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Ghafari T, Mazzetti C, Garner K, Gutteling T, Jensen O. Modulation of alpha oscillations by attention is predicted by hemispheric asymmetry of subcortical regions. eLife 2024; 12:RP91650. [PMID: 39017666 PMCID: PMC11254381 DOI: 10.7554/elife.91650] [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] [Indexed: 07/18/2024] Open
Abstract
Evidence suggests that subcortical structures play a role in high-level cognitive functions such as the allocation of spatial attention. While there is abundant evidence in humans for posterior alpha band oscillations being modulated by spatial attention, little is known about how subcortical regions contribute to these oscillatory modulations, particularly under varying conditions of cognitive challenge. In this study, we combined MEG and structural MRI data to investigate the role of subcortical structures in controlling the allocation of attentional resources by employing a cued spatial attention paradigm with varying levels of perceptual load. We asked whether hemispheric lateralization of volumetric measures of the thalamus and basal ganglia predicted the hemispheric modulation of alpha-band power. Lateral asymmetry of the globus pallidus, caudate nucleus, and thalamus predicted attention-related modulations of posterior alpha oscillations. When the perceptual load was applied to the target and the distractor was salient caudate nucleus asymmetry predicted alpha-band modulations. Globus pallidus was predictive of alpha-band modulations when either the target had a high load, or the distractor was salient, but not both. Finally, the asymmetry of the thalamus predicted alpha band modulation when neither component of the task was perceptually demanding. In addition to delivering new insight into the subcortical circuity controlling alpha oscillations with spatial attention, our finding might also have clinical applications. We provide a framework that could be followed for detecting how structural changes in subcortical regions that are associated with neurological disorders can be reflected in the modulation of oscillatory brain activity.
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Affiliation(s)
- Tara Ghafari
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Cecilia Mazzetti
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
| | - Kelly Garner
- School of Psychology, University of New South WalesKensingtonAustralia
| | - Tjerk Gutteling
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
- CERMEP-Imagerie du Vivant, MEG DepartmentLyonFrance
| | - Ole Jensen
- Centre for Human Brain Health, School of Psychology, University of BirminghamBirminghamUnited Kingdom
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Afnan J, Cai Z, Lina JM, Abdallah C, Delaire E, Avigdor T, Ros V, Hedrich T, von Ellenrieder N, Kobayashi E, Frauscher B, Gotman J, Grova C. EEG/MEG source imaging of deep brain activity within the maximum entropy on the mean framework: Simulations and validation in epilepsy. Hum Brain Mapp 2024; 45:e26720. [PMID: 38994740 PMCID: PMC11240147 DOI: 10.1002/hbm.26720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/16/2024] [Accepted: 05/06/2024] [Indexed: 07/13/2024] Open
Abstract
Electro/Magneto-EncephaloGraphy (EEG/MEG) source imaging (EMSI) of epileptic activity from deep generators is often challenging due to the higher sensitivity of EEG/MEG to superficial regions and to the spatial configuration of subcortical structures. We previously demonstrated the ability of the coherent Maximum Entropy on the Mean (cMEM) method to accurately localize the superficial cortical generators and their spatial extent. Here, we propose a depth-weighted adaptation of cMEM to localize deep generators more accurately. These methods were evaluated using realistic MEG/high-density EEG (HD-EEG) simulations of epileptic activity and actual MEG/HD-EEG recordings from patients with focal epilepsy. We incorporated depth-weighting within the MEM framework to compensate for its preference for superficial generators. We also included a mesh of both hippocampi, as an additional deep structure in the source model. We generated 5400 realistic simulations of interictal epileptic discharges for MEG and HD-EEG involving a wide range of spatial extents and signal-to-noise ratio (SNR) levels, before investigating EMSI on clinical HD-EEG in 16 patients and MEG in 14 patients. Clinical interictal epileptic discharges were marked by visual inspection. We applied three EMSI methods: cMEM, depth-weighted cMEM and depth-weighted minimum norm estimate (MNE). The ground truth was defined as the true simulated generator or as a drawn region based on clinical information available for patients. For deep sources, depth-weighted cMEM improved the localization when compared to cMEM and depth-weighted MNE, whereas depth-weighted cMEM did not deteriorate localization accuracy for superficial regions. For patients' data, we observed improvement in localization for deep sources, especially for the patients with mesial temporal epilepsy, for which cMEM failed to reconstruct the initial generator in the hippocampus. Depth weighting was more crucial for MEG (gradiometers) than for HD-EEG. Similar findings were found when considering depth weighting for the wavelet extension of MEM. In conclusion, depth-weighted cMEM improved the localization of deep sources without or with minimal deterioration of the localization of the superficial sources. This was demonstrated using extensive simulations with MEG and HD-EEG and clinical MEG and HD-EEG for epilepsy patients.
