1
|
Süzen E, Şavklıyıldız A, Özkan Ö, Çolak ÖH, Apaydın Doğan E, Özkan Ö, Şimşek B, Uluşar ÜD, Carlak HF, Polat Ö, Uysal H. Delta waves as a sign of cortical plasticity after full-face transplantation. Sci Rep 2024; 14:16454. [PMID: 39014053 PMCID: PMC11252439 DOI: 10.1038/s41598-024-67469-w] [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: 04/27/2024] [Accepted: 07/11/2024] [Indexed: 07/18/2024] Open
Abstract
This study focused on detecting the reflections of healing and change in cortex activation in full-face transplantation and lesions patients on EEG activity. Face transplant patients have facial lesions before transplantation and, to identify pre-face transplant patients' brain activity in the absence of pre-transplant recordings, we used data obtained from pre-transplant facial lesion patients. Ten healthy, four facial lesion and three full-face transplant patients participated in this study. EEG data recorded for four different sensory stimuli (brush from the right face, right hand, left face, and left-hand regions) were analyzed using wavelet packet transform method. EEG waves were analyzed for standard bands. Our findings indicate significant change in the 2-4 Hz frequency range which may be a result of ongoing or previous cortical reorganization for face lesion and transplant patients. Alterations of the delta wave seen in patients with facial lesion and face transplant can also be explained by the intense central plasticity. Our findings show that the delta band differences might be used as a marker in the evaluation of post-transplant cortical plasticity in the future.
Collapse
Affiliation(s)
- Esra Süzen
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey
| | - Ayhan Şavklıyıldız
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey
| | - Ömer Özkan
- Faculty of Medicine, Department of Plastic and Reconstructive Surgery, Akdeniz University, Antalya, Turkey
| | - Ömer Halil Çolak
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey.
| | - Ebru Apaydın Doğan
- Faculty of Medicine, Department of Neurology, Akdeniz University, Antalya, Turkey
| | - Özlenen Özkan
- Faculty of Medicine, Department of Plastic and Reconstructive Surgery, Akdeniz University, Antalya, Turkey
| | - Buket Şimşek
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey
| | - Ümit Deniz Uluşar
- Faculty of Engineering, Department of Computer Engineering, Akdeniz University, Antalya, Turkey
| | - Hamza Feza Carlak
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey
| | - Övünç Polat
- Faculty of Engineering, Department of Electrical and Electronics Engineering, Akdeniz University, Pınarbasi Blvd., Antalya, Turkey
| | - Hilmi Uysal
- Faculty of Medicine, Department of Neurology, Akdeniz University, Antalya, Turkey
| |
Collapse
|
2
|
Guerreiro Fernandes F, Raemaekers M, Freudenburg Z, Ramsey N. Considerations for implanting speech brain computer interfaces based on functional magnetic resonance imaging. J Neural Eng 2024; 21:036005. [PMID: 38648782 DOI: 10.1088/1741-2552/ad4178] [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: 06/27/2023] [Accepted: 04/22/2024] [Indexed: 04/25/2024]
Abstract
Objective.Brain-computer interfaces (BCIs) have the potential to reinstate lost communication faculties. Results from speech decoding studies indicate that a usable speech BCI based on activity in the sensorimotor cortex (SMC) can be achieved using subdurally implanted electrodes. However, the optimal characteristics for a successful speech implant are largely unknown. We address this topic in a high field blood oxygenation level dependent functional magnetic resonance imaging (fMRI) study, by assessing the decodability of spoken words as a function of hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal-axis.Approach.Twelve subjects conducted a 7T fMRI experiment in which they pronounced 6 different pseudo-words over 6 runs. We divided the SMC by hemisphere, gyrus, sulcal depth, and position along the ventral/dorsal axis. Classification was performed on in these SMC areas using multiclass support vector machine (SVM).Main results.Significant classification was possible from the SMC, but no preference for the left or right hemisphere, nor for the precentral or postcentral gyrus for optimal word classification was detected. Classification while using information from the cortical surface was slightly better than when using information from deep in the central sulcus and was highest within the ventral 50% of SMC. Confusion matrices where highly similar across the entire SMC. An SVM-searchlight analysis revealed significant classification in the superior temporal gyrus and left planum temporale in addition to the SMC.Significance.The current results support a unilateral implant using surface electrodes, covering the ventral 50% of the SMC. The added value of depth electrodes is unclear. We did not observe evidence for variations in the qualitative nature of information across SMC. The current results need to be confirmed in paralyzed patients performing attempted speech.
Collapse
Affiliation(s)
- F Guerreiro Fernandes
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - M Raemaekers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - Z Freudenburg
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| | - N Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands
| |
Collapse
|
3
|
Carmichael DW, Vulliemoz S, Murta T, Chaudhary U, Perani S, Rodionov R, Rosa MJ, Friston KJ, Lemieux L. Measurement of the Mapping between Intracranial EEG and fMRI Recordings in the Human Brain. Bioengineering (Basel) 2024; 11:224. [PMID: 38534498 DOI: 10.3390/bioengineering11030224] [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: 10/16/2023] [Revised: 12/15/2023] [Accepted: 12/20/2023] [Indexed: 03/28/2024] Open
Abstract
There are considerable gaps in our understanding of the relationship between human brain activity measured at different temporal and spatial scales. Here, electrocorticography (ECoG) measures were used to predict functional MRI changes in the sensorimotor cortex in two brain states: at rest and during motor performance. The specificity of this relationship to spatial co-localisation of the two signals was also investigated. We acquired simultaneous ECoG-fMRI in the sensorimotor cortex of three patients with epilepsy. During motor activity, high gamma power was the only frequency band where the electrophysiological response was co-localised with fMRI measures across all subjects. The best model of fMRI changes across states was its principal components, a parsimonious description of the entire ECoG spectrogram. This model performed much better than any others that were based either on the classical frequency bands or on summary measures of cross-spectral changes. The region-specific fMRI signal is reflected in spatially and spectrally distributed EEG activity.
