1
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Wang C, Sun Y, Xing Y, Liu K, Xu K. Role of electrophysiological activity and interactions of lateral habenula in the development of depression-like behavior in a chronic restraint stress model. Brain Res 2024; 1835:148914. [PMID: 38580047 DOI: 10.1016/j.brainres.2024.148914] [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: 12/14/2023] [Revised: 02/20/2024] [Accepted: 04/02/2024] [Indexed: 04/07/2024]
Abstract
Closed-loop deep brain stimulation (DBS) system offers a promising approach for treatment-resistant depression, but identifying universally accepted electrophysiological biomarkers for closed-loop DBS systems targeting depression is challenging. There is growing evidence suggesting a strong association between the lateral habenula (LHb) and depression. Here, we took LHb as a key target, utilizing multi-site local field potentials (LFPs) to study the acute and chronic changes in electrophysiology, functional connectivity, and brain network characteristics during the formation of a chronic restraint stress (CRS) model. Furthermore, our model combining the electrophysiological changes of LHb and interactions between LHb and other potential targets of depression can effectively distinguish depressive states, offering a new way for developing effective closed-loop DBS strategies.
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Affiliation(s)
- Chang Wang
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100,China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
| | - Yuting Sun
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100,China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
| | - Yanjie Xing
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100,China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
| | - Kezhou Liu
- School of Automation (Artificial Intelligence), Hangzhou Dianzi University, Hangzhou 310018, China.
| | - Kedi Xu
- Qiushi Academy for Advanced Studies (QAAS), Zhejiang University, Hangzhou, China; Nanhu Brain-computer Interface Institute, Hangzhou 311100,China; Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness Appraisal, Key Laboratory of Biomedical Engineering of Education Ministry, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; State Key Lab of Brain-Machine Intelligence, Zhejiang University, Hangzhou, China.
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2
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Schilling A, Gerum R, Boehm C, Rasheed J, Metzner C, Maier A, Reindl C, Hamer H, Krauss P. Deep learning based decoding of single local field potential events. Neuroimage 2024:120696. [PMID: 38909761 DOI: 10.1016/j.neuroimage.2024.120696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 06/12/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024] Open
Abstract
How is information processed in the cerebral cortex? In most cases, recorded brain activity is averaged over many (stimulus) repetitions, which erases the fine-structure of the neural signal. However, the brain is obviously a single-trial processor. Thus, we here demonstrate that an unsupervised machine learning approach can be used to extract meaningful information from electro-physiological recordings on a single-trial basis. We use an auto-encoder network to reduce the dimensions of single local field potential (LFP) events to create interpretable clusters of different neural activity patterns. Strikingly, certain LFP shapes correspond to latency differences in different recording channels. Hence, LFP shapes can be used to determine the direction of information flux in the cerebral cortex. Furthermore, after clustering, we decoded the cluster centroids to reverse-engineer the underlying prototypical LFP event shapes. To evaluate our approach, we applied it to both extra-cellular neural recordings in rodents, and intra-cranial EEG recordings in humans. Finally, we find that single channel LFP event shapes during spontaneous activity sample from the realm of possible stimulus evoked event shapes. A finding which so far has only been demonstrated for multi-channel population coding.
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Affiliation(s)
- Achim Schilling
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Richard Gerum
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany; Department of Physics and Center for Vision Research, York University, Toronto, Canada
| | - Claudia Boehm
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Jwan Rasheed
- Neuroscience Lab, University Hospital Erlangen, Germany; Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany
| | - Claus Metzner
- Neuroscience Lab, University Hospital Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, University Erlangen-Nürnberg, Germany
| | - Caroline Reindl
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Germany
| | - Hajo Hamer
- Epilepsy Center, Department of Neurology, University Hospital Erlangen, Germany
| | - Patrick Krauss
- Cognitive Computational Neuroscience Group, University Erlangen-Nürnberg, Germany; Pattern Recognition Lab, University Erlangen-Nürnberg, Germany.
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3
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Asaad WF, Sheth SA. What's the n? On sample size vs. subject number for brain-behavior neurophysiology and neuromodulation. Neuron 2024:S0896-6273(24)00323-4. [PMID: 38781973 DOI: 10.1016/j.neuron.2024.04.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 04/01/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Neurophysiology and neuromodulation strive to understand the neural basis of behavior through a one-to-one correspondence between a particular brain and its behavioral output. Within this framework, studies with few subjects but sufficient sample sizes can be both rigorous and impactful.
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Affiliation(s)
- Wael F Asaad
- Department of Neuroscience, Brown University, Providence, RI, USA; Department of Neurosurgery, Brown University Alpert Medical School, Providence, RI, USA; Carney Institute for Brain Science, Brown University, Providence, RI, USA; Norman Prince Neurosciences Institute, Rhode Island Hospital, Providence, RI, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA; Jan and Dan Duncan Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA
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4
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Jia Q, Jing L, Zhu Y, Han M, Jiao P, Wang Y, Xu Z, Duan Y, Wang M, Cai X. Real-Time Precise Targeting of the Subthalamic Nucleus via Transfer Learning in a Rat Model of Parkinson's Disease Based on Microelectrode Arrays. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1787-1795. [PMID: 38656860 DOI: 10.1109/tnsre.2024.3393116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
In neurodegenerative disorders, neuronal firing patterns and oscillatory activity are remarkably altered in specific brain regions, which can serve as valuable biomarkers for the identification of deep brain regions. The subthalamic nucleus (STN) has been the primary target for DBS in patients with Parkinson's disease (PD). In this study, changes in the spike firing patterns and spectral power of local field potentials (LFPs) in the pre-STN (zona incerta, ZI) and post-STN (cerebral peduncle, cp) regions were investigated in PD rats, providing crucial evidence for the functional localization of the STN. Sixteen-channel microelectrode arrays (MEAs) with sites distributed at different depths and widths were utilized to record neuronal activities. The spikes in the STN exhibited higher firing rates than those in the ZI and cp. Furthermore, the LFP power in the delta band in the STN was the greatest, followed by that in the ZI, and was greater than that in the cp. Additionally, increased LFP power was observed in the beta bands in the STN. To identify the best performing classification model, we applied various convolutional neural networks (CNNs) based on transfer learning to analyze the recorded raw data, which were processed using the Gram matrix of the spikes and the fast Fourier transform of the LFPs. The best transfer learning model achieved an accuracy of 95.16%. After fusing the spike and LFP classification results, the time precision for processing the raw data reached 500 ms. The pretrained model, utilizing raw data, demonstrated the feasibility of employing transfer learning for training models on neural activity. This approach highlights the potential for functional localization within deep brain regions.
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5
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Średniawa W, Borzymowska Z, Kondrakiewicz K, Jurgielewicz P, Mindur B, Hottowy P, Wójcik DK, Kublik E. Local contribution to the somatosensory evoked potentials in rat's thalamus. PLoS One 2024; 19:e0301713. [PMID: 38593141 PMCID: PMC11003638 DOI: 10.1371/journal.pone.0301713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 03/19/2024] [Indexed: 04/11/2024] Open
Abstract
Local Field Potential (LFP), despite its name, often reflects remote activity. Depending on the orientation and synchrony of their sources, both oscillations and more complex waves may passively spread in brain tissue over long distances and be falsely interpreted as local activity at such distant recording sites. Here we show that the whisker-evoked potentials in the thalamic nuclei are of local origin up to around 6 ms post stimulus, but the later (7-15 ms) wave is overshadowed by a negative component reaching from cortex. This component can be analytically removed and local thalamic LFP can be recovered reliably using Current Source Density analysis. We used model-based kernel CSD (kCSD) method which allowed us to study the contribution of local and distant currents to LFP from rat thalamic nuclei and barrel cortex recorded with multiple, non-linear and non-regular multichannel probes. Importantly, we verified that concurrent recordings from the cortex are not essential for reliable thalamic CSD estimation. The proposed framework can be used to analyze LFP from other brain areas and has consequences for general LFP interpretation and analysis.
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Affiliation(s)
- Władysław Średniawa
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Zuzanna Borzymowska
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Kacper Kondrakiewicz
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
| | - Paweł Jurgielewicz
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Bartosz Mindur
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Paweł Hottowy
- AGH University of Science and Technology in Kraków, Faculty of Physics and Applied Computer Science, Krakow, Poland
| | - Daniel K. Wójcik
- Laboratory of Neuroinformatics, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
- Jagiellonian University, Faculty of Management and Social Communication, Jagiellonian University, Krakow, Poland
| | - Ewa Kublik
- Neurobiology of Emotions Laboratory, Nencki Institute of Experimental Biology of Polish Academy of Sciences, Warsaw, Poland
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6
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Sharma D, Lupkin SM, McGinty VB. Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.02.587758. [PMID: 38617349 PMCID: PMC11014579 DOI: 10.1101/2024.04.02.587758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.
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Affiliation(s)
- Dixit Sharma
- Center for Molecular and Behavioral Neuroscience, Rutgers University – Newark
- Graduate Program in Neuroscience, Rutgers University – Newark
| | - Shira M. Lupkin
- Center for Molecular and Behavioral Neuroscience, Rutgers University – Newark
- Graduate Program in Neuroscience, Rutgers University – Newark
| | - Vincent B. McGinty
- Center for Molecular and Behavioral Neuroscience, Rutgers University – Newark
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7
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Singh B, Wang Z, Madiah LM, Gatti SE, Fulton JN, Johnson GW, Li R, Dawant BM, Englot DJ, Bick SK, Roberson SW, Constantinidis C. Brain-wide human oscillatory local field potential activity during visual working memory. iScience 2024; 27:109130. [PMID: 38380249 PMCID: PMC10877957 DOI: 10.1016/j.isci.2024.109130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 02/22/2024] Open
Abstract
Oscillatory activity in the local field potential (LFP) is thought to be a marker of cognitive processes. To understand how it differentiates tasks and brain areas in humans, we recorded LFPs in 15 adults with intracranial depth electrodes, as they performed visual-spatial and shape working memory tasks. Stimulus appearance produced widespread, broad-band activation, including in occipital, parietal, temporal, insular, and prefrontal cortex, and the amygdala and hippocampus. Occipital cortex was characterized by most elevated power in the high-gamma (100-150 Hz) range during the visual stimulus presentation. The most consistent feature of the delay period was a systematic pattern of modulation in the beta frequency (16-40 Hz), which included a decrease in power of variable timing across areas, and rebound during the delay period. These results reveal the widespread nature of oscillatory activity across a broad brain network and region-specific signatures of oscillatory processes associated with visual working memory.
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Affiliation(s)
- Balbir Singh
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Zhengyang Wang
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA
| | - Leen M. Madiah
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - S. Elizabeth Gatti
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Jenna N. Fulton
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Graham W. Johnson
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Rui Li
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Benoit M. Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Dario J. Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Sarah K. Bick
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Neuroscience Program, Vanderbilt University, Nashville, TN, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
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8
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Orsher Y, Rom A, Perel R, Lahini Y, Blinder P, Shein-Idelson M. Sequentially activated discrete modules appear as traveling waves in neuronal measurements with limited spatiotemporal sampling. eLife 2024; 12:RP92254. [PMID: 38451063 PMCID: PMC10942589 DOI: 10.7554/elife.92254] [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: 03/08/2024] Open
Abstract
Numerous studies have identified traveling waves in the cortex and suggested they play important roles in brain processing. These waves are most often measured using macroscopic methods that are unable to assess the local spiking activity underlying wave dynamics. Here, we investigated the possibility that waves may not be traveling at the single neuron scale. We first show that sequentially activating two discrete brain areas can appear as traveling waves in EEG simulations. We next reproduce these results using an analytical model of two sequentially activated regions. Using this model, we were able to generate wave-like activity with variable directions, velocities, and spatial patterns, and to map the discriminability limits between traveling waves and modular sequential activations. Finally, we investigated the link between field potentials and single neuron excitability using large-scale measurements from turtle cortex ex vivo. We found that while field potentials exhibit wave-like dynamics, the underlying spiking activity was better described by consecutively activated spatially adjacent groups of neurons. Taken together, this study suggests caution when interpreting phase delay measurements as continuously propagating wavefronts in two different spatial scales. A careful distinction between modular and wave excitability profiles across scales will be critical for understanding the nature of cortical computations.