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Affiliation(s)
- Jawata Afnan
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Zhengchen Cai
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Jean-Marc Lina
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Electrical Engineering Department, École De Technologie Supérieure, Montréal, Québec, Canada
- Center for Advanced Research in Sleep Medicine, Sacré-Coeur Hospital, Montréal, Québec, Canada
| | - Chifaou Abdallah
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Edouard Delaire
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
| | - Tamir Avigdor
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Integrated Program in Neuroscience, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Victoria Ros
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Tanguy Hedrich
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
| | - Nicolas von Ellenrieder
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Eliane Kobayashi
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Birgit Frauscher
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Analytical Neurophysiology Lab, Department of Neurology, Duke University School of Medicine, Durham, North Carolina, USA
| | - Jean Gotman
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
| | - Christophe Grova
- Multimodal Functional Imaging Lab, Biomedical Engineering Department, McGill University, Montréal, Québec, Canada
- Montreal Neurological Institute, Department of Neurology and Neurosurgery, McGill University, Montréal, Québec, Canada
- Physnum Team, Centre De Recherches Mathématiques, Montréal, Québec, Canada
- Multimodal Functional Imaging Lab, Department of Physics and Concordia School of Health, Concordia University, Montréal, Québec, Canada
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Siemann J, Kroeger A, Bender S, Muthuraman M, Siniatchkin M. Segregated Dynamical Networks for Biological Motion Perception in the Mu and Beta Range Underlie Social Deficits in Autism. Diagnostics (Basel) 2024; 14:408. [PMID: 38396447 PMCID: PMC10887711 DOI: 10.3390/diagnostics14040408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024] Open
Abstract
OBJECTIVE Biological motion perception (BMP) correlating with a mirror neuron system (MNS) is attenuated in underage individuals with autism spectrum disorder (ASD). While BMP in typically-developing controls (TDCs) encompasses interconnected MNS structures, ASD data hint at segregated form and motion processing. This coincides with less fewer long-range connections in ASD than TDC. Using BMP and electroencephalography (EEG) in ASD, we characterized directionality and coherence (mu and beta frequencies). Deficient BMP may stem from desynchronization thereof in MNS and may predict social-communicative deficits in ASD. Clinical considerations thus profit from brain-behavior associations. METHODS Point-like walkers elicited BMP using 15 white dots (walker vs. scramble in 21 ASD (mean: 11.3 ± 2.3 years) vs. 23 TDC (mean: 11.9 ± 2.5 years). Dynamic Imaging of Coherent Sources (DICS) characterized the underlying EEG time-frequency causality through time-resolved Partial Directed Coherence (tPDC). Support Vector Machine (SVM) classification validated the group effects (ASD vs. TDC). RESULTS TDC showed MNS sources and long-distance paths (both feedback and bidirectional); ASD demonstrated distinct from and motion sources, predominantly local feedforward connectivity, and weaker coherence. Brain-behavior correlations point towards dysfunctional networks. SVM successfully classified ASD regarding EEG and performance. CONCLUSION ASD participants showed segregated local networks for BMP potentially underlying thwarted complex social interactions. Alternative explanations include selective attention and global-local processing deficits. SIGNIFICANCE This is the first study applying source-based connectivity to reveal segregated BMP networks in ASD regarding structure, cognition, frequencies, and temporal dynamics that may explain socio-communicative aberrancies.
