo Lab, Department of Psychology, Université de Montréal, Canada
| | - Laura Knight
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - James Walters
- MRC Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Krish D Singh
- CUBRIC, School of Psychology, College of Biomedical and Life Sciences, Cardiff University, UK
| | - Karim Jerbi
- CoCo Lab, Department of Psychology, Université de Montréal, Canada; MEG Center, University of Montreal, Canada; UNIQUE Centre (Unifying AI and Neuroscience - Québec), Quebec, Canada; Mila (Quebec AI Institute), Montreal, QC, Canada; Centre de recherche de l'Institut universitaire en santé mentale de Montréal, Montreal, QC, Canada
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17
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Uddin LQ. Bring the Noise: Reconceptualizing Spontaneous Neural Activity. Trends Cogn Sci 2020; 24:734-746. [PMID: 32600967 PMCID: PMC7429348 DOI: 10.1016/j.tics.2020.06.003] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/04/2020] [Accepted: 06/05/2020] [Indexed: 12/17/2022]
Abstract
Definitions of what constitutes the 'signal of interest' in neuroscience can be controversial, due in part to continuously evolving notions regarding the significance of spontaneous neural activity. This review highlights how the challenge of separating brain signal from noise has led to new conceptualizations of brain functional organization at both the micro- and macroscopic level. Recent debates in the functional neuroimaging community surrounding artifact removal processes have revived earlier discussions surrounding how to appropriately isolate and measure neuronal signals against a background of noise from various sources. Insights from electrophysiological studies and computational modeling can inform current theory and data analytic practices in human functional neuroimaging, given that signal and noise may be inextricably linked in the brain.
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Affiliation(s)
- Lucina Q Uddin
- Department of Psychology, University of Miami, PO Box 248185-0751, Coral Gables, FL 33124, USA; Neuroscience Program, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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18
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Li SJ, Lo YC, Lai HY, Lin SH, Lin HC, Lin TC, Chang CW, Chen TC, Chin-Jung Hsieh C, Yang SH, Chiu FM, Kuo CH, Chen YY. Uncovering the Modulatory Interactions of Brain Networks in Cognition with Central Thalamic Deep Brain Stimulation Using Functional Magnetic Resonance Imaging. Neuroscience 2020; 440:65-84. [DOI: 10.1016/j.neuroscience.2020.05.022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 05/05/2020] [Accepted: 05/12/2020] [Indexed: 01/04/2023]
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19
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Canales-Johnson A, Billig AJ, Olivares F, Gonzalez A, Garcia MDC, Silva W, Vaucheret E, Ciraolo C, Mikulan E, Ibanez A, Huepe D, Noreika V, Chennu S, Bekinschtein TA. Dissociable Neural Information Dynamics of Perceptual Integration and Differentiation during Bistable Perception. Cereb Cortex 2020; 30:4563-4580. [PMID: 32219312 PMCID: PMC7325715 DOI: 10.1093/cercor/bhaa058] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
At any given moment, we experience a perceptual scene as a single whole and yet we may distinguish a variety of objects within it. This phenomenon instantiates two properties of conscious perception: integration and differentiation. Integration is the property of experiencing a collection of objects as a unitary percept and differentiation is the property of experiencing these objects as distinct from each other. Here, we evaluated the neural information dynamics underlying integration and differentiation of perceptual contents during bistable perception. Participants listened to a sequence of tones (auditory bistable stimuli) experienced either as a single stream (perceptual integration) or as two parallel streams (perceptual differentiation) of sounds. We computed neurophysiological indices of information integration and information differentiation with electroencephalographic and intracranial recordings. When perceptual alternations were endogenously driven, the integrated percept was associated with an increase in neural information integration and a decrease in neural differentiation across frontoparietal regions, whereas the opposite pattern was observed for the differentiated percept. However, when perception was exogenously driven by a change in the sound stream (no bistability), neural oscillatory power distinguished between percepts but information measures did not. We demonstrate that perceptual integration and differentiation can be mapped to theoretically motivated neural information signatures, suggesting a direct relationship between phenomenology and neurophysiology.
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Affiliation(s)
- Andrés Canales-Johnson
- Department of Psychology, University of Cambridge, CB2 3EB Cambridge, UK
- Vicerectoria de Investigacion y Posgrado, Universidad Catolica del Maule, Talca 3480112, Chile
| | - Alexander J Billig
- Brain and Mind Institute, University of Western Ontario, London, N6A 3K7, Canada
- UCL Ear Institute, University College London, London, UK
| | - Francisco Olivares
- Facultad de Psicologia, Universidad Diego Portales, Santiago 8370076, Chile
| | - Andrés Gonzalez
- Facultad de Psicologia, Universidad Diego Portales, Santiago 8370076, Chile
| | - María del Carmen Garcia
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Walter Silva
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Esteban Vaucheret
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Carlos Ciraolo
- Programa de Cirugía de Epilepsia, Hospital Italiano de Buenos Aires, Buenos Aires C1199ABB, Argentina
| | - Ezequiel Mikulan
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires 1126, Argentina
| | - Agustín Ibanez
- Laboratory of Experimental Psychology and Neuroscience (LPEN), Institute of Cognitive and Translational Neuroscience (INCyT), INECO Foundation, Favaloro University, Buenos Aires 1126, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibáñez, Santiago 2485, Chile
| | - David Huepe
- School of Psychology, Center for Social and Cognitive Neuroscience (CSCN), Universidad Adolfo Ibáñez, Santiago 2485, Chile
| | - Valdas Noreika
- Department of Psychology, University of Cambridge, CB2 3EB Cambridge, UK
| | - Srivas Chennu
- School of Computing, University of Kent, ME4 4AG Chatham, UK
- Department of Clinical Neurosciences, University of Cambridge, CB2 3EB Cambridge, UK
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20
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Chen T, Su H, Zhong N, Tan H, Li X, Meng Y, Duan C, Zhang C, Bao J, Xu D, Song W, Zou J, Liu T, Zhan Q, Jiang H, Zhao M. Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates. BMC Psychiatry 2020; 20:334. [PMID: 32580716 PMCID: PMC7315471 DOI: 10.1186/s12888-020-02743-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 06/18/2020] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC). METHODS A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions. RESULTS MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively. CONCLUSIONS Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.