Collapse
Affiliation(s)
- David W Carmichael
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London SE1 7EH, UK
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Serge Vulliemoz
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Epilepsy Unit, Neurology Department, University Hospital and University of Geneva, 1211 Geneva 14, Switzerland
| | - Teresa Murta
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
- Department of Bioengineering, Institute for Systems and Robotics, Instituto Superior Tecnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
| | - Umair Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Suejen Perani
- Developmental Imaging and Biophysics section, UCL Great Ormond Street Institute of Child Health, London WC1N 1EH, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| | - Maria Joao Rosa
- Department of Computer Science, University College London, London WC1E 6BT, UK
| | - Karl J Friston
- Wellcome Trust Centre for Human Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1E 6BT, UK
| |
Collapse
|
4
|
Socolovsky M, di Masi G, Bonilla G, Lovaglio A, Battaglia D, Rosler R, Malessy M. Brain plasticity in neonatal brachial plexus palsies: quantification and comparison with adults' brachial plexus injuries. Childs Nerv Syst 2024; 40:479-486. [PMID: 37436472 DOI: 10.1007/s00381-023-06072-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 07/05/2023] [Indexed: 07/13/2023]
Abstract
PURPOSE To compare two populations of brachial plexus palsies, one neonatal (NBPP) and the other traumatic (NNBPP) who underwent different nerve transfers, using the plasticity grading scale (PGS) for detecting differences in brain plasticity between both groups. METHODS To be included, all patients had to have undergone a nerve transfer as the unique procedure to recover one lost function. The primary outcome was the PGS score. We also assessed patient compliance to rehabilitation using the rehabilitation quality scale (RQS). Statistical analysis of all variables was performed. A p ≤ 0.050 set as criterion for statistical significance. RESULTS A total of 153 NNBPP patients and 35 NBPP babies (with 38 nerve transfers) met the inclusion criteria. The mean age at surgery of the NBPP group was 9 months (SD 5.42, range 4 to 23 months). The mean age of NNBPP patients was 22 years (SD 12 years, range 3 to 69). They were operated around sixth months after the trauma. All transfers performed in NBPP patients had a maximum PGS score of 4. This was not the case for the NNBPP population that reached a PGS score of 4 in approximately 20% of the cases. This difference was statistically significant (p < 0.001). The RQS was not significantly different between groups. CONCLUSION We found that babies with NBPP have a significantly greater capacity for plastic rewiring than adults with NNBPP. The brain in the very young patient can process the changes induced by the peripheral nerve transfer better than in adults.
Collapse
Affiliation(s)
- Mariano Socolovsky
- Peripheral Nerve & Brachial Plexus Surgery Program, Department of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, La Pampa 1175 Torre 2 5A, 1428, Buenos Aires, Argentina.
- Peripheral Nerve & Brachial Plexus Surgery Unit, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Torre 2 5A, Buenos Aires, Argentina.
| | - Gilda di Masi
- Peripheral Nerve & Brachial Plexus Surgery Program, Department of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, La Pampa 1175 Torre 2 5A, 1428, Buenos Aires, Argentina
- Peripheral Nerve & Brachial Plexus Surgery Unit, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Torre 2 5A, Buenos Aires, Argentina
| | - Gonzalo Bonilla
- Peripheral Nerve & Brachial Plexus Surgery Program, Department of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, La Pampa 1175 Torre 2 5A, 1428, Buenos Aires, Argentina
- Peripheral Nerve & Brachial Plexus Surgery Unit, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Torre 2 5A, Buenos Aires, Argentina
| | - Ana Lovaglio
- Peripheral Nerve & Brachial Plexus Surgery Program, Department of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, La Pampa 1175 Torre 2 5A, 1428, Buenos Aires, Argentina
- Peripheral Nerve & Brachial Plexus Surgery Unit, Division of Neurosurgery, Hospital de Clínicas, University of Buenos Aires School of Medicine, Torre 2 5A, Buenos Aires, Argentina
| | - Danilo Battaglia
- Peripheral Nerve & Brachial Plexus Surgery Program, Department of Physiotherapy and Rehabilitation, Hospital de Clínicas, University of Buenos Aires School of Medicine, Buenos Aires, Argentina
| | - Roberto Rosler
- Department of Neurology, Universidad Abierta Interamericana, Buenos Aires, Argentina
| | - Martijn Malessy
- Department of Neurosurgery, University of Leiden School of Medicine, Leiden, Holland
| |
Collapse
|
5
|
Vitória MA, Fernandes FG, van den Boom M, Ramsey N, Raemaekers M. Decoding Single and Paired Phonemes Using 7T Functional MRI. Brain Topogr 2024:10.1007/s10548-024-01034-6. [PMID: 38261272 DOI: 10.1007/s10548-024-01034-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Accepted: 01/12/2024] [Indexed: 01/24/2024]
Abstract
Several studies have shown that mouth movements related to the pronunciation of individual phonemes are represented in the sensorimotor cortex. This would theoretically allow for brain computer interfaces that are capable of decoding continuous speech by training classifiers based on the activity in the sensorimotor cortex related to the production of individual phonemes. To address this, we investigated the decodability of trials with individual and paired phonemes (pronounced consecutively with one second interval) using activity in the sensorimotor cortex. Fifteen participants pronounced 3 different phonemes and 3 combinations of two of the same phonemes in a 7T functional MRI experiment. We confirmed that support vector machine (SVM) classification of single and paired phonemes was possible. Importantly, by combining classifiers trained on single phonemes, we were able to classify paired phonemes with an accuracy of 53% (33% chance level), demonstrating that activity of isolated phonemes is present and distinguishable in combined phonemes. A SVM searchlight analysis showed that the phoneme representations are widely distributed in the ventral sensorimotor cortex. These findings provide insights about the neural representations of single and paired phonemes. Furthermore, it supports the notion that speech BCI may be feasible based on machine learning algorithms trained on individual phonemes using intracranial electrode grids.
Collapse
Affiliation(s)
- Maria Araújo Vitória
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Francisco Guerreiro Fernandes
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Max van den Boom
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Nick Ramsey
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mathijs Raemaekers
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
| |
Collapse
|
6
|
Tai P, Ding P, Wang F, Gong A, Li T, Zhao L, Su L, Fu Y. Brain-computer interface paradigms and neural coding. Front Neurosci 2024; 17:1345961. [PMID: 38287988 PMCID: PMC10822902 DOI: 10.3389/fnins.2023.1345961] [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: 11/28/2023] [Accepted: 12/28/2023] [Indexed: 01/31/2024] Open
Abstract
Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.
Collapse
Affiliation(s)
- Pengrui Tai
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Peng Ding
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Fan Wang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Anmin Gong
- School of Information Engineering, Chinese People’s Armed Police Force Engineering University, Xi’an, China
| | - Tianwen Li
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Zhao
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
- Faculty of Science, Kunming University of Science and Technology, Kunming, China
| | - Lei Su
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| | - Yunfa Fu
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
- Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China
| |
Collapse
|
7
|
Merino EC, Faes A, Van Hulle MM. The role of distinct ECoG frequency features in decoding finger movement. J Neural Eng 2023; 20:066014. [PMID: 37963397 DOI: 10.1088/1741-2552/ad0c5e] [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: 06/26/2023] [Accepted: 11/14/2023] [Indexed: 11/16/2023]
Abstract
Objective.To identify the electrocorticography (ECoG) frequency features that encode distinct finger movement states during repeated finger flexions.Approach.We used the publicly available Stanford ECoG dataset of cue-based, repeated single finger flexions. Using linear regression, we identified the spectral features that contributed most to the encoding of movement dynamics and discriminating movement events from rest, and combined them to predict finger movement trajectories. Furthermore, we also looked into the effect of the used frequency range and the spatial distribution of the identified features.Main results.Two frequency features generate superior performance, each one for a different movement aspect: high gamma band activity distinguishes movement events from rest, whereas the local motor potential (LMP) codes for movement dynamics. Combining these two features in a finger movement decoder outperformed comparable prior work where the entire spectrum was used as the average correlation coefficient with the true trajectories increased from 0.45 to 0.5, both applied to the Stanford dataset, and erroneous predictions during rest were demoted. In addition, for the first time, our results show the influence of the upper cut-off frequency used to extract LMP, yielding a higher performance when this range is adjusted to the finger movement rate.Significance.This study shows the benefit of a detailed feature analysis prior to designing the finger movement decoder.