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Affiliation(s)
- Yuval Orsher
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- School of Physics & Astronomy, Faculty of Exact Sciences, Tel Aviv UniversityTel AvivIsrael
| | - Ariel Rom
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Rotem Perel
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
| | - Yoav Lahini
- School of Physics & Astronomy, Faculty of Exact Sciences, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Pablo Blinder
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
| | - Mark Shein-Idelson
- School of Neurobiology, Biochemistry, and Biophysics, Tel Aviv UniversityTel AvivIsrael
- Sagol School of Neuroscience, Tel Aviv University, IsraelTel AvivIsrael
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9
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Orellana V. D, Donoghue JP, Vargas-Irwin CE. Low frequency independent components: Internal neuromarkers linking cortical LFPs to behavior. iScience 2024; 27:108310. [PMID: 38303697 PMCID: PMC10831875 DOI: 10.1016/j.isci.2023.108310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 12/08/2022] [Accepted: 10/10/2023] [Indexed: 02/03/2024] Open
Abstract
Local field potentials (LFPs) in the primate motor cortex have been shown to reflect information related to volitional movements. However, LFPs are composite signals that receive contributions from multiple neural sources, producing a complex mix of component signals. Using a blind source separation approach, we examined the components of neural activity recorded using multielectrode arrays in motor areas of macaque monkeys during a grasping and lifting task. We found a set of independent components in the low-frequency LFP with high temporal and spatial consistency associated with each task stage. We observed that ICs often arise from electrodes distributed across multiple cortical areas and provide complementary information to external behavioral markers, specifically in task stage detection and trial alignment. Taken together, our results show that it is possible to separate useful independent components of the LFP associated with specific task-related events, potentially representing internal markers of transition between cortical network states.
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Affiliation(s)
- Diego Orellana V.
- Engineering Faculty, Pontificia Universidad Javeriana, Bogotá 110231, Colombia
- Faculty of Energy, Universidad Nacional de Loja, Loja 110101, Ecuador
| | - John P. Donoghue
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Robert J and Nancy D Carney Institute for Brain Science, Providence, RI 02912, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA
| | - Carlos E. Vargas-Irwin
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Robert J and Nancy D Carney Institute for Brain Science, Providence, RI 02912, USA
- Center for Neurorestoration and Neurotechnology, Rehabilitation Research and Development Service, Department of Veterans Affairs Medical Center, Providence, RI 02908, USA
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10
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Stout JJ, George AE, Kim S, Hallock HL, Griffin AL. Using synchronized brain rhythms to bias memory-guided decisions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.04.02.535279. [PMID: 37034665 PMCID: PMC10081324 DOI: 10.1101/2023.04.02.535279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Functional interactions between the prefrontal cortex and hippocampus, as revealed by strong oscillatory synchronization in the theta (6-11 Hz) frequency range, correlate with memory-guided decision-making. However, the degree to which this form of long-range synchronization influences memory-guided choice remains unclear. We developed a brain machine interface that initiated task trials based on the magnitude of prefrontal hippocampal theta synchronization, then measured choice outcomes. Trials initiated based on strong prefrontal-hippocampal theta synchrony were more likely to be correct compared to control trials on both working memory-dependent and -independent tasks. Prefrontal-thalamic neural interactions increased with prefrontal-hippocampal synchrony and optogenetic activation of the ventral midline thalamus primarily entrained prefrontal theta rhythms, but dynamically modulated synchrony. Together, our results show that prefrontal-hippocampal theta synchronization leads to a higher probability of a correct choice and strengthens prefrontal-thalamic dialogue. Our findings reveal new insights into the neural circuit dynamics underlying memory-guided choices and highlight a promising technique to potentiate cognitive processes or behavior via brain machine interfacing.
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11
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Shi C, Zhang C, Chen JF, Yao Z. Enhancement of low gamma oscillations by volitional conditioning of local field potential in the primary motor and visual cortex of mice. Cereb Cortex 2024; 34:bhae051. [PMID: 38425214 DOI: 10.1093/cercor/bhae051] [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: 07/11/2023] [Revised: 01/04/2024] [Accepted: 01/25/2024] [Indexed: 03/02/2024] Open
Abstract
Volitional control of local field potential oscillations in low gamma band via brain machine interface can not only uncover the relationship between low gamma oscillation and neural synchrony but also suggest a therapeutic potential to reverse abnormal local field potential oscillation in neurocognitive disorders. In nonhuman primates, the volitional control of low gamma oscillations has been demonstrated by brain machine interface techniques in the primary motor and visual cortex. However, it is not clear whether this holds in other brain regions and other species, for which gamma rhythms might involve in highly different neural processes. Here, we established a closed-loop brain-machine interface and succeeded in training mice to volitionally elevate low gamma power of local field potential in the primary motor and visual cortex. We found that the mice accomplished the task in a goal-directed manner and spiking activity exhibited phase-locking to the oscillation in local field potential in both areas. Moreover, long-term training made the power enhancement specific to direct and adjacent channel, and increased the transcriptional levels of NMDA receptors as well as that of hypoxia-inducible factor relevant to metabolism. Our results suggest that volitionally generated low gamma rhythms in different brain regions share similar mechanisms and pave the way for employing brain machine interface in therapy of neurocognitive disorders.
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Affiliation(s)
- Chennan Shi
- The Molecular Neuropharmacology Laboratory and the Eye-Brain Research Center, The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325001, China
| | - Chenyu Zhang
- The Molecular Neuropharmacology Laboratory and the Eye-Brain Research Center, The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
| | - Jiang-Fan Chen
- The Molecular Neuropharmacology Laboratory and the Eye-Brain Research Center, The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang 325001, China
| | - Zhimo Yao
- The Molecular Neuropharmacology Laboratory and the Eye-Brain Research Center, The State Key Laboratory of Ophthalmology, Optometry and Vision Science, Wenzhou Medical University, Wenzhou, Zhejiang 325027, China
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12
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Mai A, Riès S, Ben-Haim S, Shih JJ, Gentner TQ. Acoustic and language-specific sources for phonemic abstraction from speech. Nat Commun 2024; 15:677. [PMID: 38263364 PMCID: PMC10805762 DOI: 10.1038/s41467-024-44844-9] [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: 01/05/2023] [Accepted: 01/03/2024] [Indexed: 01/25/2024] Open
Abstract
Spoken language comprehension requires abstraction of linguistic information from speech, but the interaction between auditory and linguistic processing of speech remains poorly understood. Here, we investigate the nature of this abstraction using neural responses recorded intracranially while participants listened to conversational English speech. Capitalizing on multiple, language-specific patterns where phonological and acoustic information diverge, we demonstrate the causal efficacy of the phoneme as a unit of analysis and dissociate the unique contributions of phonemic and spectrographic information to neural responses. Quantitive higher-order response models also reveal that unique contributions of phonological information are carried in the covariance structure of the stimulus-response relationship. This suggests that linguistic abstraction is shaped by neurobiological mechanisms that involve integration across multiple spectro-temporal features and prior phonological information. These results link speech acoustics to phonology and morphosyntax, substantiating predictions about abstractness in linguistic theory and providing evidence for the acoustic features that support that abstraction.
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Affiliation(s)
- Anna Mai
- University of California, San Diego, Linguistics, 9500 Gilman Dr., La Jolla, CA, 92093, USA.
| | - Stephanie Riès
- San Diego State University, School of Speech, Language, and Hearing Sciences, 5500 Campanile Drive, San Diego, CA, 92182, USA
- San Diego State University, Center for Clinical and Cognitive Sciences, 5500 Campanile Drive, San Diego, CA, 92182, USA
| | - Sharona Ben-Haim
- University of California, San Diego, Neurological Surgery, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Jerry J Shih
- University of California, San Diego, Neurosciences, 9500 Gilman Dr., La Jolla, CA, 92093, USA
| | - Timothy Q Gentner
- University of California, San Diego, Psychology, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- University of California, San Diego, Neurobiology, 9500 Gilman Dr., La Jolla, CA, 92093, USA
- University of California, San Diego, Kavli Institute for Brain and Mind, 9500 Gilman Dr., La Jolla, CA, 92093, USA
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13
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Walder-Christensen K, Abdelaal K, Klein H, Thomas GE, Gallagher NM, Talbot A, Adamson E, Rawls A, Hughes D, Mague SD, Dzirasa K, Carlson DE. Electome network factors: Capturing emotional brain networks related to health and disease. CELL REPORTS METHODS 2024; 4:100691. [PMID: 38215761 PMCID: PMC10832286 DOI: 10.1016/j.crmeth.2023.100691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/17/2023] [Accepted: 12/21/2023] [Indexed: 01/14/2024]
Abstract
Therapeutic development for mental disorders has been slow despite the high worldwide prevalence of illness. Unfortunately, cellular and circuit insights into disease etiology have largely failed to generalize across individuals that carry the same diagnosis, reflecting an unmet need to identify convergent mechanisms that would facilitate optimal treatment. Here, we discuss how mesoscale networks can encode affect and other cognitive processes. These networks can be discovered through electrical functional connectome (electome) analysis, a method built upon explainable machine learning models for analyzing and interpreting mesoscale brain-wide signals in a behavioral context. We also outline best practices for identifying these generalizable, interpretable, and biologically relevant networks. Looking forward, translational electome analysis can span species and various moods, cognitive processes, or other brain states, supporting translational medicine. Thus, we argue that electome analysis provides potential translational biomarkers for developing next-generation therapeutics that exhibit high efficacy across heterogeneous disorders.
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Affiliation(s)
- Kathryn Walder-Christensen
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Karim Abdelaal
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Hunter Klein
- Department of Electrical and Computer Engineering, Duke University, Durham, NC 27710, USA
| | - Gwenaëlle E Thomas
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Neil M Gallagher
- Department of Psychiatry, Weill Cornell Medical Center, New York City, NY 10065, USA
| | - Austin Talbot
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Elise Adamson
- Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27710, USA
| | - Ashleigh Rawls
- Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Dalton Hughes
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Stephen D Mague
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Kafui Dzirasa
- Department of Psychiatry and Behavioral Sciences, Duke University Medical Center, Durham, NC 27710, USA; Department of Neurobiology, Duke University Medical Center, Durham, NC 27710, USA; Department of Neurosurgery, Duke University Medical Center, Durham, NC 27710, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA; Department of Biomedical Engineering, Duke University, Durham, NC 27710, USA.
| | - David E Carlson
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27710, USA; Department of Civil and Environmental Engineering, Duke University, Durham, NC 27710, USA.
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14
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Juventin M, Zbili M, Fourcaud-Trocmé N, Garcia S, Buonviso N, Amat C. Respiratory rhythm modulates membrane potential and spiking of nonolfactory neurons. J Neurophysiol 2023; 130:1552-1566. [PMID: 37964739 DOI: 10.1152/jn.00487.2022] [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: 12/01/2022] [Revised: 10/23/2023] [Accepted: 11/08/2023] [Indexed: 11/16/2023] Open
Abstract
In recent years, several studies have shown a respiratory drive of the local field potential (LFP) in numerous brain areas so that the respiratory rhythm could be considered as a master clock promoting communication between distant brain locations. However, outside of the olfactory system, it remains unknown whether the respiratory rhythm could shape membrane potential (MP) oscillations. To fill this gap, we co-recorded MP and LFP activities in different nonolfactory brain areas, medial prefrontal cortex (mPFC), primary somatosensory cortex (S1), primary visual cortex (V1), and hippocampus (HPC), in urethane-anesthetized rats. Using respiratory cycle-by-cycle analysis, we observed that respiration could modulate both MP and spiking discharges in all recorded areas during episodes that we called respiration-related oscillations (RRo). Further quantifications revealed that RRo episodes were transient in most neurons (5 consecutive respiratory cycles in average). RRo development in MP was largely correlated with the presence of respiratory modulation in the LFP. By showing that the respiratory rhythm influenced brain activities deep to the MP of nonolfactory neurons, our data support the idea that respiratory rhythm could mediate long-range communication between brain areas.NEW & NOTEWORTHY In this study, we evidenced strong respiratory-driven oscillations of neuronal membrane potential and spiking discharge in various nonolfactory areas of the mammal brain. These oscillations were found in the medial prefrontal cortex, primary somatosensory cortex, primary visual cortex, and hippocampus. These findings support the idea that respiratory rhythm could be used as a common clock to set the dynamics of large-scale neuronal networks on the same slow rhythm.