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Affiliation(s)
- Tianzhen Chen
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Hang Su
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Na Zhong
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Haoye Tan
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Xiaotong Li
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China
| | - Yiran Meng
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Chunmei Duan
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Congbin Zhang
- Yunnan Institute on Drug Dependence, Kunming, Yunnan China
| | - Juwang Bao
- grid.28703.3e0000 0000 9040 3743Institute of Higher Education, Beijing University of Technology, Beijing, China
| | - Ding Xu
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Weidong Song
- Shanghai Bureau of Drug Rehabilitation Administration, Shanghai, China
| | - Jixue Zou
- Department of Health, Yunnan Bureau of Drug Rehabilitation Administration, Kunming, Yunnan China
| | - Tao Liu
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Qingqing Zhan
- Yunnan Third Compulsory Drug Dependence Rehablitation Center Hospital, Kunming, Yunnan China
| | - Haifeng Jiang
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030, China. .,Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.
| | - Min Zhao
- grid.16821.3c0000 0004 0368 8293Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, Shanghai, 200030 China ,grid.415630.50000 0004 1782 6212Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China ,grid.16821.3c0000 0004 0368 8293Institute of Psychological and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China ,grid.9227.e0000000119573309CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Sciences, Shanghai, China
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21
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Whitwell HJ, Bacalini MG, Blyuss O, Chen S, Garagnani P, Gordleeva SY, Jalan S, Ivanchenko M, Kanakov O, Kustikova V, Mariño IP, Meyerov I, Ullner E, Franceschi C, Zaikin A. The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging. Front Aging Neurosci 2020; 12:136. [PMID: 32523526 PMCID: PMC7261843 DOI: 10.3389/fnagi.2020.00136] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Accepted: 04/22/2020] [Indexed: 12/13/2022] Open
Abstract
Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.
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Affiliation(s)
- Harry J Whitwell
- Department of Chemical Engineering, Imperial College London, London, United Kingdom
| | | | - Oleg Blyuss
- School of Physics, Astronomy and Mathematics, University of Hertfordshire, Harfield, United Kingdom.,Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Shangbin Chen
- Britton Chance Centre for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China
| | - Paolo Garagnani
- Department of Experimental, Diagnostic and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy
| | - Susan Yu Gordleeva
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sarika Jalan
- Complex Systems Laboratory, Discipline of Physics, Indian Institute of Technology Indore, Indore, India.,Centre for Bio-Science and Bio-Medical Engineering, Indian Institute of Technology Indore, Indore, India
| | - Mikhail Ivanchenko
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Oleg Kanakov
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Valentina Kustikova
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ines P Mariño
- Department of Biology and Geology, Physics and Inorganic Chemistry, Universidad Rey Juan Carlos, Madrid, Spain
| | - Iosif Meyerov
- Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Ekkehard Ullner
- Department of Physics (SUPA), Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, United Kingdom
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Zaikin
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.,Institute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Mathematics, Institute for Women's Health, University College London, London, United Kingdom
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22
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Leng X, Xiang J, Yang Y, Yu T, Qi X, Zhang X, Wu S, Wang Y. Frequency-specific changes in the default mode network in patients with cingulate gyrus epilepsy. Hum Brain Mapp 2020; 41:2447-2459. [PMID: 32096905 PMCID: PMC7268086 DOI: 10.1002/hbm.24956] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 11/25/2019] [Accepted: 02/11/2020] [Indexed: 12/13/2022] Open
Abstract
To identify abnormal functional connectivity of the default mode network in cingulate gyrus epilepsy, which may yield new information about the default mode network and suggest a new cingulate gyrus epilepsy biomarker. Fifteen patients with cingulate gyrus epilepsy (mean age = 21 years) and 15 healthy controls (mean age = 24 years) were studied in the resting state using magnetoencephalography. Twelve brain areas of interest in the default mode network were extracted and investigated with multifrequency signals that included alpha (α, 8–13 Hz), beta (β, 14–30 Hz), and gamma (γ, 31–80 Hz) band oscillations. Patients with cingulate gyrus epilepsy had significantly greater connectivity in all three frequency bands (α, β, γ). A frequency‐specific elevation of functional connectivity was found in patients compared to controls. The greater functional connectivity in the γ band was significantly more prominent than that of the α and β bands. Patients with cingulate gyrus epilepsy and controls differed significantly in functional connectivity between the left angular gyrus and left posterior cingulate cortex in the α, β, and γ bands. The results of the node degree analysis were similar to those of the functional connectivity analysis. Our findings reveal for the first time that brain activity in the γ band may play a key role in the default mode network in cingulate gyrus epilepsy. Altered functional connectivity of the left angular gyrus and left posterior cingulate cortex may be a new biomarker for cingulate gyrus epilepsy.