Collapse
Affiliation(s)
- Eva Calvo Merino
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - A Faes
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| | - M M Van Hulle
- Laboratory for Neuro- and Psychophysiology, KU Leuven, Leuven, Belgium
| |
Collapse
|
8
|
Leinders S, Vansteensel MJ, Piantoni G, Branco MP, Freudenburg ZV, Gebbink TA, Pels EGM, Raemaekers MAH, Schippers A, Aarnoutse EJ, Ramsey NF. Using fMRI to localize target regions for implanted brain-computer interfaces in locked-in syndrome. Clin Neurophysiol 2023; 155:1-15. [PMID: 37657190 DOI: 10.1016/j.clinph.2023.08.003] [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: 02/10/2023] [Revised: 06/29/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
OBJECTIVE Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have the potential to improve quality of life of people with locked-in syndrome (LIS) by restoring their ability to communicate independently. Before implantation of such a system, it is important to localize ECoG electrode target regions. Here, we assessed the predictive value of functional magnetic resonance imaging (fMRI) for the localization of suitable target regions on the sensorimotor cortex for ECoG-based BCI in people with locked-in syndrome. METHODS Three people with locked-in syndrome were implanted with a chronic, fully implantable ECoG-BCI system. We compared pre-surgical fMRI activity with post-implantation ECoG activity from areas known to be active and inactive during attempted hand movement (sensorimotor hand region and dorsolateral prefrontal cortex, respectively). RESULTS Results showed a spatial match between fMRI activity and changes in ECoG low and high frequency band power (10 - 30 and 65 - 95 Hz, respectively) during attempted movement. Also, we found that fMRI can be used to select a sub-set of electrodes that show strong task-related signal changes that are therefore likely to generate adequate BCI control. CONCLUSIONS Our findings indicate that fMRI is a useful non-invasive tool for the pre-surgical workup of BCI implant candidates. SIGNIFICANCE If these results are confirmed in more BCI studies, fMRI might be used for more efficient surgical BCI procedures with focused cortical coverage and lower participant burden.
Collapse
Affiliation(s)
- Sacha Leinders
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Giovanni Piantoni
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mariana P Branco
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Zac V Freudenburg
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Tineke A Gebbink
- Department of Clinical Neurophysiology, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Elmar G M Pels
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Mathijs A H Raemaekers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Anouck Schippers
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, University Medical Center Utrecht Brain Center, Utrecht University, 3584 CX, Utrecht, The Netherlands.
| |
Collapse
|
9
|
Lakhani DA, Sabsevitz DS, Chaichana KL, Quiñones-Hinojosa A, Middlebrooks EH. Current State of Functional MRI in the Presurgical Planning of Brain Tumors. Radiol Imaging Cancer 2023; 5:e230078. [PMID: 37861422 DOI: 10.1148/rycan.230078] [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] [Indexed: 10/21/2023]
Abstract
Surgical resection of brain tumors is challenging because of the delicate balance between maximizing tumor removal and preserving vital brain functions. Functional MRI (fMRI) offers noninvasive preoperative mapping of widely distributed brain areas and is increasingly used in presurgical functional mapping. However, its impact on survival and functional outcomes is still not well-supported by evidence. Task-based fMRI (tb-fMRI) maps blood oxygen level-dependent (BOLD) signal changes during specific tasks, while resting-state fMRI (rs-fMRI) examines spontaneous brain activity. rs-fMRI may be useful for patients who cannot perform tasks, but its reliability is affected by tumor-induced changes, challenges in data processing, and noise. Validation studies comparing fMRI with direct cortical stimulation (DCS) show variable concordance, particularly for cognitive functions such as language; however, concordance for tb-fMRI is generally greater than that for rs-fMRI. Preoperative fMRI, in combination with MRI tractography and intraoperative DCS, may result in improved survival and extent of resection and reduced functional deficits. fMRI has the potential to guide surgical planning and help identify targets for intraoperative mapping, but there is currently limited prospective evidence of its impact on patient outcomes. This review describes the current state of fMRI for preoperative assessment in patients undergoing brain tumor resection. Keywords: MR-Functional Imaging, CNS, Brain/Brain Stem, Anatomy, Oncology, Functional MRI, Functional Anatomy, Task-based, Resting State, Surgical Planning, Brain Tumor © RSNA, 2023.
Collapse
Affiliation(s)
- Dhairya A Lakhani
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - David S Sabsevitz
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Kaisorn L Chaichana
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Alfredo Quiñones-Hinojosa
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| | - Erik H Middlebrooks
- From the Department of Radiology, West Virginia University, Morgantown, WV (D.A.L.); and Departments of Psychiatry and Psychology (D.S.S.), Neurosurgery (K.L.C., A.Q.H., E.H.M.), and Radiology (E.H.M.), Mayo Clinic Florida, 4500 San Pablo Rd, Jacksonville, FL 32224
| |
Collapse
|
10
|
Easthope E, Shamei A, Liu Y, Gick B, Fels S. Cortical control of posture in fine motor skills: evidence from inter-utterance rest position. Front Hum Neurosci 2023; 17:1139569. [PMID: 37662639 PMCID: PMC10469778 DOI: 10.3389/fnhum.2023.1139569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 06/12/2023] [Indexed: 09/05/2023] Open
Abstract
The vocal tract continuously employs tonic muscle activity in the maintenance of postural configurations. Gamma-band activity in the sensorimotor cortex underlies transient movements during speech production, yet little is known about the neural control of postural states in the vocal tract. Simultaneously, there is evidence that sensorimotor beta-band activations contribute to a system of inhibition and state maintenance that is integral to postural control in the body. Here we use electrocorticography to assess the contribution of sensorimotor beta-band activity during speech articulation and postural maintenance, and demonstrate that beta-band activity corresponds to the inhibition of discrete speech movements and the maintenance of tonic postural states in the vocal tract. Our findings identify consistencies between the neural control of posture in speech and what is previously reported in gross motor contexts, providing support for a unified theory of postural control across gross and fine motor skills.