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Affiliation(s)
- Maxime Juventin
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
| | - Mickael Zbili
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
- Université Clermont Auvergne, CHU Clermont-Ferrand, INSERM, Clermont-Ferrand, France
| | - Nicolas Fourcaud-Trocmé
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
| | - Samuel Garcia
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
| | - Nathalie Buonviso
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
| | - Corine Amat
- Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, Université Claude Bernard Lyon 1, CNRS, INSERM, Bron, France
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15
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Noor MS, Howell B, McIntyre CC. Role of the volume conductor on simulations of local field potential recordings from deep brain stimulation electrodes. PLoS One 2023; 18:e0294512. [PMID: 38011104 PMCID: PMC10681243 DOI: 10.1371/journal.pone.0294512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 11/02/2023] [Indexed: 11/29/2023] Open
Abstract
OBJECTIVE Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are commonly used in research analyses, and are beginning to be used in clinical practice. Computational models of DBS LFPs provide tools for investigating the biophysics and neural synchronization that underlie LFP signals. However, technical standards for DBS LFP model parameterization remain to be established. Therefore, the goal of this study was to evaluate the role of the volume conductor (VC) model complexity on simulated LFP signals in the subthalamic nucleus (STN). APPROACH We created a detailed human head VC model that explicitly represented the inhomogeneity and anisotropy associated with 12 different tissue structures. This VC model represented our "gold standard" for technical detail and electrical realism. We then incrementally decreased the complexity of the VC model and quantified the impact on the simulated LFP recordings. Identical STN neural source activity was used when comparing the different VC model variants. Results Ignoring tissue anisotropy reduced the simulated LFP amplitude by ~12%, while eliminating soft tissue heterogeneity had a negligible effect on the recordings. Simplification of the VC model to consist of a single homogenous isotropic tissue medium with a conductivity of 0.215 S/m contributed an additional ~3% to the error. SIGNIFICANCE Highly detailed VC models do generate different results than simplified VC models. However, with errors in the range of ~15%, the use of a well-parameterized simple VC model is likely to be acceptable in most contexts for DBS LFP modeling.
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Affiliation(s)
- M. Sohail Noor
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Bryan Howell
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Cameron C. McIntyre
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
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16
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Oliveira AM, Coelho L, Carvalho E, Ferreira-Pinto MJ, Vaz R, Aguiar P. Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop. J Neurol 2023; 270:5313-5326. [PMID: 37530789 PMCID: PMC10576725 DOI: 10.1007/s00415-023-11873-1] [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: 05/09/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/03/2023]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.
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Affiliation(s)
- Andreia M Oliveira
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
| | - Luis Coelho
- Instituto Superior de Engenharia do Porto, Porto, Portugal
| | - Eduardo Carvalho
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal
- ICBAS-School of Medicine and Biomedical Sciences, University of Porto, Porto, Portugal
| | - Manuel J Ferreira-Pinto
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Rui Vaz
- Centro Hospitalar Universitário de São João, Porto, Portugal
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal
| | - Paulo Aguiar
- Faculdade de Engenharia da Universidade do Porto, Porto, Portugal.
- Neuroengineering and Computational Neuroscience Lab, Instituto de Investigação e Inovação da Universidade do Porto, Porto, Portugal.
- Faculdade de Medicina da Universidade do Porto, Porto, Portugal.
- i3S-Instituto de Investigação e Inovação em Saúde, Rua Alfredo Allen, 208, 4200-135, Porto, Portugal.
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17
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Lu X, Wang Y, Liu Z, Gou Y, Jaeger D, St-Pierre F. Widefield imaging of rapid pan-cortical voltage dynamics with an indicator evolved for one-photon microscopy. Nat Commun 2023; 14:6423. [PMID: 37828037 PMCID: PMC10570354 DOI: 10.1038/s41467-023-41975-3] [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: 08/23/2022] [Accepted: 09/20/2023] [Indexed: 10/14/2023] Open
Abstract
Widefield imaging with genetically encoded voltage indicators (GEVIs) is a promising approach for understanding the role of large cortical networks in the neural coding of behavior. However, the limited performance of current GEVIs restricts their deployment for single-trial imaging of rapid neuronal voltage dynamics. Here, we developed a high-throughput platform to screen for GEVIs that combine fast kinetics with high brightness, sensitivity, and photostability under widefield one-photon illumination. Rounds of directed evolution produced JEDI-1P, a green-emitting fluorescent indicator with enhanced performance across all metrics. Next, we optimized a neonatal intracerebroventricular delivery method to achieve cost-effective and wide-spread JEDI-1P expression in mice. We also developed an approach to correct optical measurements from hemodynamic and motion artifacts effectively. Finally, we achieved stable brain-wide voltage imaging and successfully tracked gamma-frequency whisker and visual stimulations in awake mice in single trials, opening the door to investigating the role of high-frequency signals in brain computations.
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Affiliation(s)
- Xiaoyu Lu
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, 77005, USA
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yunmiao Wang
- Neuroscience Graduate Program, Emory University, Atlanta, GA, 30322, USA
- Biology Department, Emory University, Atlanta, GA, 30322, USA
| | - Zhuohe Liu
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Yueyang Gou
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Dieter Jaeger
- Biology Department, Emory University, Atlanta, GA, 30322, USA.
| | - François St-Pierre
- Systems, Synthetic, and Physical Biology Program, Rice University, Houston, TX, 77005, USA.
- Department of Electrical and Computer Engineering, Rice University, Houston, TX, 77005, USA.
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA.
- Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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18
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Wang HL, Kuo YT, Lo YC, Kuo CH, Chen BW, Wang CF, Wu ZY, Lee CE, Yang SH, Lin SH, Chen PC, Chen YY. Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task. Int J Neural Syst 2023; 33:2350051. [PMID: 37632142 DOI: 10.1142/s012906572350051x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/27/2023]
Abstract
Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.
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Affiliation(s)
- Han-Lin Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yun-Ting Kuo
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Yu-Chun Lo
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
| | - Chao-Hung Kuo
- Department of Neurosurgery, Neurological Institute Taipei Veterans General Hospital, No. 201, Sec. 2 Shipai Rd., Taipei 11217, Taiwan
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Zu-Yu Wu
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Chi-En Lee
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
| | - Shih-Hung Yang
- Department of Mechanical Engineering, National Cheng Kung University, No. 1, University Rd., Tainan 70101, Taiwan
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, No. 707, Sec. 3 Zhongyang Rd., Hualien 97002, Taiwan
- Department of Neurology, School of Medicine, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien 97004, Taiwan
| | - Po-Chuan Chen
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, No. 155, Sec. 2 Linong St., Taipei 112304, Taiwan
- The Ph.D. Program in Medical Neuroscience, College of Medical Science and Technology, Taipei Medical University, 12F., Education & Research Building, Shuang-Ho Campus, No. 301, Yuantong Rd., New Taipei City 235235, Taiwan
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19
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Singh B, Wang Z, Madiah LM, Gatti SE, Fulton JN, Johnson GW, Li R, Dawant BM, Englot DJ, Bick SK, Roberson SW, Constantinidis C. Brain-wide human oscillatory LFP activity during visual working memory. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.06.556554. [PMID: 37732263 PMCID: PMC10508766 DOI: 10.1101/2023.09.06.556554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Oscillatory activity is thought to be a marker of cognitive processes, although its role and distribution across the brain during working memory has been a matter of debate. To understand how oscillatory activity differentiates tasks and brain areas in humans, we recorded local field potentials (LFPs) in 12 adults as they performed visual-spatial and shape-matching memory tasks. Tasks were designed to engage working memory processes at a range of delay intervals between stimulus delivery and response initiation. LFPs were recorded using intracranial depth electrodes implanted to localize seizures for management of intractable epilepsy. Task-related LFP power analyses revealed an extensive network of cortical regions that were activated during the presentation of visual stimuli and during their maintenance in working memory, including occipital, parietal, temporal, insular, and prefrontal cortical areas, and subcortical structures including the amygdala and hippocampus. Across most brain areas, the appearance of a stimulus produced broadband power increase, while gamma power was evident during the delay interval of the working memory task. Notable differences between areas included that occipital cortex was characterized by elevated power in the high gamma (100-150 Hz) range during the 500 ms of visual stimulus presentation, which was less pronounced or absent in other areas. A decrease in power centered in beta frequency (16-40 Hz) was also observed after the stimulus presentation, whose magnitude differed across areas. These results reveal the interplay of oscillatory activity across a broad network, and region-specific signatures of oscillatory processes associated with visual working memory.
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Affiliation(s)
- Balbir Singh
- Department of Biomedical Engineering, Vanderbilt University
| | | | - Leen M Madiah
- Department of Biomedical Engineering, Vanderbilt University
| | | | - Jenna N Fulton
- Department of Neurology, Vanderbilt University Medical Center
| | - Graham W Johnson
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Rui Li
- Department of Electrical and Computer Engineering, Vanderbilt University
| | - Benoit M Dawant
- Department of Electrical and Computer Engineering, Vanderbilt University
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Sarah K Bick
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurological Surgery, Vanderbilt University Medical Center
| | - Shawniqua Williams Roberson
- Department of Biomedical Engineering, Vanderbilt University
- Department of Neurology, Vanderbilt University Medical Center
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University
- Neuroscience Program, Vanderbilt University
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center
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20
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Maiseli B, Abdalla AT, Massawe LV, Mbise M, Mkocha K, Nassor NA, Ismail M, Michael J, Kimambo S. Brain-computer interface: trend, challenges, and threats. Brain Inform 2023; 10:20. [PMID: 37540385 PMCID: PMC10403483 DOI: 10.1186/s40708-023-00199-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 07/01/2023] [Indexed: 08/05/2023] Open
Abstract
Brain-computer interface (BCI), an emerging technology that facilitates communication between brain and computer, has attracted a great deal of research in recent years. Researchers provide experimental results demonstrating that BCI can restore the capabilities of physically challenged people, hence improving the quality of their lives. BCI has revolutionized and positively impacted several industries, including entertainment and gaming, automation and control, education, neuromarketing, and neuroergonomics. Notwithstanding its broad range of applications, the global trend of BCI remains lightly discussed in the literature. Understanding the trend may inform researchers and practitioners on the direction of the field, and on where they should invest their efforts more. Noting this significance, we have analyzed 25,336 metadata of BCI publications from Scopus to determine advancement of the field. The analysis shows an exponential growth of BCI publications in China from 2019 onwards, exceeding those from the United States that started to decline during the same period. Implications and reasons for this trend are discussed. Furthermore, we have extensively discussed challenges and threats limiting exploitation of BCI capabilities. A typical BCI architecture is hypothesized to address two prominent BCI threats, privacy and security, as an attempt to make the technology commercially viable to the society.