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Affiliation(s)
- Xuerong Leng
- Department of Pediatrics, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Jing Xiang
- MEG Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - Yingxue Yang
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Tao Yu
- Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Xiaohong Qi
- Department of Pediatrics, Xuanwu Hospital Capital Medical University, Beijing, China
| | - Xiating Zhang
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Siqi Wu
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital Capital Medical University, Beijing, China.,Beijing Key Laboratory of Neuromodulation, Beijing, China.,Center of Epilepsy, Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
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23
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Pang JC, Robinson PA. Power spectrum of resting-state blood-oxygen-level-dependent signal. Phys Rev E 2020; 100:022418. [PMID: 31574765 DOI: 10.1103/physreve.100.022418] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Indexed: 12/25/2022]
Abstract
Hemodynamic modeling is used to explore the origin, predict, and analyze the power spectrum of the resting-state blood-oxygen-level-dependent (BOLD) signal measured by functional magnetic resonance imaging (fMRI), which has been reported to have a power-law form, i.e., P(f)∝f^{-s}, where P(f) is the power, f is the frequency, and s>0 is the power-law exponent. However, current fMRI experimental paradigms have limited acquisition durations, affecting the spectral resolution of fMRI data at the low-frequency regime. Here, the claimed power-law spectrum is investigated by using a recent hemodynamic model to analytically derive the BOLD power spectrum, with parameters that are related to neurophysiology. The theoretical results show that, for all realistic parameter combinations, the BOLD power spectrum is flat at f≲0.01Hz, has a weak resonance originating from intrinsic oscillations of vasodilatory response, and becomes a power law for high frequencies, all of which is in agreement with an empirical data set that describes the spectrum of one subject and brain region. However, the results are contrary to studies reporting a pure power-law spectrum at f≲0.2Hz. The discrepancy is attributed largely to data averaging employed by current approaches that averages together important properties of the BOLD power spectrum, such as its resonance, that biases the spectrum to only show a power law. Data averaging also reduces the high-frequency power-law exponent relative to individual cases. Overall, this work demonstrates how the model can reproduce BOLD dynamics and further analyze its low-frequency behavior. Moreover, it also uses the model to explain the impact of procedures, such as data averaging, on the reported features of the BOLD power spectrum.
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Affiliation(s)
- J C Pang
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia.,Center for Integrative Brain Function, University of Sydney, Sydney, NSW 2006, Australia.,QIMR Berghofer Medical Research Institute, Herston, QLD 4006, Australia
| | - P A Robinson
- School of Physics, University of Sydney, Sydney, NSW 2006, Australia.,Center for Integrative Brain Function, University of Sydney, Sydney, NSW 2006, Australia
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Rolston JD, Chang EF. Critical Language Areas Show Increased Functional Connectivity in Human Cortex. Cereb Cortex 2019; 28:4161-4168. [PMID: 29045564 DOI: 10.1093/cercor/bhx271] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2016] [Indexed: 11/13/2022] Open
Abstract
Electrocortical stimulation (ECS) mapping is routinely used to identify critical language sites before resective neurosurgery. The precise locations of these sites are highly variable across patients, occurring in the frontal, temporal, and parietal lobes-it is this variability that necessitates individual patient mapping. But why these particular anatomical sites are so privileged in each patient is unknown. We hypothesized that critical language sites have greater functional connectivity with nearby cortex than sites without critical functions, since they serve as central nodes within the language network. Functional connectivity across language, motor, and cleared sites was measured in 15 patients undergoing electrocortiographic (ECoG) mapping for epilepsy surgery. Critical language sites had significantly higher connectivity than sites without critical functions (P = 0.001), and this also held for motor sites (P = 0.022). These data support the hypothesis that critical language sites are highly connected within the local cortical network, perhaps explaining why their disruption with ECS leads to transient disturbances in language function. It is our hope that improved understanding of the mechanisms of ECS will permit improved surgical planning and perhaps contribute to the understanding of normal language physiology.
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Affiliation(s)
- John D Rolston
- Department of Neurosurgery, University of Utah, Salt Lake City, UT, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
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25
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Okun M, Steinmetz NA, Lak A, Dervinis M, Harris KD. Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales. Cereb Cortex 2019; 29:2196-2210. [PMID: 30796825 PMCID: PMC6458908 DOI: 10.1093/cercor/bhz023] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Revised: 01/26/2019] [Accepted: 01/28/2019] [Indexed: 12/20/2022] Open
Abstract
Cortical activity is organized across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms-1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons' spiking activity and its coupling to local population rate and to arousal level across 0.01-100 Hz frequency range. In mouse medial prefrontal cortex, the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anticorrelated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of individual neurons' population coupling strength. These observations demonstrate how neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI.
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Affiliation(s)
- Michael Okun
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
- Institute of Neurology, University College London, London, UK
| | | | - Armin Lak
- Institute of Neurology, University College London, London, UK
| | - Martynas Dervinis
- Centre for Systems Neuroscience and Department of Neuroscience, Psychology and Behaviour, University of Leicester, Leicester, UK
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26
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Abbas A, Belloy M, Kashyap A, Billings J, Nezafati M, Schumacher EH, Keilholz S. Quasi-periodic patterns contribute to functional connectivity in the brain. Neuroimage 2019; 191:193-204. [PMID: 30753928 DOI: 10.1016/j.neuroimage.2019.01.076] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 01/07/2019] [Accepted: 01/30/2019] [Indexed: 02/03/2023] Open
Abstract
Functional connectivity is widely used to study the coordination of activity between brain regions over time. Functional connectivity in the default mode and task positive networks is particularly important for normal brain function. However, the processes that give rise to functional connectivity in the brain are not fully understood. It has been postulated that low-frequency neural activity plays a key role in establishing the functional architecture of the brain. Quasi-periodic patterns (QPPs) are a reliably observable form of low-frequency neural activity that involve the default mode and task positive networks. Here, QPPs from resting-state and working memory task-performing individuals were acquired. The spatiotemporal pattern, strength, and frequency of the QPPs between the two groups were compared and the contribution of QPPs to functional connectivity in the brain was measured. In task-performing individuals, the spatiotemporal pattern of the QPP changes, particularly in task-relevant regions, and the QPP tends to occur with greater strength and frequency. Differences in the QPPs between the two groups could partially account for the variance in functional connectivity between resting-state and task-performing individuals. The QPPs contribute strongly to connectivity in the default mode and task positive networks and to the strength of anti-correlation seen between the two networks. Many of the connections affected by QPPs are also disrupted during several neurological disorders. These findings contribute to understanding the dynamic neural processes that give rise to functional connectivity in the brain and how they may be disrupted during disease.