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Affiliation(s)
- Julia Siemann
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany;
| | - Anne Kroeger
- Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany (S.B.)
| | - Stephan Bender
- Clinic of Child and Adolescent Psychiatry, Goethe-University of Frankfurt am Main, 60389 Frankfurt, Germany (S.B.)
- Department for Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Faculty of Medicine and University Hospital Cologne, University of Cologne, 50937 Cologne, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), University Clinic Würzburg, 97080 Würzburg, Germany;
| | - Michael Siniatchkin
- Department of Child and Adolescent Psychiatry and Psychotherapy Bethel, Evangelical Hospital Bielefeld, 33617 Bielefeld, Germany;
- University Clinic of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, 52074 Aachen, Germany
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Das D, Shaw ME, Hämäläinen MS, Dykstra AR, Doll L, Gutschalk A. A role for retro-splenial cortex in the task-related P3 network. Clin Neurophysiol 2024; 157:96-109. [PMID: 38091872 DOI: 10.1016/j.clinph.2023.11.014] [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: 03/23/2023] [Revised: 10/12/2023] [Accepted: 11/19/2023] [Indexed: 12/26/2023]
Abstract
OBJECTIVE The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified. METHODS We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm. RESULTS Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses. CONCLUSIONS The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG. SIGNIFICANCE These results provide a new perspective for the interpretation of the extensive research based on the P3 response.
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Affiliation(s)
- Diptyajit Das
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Marnie E Shaw
- College of Engineering & Computer Science, Australian National University, Canberra, Australia
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA; Harvard, MIT Division of Health Science and Technology, USA; Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Finland
| | - Andrew R Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, USA
| | - Laura Doll
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Alexander Gutschalk
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
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Sarma AK, Popli G, Anzalone A, Contillo N, Cornell C, Nunn AM, Rowland JA, Godwin DW, Flashman LA, Couture D, Stapleton-Kotloski JR. Use of magnetic source imaging to assess recovery after severe traumatic brain injury-an MEG pilot study. Front Neurol 2023; 14:1257886. [PMID: 38020602 PMCID: PMC10656620 DOI: 10.3389/fneur.2023.1257886] [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: 07/14/2023] [Accepted: 10/13/2023] [Indexed: 12/01/2023] Open
Abstract
Rationale Severe TBI (sTBI) is a devastating neurological injury that comprises a significant global trauma burden. Early comprehensive neurocritical care and rehabilitation improve outcomes for such patients, although better diagnostic and prognostic tools are necessary to guide personalized treatment plans. Methods In this study, we explored the feasibility of conducting resting state magnetoencephalography (MEG) in a case series of sTBI patients acutely after injury (~7 days), and then about 1.5 and 8 months after injury. Synthetic aperture magnetometry (SAM) was utilized to localize source power in the canonical frequency bands of delta, theta, alpha, beta, and gamma, as well as DC-80 Hz. Results At the first scan, SAM source maps revealed zones of hypofunction, islands of preserved activity, and hemispheric asymmetry across bandwidths, with markedly reduced power on the side of injury for each patient. GCS scores improved at scan 2 and by scan 3 the patients were ambulatory. The SAM maps for scans 2 and 3 varied, with most patients showing increasing power over time, especially in gamma, but a continued reduction in power in damaged areas and hemispheric asymmetry and/or relative diminishment in power at the site of injury. At the group level for scan 1, there was a large excess of neural generators operating within the delta band relative to control participants, while the number of neural generators for beta and gamma were significantly reduced. At scan 2 there was increased beta power relative to controls. At scan 3 there was increased group-wise delta power in comparison to controls. Conclusion In summary, this pilot study shows that MEG can be safely used to monitor and track the recovery of brain function in patients with severe TBI as well as to identify patient-specific regions of decreased or altered brain function. Such MEG maps of brain function may be used in the future to tailor patient-specific rehabilitation plans to target regions of altered spectral power with neurostimulation and other treatments.