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Affiliation(s)
- Baraka Maiseli
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania.
| | - Abdi T Abdalla
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Libe V Massawe
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Mercy Mbise
- Department of Computer Science and Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Khadija Mkocha
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Nassor Ally Nassor
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Moses Ismail
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - James Michael
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
| | - Samwel Kimambo
- Department of Electronics and Telecommunications Engineering, College of Information and Communication Technologies, University of Dar es Salaam, 14113, Dar es Salaam, Tanzania
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21
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Johnson GW, Doss DJ, Morgan VL, Paulo DL, Cai LY, Shless JS, Negi AS, Gummadavelli A, Kang H, Reddy SB, Naftel RP, Bick SK, Williams Roberson S, Dawant BM, Wallace MT, Englot DJ. The Interictal Suppression Hypothesis in focal epilepsy: network-level supporting evidence. Brain 2023; 146:2828-2845. [PMID: 36722219 PMCID: PMC10316780 DOI: 10.1093/brain/awad016] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/24/2022] [Accepted: 01/08/2023] [Indexed: 02/02/2023] Open
Abstract
Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10-13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10-3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10-12). Structure-function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10-21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest that seizure-onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure-onset zones. These findings have implications for the identification of seizure-onset zones using only brief electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative and neuromodulation approaches to improve surgical success rates in those suffering from drug-resistant focal epilepsy.
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Affiliation(s)
- Graham W Johnson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Derek J Doss
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Victoria L Morgan
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Danika L Paulo
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Leon Y Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
| | - Jared S Shless
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Aarushi S Negi
- Department of Neuroscience, Vanderbilt University, Nashville, TN 37232, USA
| | - Abhijeet Gummadavelli
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37232, USA
| | - Shilpa B Reddy
- Department of Pediatrics, Vanderbilt Children’s Hospital, Nashville, TN 37232, USA
| | - Robert P Naftel
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Sarah K Bick
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Benoit M Dawant
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
| | - Mark T Wallace
- Department of Hearing and Speech Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychology, Vanderbilt University, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University, Nashville, TN 37232, USA
- Department of Pharmacology, Vanderbilt University, Nashville, TN 37232, USA
| | - Dario J Englot
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Vanderbilt University Institute of Imaging Science (VUIIS), Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Vanderbilt Institute for Surgery and Engineering (VISE), Vanderbilt University, Nashville, TN 37235, USA
- Department of Neurological Surgery, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA
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22
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Eiber CD, Aditya Tarigoppula VS, Rind GS. A 'Total Unique Variation Analysis' for Brain-Machine Interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083167 DOI: 10.1109/embc40787.2023.10340518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
When designing a fully implantable brain-machine interface (BMI), the primary aim is to detect as much neural information as possible with as few channels as possible. In this paper, we present a total unique variance analysis (TUVA) for evaluating the signal unique to each channel that cannot be predicted by linear combination of signals on other channels. TUVA is a statistical method for determining the total unique variance in multidimensional data, ordering channels from most to least informative, to aid in the design of maximally-efficacious BMIs. We demonstrate how this method can be applied to the design of BMIs by comparing TUVA values computed for simulated lead-field maps for high-channel-count electrocorticography (ECoG) with values computed for recordings in the interictal period in the context of surgery planning for epileptic resection.Clinical Relevance- This paper introduces a new statistical method for comparison of neural interface designs, focused on quantifying recording efficiency by minimizing channel crosstalk, which may help improve the risk-benefit profile of invasive neural recording.
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23
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Osanai H, Yamamoto J, Kitamura T. Extracting electromyographic signals from multi-channel LFPs using independent component analysis without direct muscular recording. CELL REPORTS METHODS 2023; 3:100482. [PMID: 37426755 PMCID: PMC10326347 DOI: 10.1016/j.crmeth.2023.100482] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/12/2023] [Accepted: 04/25/2023] [Indexed: 07/11/2023]
Abstract
Electromyography (EMG) has been commonly used for the precise identification of animal behavior. However, it is often not recorded together with in vivo electrophysiology due to the need for additional surgeries and setups and the high risk of mechanical wire disconnection. While independent component analysis (ICA) has been used to reduce noise from field potential data, there has been no attempt to proactively use the removed "noise," of which EMG signals are thought to be one of the major sources. Here, we demonstrate that EMG signals can be reconstructed without direct EMG recording using the "noise" ICA component from local field potentials. The extracted component is highly correlated with directly measured EMG, termed IC-EMG. IC-EMG is useful for measuring an animal's sleep/wake, freezing response, and non-rapid eye movement (NREM)/REM sleep states consistently with actual EMG. Our method has advantages in precise and long-term behavioral measurement in wide-ranging in vivo electrophysiology experiments.
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Affiliation(s)
- Hisayuki Osanai
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Jun Yamamoto
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Takashi Kitamura
- Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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24
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Claar LD, Rembado I, Kuyat JR, Russo S, Marks LC, Olsen SR, Koch C. Cortico-thalamo-cortical interactions modulate electrically evoked EEG responses in mice. eLife 2023; 12:RP84630. [PMID: 37358562 DOI: 10.7554/elife.84630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023] Open
Abstract
Perturbational complexity analysis predicts the presence of consciousness in volunteers and patients by stimulating the brain with brief pulses, recording EEG responses, and computing their spatiotemporal complexity. We examined the underlying neural circuits in mice by directly stimulating cortex while recording with EEG and Neuropixels probes during wakefulness and isoflurane anesthesia. When mice are awake, stimulation of deep cortical layers reliably evokes locally a brief pulse of excitation, followed by a biphasic sequence of 120 ms profound off period and a rebound excitation. A similar pattern, partially attributed to burst spiking, is seen in thalamic nuclei and is associated with a pronounced late component in the evoked EEG. We infer that cortico-thalamo-cortical interactions drive the long-lasting evoked EEG signals elicited by deep cortical stimulation during the awake state. The cortical and thalamic off period and rebound excitation, and the late component in the EEG, are reduced during running and absent during anesthesia.
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Affiliation(s)
- Leslie D Claar
- MindScope Program, Allen Institute, Seattle, United States
| | - Irene Rembado
- MindScope Program, Allen Institute, Seattle, United States
| | | | - Simone Russo
- MindScope Program, Allen Institute, Seattle, United States
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - Lydia C Marks
- MindScope Program, Allen Institute, Seattle, United States
| | - Shawn R Olsen
- MindScope Program, Allen Institute, Seattle, United States
| | - Christof Koch
- MindScope Program, Allen Institute, Seattle, United States
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25
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Kennedy JP, Zhou Y, Qin Y, Lovett SD, Cooper T, Sheremet A, Burke SN, Maurer AP. Visual cortical LFP in relation to the hippocampal theta rhythm in track running rats. Front Cell Neurosci 2023; 17:1144260. [PMID: 37408856 PMCID: PMC10318345 DOI: 10.3389/fncel.2023.1144260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Accepted: 06/01/2023] [Indexed: 07/07/2023] Open
Abstract
Theta oscillations in the primary visual cortex (VC) have been observed during running tasks, but the mechanism behind their generation is not well understood. Some studies have suggested that theta in the VC is locally generated, while others have proposed that it is volume conducted from the hippocampus. The present study aimed to investigate the relationship between hippocampal and VC LFP dynamics. Analysis of power spectral density revealed that LFP in the VC was similar to that in the hippocampus, but with lower overall magnitude. As running velocity increased, both the power and frequency of theta and its harmonics increased in the VC, similarly to what is observed in the hippocampus. Current source density analysis triggered to theta did not identify distinct current sources and sinks in the VC, supporting the idea that theta in the VC is conducted from the adjacent hippocampus. Phase coupling between theta, its harmonics, and gamma is a notable feature in the hippocampus, particularly in the lacunosum moleculare. While some evidence of coupling between theta and its harmonics in the VC was found, bicoherence estimates did not reveal significant phase coupling between theta and gamma. Similar results were seen in the cross-region bicoherence analysis, where theta showed strong coupling with its harmonics with increasing velocity. Thus, theta oscillations observed in the VC during running tasks are likely due to volume conduction from the hippocampus.
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Affiliation(s)
- Jack P. Kennedy
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Yuchen Zhou
- Department of Psychiatry, School of Medicine, Yale University, New Haven, CT, United States
| | - Yu Qin
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- Engineering School of Sustainable Infrastructure and Environment, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States
| | - Sarah D. Lovett
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Tara Cooper
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Alex Sheremet
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- Engineering School of Sustainable Infrastructure and Environment, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States
| | - Sara N. Burke
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Andrew P. Maurer
- Department of Neuroscience, College of Medicine, McKnight Brain Institute, University of Florida, Gainesville, FL, United States
- Engineering School of Sustainable Infrastructure and Environment, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL, United States
- Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
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26
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Tovar DA, Westerberg JA, Cox MA, Dougherty K, Wallace MT, Bastos AM, Maier A. Near-field potentials index local neural computations more accurately than population spiking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.11.540026. [PMID: 37214905 PMCID: PMC10197629 DOI: 10.1101/2023.05.11.540026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Local field potentials (LFP) are low-frequency extracellular voltage fluctuations thought to primarily arise from synaptic activity. However, unlike highly localized neuronal spiking, LFP is spatially less specific. LFP measured at one location is not entirely generated there due to far-field contributions that are passively conducted across volumes of neural tissue. We sought to quantify how much information within the locally generated, near-field low-frequency activity (nfLFP) is masked by volume-conducted far-field signals. To do so, we measured laminar neural activity in primary visual cortex (V1) of monkeys viewing sequences of multifeatured stimuli. We compared information content of regular LFP and nfLFP that was mathematically stripped of volume-conducted far-field contributions. Information content was estimated by decoding stimulus properties from neural responses via spatiotemporal multivariate pattern analysis. Volume-conducted information differed from locally generated information in two important ways: (1) for stimulus features relevant to V1 processing (orientation and eye-of-origin), nfLFP contained more information. (2) in contrast, the volume-conducted signal was more informative regarding temporal context (relative stimulus position in a sequence), a signal likely to be coming from elsewhere. Moreover, LFP and nfLFP differed both spectrally as well as spatially, urging caution regarding the interpretations of individual frequency bands and/or laminar patterns of LFP. Most importantly, we found that population spiking of local neurons was less informative than either the LFP or nfLFP, with nfLFP containing most of the relevant information regarding local stimulus processing. These findings suggest that the optimal way to read out local computational processing from neural activity is to decode the local contributions to LFP, with significant information loss hampering both regular LFP and local spiking. Author’s Contributions Conceptualization, D.A.T., J.A.W, and A.M.; Data Collection, J.A.W., M.A.C., K.D.; Formal Analysis, D.A.T. and J.A.W.; Data Visualization, D.A.T. and J.A.W.; Original Draft, D.A.T., J.A.W., and A.M.; Revisions and Final Draft, D.A.T., J.A.W., M.A.C., K.D., M.T.W., A.M.B., and A.M. Competing Interests The authors declare no conflicts of interest.
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Stöber TM, Batulin D, Triesch J, Narayanan R, Jedlicka P. Degeneracy in epilepsy: multiple routes to hyperexcitable brain circuits and their repair. Commun Biol 2023; 6:479. [PMID: 37137938 PMCID: PMC10156698 DOI: 10.1038/s42003-023-04823-0] [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: 05/24/2022] [Accepted: 04/06/2023] [Indexed: 05/05/2023] Open
Abstract
Due to its complex and multifaceted nature, developing effective treatments for epilepsy is still a major challenge. To deal with this complexity we introduce the concept of degeneracy to the field of epilepsy research: the ability of disparate elements to cause an analogous function or malfunction. Here, we review examples of epilepsy-related degeneracy at multiple levels of brain organisation, ranging from the cellular to the network and systems level. Based on these insights, we outline new multiscale and population modelling approaches to disentangle the complex web of interactions underlying epilepsy and to design personalised multitarget therapies.
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Affiliation(s)
- Tristan Manfred Stöber
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, 44801, Bochum, Germany
- Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University, 60590, Frankfurt, Germany
| | - Danylo Batulin
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
- CePTER - Center for Personalized Translational Epilepsy Research, Goethe University, 60590, Frankfurt, Germany
- Faculty of Computer Science and Mathematics, Goethe University, 60486, Frankfurt, Germany
| | - Jochen Triesch
- Frankfurt Institute for Advanced Studies, 60438, Frankfurt am Main, Germany
| | - Rishikesh Narayanan
- Cellular Neurophysiology Laboratory, Molecular Biophysics Unit, Indian Institute of Science, Bangalore, 560012, India
| | - Peter Jedlicka
- ICAR3R - Interdisciplinary Centre for 3Rs in Animal Research, Faculty of Medicine, Justus Liebig University Giessen, 35390, Giessen, Germany.