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Affiliation(s)
- Anzar Abbas
- Neuroscience, Emory University, 1760 Haygood Dr NE Suite W-200, Atlanta, GA, 30322, USA
| | - Michaël Belloy
- Bio-Imaging Lab, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Antwerp, Belgium
| | - Amrit Kashyap
- Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760, Haygood Dr NE Suite, W-200, Atlanta, GA, 30322, USA
| | - Jacob Billings
- Neuroscience, Emory University, 1760 Haygood Dr NE Suite W-200, Atlanta, GA, 30322, USA
| | - Maysam Nezafati
- Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760, Haygood Dr NE Suite, W-200, Atlanta, GA, 30322, USA
| | - Eric H Schumacher
- School of Psychology, Georgia Institute of Technology, 654 Cherry Street, Atlanta, GA 30332, USA
| | - Shella Keilholz
- Neuroscience, Emory University, 1760 Haygood Dr NE Suite W-200, Atlanta, GA, 30322, USA; Biomedical Engineering, Emory University and Georgia Institute of Technology, 1760, Haygood Dr NE Suite, W-200, Atlanta, GA, 30322, USA.
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27
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La Rocca D, Zilber N, Abry P, van Wassenhove V, Ciuciu P. Self-similarity and multifractality in human brain activity: A wavelet-based analysis of scale-free brain dynamics. J Neurosci Methods 2018; 309:175-187. [DOI: 10.1016/j.jneumeth.2018.09.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/16/2022]
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28
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Hong KS, Khan MJ, Hong MJ. Feature Extraction and Classification Methods for Hybrid fNIRS-EEG Brain-Computer Interfaces. Front Hum Neurosci 2018; 12:246. [PMID: 30002623 PMCID: PMC6032997 DOI: 10.3389/fnhum.2018.00246] [Citation(s) in RCA: 105] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2018] [Accepted: 05/29/2018] [Indexed: 11/13/2022] Open
Abstract
In this study, a brain-computer interface (BCI) framework for hybrid functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) for locked-in syndrome (LIS) patients is investigated. Brain tasks, channel selection methods, and feature extraction and classification algorithms available in the literature are reviewed. First, we categorize various types of patients with cognitive and motor impairments to assess the suitability of BCI for each of them. The prefrontal cortex is identified as a suitable brain region for imaging. Second, the brain activity that contributes to the generation of hemodynamic signals is reviewed. Mental arithmetic and word formation tasks are found to be suitable for use with LIS patients. Third, since a specific targeted brain region is needed for BCI, methods for determining the region of interest are reviewed. The combination of a bundled-optode configuration and threshold-integrated vector phase analysis turns out to be a promising solution. Fourth, the usable fNIRS features and EEG features are reviewed. For hybrid BCI, a combination of the signal peak and mean fNIRS signals and the highest band powers of EEG signals is promising. For classification, linear discriminant analysis has been most widely used. However, further research on vector phase analysis as a classifier for multiple commands is desirable. Overall, proper brain region identification and proper selection of features will improve classification accuracy. In conclusion, five future research issues are identified, and a new BCI scheme, including brain therapy for LIS patients and using the framework of hybrid fNIRS-EEG BCI, is provided.
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Affiliation(s)
- Keum-Shik Hong
- Department of Cogno-Mechatronics Engineering, Pusan National University, Busan, South Korea.,School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - M Jawad Khan
- School of Mechanical Engineering, Pusan National University, Busan, South Korea
| | - Melissa J Hong
- Early Learning, FIRST 5 Santa Clara County, San Jose, CA, United States
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29
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Palva JM, Wang SH, Palva S, Zhigalov A, Monto S, Brookes MJ, Schoffelen JM, Jerbi K. Ghost interactions in MEG/EEG source space: A note of caution on inter-areal coupling measures. Neuroimage 2018; 173:632-643. [PMID: 29477441 DOI: 10.1016/j.neuroimage.2018.02.032] [Citation(s) in RCA: 153] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 11/01/2017] [Accepted: 02/16/2018] [Indexed: 11/20/2022] Open
Abstract
When combined with source modeling, magneto- (MEG) and electroencephalography (EEG) can be used to study long-range interactions among cortical processes non-invasively. Estimation of such inter-areal connectivity is nevertheless hindered by instantaneous field spread and volume conduction, which artificially introduce linear correlations and impair source separability in cortical current estimates. To overcome the inflating effects of linear source mixing inherent to standard interaction measures, alternative phase- and amplitude-correlation based connectivity measures, such as imaginary coherence and orthogonalized amplitude correlation have been proposed. Being by definition insensitive to zero-lag correlations, these techniques have become increasingly popular in the identification of correlations that cannot be attributed to field spread or volume conduction. We show here, however, that while these measures are immune to the direct effects of linear mixing, they may still reveal large numbers of spurious false positive connections through field spread in the vicinity of true interactions. This fundamental problem affects both region-of-interest-based analyses and all-to-all connectome mappings. Most importantly, beyond defining and illustrating the problem of spurious, or "ghost" interactions, we provide a rigorous quantification of this effect through extensive simulations. Additionally, we further show that signal mixing also significantly limits the separability of neuronal phase and amplitude correlations. We conclude that spurious correlations must be carefully considered in connectivity analyses in MEG/EEG source space even when using measures that are immune to zero-lag correlations.