Collapse
Affiliation(s)
- Eric Easthope
- Human Communication Technologies Lab, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| | - Arian Shamei
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
| | - Yadong Liu
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
| | - Bryan Gick
- Integrated Speech Research Lab, Department of Linguistics, University of British Columbia, Vancouver, BC, Canada
- Haskins Laboratories, New Haven, CT, United States
| | - Sidney Fels
- Human Communication Technologies Lab, Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada
| |
Collapse
|
11
|
Kreitz S, Mennecke A, Konerth L, Rösch J, Nagel AM, Laun FB, Uder M, Dörfler A, Hess A. 3T vs. 7T fMRI: capturing early human memory consolidation after motor task utilizing the observed higher functional specificity of 7T. Front Neurosci 2023; 17:1215400. [PMID: 37638321 PMCID: PMC10448826 DOI: 10.3389/fnins.2023.1215400] [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: 05/01/2023] [Accepted: 07/17/2023] [Indexed: 08/29/2023] Open
Abstract
Objective Functional magnetic resonance imaging (fMRI) visualizes brain structures at increasingly higher resolution and better signal-to-noise ratio (SNR) as field strength increases. Yet, mapping the blood oxygen level dependent (BOLD) response to distinct neuronal processes continues to be challenging. Here, we investigated the characteristics of 7 T-fMRI compared to 3 T-fMRI in the human brain beyond the effect of increased SNR and verified the benefits of 7 T-fMRI in the detection of tiny, highly specific modulations of functional connectivity in the resting state following a motor task. Methods 18 healthy volunteers underwent two resting state and a stimulus driven measurement using a finger tapping motor task at 3 and 7 T, respectively. The SNR for each field strength was adjusted by targeted voxel size variation to minimize the effect of SNR on the field strength specific outcome. Spatial and temporal characteristics of resting state ICA, network graphs, and motor task related activated areas were compared. Finally, a graph theoretical approach was used to detect resting state modulation subsequent to a simple motor task. Results Spatial extensions of resting state ICA and motor task related activated areas were consistent between field strengths, but temporal characteristics varied, indicating that 7 T achieved a higher functional specificity of the BOLD response than 3 T-fMRI. Following the motor task, only 7 T-fMRI enabled the detection of highly specific connectivity modulations representing an "offline replay" of previous motor activation. Modulated connections of the motor cortex were directly linked to brain regions associated with memory consolidation. Conclusion These findings reveal how memory processing is initiated even after simple motor tasks, and that it begins earlier than previously shown. Thus, the superior capability of 7 T-fMRI to detect subtle functional dynamics promises to improve diagnostics and therapeutic assessment of neurological diseases.
Collapse
Affiliation(s)
- Silke Kreitz
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Angelika Mennecke
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Laura Konerth
- Institute for Pharmacology and Toxicology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Julie Rösch
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Armin M. Nagel
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik B. Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Arnd Dörfler
- Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Andreas Hess
- Institute for Pharmacology and Toxicology, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Erlangen, Germany
- FAU NeW—Research Center for New Bioactive Compounds, Erlangen, Germany
| |
Collapse
|
12
|
Meyer NK, Kang D, Black DF, Campeau NG, Welker KM, Gray EM, In MH, Shu Y, Huston III J, Bernstein MA, Trzasko JD. Enhanced clinical task-based fMRI metrics through locally low-rank denoising of complex-valued data. Neuroradiol J 2023; 36:273-288. [PMID: 36063799 PMCID: PMC10268095 DOI: 10.1177/19714009221122171] [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: 11/15/2022] Open
Abstract
OBJECTIVE This study investigates a locally low-rank (LLR) denoising algorithm applied to source images from a clinical task-based functional MRI (fMRI) exam before post-processing for improving statistical confidence of task-based activation maps. METHODS Task-based motor and language fMRI was obtained in eleven healthy volunteers under an IRB approved protocol. LLR denoising was then applied to raw complex-valued image data before fMRI processing. Activation maps generated from conventional non-denoised (control) data were compared with maps derived from LLR-denoised image data. Four board-certified neuroradiologists completed consensus assessment of activation maps; region-specific and aggregate motor and language consensus thresholds were then compared with nonparametric statistical tests. Additional evaluation included retrospective truncation of exam data without and with LLR denoising; a ROI-based analysis tracked t-statistics and temporal SNR (tSNR) as scan durations decreased. A test-retest assessment was performed; retest data were matched with initial test data and compared for one subject. RESULTS fMRI activation maps generated from LLR-denoised data predominantly exhibited statistically significant (p = 4.88×10-4 to p = 0.042; one p = 0.062) increases in consensus t-statistic thresholds for motor and language activation maps. Following data truncation, LLR data showed task-specific increases in t-statistics and tSNR respectively exceeding 20 and 50% compared to control. LLR denoising enabled truncation of exam durations while preserving cluster volumes at fixed thresholds. Test-retest showed variable activation with LLR data thresholded higher in matching initial test data. CONCLUSION LLR denoising affords robust increases in t-statistics on fMRI activation maps compared to routine processing, and offers potential for reduced scan duration while preserving map quality.
Collapse
Affiliation(s)
- Nolan K Meyer
- Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN, USA
| | - Daehun Kang
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - David F Black
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | - Kirk M Welker
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Erin M Gray
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Myung-Ho In
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Yunhong Shu
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | | | | | | |
Collapse
|
13
|
Charyasz E, Heule R, Molla F, Erb M, Kumar VJ, Grodd W, Scheffler K, Bause J. Functional mapping of sensorimotor activation in the human thalamus at 9.4 Tesla. Front Neurosci 2023; 17:1116002. [PMID: 37008235 PMCID: PMC10050447 DOI: 10.3389/fnins.2023.1116002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 02/27/2023] [Indexed: 03/17/2023] Open
Abstract
Although the thalamus is perceived as a passive relay station for almost all sensory signals, the function of individual thalamic nuclei remains unresolved. In the present study, we aimed to identify the sensorimotor nuclei of the thalamus in humans using task-based fMRI at a field strength of 9.4T by assessing the individual subject-specific sensorimotor BOLD response during a combined active motor (finger-tapping) and passive sensory (tactile-finger) stimulation. We demonstrate that both tasks increase BOLD signal response in the lateral nuclei group (VPL, VA, VLa, and VLp), and in the pulvinar nuclei group (PuA, PuM, and PuL). Finger-tapping stimuli evokes a stronger BOLD response compared to the tactile stimuli, and additionally engages the intralaminar nuclei group (CM and Pf). In addition, our results demonstrate reproducible thalamic nuclei activation during motor and tactile stimuli. This work provides important insight into understanding the function of individual thalamic nuclei in processing various input signals and corroborates the benefits of using ultra-high-field MR scanners for functional imaging of fine-scale deeply located brain structures.
Collapse
Affiliation(s)
- Edyta Charyasz
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Graduate Training Centre of Neuroscience, Tübingen, Germany
- *Correspondence: Edyta Charyasz,
| | - Rahel Heule
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Center for MR Research, University Children’s Hospital, Zurich, Switzerland
| | - Francesko Molla
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
- Graduate Training Centre of Neuroscience, Tübingen, Germany
- Center for Neurology, Hertie-Institute for Clinical Brain Research, Tübingen, Germany
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Vinod Jangir Kumar
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Wolfgang Grodd
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Klaus Scheffler
- Department of Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Jonas Bause
- Department for High Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| |
Collapse
|
14
|
Schellekens W, Bhogal AA, Roefs ECA, Báez-Yáñez MG, Siero JCW, Petridou N. The many layers of BOLD. The effect of hypercapnic and hyperoxic stimuli on macro- and micro-vascular compartments quantified by CVR, M, and CBV across cortical depth. J Cereb Blood Flow Metab 2023; 43:419-432. [PMID: 36262088 PMCID: PMC9941862 DOI: 10.1177/0271678x221133972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 09/01/2022] [Accepted: 09/05/2022] [Indexed: 11/16/2022]
Abstract
Ultra-high field functional magnetic resonance imaging (fMRI) offers the spatial resolution to measure neuronal activity at the scale of cortical layers. However, cortical depth dependent vascularization differences, such as a higher prevalence of macro-vascular compartments near the pial surface, have a confounding effect on depth-resolved blood-oxygen-level dependent (BOLD) fMRI signals. In the current study, we use hypercapnic and hyperoxic breathing conditions to quantify the influence of all venous vascular and micro-vascular compartments on laminar BOLD fMRI, as measured with gradient-echo (GE) and spin-echo (SE) scan sequences, respectively. We find that all venous vascular and micro-vascular compartments are capable of comparable theoretical maximum signal intensities, as represented by the M-value parameter. However, the capacity for vessel dilation, as reflected by the cerebrovascular reactivity (CVR), is approximately two and a half times larger for all venous vascular compartments combined compared to the micro-vasculature at superficial layers. Finally, there is roughly a 35% difference in estimates of CBV changes between all venous vascular and micro-vascular compartments, although this relative difference was approximately uniform across cortical depth. Thus, our results suggest that fMRI BOLD signal differences across cortical depth are likely caused by differences in dilation properties between macro- and micro-vascular compartments.