- Institute of Clinical Neuroanatomy, Neuroscience Center, Goethe University, 60590, Frankfurt am Main, Germany.
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28
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Ohmori H, Hirai Y, Matsui R, Watanabe D. High resolution recording of local field currents simultaneously with sound-evoked calcium signals by a photometric patch electrode in the auditory cortex field L of the chick. J Neurosci Methods 2023; 392:109863. [PMID: 37075913 DOI: 10.1016/j.jneumeth.2023.109863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 03/06/2023] [Accepted: 04/15/2023] [Indexed: 04/21/2023]
Abstract
BACKGROUND Functioning of the brain is based on both electrical and metabolic activity of neural ensembles. Accordingly, it would be useful to measure intracellular metabolic signaling simultaneously with electrical activity in the brain in vivo. NEW METHOD We innovated a PhotoMetric-patch-Electrode (PME) recording system that has a high temporal resolution incorporating a photomultiplier tube as a light detector. The PME is fabricated from a quartz glass capillary to transmit light as a light guide, and it can detect electrical signals as a patch electrode simultaneously with a fluorescence signal. RESULTS We measured the sound-evoked Local Field Current (LFC) and fluorescence Ca2+ signal from neurons labeled with Ca2+-sensitive dye Oregon Green BAPTA1 in field L, the avian auditory cortex. Sound stimulation evoked multi-unit spike bursts and Ca2+ signals, and enhanced the fluctuation of LFC. After a brief sound stimulation, the cross-correlation between LFC and Ca2+ signal was prolonged. D-AP5 (antagonist for NMDA receptors) suppressed the sound-evoked Ca2+ signal when applied locally by pressure from the tip of PME. COMPARISON WITH EXISTING METHODS In contrast to existing multiphoton imaging or optical fiber recording methods, the PME is a patch electrode pulled simply from a quartz glass capillary and can measure fluorescence signals at the tip simultaneously with electrical signal at any depth of the brain structure. CONCLUSION The PME is devised to record electrical and optical signals simultaneously with high temporal resolution. Moreover, it can inject chemical agents dissolved in the tip-filling medium locally by pressure, allowing manipulation of neural activity pharmacologically.
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Affiliation(s)
- Harunori Ohmori
- Department of Physiology & Neurobiology, Faculty of Medicine, Kyoto University, Kyoto, Japan.
| | - Yasuharu Hirai
- Department of Physiology & Neurobiology, Faculty of Medicine, Kyoto University, Kyoto, Japan
| | - Ryosuke Matsui
- Department of Biological Sciences, Faculty of Medicine, Kyoto University, Kyoto, Japan
| | - Dai Watanabe
- Department of Biological Sciences, Faculty of Medicine, Kyoto University, Kyoto, Japan
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29
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Shin D, Peelman K, Lien AD, Del Rosario J, Haider B. Narrowband gamma oscillations propagate and synchronize throughout the mouse thalamocortical visual system. Neuron 2023; 111:1076-1085.e8. [PMID: 37023711 PMCID: PMC10112544 DOI: 10.1016/j.neuron.2023.03.006] [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: 05/02/2022] [Revised: 12/16/2022] [Accepted: 03/06/2023] [Indexed: 04/08/2023]
Abstract
Oscillations of neural activity permeate sensory systems. In the visual system, broadband gamma oscillations (30-80 Hz) are thought to act as a communication mechanism underlying perception. However, these oscillations show widely varying frequency and phase, providing constraints for coordinating spike timing across areas. Here, we examined Allen Brain Observatory data and performed causal experiments to show that narrowband gamma (NBG) oscillations (50-70 Hz) propagate and synchronize throughout the awake mouse visual system. Lateral geniculate nucleus (LGN) neurons fired precisely relative to NBG phase in primary visual cortex (V1) and multiple higher visual areas (HVAs). NBG neurons across areas showed a higher likelihood of functional connectivity and stronger visual responses; remarkably, NBG neurons in LGN, preferring bright (ON) versus dark (OFF), fired at distinct NBG phases aligned across the cortical hierarchy. NBG oscillations may thus serve to coordinate spike timing across brain areas and facilitate communication of distinct visual features during perception.
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Affiliation(s)
- Donghoon Shin
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA; Electrical and Computer Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA; Bioengineering, UCSF - UC Berkeley Joint PhD Program, San Francisco, CA, USA
| | - Kayla Peelman
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Anthony D Lien
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Joseph Del Rosario
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA
| | - Bilal Haider
- Biomedical Engineering, Georgia Institute of Technology & Emory University, Atlanta, GA, USA.
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30
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Jeakle EN, Abbott JR, Usoro JO, Wu Y, Haghighi P, Radhakrishna R, Sturgill BS, Nakajima S, Thai TTD, Pancrazio JJ, Cogan SF, Hernandez-Reynoso AG. Chronic Stability of Local Field Potentials Using Amorphous Silicon Carbide Microelectrode Arrays Implanted in the Rat Motor Cortex. MICROMACHINES 2023; 14:680. [PMID: 36985087 PMCID: PMC10054633 DOI: 10.3390/mi14030680] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 03/15/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
Implantable microelectrode arrays (MEAs) enable the recording of electrical activity of cortical neurons, allowing the development of brain-machine interfaces. However, MEAs show reduced recording capabilities under chronic conditions, prompting the development of novel MEAs that can improve long-term performance. Conventional planar, silicon-based devices and ultra-thin amorphous silicon carbide (a-SiC) MEAs were implanted in the motor cortex of female Sprague-Dawley rats, and weekly anesthetized recordings were made for 16 weeks after implantation. The spectral density and bandpower between 1 and 500 Hz of recordings were compared over the implantation period for both device types. Initially, the bandpower of the a-SiC devices and standard MEAs was comparable. However, the standard MEAs showed a consistent decline in both bandpower and power spectral density throughout the 16 weeks post-implantation, whereas the a-SiC MEAs showed substantially more stable performance. These differences in bandpower and spectral density between standard and a-SiC MEAs were statistically significant from week 6 post-implantation until the end of the study at 16 weeks. These results support the use of ultra-thin a-SiC MEAs to develop chronic, reliable brain-machine interfaces.
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Affiliation(s)
- Eleanor N. Jeakle
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Justin R. Abbott
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Joshua O. Usoro
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Yupeng Wu
- Department of Materials Science and Engineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Pegah Haghighi
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Rahul Radhakrishna
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Brandon S. Sturgill
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Shido Nakajima
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Teresa T. D. Thai
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Joseph J. Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Stuart F. Cogan
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
| | - Ana G. Hernandez-Reynoso
- Department of Bioengineering, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX 75080-3021, USA
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31
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Zhang Q, Cramer SR, Turner KL, Neuberger T, Drew PJ, Zhang N. High-frequency neuronal signal better explains multi-phase BOLD response. Neuroimage 2023; 268:119887. [PMID: 36681134 PMCID: PMC9962576 DOI: 10.1016/j.neuroimage.2023.119887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 01/20/2023] Open
Abstract
Visual stimulation-evoked blood-oxygen-level dependent (BOLD) responses can exhibit more complex temporal dynamics than a simple monophasic response. For instance, BOLD responses sometimes include a phase of positive response followed by a phase of post-stimulus undershoot. Whether the BOLD response during these phases reflects the underlying neuronal signal fluctuations or is contributed by non-neuronal physiological factors remains elusive. When presenting blocks of sustained (i.e. DC) light ON-OFF stimulations to unanesthetized rats, we observed that the response following a decrease in illumination (i.e. OFF stimulation-evoked BOLD response) in the visual cortices displayed reproducible multiple phases, including an initial positive BOLD response, followed by an undershoot and then an overshoot before the next ON trial. This multi-phase BOLD response did not result from the entrainment of the periodic stimulation structure. When we measured the neural correlates of these responses, we found that the high-frequency band from the LFP power (300 - 3000 Hz, multi-unit activity (MUA)), but not the power in the gamma band (30 - 100 Hz) exhibited the same multiphasic dynamics as the BOLD signal. This study suggests that the post-stimulus phases of the BOLD response can be better explained by the high-frequency neuronal signal.
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Affiliation(s)
- Qingqing Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA; Center for Neural Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Samuel R Cramer
- The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA; Center for Neural Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Kevin L Turner
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA; Center for Neural Engineering, The Pennsylvania State University, University Park 16802, USA
| | - Thomas Neuberger
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA
| | - Patrick J Drew
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA; Center for Neural Engineering, The Pennsylvania State University, University Park 16802, USA; Departments of Engineering Science and Mechanics, Neurosurgery, and Biology, The Pennsylvania State University, University Park, USA
| | - Nanyin Zhang
- Department of Biomedical Engineering, The Pennsylvania State University, University Park, USA; The Neuroscience Graduate Program, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, USA; Center for Neurotechnology in Mental Health Research, The Pennsylvania State University, University Park 16802, USA; Center for Neural Engineering, The Pennsylvania State University, University Park 16802, USA.
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32
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D’Onofrio V, Manzo N, Guerra A, Landi A, Baro V, Määttä S, Weis L, Porcaro C, Corbetta M, Antonini A, Ferreri F. Combining Transcranial Magnetic Stimulation and Deep Brain Stimulation: Current Knowledge, Relevance and Future Perspectives. Brain Sci 2023; 13:brainsci13020349. [PMID: 36831892 PMCID: PMC9954740 DOI: 10.3390/brainsci13020349] [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: 01/03/2023] [Revised: 02/13/2023] [Accepted: 02/16/2023] [Indexed: 02/22/2023] Open
Abstract
Deep brain stimulation (DBS) has emerged as an invasive neuromodulation technique for the treatment of several neurological disorders, but the mechanisms underlying its effects remain partially elusive. In this context, the application of Transcranial Magnetic Stimulation (TMS) in patients treated with DBS represents an intriguing approach to investigate the neurophysiology of cortico-basal networks. Experimental studies combining TMS and DBS that have been performed so far have mainly aimed to evaluate the effects of DBS on the cerebral cortex and thus to provide insights into DBS's mechanisms of action. The modulation of cortical excitability and plasticity by DBS is emerging as a potential contributor to its therapeutic effects. Moreover, pairing DBS and TMS stimuli could represent a method to induce cortical synaptic plasticity, the therapeutic potential of which is still unexplored. Furthermore, the advent of new DBS technologies and novel treatment targets will present new research opportunities and prospects to investigate brain networks. However, the application of the combined TMS-DBS approach is currently limited by safety concerns. In this review, we sought to present an overview of studies performed by combining TMS and DBS in neurological disorders, as well as available evidence and recommendations on the safety of their combination. Additionally, we outline perspectives for future research by highlighting knowledge gaps and possible novel applications of this approach.
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Affiliation(s)
| | - Nicoletta Manzo
- IRCCS San Camillo Hospital, Via Alberoni 70, 0126 Venice, Italy
| | - Andrea Guerra
- IRCCS Neuromed, 86077 Pozzilli, Italy
- Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy
| | - Andrea Landi
- Academic Neurosurgery, Department of Neurosciences, University of Padova, 35128 Padova, Italy
| | - Valentina Baro
- Academic Neurosurgery, Department of Neurosciences, University of Padova, 35128 Padova, Italy
| | - Sara Määttä
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, 70211 Kuopio, Finland
| | - Luca Weis
- Parkinson’s Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, 35128 Padova, Italy
| | - Camillo Porcaro
- Padova Neuroscience Center (PNC), University of Padova, 35129 Padova, Italy
- Department of Neuroscience, University of Padova, 35128 Padova, Italy
- Institute of Cognitive Sciences, and Technologies (ISTC)-National Research Council (CNR), 00185 Rome, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK
| | - Maurizio Corbetta
- Padova Neuroscience Center (PNC), University of Padova, 35129 Padova, Italy
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, 35128 Padova, Italy
- Venetian Institute of Molecular Medicine, 35129 Padova, Italy
| | - Angelo Antonini
- Parkinson’s Disease and Movement Disorders Unit, Department of Neuroscience, Centre for Rare Neurological Diseases (ERN-RND), University of Padova, 35128 Padova, Italy
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, 35128 Padova, Italy
- Department of Neurology, Washington University, St. Louis, MO 63108, USA
- Department of Neuroscience, Washington University, St. Louis, MO 63108, USA
- Correspondence: (A.A.); (F.F.)
| | - Florinda Ferreri
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, 70211 Kuopio, Finland
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padova, 35128 Padova, Italy
- Correspondence: (A.A.); (F.F.)