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Affiliation(s)
- J Matias Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
| | - Sheng H Wang
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; Doctoral Programme Brain & Mind, University of Helsinki, Finland
| | - Satu Palva
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland; BioMag Laboratory, HUS Medical Imaging Center, Helsinki, Finland
| | - Alexander Zhigalov
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Simo Monto
- Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Matthew J Brookes
- Sir Peter Mansfield Imaging Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, United Kingdom
| | - Jan-Mathijs Schoffelen
- Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Karim Jerbi
- Psychology Department, University of Montreal, Montreal, QC, Canada; MEG Unit, University of Montreal, QC, Canada
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30
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Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks. J Neurosci 2018; 38:4230-4242. [PMID: 29626167 DOI: 10.1523/jneurosci.0217-18.2018] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/03/2018] [Accepted: 03/31/2018] [Indexed: 11/21/2022] Open
Abstract
Evidence for intrinsic functional connectivity (FC) within the human brain is largely from neuroimaging studies of hemodynamic activity. Data are lacking from anatomically precise electrophysiological recordings in the most widely studied nodes of human brain networks. Here we used a combination of fMRI and electrocorticography (ECoG) in five human neurosurgical patients with electrodes in the canonical "default" (medial prefrontal and posteromedial cortex), "dorsal attention" (frontal eye fields and superior parietal lobule), and "frontoparietal control" (inferior parietal lobule and dorsolateral prefrontal cortex) networks. In this unique cohort, simultaneous intracranial recordings within these networks were anatomically matched across different individuals. Within each network and for each individual, we found a positive, and reproducible, spatial correlation for FC measures obtained from resting-state fMRI and separately recorded ECoG in the same brains. This relationship was reliably identified for electrophysiological FC based on slow (<1 Hz) fluctuations of high-frequency broadband (70-170 Hz) power, both during wakeful rest and sleep. A similar FC organization was often recovered when using lower-frequency (1-70 Hz) power, but anatomical specificity and consistency were greatest for the high-frequency broadband range. An interfrequency comparison of fluctuations in FC revealed that high and low-frequency ranges often temporally diverged from one another, suggesting that multiple neurophysiological sources may underlie variations in FC. Together, our work offers a generalizable electrophysiological basis for intrinsic FC and its dynamics across individuals, brain networks, and behavioral states.SIGNIFICANCE STATEMENT The study of human brain networks during wakeful "rest", largely with fMRI, is now a major focus in both cognitive and clinical neuroscience. However, little is known about the neurophysiology of these networks and their dynamics. We studied neural activity during wakeful rest and sleep within neurosurgical patients with directly implanted electrodes. We found that network activity patterns showed striking similarities between fMRI and direct recordings in the same brains. With improved resolution of direct recordings, we also found that networks were best characterized with specific activity frequencies and that different frequencies show different profiles of within-network activity over time. Our work clarifies how networks spontaneously organize themselves across individuals, brain networks, and behavioral states.
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31
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The Basal Forebrain Regulates Global Resting-State fMRI Fluctuations. Neuron 2018; 97:940-952.e4. [PMID: 29398365 DOI: 10.1016/j.neuron.2018.01.032] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Revised: 12/10/2017] [Accepted: 01/12/2018] [Indexed: 01/06/2023]
Abstract
Patterns of spontaneous brain activity, typically measured in humans at rest with fMRI, are used routinely to assess the brain's functional organization. The mechanisms that generate and coordinate the underlying neural fluctuations are largely unknown. Here we investigate the hypothesis that the nucleus basalis of Meynert (NBM), the principal source of widespread cholinergic and GABAergic projections to the cortex, contributes critically to such activity. We reversibly inactivated two distinct sites of the NBM in macaques while measuring fMRI activity across the brain. We found that inactivation led to strong, regionalized suppression of shared or "global" signal components of cortical fluctuations ipsilateral to the injection. At the same time, the commonly studied resting-state networks retained their spatial structure under this suppression. The results indicate that the NBM contributes selectively to the global component of functional connectivity but plays little if any role in the specific correlations that define resting-state networks.
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32
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Shine JM, Aburn MJ, Breakspear M, Poldrack RA. The modulation of neural gain facilitates a transition between functional segregation and integration in the brain. eLife 2018; 7:e31130. [PMID: 29376825 PMCID: PMC5818252 DOI: 10.7554/elife.31130] [Citation(s) in RCA: 87] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2017] [Accepted: 01/26/2018] [Indexed: 12/13/2022] Open
Abstract
Cognitive function relies on a dynamic, context-sensitive balance between functional integration and segregation in the brain. Previous work has proposed that this balance is mediated by global fluctuations in neural gain by projections from ascending neuromodulatory nuclei. To test this hypothesis in silico, we studied the effects of neural gain on network dynamics in a model of large-scale neuronal dynamics. We found that increases in neural gain directed the network through an abrupt dynamical transition, leading to an integrated network topology that was maximal in frontoparietal 'rich club' regions. This gain-mediated transition was also associated with increased topological complexity, as well as increased variability in time-resolved topological structure, further highlighting the potential computational benefits of the gain-mediated network transition. These results support the hypothesis that neural gain modulation has the computational capacity to mediate the balance between integration and segregation in the brain.
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Affiliation(s)
- James M Shine
- Department of PsychologyStanford UniversityStanfordUnited States
- Central Clinical SchoolThe University of SydneySydneyAustralia
| | | | - Michael Breakspear
- QIMR Berghofer Medical Research InstituteBrisbaneAustralia
- Metro North Mental Health ServiceBrisbaneAustralia
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33
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Michel CM, Koenig T. EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review. Neuroimage 2017; 180:577-593. [PMID: 29196270 DOI: 10.1016/j.neuroimage.2017.11.062] [Citation(s) in RCA: 510] [Impact Index Per Article: 72.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Revised: 11/07/2017] [Accepted: 11/27/2017] [Indexed: 12/27/2022] Open
Abstract
The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.