Collapse
Affiliation(s)
- Wouter Schellekens
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
| | - Alex A Bhogal
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
| | - Emiel CA Roefs
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
| | - Mario G Báez-Yáñez
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
| | - Jeroen CW Siero
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
- Spinoza Centre for Neuroimaging, Amsterdam, The
Netherlands
| | - Natalia Petridou
- Radiology Department, Center for Image Sciences, UMC Utrecht,
Netherlands
| |
Collapse
|
15
|
Morphology, Connectivity, and Encoding Features of Tactile and Motor Representations of the Fingers in the Human Precentral and Postcentral Gyrus. J Neurosci 2023; 43:1572-1589. [PMID: 36717227 PMCID: PMC10008061 DOI: 10.1523/jneurosci.1976-21.2022] [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: 09/08/2021] [Revised: 09/11/2022] [Accepted: 09/14/2022] [Indexed: 02/01/2023] Open
Abstract
Despite the tight coupling between sensory and motor processing for fine manipulation in humans, it is not yet totally clear which specific properties of the fingers are mapped in the precentral and postcentral gyrus. We used fMRI to compare the morphology, connectivity, and encoding of the motor and tactile finger representations (FRs) in the precentral and postcentral gyrus of 25 5-fingered participants (8 females). Multivoxel pattern and structural and functional connectivity analyses demonstrated the existence of distinct motor and tactile FRs within both the precentral and postcentral gyrus, integrating finger-specific motor and tactile information. Using representational similarity analysis, we found that the motor and tactile FRs in the sensorimotor cortex were described by the perceived structure of the hand better than by the actual hand anatomy or other functional models (finger kinematics, muscles synergies). We then studied a polydactyly individual (i.e., with a congenital 6-fingered hand) showing superior manipulation abilities and divergent anatomic-functional hand properties. The perceived hand model was still the best model for tactile representations in the precentral and postcentral gyrus, while finger kinematics better described motor representations in the precentral gyrus. We suggest that, under normal conditions (i.e., in subjects with a standard hand anatomy), the sensorimotor representations of the 5 fingers in humans converge toward a model of perceived hand anatomy, deviating from the real hand structure, as the best synthesis between functional and structural features of the hand.SIGNIFICANCE STATEMENT Distinct motor and tactile finger representations exist in both the precentral and postcentral gyrus, supported by a finger-specific pattern of anatomic and functional connectivity across modalities. At the representational level, finger representations reflect the perceived structure of the hand, which might result from an adapting process harmonizing (i.e., uniformizing) the encoding of hand function and structure in the precentral and postcentral gyrus. The same analyses performed in an extremely rare polydactyly subject showed that the emergence of such representational geometry is also found in neuromechanical variants with different hand anatomy and function. However, the harmonization process across the precentral and postcentral gyrus might not be possible because of divergent functional-structural properties of the hand and associated superior manipulation abilities.
Collapse
|
16
|
The Perturbational Map of Low Frequency Repetitive Transcranial Magnetic Stimulation of Primary Motor Cortex in Movement Disorders. BRAIN DISORDERS 2023. [DOI: 10.1016/j.dscb.2023.100071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
|
17
|
de Oliveira ÍAF, Siero JCW, Dumoulin SO, van der Zwaag W. Improved Selectivity in 7 T Digit Mapping Using VASO-CBV. Brain Topogr 2023; 36:23-31. [PMID: 36517699 PMCID: PMC9834127 DOI: 10.1007/s10548-022-00932-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/28/2022] [Indexed: 12/15/2022]
Abstract
Functional magnetic resonance imaging (fMRI) at Ultra-high field (UHF, ≥ 7 T) benefits from significant gains in the BOLD contrast-to-noise ratio (CNR) and temporal signal-to-noise ratio (tSNR) compared to conventional field strengths (3 T). Although these improvements enabled researchers to study the human brain to unprecedented spatial resolution, the blood pooling effect reduces the spatial specificity of the widely-used gradient-echo BOLD acquisitions. In this context, vascular space occupancy (VASO-CBV) imaging may be advantageous since it is proposed to have a higher spatial specificity than BOLD. We hypothesized that the assumed higher specificity of VASO-CBV imaging would translate to reduced overlap in fine-scale digit representation maps compared to BOLD-based digit maps. We used sub-millimeter resolution VASO fMRI at 7 T to map VASO-CBV and BOLD responses simultaneously in the motor and somatosensory cortices during individual finger movement tasks. We assessed the cortical overlap in different ways, first by calculating similarity coefficient metrics (DICE and Jaccard) and second by calculating selectivity measures. In addition, we demonstrate a consistent topographical organization of the targeted digit representations (thumb-index-little finger) in the motor areas. We show that the VASO-CBV responses yielded less overlap between the digit clusters than BOLD, and other selectivity measures were higher for VASO-CBV too. In summary, these results were consistent across metrics and participants, confirming the higher spatial specificity of VASO-CBV compared to BOLD.
Collapse
Affiliation(s)
- Ícaro A. F. de Oliveira
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Jeroen C. W. Siero
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.7692.a0000000090126352Radiology, Utrecht Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Serge O. Dumoulin
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.12380.380000 0004 1754 9227Experimental and Applied Psychology, VU University, Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands ,grid.5477.10000000120346234Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
| | - Wietske van der Zwaag
- grid.458380.20000 0004 0368 8664Spinoza Centre for Neuroimaging, Meibergdreef 75, 1105 BK Amsterdam, The Netherlands ,grid.419918.c0000 0001 2171 8263Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| |
Collapse
|
18
|
Kung YC, Li CW, Hsiao FC, Tsai PJ, Chen S, Li MK, Lee HC, Chang CY, Wu CW, Lin CP. Cross-Scale Dynamicity of Entropy and Connectivity in the Sleeping Brain. Brain Connect 2022; 12:835-845. [PMID: 35343241 PMCID: PMC9839343 DOI: 10.1089/brain.2021.0174] [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] [Indexed: 01/22/2023] Open
Abstract
Introduction: The concept of local sleep refers to the phenomenon of local brain activity that modifies neural networks during unresponsive global sleep. Such network rewiring may differ across spatial scales; however, the global and local alterations in brain systems remain elusive in human sleep. Materials and Methods: We examined cross-scale changes of brain networks in sleep. Functional magnetic resonance imaging data were acquired from 28 healthy participants during nocturnal sleep. We adopted both metrics of connectivity (functional connectivity [FC] and regional homogeneity [ReHo]) and complexity (multiscale entropy) to explore the global and local functionality of the neural assembly across nonrapid eye movement sleep stages. Results: Long-range FC decreased with sleep depth, whereas local ReHo peaked at the N2 stage and reached its lowest level at the N3 stage. Entropy exhibited a general decline at the local scale (Scale 1) as sleep deepened, whereas the coarse-scale entropy (Scale 3) was consistent across stages. Discussion: The negative correlation between Scale-1 entropy and ReHo reflects the enhanced signal regularity and synchronization in sleep, identifying the information exchange at the local scale. The N2 stage showed a distinctive pattern toward local information processing with scrambled long-distance information exchange, indicating a specific time window for network reorganization. Collectively, the multidimensional metrics indicated an imbalanced global-local relationship among brain functional networks across sleep-wake stages.