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Kutafina E, Becker S, Namer B. Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods. FRONTIERS IN NETWORK PHYSIOLOGY 2023; 3:1099282. [PMID: 36926544 PMCID: PMC10013045 DOI: 10.3389/fnetp.2023.1099282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/04/2023] [Indexed: 02/12/2023]
Abstract
In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.
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Affiliation(s)
- Ekaterina Kutafina
- Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
- Faculty of Applied Mathematics, AGH University of Science and Technology, Krakow, Poland
| | - Susanne Becker
- Clinical Psychology, Department of Experimental Psychology, Heinrich Heine University, Düsseldorf, Germany
- Integrative Spinal Research, Department of Chiropractic Medicine, University Hospital Balgrist, University of Zurich, Zurich, Switzerland
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Barbara Namer
- Junior Research Group Neuroscience, Interdisciplinary Center for Clinical Research Within the Faculty of Medicine, RWTH Aachen University, Aachen, Germany
- Institute of Physiology, Medical Faculty, RWTH Aachen University, Aachen, Germany
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Abrego AM, Khan W, Wright CE, Islam MR, Ghajar MH, Bai X, Tandon N, Seymour JP. Sensing local field potentials with a directional and scalable depth electrode array. J Neural Eng 2023; 20:016041. [PMID: 36630716 DOI: 10.1088/1741-2552/acb230] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Objective. A variety of electrophysiology tools are available to the neurosurgeon for diagnosis, functional therapy, and neural prosthetics. However, no tool can currently address these three critical needs: (a) access to all cortical regions in a minimally invasive manner; (b) recordings with microscale, mesoscale, and macroscale resolutions simultaneously; and (c) access to spatially distant multiple brain regions that constitute distributed cognitive networks.Approach.We modeled, designed, and demonstrated a novel device for recording local field potentials (LFPs) with the form factor of a stereo-electroencephalographic electrode and combined with radially distributed microelectrodes.Main results. Electro-quasistatic models demonstrate that the lead body amplifies and shields LFP sources based on direction, enablingdirectional sensitivity andscalability, referred to as thedirectional andscalable (DISC) array.In vivo,DISC demonstrated significantly improved signal-to-noise ratio, directional sensitivity, and decoding accuracy from rat barrel cortex recordings during whisker stimulation. Critical for future translation, DISC demonstrated a higher signal to noise ratio (SNR) than virtual ring electrodes and a noise floor approaching that of large ring electrodes in an unshielded environment after common average referencing. DISC also revealed independent, stereoscopic current source density measures whose direction was verified after histology.Significance. Directional sensitivity of LFPs may significantly improve brain-computer interfaces and many diagnostic procedures, including epilepsy foci detection and deep brain targeting.
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Affiliation(s)
- Amada M Abrego
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - Wasif Khan
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - Christopher E Wright
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
- Department of Bioengineering, Rice University, Houston, TX 77030, United States of America
| | - M Rabiul Islam
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - Mohammad H Ghajar
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - Xiaokang Bai
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - Nitin Tandon
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
| | - John P Seymour
- Department of Neurosurgery, University of Texas Health Science Center, Houston, TX 77030, United States of America
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77030, United States of America
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Liu Y, Chen X, Liang Y, Song H, Yu P, Guan S, Liu Z, Yang A, Tang M, Zhou Y, Zheng Y, Yang Z, Jiang L, He J, Tan N, Xu B, Lin X. Ferromagnetic Flexible Electronics for Brain-Wide Selective Neural Recording. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2208251. [PMID: 36451587 DOI: 10.1002/adma.202208251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 11/09/2022] [Indexed: 06/17/2023]
Abstract
Flexible microelectronics capable of straightforward implantation, remotely controlled navigation, and stable long-term recording hold great promise in diverse medical applications, particularly in deciphering complex functions of neural circuits in the brain. Existing flexible electronics, however, are often limited in bending and buckling during implantation, and unable to access a large brain region. Here, an injectable class of electronics with stable recording, omnidirectional steering, and precise navigating capabilities based on magnetic actuation is presented. After simple transcriptional injection, the rigid coatings are biodegraded quickly and the bundles of magnetic-nanoparticles-coated microelectrodes become separated, ultra-flexible, and magnetic actuated for further minimally invasive three-dimensional interpenetration in the brain. As proof of concept, this paradigm-shifting approach is demonstrated for selective and multiplexed neural activities recording across distant regions in the deep rodent brains. Coupling with optogenetic neural stimulation, the unique capabilities of this platform in electrophysiological readouts of projection dynamics in vivo are also demonstrated. The ability of these miniaturized, remotely controllable, and biocompatible ferromagnetic flexible electronics to afford minimally invasive manipulations in the soft tissues of the mammalian brain foreshadows applications in other organ systems, with great potential for broad utility in biomedical science and engineering.
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Affiliation(s)
- Yuxin Liu
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Xi Chen
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, 999077, China
| | - Ye Liang
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, 999077, China
| | - Hao Song
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Peng Yu
- School of Computer Science and Engineering, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China
| | - Shunmin Guan
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Zijian Liu
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Anqi Yang
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Minghui Tang
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Yajing Zhou
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Ying Zheng
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Zhilun Yang
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Lelun Jiang
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Jufang He
- Department of Neuroscience, City University of Hong Kong, Kowloon Tong, Hong Kong SAR, 999077, China
| | - Ning Tan
- School of Computer Science and Engineering, Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, Guangdong, 510275, China
| | - Bingzhe Xu
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
| | - Xudong Lin
- Guangdong Provincial Key Laboratory of Sensor Technology and Biomedical Instrument, School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, 518107, China
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Li D, Qin Q, Xia Y, Cheng S, Zhang J, Duan X, Qin X, Tian X, Mao L, Qiu J, Jiang X, Zou Z, Chen C. Heterozygous disruption of beclin 1 alleviates neurotoxicity induced by sub-chronic exposure of arsenite in mice. Neurotoxicology 2023; 94:11-23. [PMID: 36374725 DOI: 10.1016/j.neuro.2022.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 11/11/2022]
Abstract
Arsenite is a well-documented neurotoxicant that widely exists in the environment. However, the detailed mechanisms of arsenite neurotoxicity are not fully clarified. Autophagy has been reported to be involved in many neurological problems induced by arsenite. Since beclin 1 is an essential mediator of autophagy, we herein used both adult wild-type (beclin 1+/+) and heterozygous disruption of beclin 1 (beclin 1+/-) mice for chronic administration of 50 mg/L arsenite via drinking water for 3 months. Our results demonstrated that exposure of arsenite caused the working memory deficit, anxiety-like behavior and motor coordination disorder in beclin 1+/+ mice, accompanied with pathological changes in morphology and electrophysiology in the cortical tissues. This treatment of arsenite significantly reduced the number of neuronal cells and induced microglia activation and synaptic transmission disorders in the wild-type mice as compared with vehicle controls. Intriguingly, by using beclin 1+/- mice, we found that heterozygous disruption of beclin 1 profoundly attenuated these neurotoxic effects induced by arsenite, mainly manifested by improvements in the neurobehavioral impairments, abnormal electrophysiologic alterations as well as dysregulation of synaptic transmission. These findings together indicate that regulation of autophagy via beclin 1 would be a potential strategy for treatment against arsenite neurotoxicity.
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Affiliation(s)
- Danyang Li
- Department of Occupational and Environmental Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Qizhong Qin
- Center of Experimental Teaching for Public Health, Experimental Teaching and Management Center, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Yinyin Xia
- Department of Occupational and Environmental Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Shuqun Cheng
- Department of Occupational and Environmental Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Jun Zhang
- Molecular Biology Laboratory of Respiratory Disease, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xinhao Duan
- Department of Health Laboratory Technology, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xia Qin
- Department of Pharmacy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xin Tian
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lejiao Mao
- Molecular Biology Laboratory of Respiratory Disease, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Jingfu Qiu
- Department of Health Laboratory Technology, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China; Research Center for Environment and Human Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China
| | - Xuejun Jiang
- Center of Experimental Teaching for Public Health, Experimental Teaching and Management Center, Chongqing Medical University, Chongqing 400016, People's Republic of China; Research Center for Environment and Human Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China.
| | - Zhen Zou
- Molecular Biology Laboratory of Respiratory Disease, Institute of Life Sciences, Chongqing Medical University, Chongqing 400016, People's Republic of China; Research Center for Environment and Human Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China.
| | - Chengzhi Chen
- Department of Occupational and Environmental Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China; Research Center for Environment and Human Health, School of Public Health, Chongqing Medical University, Chongqing 400016, People's Republic of China.
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37
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Khodaei F, Sadati SH, Doost M, Lashgari R. LFP polarity changes across cortical and eccentricity in primary visual cortex. Front Neurosci 2023; 17:1138602. [PMID: 36922925 PMCID: PMC10008888 DOI: 10.3389/fnins.2023.1138602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 02/08/2023] [Indexed: 03/01/2023] Open
Abstract
Local field potentials (LFPs) can evaluate neural population activity in the cortex and their interaction with other cortical areas. Analyzing current source density (CSD) rather than LFPs is very significant due to the reduction of volume conduction effects. Current sinks are construed as net inward transmembrane currents, while current sources are net outward ones. Despite extensive studies of LFPs and CSDs, their morphology in different cortical layers and eccentricities are still largely unknown. Because LFP polarity changes provide a measure of neural activity, they can be useful in implanting brain-computer interface (BCI) chips and effectively communicating the BCI devices to the brain. We hypothesize that sinks and sources analyses could be a way to quantitatively achieve their characteristics in response to changes in stimulus size and layer-dependent differences with increasing eccentricities. In this study, we show that stimulus properties play a crucial role in determining the flow. The present work focusses on the primary visual cortex (V1). In this study, we investigate a map of the LFP-CSD in V1 area by presenting different stimulus properties (e.g., size and type) in the visual field area of Macaque monkeys. Our aim is to use the morphology of sinks and sources to measure the input and output information in different layers as well as different eccentricities. According to the value of CSDs, the results show that the stimuli smaller than RF's size had lower strength than the others and the larger RF's stimulus size showed smaller strength than the optimized stimulus size, which indicated the suppression phenomenon. Additionally, with the increased eccentricity, CSD's strengths were increased across cortical layers.
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Affiliation(s)
- Fereshteh Khodaei
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.,Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - S H Sadati
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Mahyar Doost
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
| | - Reza Lashgari
- Institute of Medical Science and Technology, Shahid Beheshti University, Tehran, Iran
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38
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Wilson MN, Thunemann M, Liu X, Lu Y, Puppo F, Adams JW, Kim JH, Ramezani M, Pizzo DP, Djurovic S, Andreassen OA, Mansour AA, Gage FH, Muotri AR, Devor A, Kuzum D. Multimodal monitoring of human cortical organoids implanted in mice reveal functional connection with visual cortex. Nat Commun 2022; 13:7945. [PMID: 36572698 PMCID: PMC9792589 DOI: 10.1038/s41467-022-35536-3] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 12/09/2022] [Indexed: 12/27/2022] Open
Abstract
Human cortical organoids, three-dimensional neuronal cultures, are emerging as powerful tools to study brain development and dysfunction. However, whether organoids can functionally connect to a sensory network in vivo has yet to be demonstrated. Here, we combine transparent microelectrode arrays and two-photon imaging for longitudinal, multimodal monitoring of human cortical organoids transplanted into the retrosplenial cortex of adult mice. Two-photon imaging shows vascularization of the transplanted organoid. Visual stimuli evoke electrophysiological responses in the organoid, matching the responses from the surrounding cortex. Increases in multi-unit activity (MUA) and gamma power and phase locking of stimulus-evoked MUA with slow oscillations indicate functional integration between the organoid and the host brain. Immunostaining confirms the presence of human-mouse synapses. Implantation of transparent microelectrodes with organoids serves as a versatile in vivo platform for comprehensive evaluation of the development, maturation, and functional integration of human neuronal networks within the mouse brain.