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Affiliation(s)
- Christoph M Michel
- Department of Basic Neurosciences, University of Geneva, Campus Biotech, Switzerland; Lemanic Biomedical Imaging Centre (CIBM), Lausanne and Geneva, Switzerland.
| | - Thomas Koenig
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Switzerland
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34
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Branco MP, Gaglianese A, Glen DR, Hermes D, Saad ZS, Petridou N, Ramsey NF. ALICE: A tool for automatic localization of intra-cranial electrodes for clinical and high-density grids. J Neurosci Methods 2017; 301:43-51. [PMID: 29100838 DOI: 10.1016/j.jneumeth.2017.10.022] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Accepted: 10/24/2017] [Indexed: 12/01/2022]
Abstract
BACKGROUND Electrocorticographic (ECoG) measurements require the accurate localization of implanted electrodes with respect to the subject's neuroanatomy. Electrode localization is particularly relevant to associate structure with function. Several procedures have attempted to solve this problem, namely by co-registering a post-operative computed tomography (CT) scan, with a pre-operative magnetic resonance imaging (MRI) anatomy scan. However, this type of procedure requires a manual and time-consuming detection and transcription of the electrode coordinates from the CT volume scan and restricts the extraction of smaller high-resolution ECoG grid electrodes due to the downsampling of the CT. NEW METHOD ALICE automatically detects electrodes on the post-operative high-resolution CT scan, visualizes them in a combined 2D and 3D volume space using AFNI and SUMA software and then projects the electrodes on the individual's cortical surface rendering. The pipeline integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for ECoG electrode localization. RESULTS ALICE was validated in 13 subjects implanted with clinical ECoG grids by comparing the calculated electrode center-of-mass coordinates with those computed using a commonly used method. COMPARISON WITH EXISTING METHODS A novel aspect of ALICE is the combined 2D-3D visualization of the electrodes on the CT scan and the option to also detect high-density ECoG grids. Feasibility was shown in 5 subjects and validated for 2 subjects. CONCLUSIONS The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart.
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Affiliation(s)
- Mariana P Branco
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands
| | - Anna Gaglianese
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Daniel R Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Dora Hermes
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands; Department of Psychology, New York University, New York, NY, United States; Department of Psychology, Stanford University, Stanford, CA, United States
| | - Ziad S Saad
- Scientific and Statistical Computing Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD 20892, United States
| | - Natalia Petridou
- Department of Radiology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center, The Netherlands.
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35
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Alamian G, Hincapié AS, Pascarella A, Thiery T, Combrisson E, Saive AL, Martel V, Althukov D, Haesebaert F, Jerbi K. Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges. Clin Neurophysiol 2017; 128:1719-1736. [DOI: 10.1016/j.clinph.2017.06.246] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/08/2017] [Accepted: 06/19/2017] [Indexed: 02/06/2023]
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36
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Dynamic connectivity modulates local activity in the core regions of the default-mode network. Proc Natl Acad Sci U S A 2017; 114:9713-9718. [PMID: 28827337 DOI: 10.1073/pnas.1702027114] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Segregation and integration are distinctive features of large-scale brain activity. Although neuroimaging studies have been unraveling their neural correlates, how integration takes place over segregated modules remains elusive. Central to this problem is the mechanism by which a brain region adjusts its activity according to the influence it receives from other regions. In this study, we explore how dynamic connectivity between two regions affects the neural activity within a participating region. Combining functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) in the same group of subjects, we analyzed resting-state data from the core of the default-mode network. We observed directed influence from the posterior cingulate cortex (PCC) to the anterior cingulate cortex (ACC) in the 10-Hz range. This time-varying influence was associated with the power alteration in the ACC: strong influence corresponded with a decrease of power around 13-16 Hz and an increase of power in the lower (1-7 Hz) and higher (30-55 Hz) ends of the spectrum. We also found that the amplitude of the 30- to 55-Hz activity was coupled to the phase of the 3- to 4-Hz activity in the ACC. These results characterized the local spectral changes associated with network interactions. The specific spectral information both highlights the functional roles of PCC-ACC connectivity in the resting state and provides insights into the dynamic relationship between local activity and coupling dynamics of a network.
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37
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Kucyi A. Just a thought: How mind-wandering is represented in dynamic brain connectivity. Neuroimage 2017; 180:505-514. [PMID: 28684334 DOI: 10.1016/j.neuroimage.2017.07.001] [Citation(s) in RCA: 64] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2017] [Revised: 06/14/2017] [Accepted: 07/01/2017] [Indexed: 01/24/2023] Open
Abstract
The neuroscience of mind-wandering has begun to flourish, with roles of brain regions and networks being defined for various components of spontaneous thought. However, most of brain activity does not represent immediately occurring thoughts. Instead, spontaneous, organized network activity largely reflects "intrinsic" functions that are unrelated to the current experience. There remains no consensus on how brain networks represent mind-wandering in parallel to functioning in other ongoing, predominantly unconscious processes. Commonly, in network analysis of functional neuroimaging data, functional connectivity (FC; correlated time series) between remote brain regions is considered over several minutes or longer. In contrast, dynamic functional connectivity (dFC) is a new, promising approach to characterizing spontaneous changes in neural network communication on the faster time-scale at which intra-individual fluctuations in thought contents may occur. Here I describe how a potential relationship between mind-wandering and FC has traditionally been considered in the literature, and I review methods and results pertaining to the study of the dFC-mind-wandering relationship. While acknowledging challenges to the dFC approach and to behaviorally capturing fluctuations in inner experiences, I describe a framework for describing spontaneous thoughts in terms of brain-network activity patterns that are comprised of connections weighted by time-varying relevance to conscious and unconscious processing. This perspective suggests preferential roles of certain anatomical communication avenues (e.g., via the default mode network) in mind-wandering, while also implying that a region's connectivity fluctuates over time in its immediate degree of relevance to conscious contents, ultimately allowing novelty and diversity of thought.
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Affiliation(s)
- Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA 94304, United States.