Collapse
Affiliation(s)
- Yi-Chia Kung
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Chia-Wei Li
- Department of Radiology, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
| | - Fan-Chi Hsiao
- Department of Counseling and Industrial/Organizational Psychology, Ming Chuan University, Taoyuan, Taiwan
| | - Pei-Jung Tsai
- Neuroimaging Research Branch, National Institute on Drug Abuse, Baltimore, Maryland, USA
| | - Shuo Chen
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - Ming-Kang Li
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Chun-Yen Chang
- Science Education Center, National Taiwan Normal University, Taipei, Taiwan
| | - Changwei W. Wu
- Graduate Institute of Mind, Brain and Consciousness, Taipei Medical University, Taipei, Taiwan
- Brain and Consciousness Research Center, Shuang-Ho Hospital,Taipei Medical University, New Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| |
Collapse
|
19
|
Vansteensel MJ, Branco MP, Leinders S, Freudenburg ZF, Schippers A, Geukes SH, Gaytant MA, Gosselaar PH, Aarnoutse EJ, Ramsey NF. Methodological Recommendations for Studies on the Daily Life Implementation of Implantable Communication-Brain-Computer Interfaces for Individuals With Locked-in Syndrome. Neurorehabil Neural Repair 2022; 36:666-677. [PMID: 36124975 DOI: 10.1177/15459683221125788] [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] [Indexed: 11/15/2022]
Abstract
Implantable brain-computer interfaces (BCIs) promise to be a viable means to restore communication in individuals with locked-in syndrome (LIS). In 2016, we presented the world-first fully implantable BCI system that uses subdural electrocorticography electrodes to record brain signals and a subcutaneous amplifier to transmit the signals to the outside world, and that enabled an individual with LIS to communicate via a tablet computer by selecting icons in spelling software. For future clinical implementation of implantable communication-BCIs, however, much work is still needed, for example, to validate these systems in daily life settings with more participants, and to improve the speed of communication. We believe the design and execution of future studies on these and other topics may benefit from the experience we have gained. Therefore, based on relevant literature and our own experiences, we here provide an overview of procedures, as well as recommendations, for recruitment, screening, inclusion, imaging, hospital admission, implantation, training, and support of participants with LIS, for studies on daily life implementation of implantable communication-BCIs. With this article, we not only aim to inform the BCI community about important topics of concern, but also hope to contribute to improved methodological standardization of implantable BCI research.
Collapse
Affiliation(s)
- Mariska J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariana P Branco
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Sacha Leinders
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Zac F Freudenburg
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anouck Schippers
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Simon H Geukes
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Michael A Gaytant
- Department of Pulmonary Diseases/Home Mechanical Ventilation, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Peter H Gosselaar
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nick F Ramsey
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
20
|
Bonzano L, Bortoletto M, Zazio A, Iester C, Stango A, Gasparotti R, Miniussi C, Bove M. The hand motor hotspot for seed-based functional connectivity of hand motor networks at rest. Front Neurosci 2022; 16:896746. [PMID: 36033609 PMCID: PMC9412736 DOI: 10.3389/fnins.2022.896746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 07/21/2022] [Indexed: 12/05/2022] Open
Abstract
In the seed-based method for studying functional connectivity (FC), seed selection is relevant. Here, we propose a new methodological approach for resting-state FC analysis of hand motor networks using the individual hand motor hotspot (hMHS) as seed. Nineteen right-handed healthy volunteers underwent a transcranial magnetic stimulation (TMS) session and resting-state fMRI. For each subject, the hMHS in both hemispheres was identified by TMS with the contralateral abductor pollicis brevis muscle as the target, the site eliciting the highest and most reliable motor-evoked potentials. Seed regions were built on coordinates on the cortex corresponding to the individual left and right hMHSs. For comparison, the left and right Brodmann’s area 4 (BA4) masks extracted from a standard atlas were used as seed. The left and right hMHSs showed FC patterns at rest mainly including sensorimotor regions, with a bilateral connectivity only for the left hMHS. The statistical contrast BA4 > hMHS for both hemispheres showed different extension and lateralization of the functionally connected cortical regions. On the contrary, no voxels survived the opposite contrast (hMHS > BA4). This suggests that detection of individual hand motor seeds by TMS allows to identify functionally connected motor networks that are more specific with respect to those obtained starting from the a priori atlas-based identification of the primary motor cortex.
Collapse
Affiliation(s)
- Laura Bonzano
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Marta Bortoletto
- Neurophysiology Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Agnese Zazio
- Neurophysiology Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Costanza Iester
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Antonietta Stango
- Neurophysiology Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Roberto Gasparotti
- Section of Neuroradiology, Department of Medical and Surgical Specialties, Radiological Sciences, and Public Health, University of Brescia, Brescia, Italy
| | - Carlo Miniussi
- Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy
| | - Marco Bove
- Section of Human Physiology, Department of Experimental Medicine, University of Genoa, Genoa, Italy
- Ospedale Policlinico San Martino IRCCS, Genoa, Italy
- *Correspondence: Marco Bove,
| |
Collapse
|
21
|
Vachha BA, Middlebrooks EH. Brain Functional Imaging Anatomy. Neuroimaging Clin N Am 2022; 32:491-505. [PMID: 35843658 DOI: 10.1016/j.nic.2022.04.001] [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: 10/17/2022]
Abstract
Human brain function is an increasingly complex framework that has important implications in clinical medicine. In this review, the anatomy of the most commonly assessed brain functions in clinical neuroradiology, including motor, language, and vision, is discussed. The anatomy and function of the primary and secondary sensorimotor areas are discussed with clinical case examples. Next, the dual stream of language processing is reviewed, as well as its implications in clinical medicine and surgical planning. Last, the authors discuss the striate and extrastriate visual cortex and review the dual stream model of visual processing.