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Affiliation(s)
- Anand Karthik Sarma
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Neurocritical Care, Piedmont Atlanta Hospital, Atlanta, GA, United States
| | - Gautam Popli
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Anthony Anzalone
- Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Neurosurgery, Henry Ford Health System, Detroit, MI, United States
| | - Nicholas Contillo
- Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Cassandra Cornell
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Andrew M. Nunn
- Department of Surgery, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Jared A. Rowland
- Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Research and Education Department, W.G. (Bill) Hefner VA Healthcare System, Salisbury, NC, United States
| | - Dwayne W. Godwin
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Research and Education Department, W.G. (Bill) Hefner VA Healthcare System, Salisbury, NC, United States
| | - Laura A. Flashman
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Daniel Couture
- Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Jennifer R. Stapleton-Kotloski
- Department of Neurology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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Das D, Shaw ME, Hämäläinen MS, Dykstra AR, Doll L, Gutschalk A. A role for retro-splenial cortex in the task-related P3 network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.03.530970. [PMID: 36945516 PMCID: PMC10028840 DOI: 10.1101/2023.03.03.530970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Objective The P3 is an event-related response observed in relation to task-relevant sensory events. Despite its ubiquitous presence, the neural generators of the P3 are controversial and not well identified. Methods We compared source analysis of combined magneto- and electroencephalography (M/EEG) data with functional magnetic resonance imaging (fMRI) and simulation studies to better understand the sources of the P3 in an auditory oddball paradigm. Results Our results suggest that the dominant source of the classical, postero-central P3 lies in the retro-splenial cortex of the ventral cingulate gyrus. A second P3 source in the anterior insular cortex contributes little to the postero-central maximum. Multiple other sources in the auditory, somatosensory, and anterior midcingulate cortex are active in an overlapping time window but can be functionally dissociated based on their activation time courses. Conclusion The retro-splenial cortex is a dominant source of the parietal P3 maximum in EEG. Significance These results provide a new perspective for the interpretation of the extensive research based on the P3 response.
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Affiliation(s)
- Diptyajit Das
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Marnie E. Shaw
- College of Engineering & Computer Science, Australian National University, Canberra, Australia
| | - Matti S. Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, USA
- Harvard, MIT Division of Health Science and Technology, USA
- Department of Neuroscience and Biomedical Engineering, Aalto University school of Science, Finland
| | - Andrew R. Dykstra
- Department of Biomedical Engineering, University of Miami, Coral Gables, USA
| | - Laura Doll
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
| | - Alexander Gutschalk
- Department of Neurology, Ruprecht-Karls-Universität Heidelberg, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany
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Koirala N, Deroche MLD, Wolfe J, Neumann S, Bien AG, Doan D, Goldbeck M, Muthuraman M, Gracco VL. Dynamic networks differentiate the language ability of children with cochlear implants. Front Neurosci 2023; 17:1141886. [PMID: 37409105 PMCID: PMC10318154 DOI: 10.3389/fnins.2023.1141886] [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: 01/10/2023] [Accepted: 05/29/2023] [Indexed: 07/07/2023] Open
Abstract
Background Cochlear implantation (CI) in prelingually deafened children has been shown to be an effective intervention for developing language and reading skill. However, there is a substantial proportion of the children receiving CI who struggle with language and reading. The current study-one of the first to implement electrical source imaging in CI population was designed to identify the neural underpinnings in two groups of CI children with good and poor language and reading skill. Methods Data using high density electroencephalography (EEG) under a resting state condition was obtained from 75 children, 50 with CIs having good (HL) or poor language skills (LL) and 25 normal hearing (NH) children. We identified coherent sources using dynamic imaging of coherent sources (DICS) and their effective connectivity computing time-frequency causality estimation based on temporal partial directed coherence (TPDC) in the two CI groups compared to a cohort of age and gender matched NH children. Findings Sources with higher coherence amplitude were observed in three frequency bands (alpha, beta and gamma) for the CI groups when compared to normal hearing children. The two groups of CI children with good (HL) and poor (LL) language ability exhibited not only different cortical and subcortical source profiles but also distinct effective connectivity between them. Additionally, a support vector machine (SVM) algorithm using these sources and their connectivity patterns for each CI group across the three frequency bands was able to predict the language and reading scores with high accuracy. Interpretation Increased coherence in the CI groups suggest overall that the oscillatory activity in some brain areas become more strongly coupled compared to the NH group. Moreover, the different sources and their connectivity patterns and their association to language and reading skill in both groups, suggest a compensatory adaptation that either facilitated or impeded language and reading development. The neural differences in the two groups of CI children may reflect potential biomarkers for predicting outcome success in CI children.