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38
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Androulakis XM, Krebs K, Peterlin BL, Zhang T, Maleki N, Sen S, Rorden C, Herath P. Modulation of intrinsic resting-state fMRI networks in women with chronic migraine. Neurology 2017; 89:163-169. [PMID: 28615426 DOI: 10.1212/wnl.0000000000004089] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 04/06/2017] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To evaluate the intrinsic resting functional connectivity of the default mode network (DMN), salience network (SN), and central executive network (CEN) network in women with chronic migraine (CM), and whether clinical features are associated with such abnormalities. METHODS We analyzed resting-state connectivity in 29 women with CM as compared to age- and sex-matched controls. Relationships between clinical characteristics and changes in targeted networks connectivity were evaluated using a multivariate linear regression model. RESULTS All 3 major intrinsic brain networks were less coherent in CM (DMN: p = 0.030, SN: p = 0.007, CEN: p = 0.002) as compared to controls. When stratified based on medication overuse headache (MOH) status, CM without MOH (DMN: p = 0.029, SN: p = 0.023, CEN: p = 0.003) and CM with MOH (DMN: p = 0.016, SN: p = 0.016, CEN: p = 0.015) were also less coherent as compared to controls. There was no difference in CM with MOH as compared to CM without MOH (DMN: p = 0.382, SN: p = 0.408, CEN: p = 0.419). The frequency of moderate and severe headache days was associated with decreased connectivity in SN (p = 0.003) and CEN (p = 0.015), while cutaneous allodynia was associated with increased connectivity in SN (p = 0.011). CONCLUSIONS Our results demonstrated decreased overall resting-state functional connectivity of the 3 major intrinsic brain networks in women with CM, and these patterns were associated with frequency of moderate to severe headache and cutaneous allodynia.
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Affiliation(s)
- X Michelle Androulakis
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston.
| | - Kaitlin Krebs
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - B Lee Peterlin
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - Tianming Zhang
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - Nasim Maleki
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - Souvik Sen
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - Chris Rorden
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
| | - Priyantha Herath
- From the Departments of Neurology (X.M.A., K.K., S.S., P.H.), Statistics (T.Z.), and Psychology (C.R.), University of South Carolina, Columbia; Department of Neurology (B.L.P.), Johns Hopkins University, Baltimore, MD; and Department of Psychiatry (N.M.), Massachusetts General Hospital, Boston
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The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. Neuroimage 2017; 170:400-411. [PMID: 28213119 DOI: 10.1016/j.neuroimage.2017.02.034] [Citation(s) in RCA: 52] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Revised: 02/13/2017] [Accepted: 02/13/2017] [Indexed: 11/23/2022] Open
Abstract
Despite the common conception of the dorsal premotor cortex (PMd) as a single brain region, its diverse connectivity profiles and behavioral heterogeneity argue for a differentiated organization of the PMd. A previous study revealed that the right PMd is characterized by a rostro-caudal and a ventro-dorsal distinction dividing it into five subregions: rostral, central, caudal, ventral and dorsal. The present study assessed whether a similar organization is present in the left hemisphere, by capitalizing on a multimodal data-driven approach combining connectivity-based parcellation (CBP) based on meta-analytic modeling, resting-state functional connectivity, and probabilistic diffusion tractography. The resulting PMd modules were then characterized based on multimodal functional connectivity and a quantitative analysis of associated behavioral functions. Analyzing the clusters consistent across all modalities revealed an organization of the left PMd that mirrored its right counterpart to a large degree. Again, caudal, central and rostral modules reflected a cognitive-motor gradient and a premotor eye-field was found in the ventral part of the left PMd. In addition, a distinct module linked to abstract cognitive functions was observed in the rostro-ventral left PMd across all CBP modalities, implying greater differentiation of higher cognitive functions for the left than the right PMd.
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40
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Distributed representation of context by intrinsic subnetworks in prefrontal cortex. Proc Natl Acad Sci U S A 2017; 114:2030-2035. [PMID: 28174269 DOI: 10.1073/pnas.1615269114] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Human prefrontal cortex supports goal-directed behavior by representing abstract information about task context. The organizational basis of these context representations, and of representations underlying other higher-order processes, is unknown. Here, we use multivariate decoding and analyses of spontaneous correlations to show that context representations are distributed across subnetworks within prefrontal cortex. Examining targeted prefrontal regions, we found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences. This subnetwork organization was stable across task-engaged and resting states, suggesting that abstract context representations are constrained by an intrinsic functional architecture. These results reveal a principle of fine-scaled functional organization in association cortex.
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Gollo LL, Roberts JA, Cocchi L. Mapping how local perturbations influence systems-level brain dynamics. Neuroimage 2017; 160:97-112. [PMID: 28126550 DOI: 10.1016/j.neuroimage.2017.01.057] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 12/12/2016] [Accepted: 01/23/2017] [Indexed: 11/15/2022] Open
Abstract
The human brain exhibits a distinct spatiotemporal organization that supports brain function and can be manipulated via local brain stimulation. Such perturbations to local cortical dynamics are globally integrated by distinct neural systems. However, it remains unclear how local changes in neural activity affect large-scale system dynamics. Here, we briefly review empirical and computational studies addressing how localized perturbations affect brain activity. We then systematically analyze a model of large-scale brain dynamics, assessing how localized changes in brain activity at the different sites affect whole-brain dynamics. We find that local stimulation induces changes in brain activity that can be summarized by relatively smooth tuning curves, which relate a region's effectiveness as a stimulation site to its position within the cortical hierarchy. Our results also support the notion that brain hubs, operating in a slower regime, are more resilient to focal perturbations and critically contribute to maintain stability in global brain dynamics. In contrast, perturbations of peripheral regions, characterized by faster activity, have greater impact on functional connectivity. As a parallel with this region-level result, we also find that peripheral systems such as the visual and sensorimotor networks were more affected by local perturbations than high-level systems such as the cingulo-opercular network. Our findings highlight the importance of a periphery-to-core hierarchy to determine the effect of local stimulation on the brain network. This study also provides novel resources to orient empirical work aiming at manipulating functional connectivity using non-invasive brain stimulation.
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Affiliation(s)
| | - James A Roberts
- QIMR Berghofer Medical Research Institute, Brisbane, Australia; Centre of Excellence for Integrative Brain Function, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Luca Cocchi
- QIMR Berghofer Medical Research Institute, Brisbane, Australia.