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Affiliation(s)
- Madison N Wilson
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Martin Thunemann
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Xin Liu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Yichen Lu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Francesca Puppo
- Department of Pediatrics, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Jason W Adams
- Department of Pediatrics, University of California San Diego, School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
| | - Jeong-Hoon Kim
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Mehrdad Ramezani
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA
| | - Donald P Pizzo
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Srdjan Djurovic
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
- NORMENT Center, Oslo, Norway
- Department of Clinical Science, University of Bergen, Bergen, Norway
- K. G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Center, Oslo, Norway
- K. G. Jebsen Center for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
- Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Oslo University Hospital, University of Oslo, Oslo, Norway
| | - Abed AlFatah Mansour
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Medical Neurobiology, The Hebrew University of Jerusalem, Ein Kerem-Jerusalem, Israel
| | - Fred H Gage
- Laboratory of Genetics, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Alysson R Muotri
- Department of Pediatrics, University of California San Diego, School of Medicine, La Jolla, CA, USA
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA
- Center for Academic Research and Training in Anthropogeny, University of California San Diego, La Jolla, CA, USA
- Archealization Center, University of California San Diego, La Jolla, CA, USA
- Kavli Institute for Brain and Mind, University of California San Diego, La Jolla, CA, USA
| | - Anna Devor
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Harvard Medical School, Massachusetts General Hospital, Charlestown, MA, USA.
| | - Duygu Kuzum
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, USA.
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Local Field Potential-Guided Contact Selection Using Chronically Implanted Sensing Devices for Deep Brain Stimulation in Parkinson's Disease. Brain Sci 2022; 12:brainsci12121726. [PMID: 36552185 PMCID: PMC9776002 DOI: 10.3390/brainsci12121726] [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: 11/25/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.
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40
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Axelrod V, Rozier C, Lehongre K, Adam C, Lambrecq V, Navarro V, Naccache L. Neural modulations in the auditory cortex during internal and external attention tasks: A single-patient intracranial recording study. Cortex 2022; 157:211-230. [PMID: 36335821 DOI: 10.1016/j.cortex.2022.09.011] [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: 10/31/2021] [Revised: 05/12/2022] [Accepted: 09/27/2022] [Indexed: 12/15/2022]
Abstract
Brain sensory processing is not passive, but is rather modulated by our internal state. Different research methods such as non-invasive imaging methods and intracranial recording of the local field potential (LFP) have been used to study to what extent sensory processing and the auditory cortex in particular are modulated by selective attention. However, at the level of the single- or multi-units the selective attention in humans has not been tested. In addition, most previous research on selective attention has explored externally-oriented attention, but attention can be also directed inward (i.e., internal attention), like spontaneous self-generated thoughts and mind-wandering. In the present study we had a rare opportunity to record multi-unit activity (MUA) in the auditory cortex of a patient. To complement, we also analyzed the LFP signal of the macro-contact in the auditory cortex. Our experiment consisted of two conditions with periodic beeping sounds. The participants were asked either to count the beeps (i.e., an "external attention" condition) or to recall the events of the previous day (i.e., an "internal attention" condition). We found that the four out of seven recorded units in the auditory cortex showed increased firing rates in "external attention" compared to "internal attention" condition. The beginning of this attentional modulation varied across multi-units between 30-50 msec and 130-150 msec from stimulus onset, a result that is compatible with an early selection view. The LFP evoked potential and induced high gamma activity both showed attentional modulation starting at about 70-80 msec. As the control, for the same experiment we recorded MUA activity in the amygdala and hippocampus of two additional patients. No major attentional modulation was found in the control regions. Overall, we believe that our results provide new empirical information and support for existing theoretical views on selective attention and spontaneous self-generated cognition.
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Affiliation(s)
- Vadim Axelrod
- The Gonda Multidisciplinary Brain Research Center, Bar Ilan University, Ramat Gan, Israel.
| | - Camille Rozier
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, ICM, INSERM U1127, CNRS UMR 7225, Paris, France
| | - Katia Lehongre
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, ICM, INSERM U1127, CNRS UMR 7225, Paris, France; Centre de NeuroImagerie de Recherche-CENIR, Paris Brain Institute, UMRS 1127, CNRS UMR 7225, Pitié-Salpêtriere Hospital, Paris, France
| | - Claude Adam
- AP-HP, GH Pitie-Salpêtrière-Charles Foix, Epilepsy Unit, Neurology Department, Paris, France
| | - Virginie Lambrecq
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, ICM, INSERM U1127, CNRS UMR 7225, Paris, France; AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, France; Sorbonne Université, UMR S1127, Paris, France
| | - Vincent Navarro
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, ICM, INSERM U1127, CNRS UMR 7225, Paris, France; AP-HP, GH Pitie-Salpêtrière-Charles Foix, Epilepsy Unit, Neurology Department, Paris, France; Sorbonne Université, UMR S1127, Paris, France
| | - Lionel Naccache
- Sorbonne Université, Institut du Cerveau - Paris Brain Institute, ICM, INSERM U1127, CNRS UMR 7225, Paris, France; AP-HP, Groupe hospitalier Pitié-Salpêtrière, Department of Neurophysiology, Paris, France
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Weakly Correlated Local Cortical State Switches under Anesthesia Lead to Strongly Correlated Global States. J Neurosci 2022; 42:8980-8996. [PMID: 36288946 PMCID: PMC9732829 DOI: 10.1523/jneurosci.0123-22.2022] [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: 01/17/2022] [Revised: 05/30/2022] [Accepted: 07/15/2022] [Indexed: 01/05/2023] Open
Abstract
During recovery from anesthesia, brain activity switches abruptly between a small set of discrete states. Surprisingly, this switching also occurs under constant doses of anesthesia, even in the absence of stimuli. These metastable states and the transitions between them are thought to form a "scaffold" that ultimately guides the brain back to wakefulness. The processes that constrain cortical activity patterns to these states and govern how states are coordinated between different cortical regions are unknown. If state transitions were driven by subcortical modulation, different cortical sites should exhibit near-synchronous state transitions. Conversely, spatiotemporal heterogeneity would suggest that state transitions are coordinated through corticocortical interactions. To differentiate between these hypotheses, we quantified synchrony of brain states in male rats exposed to a fixed isoflurane concentration. States were defined from spectra of local field potentials recorded across layers of visual and motor cortices. A transition synchrony measure shows that most state transitions are highly localized. Furthermore, while most pairs of cortical sites exhibit statistically significant coupling of both states and state transition times, coupling strength is typically weak. States and state transitions in the thalamic input layer (L4) are particularly decoupled from those in supragranular and infragranular layers. This suggests that state transitions are not imposed on the cortex by broadly projecting modulatory systems. Although each pairwise interaction is typically weak, we show that the multitude of such weak interactions is sufficient to confine global activity to a small number of discrete states.SIGNIFICANCE STATEMENT The brain consistently recovers to wakefulness after anesthesia, but this process is poorly understood. Previous work revealed that, during recovery from anesthesia, corticothalamic activity falls into one of several discrete patterns. The neuronal mechanisms constraining the cortex to just a few discrete states remain unknown. Global states could be coordinated by fluctuations in subcortical nuclei that project broadly to the cortex. Alternatively, these states may emerge from interactions within the cortex itself. Here, we provide evidence for the latter possibility by demonstrating that most pairs of cortical sites exhibit weak coupling. We thereby lay groundwork for future investigations of the specific cellular and network mechanisms of corticocortical activity state coupling.
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McCullough CM, Ramirez-Gordillo D, Hall M, Futia GL, Moran AK, Gibson EA, Restrepo D. GRINtrode: a neural implant for simultaneous two-photon imaging and extracellular electrophysiology in freely moving animals. NEUROPHOTONICS 2022; 9:045009. [PMID: 36466189 PMCID: PMC9713693 DOI: 10.1117/1.nph.9.4.045009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 11/01/2022] [Indexed: 06/11/2023]
Abstract
Significance In vivo imaging and electrophysiology are powerful tools to explore neuronal function that each offer unique complementary information with advantages and limitations. Capturing both data types from the same neural population in the freely moving animal would allow researchers to take advantage of the capabilities of both modalities and further understand how they relate to each other. Aim Here, we present a head-mounted neural implant suitable for in vivo two-photon imaging of neuronal activity with simultaneous extracellular electrical recording in head-fixed or fiber-coupled freely moving animals. Approach A gradient refractive index (GRIN) lens-based head-mounted neural implant with extracellular electrical recording provided by tetrodes on the periphery of the GRIN lens was chronically implanted. The design of the neural implant allows for recording from head-fixed animals, as well as freely moving animals by coupling the imaging system to a coherent imaging fiber bundle. Results We demonstrate simultaneous two-photon imaging of GCaMP and extracellular electrophysiology of neural activity in awake head-fixed and freely moving mice. Using the collected information, we perform correlation analysis to reveal positive correlation between optical and local field potential recordings. Conclusion Simultaneously recording neural activity using both optical and electrical methods provides complementary information from each modality. Designs that can provide such bi-modal recording in freely moving animals allow for the investigation of neural activity underlying a broader range of behavioral paradigms.
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Affiliation(s)
- Connor M. McCullough
- University of Colorado Anschutz Medical Campus, Department of Bioengineering, Aurora, Colorado, United States
| | - Daniel Ramirez-Gordillo
- University of Colorado Anschutz Medical Campus, Department of Neurosurgery, Aurora, Colorado, United States
| | - Michael Hall
- University of Colorado Anschutz Medical Campus, Neuroscience Machine Shop, Aurora, Colorado, United States
| | - Gregory L. Futia
- University of Colorado Anschutz Medical Campus, Department of Bioengineering, Aurora, Colorado, United States
| | - Andrew K. Moran
- University of Colorado Anschutz Medical Campus, Department of Cell and Development Biology, Aurora, Colorado, United States
| | - Emily A. Gibson
- University of Colorado Anschutz Medical Campus, Department of Bioengineering, Aurora, Colorado, United States
| | - Diego Restrepo
- University of Colorado Anschutz Medical Campus, Department of Cell and Development Biology, Aurora, Colorado, United States
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Prakash SS, Mayo JP, Ray S. Decoding of attentional state using local field potentials. Curr Opin Neurobiol 2022; 76:102589. [PMID: 35751949 PMCID: PMC9840850 DOI: 10.1016/j.conb.2022.102589] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 01/18/2023]
Abstract
We review recent efforts to decode visual spatial attention from different types of brain signals, such as spikes and local field potentials (LFPs). Combining signals from more electrodes improves decoding, but the pattern of improvement varies considerably depending on the signal as well as the task (for example, decoding of sensory stimulus/motor intention versus location of attention). We argue that this pattern of results conveys important information not only about the usefulness of a particular brain signal for decoding attention, but also about the spatial scale over which attention operates in the brain. The spatial scale, in turn, likely depends on the extent of underlying mechanisms such as normalization, gain control via excitation-inhibition interactions, and neuromodulatory regulation of attention.