Collapse
Affiliation(s)
- Behroze Adi Vachha
- Department of Radiology, Neuroradiology Section, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA; Brain Tumor Center, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
| | - Erik H Middlebrooks
- Department of Radiology, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA; Department of Neurosurgery, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224, USA
| |
Collapse
|
22
|
Liu YL, Xu JJ, Han LR, Liu XF, Lin MH, Wang Y, Xiao Z, Huang YK, Ren P, Huang X. Meranzin Hydrate Improves Depression-Like Behaviors and Hypomotility via Ghrelin and Neurocircuitry. Chin J Integr Med 2022; 29:490-499. [PMID: 35881212 DOI: 10.1007/s11655-022-3308-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/21/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To investigate whether meranzin hydrate (MH) can alleviate depression-like behavior and hypomotility similar to Chaihu Shugan Powder (CSP), and further explore the potential common mechanisms. METHODS Totally 120 Spraque-Dawley rats were randomly divided into 5-8 groups including sham, vehicle, fluoxetine (20 mg/kg), mosapride (10 mg/kg), CSP (30 g/kg), MH (9.18 mg/kg), [D-Lys3]-GHRP-6 (Dlys, 0.5 mg/kg), and MH+Dlys groups by a random number table, 8 rats in each group. And 32 mice were randomly divided into wild-type, MH (18 mg/kg), growth hormone secretagogue receptor-knockout (GHSR-KO), and GHSR+MH groups, 8 mice in each group. The forced swimming test (FST), open field test (OFT), tail suspension test (TST), gastric emptying (GE) test, and intestinal transit (IT) test were used to assess antidepressant and prokinetic (AP) effects after drug single administration for 30 min with absorbable identification in rats and mice, respectively. The protein expression levels of brain-derived neurotrophic factor (BDNF) and phosphorylated mammalian target of rapamycin (p-mTOR) in the hippocampus of rats were evaluated by Western blot. The differences in functional brain changes were determined via 7.0 T functional magnetic resonance imaging-blood oxygen level-dependent (fMRI-BOLD). RESULTS MH treatment improved depression-like behavior (FST, OFT) and hypomotility (GE, IT) in the acute forced swimming (FS) rats (all P<0.05), and the effects are similar to the parent formula CSP. The ghrelin antagonist [D-Lys3]-GHRP-6 inhibited the effect of MH on FST and GE (P<0.05). Similarly, MH treatment also alleviated depression-like behavior (FST, TST) in the wild-type mice, however, no effects were found in the GHSR KO mice. Additionally, administration of MH significantly stimulated BDNF and p-mTOR protein expressions in the hippocampus (both P<0.01), which were also prevented by [D-Lys3]-GHRP-6 (P<0.01). Besides, 3 main BOLD foci following acute FS rats implicated activity in hippocampus-thalamus-basal ganglia (HTB) circuits. The [D-Lys3]-GHRP-6 synchronously inhibited BOLD HTB foci. As expected, prokinetic mosapride only had effects on the thalamus and basal ganglia, but not on the hippocampus. Within the HTB, the hippocampus is implicated in depression and FD. CONCLUSIONS MH accounts for part of AP effects of parent formula CSP in acute FS rats, mainly via ghrelin-related shared regulation coupled to BOLD signals in brain areas. This novel functionally connection of HTB following acute stress, treatment, and regulation highlights anti-depression unified theory.
Collapse
Affiliation(s)
- Ya-Lin Liu
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China.,School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, 450046, China
| | - Jian-Jun Xu
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Lin-Ran Han
- Department of Outpatient, Xuzhou Central Hospital, Xuzhou, Shangdong Province, 221000, China
| | - Xiang-Fei Liu
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Mu-Hai Lin
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Yun Wang
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Zhe Xiao
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Yun-Ke Huang
- Department of Obstetrics and Gynecology, Women's Hospital School of Medicine, Zhejiang University, Hangzhou, 310006, China
| | - Ping Ren
- Department of Geriatrics, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210023, China
| | - Xi Huang
- Institute of Traditional Chinese Medicine-Related Comorbid Depression, Nanjing University of Chinese Medicine, Nanjing, 210023, China.
| |
Collapse
|
23
|
Gooijers J, Chalavi S, Koster LK, Roebroeck A, Kaas A, Swinnen SP. Representational Similarity Scores of Digits in the Sensorimotor Cortex Are Associated with Behavioral Performance. Cereb Cortex 2022; 32:3848-3863. [PMID: 35029640 DOI: 10.1093/cercor/bhab452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 02/06/2023] Open
Abstract
Previous studies aimed to unravel a digit-specific somatotopy in the primary sensorimotor (SM1) cortex. However, it remains unknown whether digit somatotopy is associated with motor preparation and/or motor execution during different types of tasks. We adopted multivariate representational similarity analysis to explore digit activation patterns in response to a finger tapping task (FTT). Sixteen healthy young adults underwent magnetic resonance imaging, and additionally performed an out-of-scanner choice reaction time task (CRTT) to assess digit selection performance. During both the FTT and CRTT, force data of all digits were acquired using force transducers. This allowed us to assess execution-related interference (i.e., digit enslavement; obtained from FTT & CRTT), as well as planning-related interference (i.e., digit selection deficit; obtained from CRTT) and determine their correlation with digit representational similarity scores of SM1. Findings revealed that digit enslavement during FTT was associated with contralateral SM1 representational similarity scores. During the CRTT, digit enslavement of both hands was also associated with representational similarity scores of the contralateral SM1. In addition, right hand digit selection performance was associated with representational similarity scores of left S1. In conclusion, we demonstrate a cortical origin of digit enslavement, and uniquely reveal that digit selection is associated with digit representations in primary somatosensory cortex (S1). Significance statement In current systems neuroscience, it is of critical importance to understand the relationship between brain function and behavioral outcome. With the present work, we contribute significantly to this understanding by uniquely assessing how digit representations in the sensorimotor cortex are associated with planning- and execution-related digit interference during a continuous finger tapping and a choice reaction time task. We observe that digit enslavement (i.e., execution-related interference) finds its origin in contralateral digit representations of SM1, and that deficits in digit selection (i.e., planning-related interference) in the right hand during a choice reaction time task are associated with more overlapping digit representations in left S1. This knowledge sheds new light on the functional contribution of the sensorimotor cortex to everyday motor skills.