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42
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Liu X, Lauer KK, Douglas Ward B, Roberts C, Liu S, Gollapudy S, Rohloff R, Gross W, Chen G, Xu Z, Binder JR, Li SJ, Hudetz AG. Propofol attenuates low-frequency fluctuations of resting-state fMRI BOLD signal in the anterior frontal cortex upon loss of consciousness. Neuroimage 2016; 147:295-301. [PMID: 27993673 DOI: 10.1016/j.neuroimage.2016.12.043] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 10/13/2016] [Accepted: 12/15/2016] [Indexed: 01/01/2023] Open
Abstract
Recent studies indicate that spontaneous low-frequency fluctuations (LFFs) of resting-state functional magnetic resonance imaging (rs-fMRI) blood oxygen level-dependent (BOLD) signals are driven by the slow (<0.1Hz) modulation of ongoing neuronal activity synchronized locally and across remote brain regions. How regional LFFs of the BOLD fMRI signal are altered during anesthetic-induced alteration of consciousness is not well understood. Using rs-fMRI in 15 healthy participants, we show that during administration of propofol to achieve loss of behavioral responsiveness indexing unconsciousness, the fractional amplitude of LFF (fALFF index) was reduced in comparison to wakeful baseline in the anterior frontal regions, temporal pole, hippocampus, parahippocampal gyrus, and amygdala. Such changes were absent in large areas of the motor, parietal, and sensory cortices. During light sedation characterized by the preservation of overt responsiveness and therefore consciousness, fALFF was reduced in the subcortical areas, temporal pole, medial orbital frontal cortex, cingulate cortex, and cerebellum. Between light sedation and deep sedation, fALFF was reduced primarily in the medial and dorsolateral frontal areas. The preferential reduction of LFFs in the anterior frontal regions is consistent with frontal to sensory-motor cortical disconnection and may contribute to the suppression of consciousness during general anesthesia.
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Affiliation(s)
- Xiaolin Liu
- Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA.
| | - Kathryn K Lauer
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - B Douglas Ward
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Christopher Roberts
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Suyan Liu
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Suneeta Gollapudy
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Robert Rohloff
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - William Gross
- Department of Anesthesiology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Guangyu Chen
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Zhan Xu
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jeffrey R Binder
- Department of Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Shi-Jiang Li
- Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Anthony G Hudetz
- Department of Anesthesiology and Center for Consciousness Science, University of Michigan, Ann Arbor, USA.
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43
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Herreras O. Local Field Potentials: Myths and Misunderstandings. Front Neural Circuits 2016; 10:101. [PMID: 28018180 PMCID: PMC5156830 DOI: 10.3389/fncir.2016.00101] [Citation(s) in RCA: 171] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 11/28/2016] [Indexed: 12/02/2022] Open
Abstract
The intracerebral local field potential (LFP) is a measure of brain activity that reflects the highly dynamic flow of information across neural networks. This is a composite signal that receives contributions from multiple neural sources, yet interpreting its nature and significance may be hindered by several confounding factors and technical limitations. By and large, the main factor defining the amplitude of LFPs is the geometry of the current sources, over and above the degree of synchronization or the properties of the media. As such, similar levels of activity may result in potentials that differ in several orders of magnitude in different populations. The geometry of these sources has been experimentally inaccessible until intracerebral high density recordings enabled the co-activating sources to be revealed. Without this information, it has proven difficult to interpret a century's worth of recordings that used temporal cues alone, such as event or spike related potentials and frequency bands. Meanwhile, a collection of biophysically ill-founded concepts have been considered legitimate, which can now be corrected in the light of recent advances. The relationship of LFPs to their sources is often counterintuitive. For instance, most LFP activity is not local but remote, it may be larger further from rather than close to the source, the polarity does not define its excitatory or inhibitory nature, and the amplitude may increase when source's activity is reduced. As technological developments foster the use of LFPs, the time is now ripe to raise awareness of the need to take into account spatial aspects of these signals and of the errors derived from neglecting to do so.
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Affiliation(s)
- Oscar Herreras
- Department of Translational Neuroscience, Cajal Institute-CSICMadrid, Spain
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44
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Kucyi A, Davis KD. The Neural Code for Pain: From Single-Cell Electrophysiology to the Dynamic Pain Connectome. Neuroscientist 2016; 23:397-414. [DOI: 10.1177/1073858416667716] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Pain occurs in time. In naturalistic settings, pain perception is sometimes stable but often varies in intensity and quality over the course of seconds, minutes, and days. A principal aim in classic electrophysiology studies of pain was to uncover a neural code based on the temporal patterns of single neuron firing. In contrast, modern neuroimaging studies have placed emphasis on uncovering the spatial pattern of brain activity (or “map”) that may reflect the pain experience. However, in the emerging field of connectomics, communication within and among brain networks is characterized as intrinsically dynamic on multiple time scales. In this review, we revisit the single-cell electrophysiological evidence for a nociceptive neural code and consider how those findings relate to recent advances in understanding systems-level dynamic processes that suggest the existence of a “dynamic pain connectome” as a spatiotemporal physiological signature of pain. We explore how spontaneous activity fluctuations in this dynamic system shape, and are shaped by, acute and chronic pain experiences and individual differences in those experiences. Highlighting the temporal dimension of pain, we aim to move pain theory beyond the concept of a static neurosignature and toward an ethologically relevant account of naturalistic dynamics.
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Affiliation(s)
- Aaron Kucyi
- Department of Neurology & Neurological Sciences, Stanford University, Stanford CA, USA
| | - Karen D. Davis
- Division of Brain, Imaging & Behaviour - Systems Neuroscience, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
- Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
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45
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Shine JM. Electrophysiological insights into freezing in Parkinson's disease. Clin Neurophysiol 2016; 127:2334-6. [PMID: 27178847 DOI: 10.1016/j.clinph.2016.03.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 11/18/2022]
Affiliation(s)
- James M Shine
- Department of Psychology, Stanford University, Stanford, CA, USA; Neuroscience Research Australia, The University of New South Wales, Sydney, NSW, Australia.
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