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Affiliation(s)
- Surya S. Prakash
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
| | - J. Patrick Mayo
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Supratim Ray
- Centre for Neuroscience, Indian Institute of Science, Bangalore, 560012, India
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Montes-Lourido P, Kar M, Pernia M, Parida S, Sadagopan S. Updates to the guinea pig animal model for in-vivo auditory neuroscience in the low-frequency hearing range. Hear Res 2022; 424:108603. [PMID: 36099806 PMCID: PMC9922531 DOI: 10.1016/j.heares.2022.108603] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 08/29/2022] [Accepted: 09/03/2022] [Indexed: 02/08/2023]
Abstract
For gaining insight into general principles of auditory processing, it is critical to choose model organisms whose set of natural behaviors encompasses the processes being investigated. This reasoning has led to the development of a variety of animal models for auditory neuroscience research, such as guinea pigs, gerbils, chinchillas, rabbits, and ferrets; but in recent years, the availability of cutting-edge molecular tools and other methodologies in the mouse model have led to waning interest in these unique model species. As laboratories increasingly look to include in-vivo components in their research programs, a comprehensive description of procedures and techniques for applying some of these modern neuroscience tools to a non-mouse small animal model would enable researchers to leverage unique model species that may be best suited for testing their specific hypotheses. In this manuscript, we describe in detail the methods we have developed to apply these tools to the guinea pig animal model to answer questions regarding the neural processing of complex sounds, such as vocalizations. We describe techniques for vocalization acquisition, behavioral testing, recording of auditory brainstem responses and frequency-following responses, intracranial neural signals including local field potential and single unit activity, and the expression of transgenes allowing for optogenetic manipulation of neural activity, all in awake and head-fixed guinea pigs. We demonstrate the rich datasets at the behavioral and electrophysiological levels that can be obtained using these techniques, underscoring the guinea pig as a versatile animal model for studying complex auditory processing. More generally, the methods described here are applicable to a broad range of small mammals, enabling investigators to address specific auditory processing questions in model organisms that are best suited for answering them.
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Affiliation(s)
- Pilar Montes-Lourido
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Manaswini Kar
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marianny Pernia
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Satyabrata Parida
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA
| | - Srivatsun Sadagopan
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, PA, USA; Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, USA; Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA; Department of Communication Science and Disorders, University of Pittsburgh, Pittsburgh, PA, USA.
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Singh B, Wang Z, Qi XL, Constantinidis C. Plasticity after cognitive training reflected in prefrontal local field potentials. iScience 2022; 25:104929. [PMID: 36065179 PMCID: PMC9440296 DOI: 10.1016/j.isci.2022.104929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 07/19/2022] [Accepted: 08/09/2022] [Indexed: 11/21/2022] Open
Abstract
Learning to perform a new cognitive task induces plasticity of the prefrontal cortex generally involving activation of more neurons and increases in firing rate; however, its effects on single neurons are diverse and complex. We sought to understand how training affects global measures of neural activity by recording and analyzing local field potentials (LFPs) in monkeys before and after they learned to perform working memory tasks. LFP power after training was characterized by a reduction in power in 20-40 Hz during the stimulus presentations and delay periods of the task. Both evoked power, synchronized to task events, and induced power exhibited this decrease after training. The effect was consistent across tasks requiring memory of spatial location and stimulus shape. Error trials were characterized by a lack of LFP power ramping around the fixation onset. Our results reveal signatures of cortical plasticity in LFPs associated with learning to perform cognitive tasks.
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Affiliation(s)
- Balbir Singh
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
| | - Zhengyang Wang
- Neuroscience Program, Vanderbilt University, Nashville, TN 37235, USA
| | - Xue-Lian Qi
- Department of Neurobiology & Anatomy, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Christos Constantinidis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA
- Neuroscience Program, Vanderbilt University, Nashville, TN 37235, USA
- Department of Ophthalmology and Visual Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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Jiang C, Luo B, Liu X, Chen GD, Salvi R. Ipsilateral auditory cortex responses to the intact ear after unilateral noise trauma in juvenile rats. Hear Res 2022; 422:108567. [PMID: 35816891 DOI: 10.1016/j.heares.2022.108567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/21/2022] [Accepted: 06/30/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND While ear stimulation produces a robust response in the contralateral auditory cortex (AC), it produces only a weak response in the ipsilateral AC, known as interhemispheric asymmetry. Unilateral deafness can lead to AC plastic changes, resulting in reduced interhemispheric asymmetry and auditory perceptual consequences. However, the unilateral hearing loss-associated plastic changes are far from fully understood. The purpose of this study was to investigate AC responses to the ipsilateral unimpaired ear after noise injury to the contralateral ear in juvenile rats. METHODS Rats (50 days) were monaurally exposed to an intense noise (10.0-12.5 kHz, 126 dB SPL) for 2 hours. The unexposed ear-induced ipsilateral AC responses were recorded 2 days and 4 months after exposure and compared between groups. RESULTS The noise exposure resulted in complete hearing loss in the exposed ear, but normal function in the other. Two days after exposure, the ipsilateral AC response induced by the intact ear was significantly enhanced and the threshold decreased (the early-onset effect). Four months after noise exposure, in addition to the increased response amplitude, the "slow-increasing" firing pattern of the neurons in the ipsilateral AC turned into the contralateral-AC-response-like "sharp-increasing" pattern (the late-onset effect) with shortened response latency. DISCUSSION The early-onset effect can result from release of inhibition due to decreased contralateral input, while the late-onset effect may imply the formation of direct connections in the ipsilateral auditory pathway. The enhanced AC response may help maintain loudness perception and monaural sound localization after unilateral deafness.
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Affiliation(s)
- Chen Jiang
- Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China
| | - Bin Luo
- Affiliated Hospital of University of Science and Technology of China, Hefei, Anhui, China
| | - Xiaopeng Liu
- Center for Hearing and Deafness, University at Buffalo, Buffalo, NY, United States
| | - Guang-Di Chen
- Center for Hearing and Deafness, University at Buffalo, Buffalo, NY, United States.
| | - Richard Salvi
- Center for Hearing and Deafness, University at Buffalo, Buffalo, NY, United States
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Raj A, Verma P, Nagarajan S. Structure-function models of temporal, spatial, and spectral characteristics of non-invasive whole brain functional imaging. Front Neurosci 2022; 16:959557. [PMID: 36110093 PMCID: PMC9468900 DOI: 10.3389/fnins.2022.959557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 07/27/2022] [Indexed: 11/29/2022] Open
Abstract
We review recent advances in using mathematical models of the relationship between the brain structure and function that capture features of brain dynamics. We argue the need for models that can jointly capture temporal, spatial, and spectral features of brain functional activity. We present recent work on spectral graph theory based models that can accurately capture spectral as well as spatial patterns across multiple frequencies in MEG reconstructions.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
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Myers JC, Smith EH, Leszczynski M, O'Sullivan J, Yates MJ, McKhann G, Mesgarani N, Schroeder C, Schevon C, Sheth SA. The Spatial Reach of Neuronal Coherence and Spike-Field Coupling across the Human Neocortex. J Neurosci 2022; 42:6285-6294. [PMID: 35790403 PMCID: PMC9374135 DOI: 10.1523/jneurosci.0050-22.2022] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 04/21/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022] Open
Abstract
Neuronal coherence is thought to be a fundamental mechanism of communication in the brain, where synchronized field potentials coordinate synaptic and spiking events to support plasticity and learning. Although the spread of field potentials has garnered great interest, little is known about the spatial reach of phase synchronization, or neuronal coherence. Functional connectivity between different brain regions is known to occur across long distances, but the locality of synchronization across the neocortex is understudied. Here we used simultaneous recordings from electrocorticography (ECoG) grids and high-density microelectrode arrays to estimate the spatial reach of neuronal coherence and spike-field coherence (SFC) across frontal, temporal, and occipital cortices during cognitive tasks in humans. We observed the strongest coherence within a 2-3 cm distance from the microelectrode arrays, potentially defining an effective range for local communication. This range was relatively consistent across brain regions, spectral frequencies, and cognitive tasks. The magnitude of coherence showed power law decay with increasing distance from the microelectrode arrays, where the highest coherence occurred between ECoG contacts, followed by coherence between ECoG and deep cortical local field potential (LFP), and then SFC (i.e., ECoG > LFP > SFC). The spectral frequency of coherence also affected its magnitude. Alpha coherence (8-14 Hz) was generally higher than other frequencies for signals nearest the microelectrode arrays, whereas delta coherence (1-3 Hz) was higher for signals that were farther away. Action potentials in all brain regions were most coherent with the phase of alpha oscillations, which suggests that alpha waves could play a larger, more spatially local role in spike timing than other frequencies. These findings provide a deeper understanding of the spatial and spectral dynamics of neuronal synchronization, further advancing knowledge about how activity propagates across the human brain.SIGNIFICANCE STATEMENT Coherence is theorized to facilitate information transfer across cerebral space by providing a convenient electrophysiological mechanism to modulate membrane potentials in spatiotemporally complex patterns. Our work uses a multiscale approach to evaluate the spatial reach of phase coherence and spike-field coherence during cognitive tasks in humans. Locally, coherence can reach up to 3 cm around a given area of neocortex. The spectral properties of coherence revealed that alpha phase-field and spike-field coherence were higher within ranges <2 cm, whereas lower-frequency delta coherence was higher for contacts farther away. Spatiotemporally shared information (i.e., coherence) across neocortex seems to reach farther than field potentials alone.
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Affiliation(s)
- John C Myers
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas 77030
| | - Elliot H Smith
- Department of Neurosurgery, University of Utah, Salt Lake City, Utah 84132
- Department of Neurology, Columbia University, New York, New York 10032
| | | | - James O'Sullivan
- Department of Electrical Engineering, Columbia University, New York, New York 10027
| | - Mark J Yates
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Guy McKhann
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Nima Mesgarani
- Department of Electrical Engineering, Columbia University, New York, New York 10027
| | - Charles Schroeder
- Department of Psychiatry, Columbia University, New York, New York 10032
| | - Catherine Schevon
- Department of Neurology, Columbia University, New York, New York 10032
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, Texas 77030
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Ghosh M, Yang FC, Rice SP, Hetrick V, Gonzalez AL, Siu D, Brennan EKW, John TT, Ahrens AM, Ahmed OJ. Running speed and REM sleep control two distinct modes of rapid interhemispheric communication. Cell Rep 2022; 40:111028. [PMID: 35793619 PMCID: PMC9291430 DOI: 10.1016/j.celrep.2022.111028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 04/08/2022] [Accepted: 06/10/2022] [Indexed: 11/30/2022] Open
Abstract
Rhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ~140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep. Gamma-rhythmic communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, Ghosh et al. identify even faster ~140 Hz rhythms, named splines, that reflect anti-phase neuronal synchrony across hemispheres. The balance of anti-phase spline and in-phase gamma communication is dynamically controlled by behavior and sleep.
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Affiliation(s)
- Megha Ghosh
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Fang-Chi Yang
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sharena P Rice
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Vaughn Hetrick
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alcides Lorenzo Gonzalez
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Danny Siu
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Ellen K W Brennan
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tibin T John
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA
| | - Allison M Ahrens
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Omar J Ahmed
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI 48109, USA; Kresge Hearing Research Institute, University of Michigan, Ann Arbor, MI 48109, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
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Mercier MR, Dubarry AS, Tadel F, Avanzini P, Axmacher N, Cellier D, Vecchio MD, Hamilton LS, Hermes D, Kahana MJ, Knight RT, Llorens A, Megevand P, Melloni L, Miller KJ, Piai V, Puce A, Ramsey NF, Schwiedrzik CM, Smith SE, Stolk A, Swann NC, Vansteensel MJ, Voytek B, Wang L, Lachaux JP, Oostenveld R. Advances in human intracranial electroencephalography research, guidelines and good practices. Neuroimage 2022; 260:119438. [PMID: 35792291 DOI: 10.1016/j.neuroimage.2022.119438] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 05/23/2022] [Accepted: 06/30/2022] [Indexed: 12/11/2022] Open
Abstract
Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.
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