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Affiliation(s)
- Nabin Koirala
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Jace Wolfe
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Sara Neumann
- Hearts for Hearing Foundation, Oklahoma City, OK, United States
| | - Alexander G. Bien
- Department of Otolaryngology – Head and Neck Surgery, University of Oklahoma Medical Center, Oklahoma City, OK, United States
| | - Derek Doan
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Michael Goldbeck
- University of Oklahoma College of Medicine, Oklahoma City, OK, United States
| | - Muthuraman Muthuraman
- Department of Neurology, Neural Engineering with Signal Analytics and Artificial Intelligence (NESA-AI), Universitätsklinikum Würzburg, Würzburg, Germany
| | - Vincent L. Gracco
- Child Study Center, Yale School of Medicine, Yale University, New Haven, CT, United States
- School of Communication Sciences and Disorders, McGill University, Montreal, QC, Canada
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Billig AJ, Lad M, Sedley W, Griffiths TD. The hearing hippocampus. Prog Neurobiol 2022; 218:102326. [PMID: 35870677 PMCID: PMC10510040 DOI: 10.1016/j.pneurobio.2022.102326] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 06/08/2022] [Accepted: 07/18/2022] [Indexed: 11/17/2022]
Abstract
The hippocampus has a well-established role in spatial and episodic memory but a broader function has been proposed including aspects of perception and relational processing. Neural bases of sound analysis have been described in the pathway to auditory cortex, but wider networks supporting auditory cognition are still being established. We review what is known about the role of the hippocampus in processing auditory information, and how the hippocampus itself is shaped by sound. In examining imaging, recording, and lesion studies in species from rodents to humans, we uncover a hierarchy of hippocampal responses to sound including during passive exposure, active listening, and the learning of associations between sounds and other stimuli. We describe how the hippocampus' connectivity and computational architecture allow it to track and manipulate auditory information - whether in the form of speech, music, or environmental, emotional, or phantom sounds. Functional and structural correlates of auditory experience are also identified. The extent of auditory-hippocampal interactions is consistent with the view that the hippocampus makes broad contributions to perception and cognition, beyond spatial and episodic memory. More deeply understanding these interactions may unlock applications including entraining hippocampal rhythms to support cognition, and intervening in links between hearing loss and dementia.
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Affiliation(s)
| | - Meher Lad
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - William Sedley
- Translational and Clinical Research Institute, Newcastle University Medical School, Newcastle upon Tyne, UK
| | - Timothy D Griffiths
- Biosciences Institute, Newcastle University Medical School, Newcastle upon Tyne, UK; Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, University College London, London, UK; Human Brain Research Laboratory, Department of Neurosurgery, University of Iowa Hospitals and Clinics, Iowa City, USA
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Joshi RB, Zaveri HP. Prognostication of responsive neurostimulation system responsiveness using presurgical magnetoencephalography. Brain Commun 2022; 4:fcac114. [PMID: 35611311 PMCID: PMC9123844 DOI: 10.1093/braincomms/fcac114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 03/30/2022] [Accepted: 05/07/2022] [Indexed: 11/13/2022] Open
Abstract
This scientific commentary refers to 'Network connectivity predicts effectiveness of responsive neurostimulation in focal epilepsy', by Fan et al. (https://doi.org/10.1093/braincomms/fcac104).
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Affiliation(s)
- Rasesh B. Joshi
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Hitten P. Zaveri
- Department of Neurology, Yale University, 333 Cedar Street, New Haven, CT 06520, USA
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