Collapse
Affiliation(s)
- J Gooijers
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
| | - S Chalavi
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
| | - L K Koster
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht 6229 EV, the Netherlands
| | - A Kaas
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht 6229 EV, the Netherlands
| | - S P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
| |
Collapse
|
24
|
Piantoni G, Hermes D, Ramsey N, Petridou N. Size of the spatial correlation between ECoG and fMRI activity. Neuroimage 2021; 242:118459. [PMID: 34371189 PMCID: PMC10627020 DOI: 10.1016/j.neuroimage.2021.118459] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 07/13/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022] Open
Abstract
Electrocorticography (ECoG) is typically employed to accurately identify the seizure focus as well as the location of brain functions to be spared during surgical resection in participants with drug-resistant epilepsy. Increasingly, this technique has become a powerful tool to map cognitive functions onto brain regions. Cortical mapping is more commonly investigated with functional MRI (fMRI), which measures blood-oxygen level dependent (BOLD) changes induced by neuronal activity. The multimodal integration between typical 3T fMRI activity maps and ECoG measurements can provide unique insight into the spatiotemporal aspects of cognition. However, the optimal integration of fMRI and ECoG requires fundamental insight into the spatial smoothness of the BOLD signal under each electrode. Here we use ECoG as ground truth for the extent of activity, as each electrode is thought to record from the cortical tissue directly underneath the contact, to estimate the spatial smoothness of the associated BOLD response at 3T fMRI. We compared the high-frequency broadband (HFB) activity recorded with ECoG while participants performed a motor task. Activity maps were obtained with fMRI at 3T for the same task in the same participant prior to surgery. We then correlated HFB power with the fMRI BOLD signal change in the area around each electrode. This latter measure was quantified by applying a 3D Gaussian kernel of varying width (sigma between 1 mm and 20 mm) to the fMRI maps including only gray-matter. We found that the correlation between HFB and BOLD activity increased sharply up to the point when the kernel width was set to 4 mm, which we defined as the kernel width of maximal spatial specificity. After this point, as the kernel width increased, the highest level of explained variance was reached at a kernel width of 9 mm for most participants. Intriguingly, maximal specificity was also limited to 4 mm for low-frequency bands, such as alpha and beta, but the kernel width with the highest explained variance was less spatially limited than the HFB. In summary, spatial specificity is limited to a kernel width of 4 mm but explained variance keeps on increasing as you average over more and more voxels containing the relatively noisy BOLD signal. Future multimodal studies should choose the kernel width based on their research goal. For maximal spatial specificity, ECoG electrodes are best compared to 3T fMRI with a kernel width of 4 mm. When optimizing the correlation between modalities, highest explained variance can be obtained at larger kernel widths of 9 mm, at the expense of spatial specificity. Finally, we release the complete pipeline so that researchers can estimate the most appropriate kernel width from their multimodal datasets.
Collapse
Affiliation(s)
- Giovanni Piantoni
- Dept Neurology & Neurosurgery, UMC Utrecht, Heidelberglaan 100, Utrecht 3584 CX, the Netherlands.
| | - Dora Hermes
- Dept Physiology & Biomedical Engineering, Mayo Clinic, Rochester, MN, United States; Dept Neurology, Mayo Clinic, Rochester, MN, United States; Dept Radiology, Mayo Clinic, Rochester, MN, United States.
| | - Nick Ramsey
- Dept Neurology & Neurosurgery, UMC Utrecht, Heidelberglaan 100, Utrecht 3584 CX, the Netherlands.
| | - Natalia Petridou
- Dept Radiology, UMC Utrecht, Heidelberglaan 100, Utrecht, the Netherlands.
| |
Collapse
|
25
|
Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Khadem A, Alizadehsani R, Zare A, Kong Y, Khosravi A, Nahavandi S, Hussain S, Acharya UR, Berk M. Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review. Comput Biol Med 2021; 139:104949. [PMID: 34737139 DOI: 10.1016/j.compbiomed.2021.104949] [Citation(s) in RCA: 80] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/02/2021] [Accepted: 10/13/2021] [Indexed: 01/23/2023]
Abstract
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
Collapse
Affiliation(s)
- Marjane Khodatars
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Delaram Sadeghi
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Navid Ghaasemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yinan Kong
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | | | - U Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489, Singapore; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Dept. of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia
| |
Collapse
|
26
|
Bruurmijn LCM, Raemaekers M, Branco MP, Vansteensel MJ, Ramsey NF. Decoding attempted phantom hand movements from ipsilateral sensorimotor areas after amputation. J Neural Eng 2021; 18. [PMID: 34433158 DOI: 10.1088/1741-2552/ac20e4] [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: 01/29/2021] [Accepted: 08/25/2021] [Indexed: 11/12/2022]
Abstract
Objective.The sensorimotor cortex is often selected as target in the development of a Brain-Computer Interface, as activation patterns from this region can be robustly decoded to discriminate between different movements the user executes. Up until recently, such BCIs were primarily based on activity in the contralateral hemisphere, where decoding movements still works even years after denervation. However, there is increasing evidence for a role of the sensorimotor cortex in controlling the ipsilateral body. The aim of this study is to investigate the effects of denervation on the movement representation on the ipsilateral sensorimotor cortex.Approach.Eight subjects with acquired above-elbow arm amputation and nine controls performed a task in which they made (or attempted to make with their phantom hand) six different gestures from the American Manual Alphabet. Brain activity was measured using 7T functional MRI, and a classifier was trained to discriminate between activation patterns on four different regions of interest (ROIs) on the ipsilateral sensorimotor cortex.Main results.Classification scores showed that decoding was possible and significantly better than chance level for both the phantom and intact hands from all ROIs. Decoding both the left (intact) and right (phantom) hand from the same hemisphere was also possible with above-chance level classification score.Significance.The possibility to decode both hands from the same hemisphere, even years after denervation, indicates that implantation of motor-electrodes for BCI control possibly need only cover a single hemisphere, making surgery less invasive, and increasing options for people with lateralized damage to motor cortex like after stroke.
Collapse
Affiliation(s)
- L C M Bruurmijn
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Raemaekers
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M P Branco
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N F Ramsey
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
27
|
Fracasso A, Gaglianese A, Vansteensel MJ, Aarnoutse EJ, Ramsey NF, Dumoulin SO, Petridou N. FMRI and intra-cranial electrocorticography recordings in the same human subjects reveals negative BOLD signal coupled with silenced neuronal activity. Brain Struct Funct 2021; 227:1371-1384. [PMID: 34363092 PMCID: PMC9046332 DOI: 10.1007/s00429-021-02342-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 07/09/2021] [Indexed: 12/27/2022]
Abstract
Positive blood oxygenation level-dependent (BOLD) responses (PBR), as measured by functional Magnetic Resonance Imaging (fMRI), are the most utilized measurements to non-invasively map activity in the brain. Recent studies have consistently shown that BOLD responses are not exclusively positive. Negative BOLD responses (NBR) have been reported in response to specific sensory stimulations and tasks. However, the exact relationship between NBR and the underlying metabolic and neuronal demand is still under debate. In this study, we investigated the neurophysiological basis of negative BOLD using fMRI and intra-cranial electrophysiology (electrocorticography, ECoG) measurements from the same human participants. We show that, for those electrodes that responded to visual stimulation, PBR are correlated with high-frequency band (HFB) responses. Crucially, NBR were associated with an absence of HFB power responses and an unpredicted decrease in the alpha power responses.
Collapse
Affiliation(s)
- Alessio Fracasso
- Institute of Neuroscience and Psychology, University of Glasgow, Glasgow, Scotland.
| | - Anna Gaglianese
- The Laboratory for Investigative Neurophysiology (The LINE), Department of Radiology, University Hospital Center, University of Lausanne, Rue Centrale 7, 1003, Lausanne, Switzerland
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Erik J Aarnoutse
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurosurgery and Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| | - Serge O Dumoulin
- Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands
- Spinoza Center for Neuroimaging, Amsterdam, The Netherlands
- Experimental and Applied Psychology, VU University Amsterdam, Amsterdam, The Netherlands
| | - Natalia Petridou
- Department of Radiology, Center for Image Sciences, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, The Netherlands
| |
Collapse
|
28
|
Miller KJ, Hermes D, Staff NP. The current state of electrocorticography-based brain-computer interfaces. Neurosurg Focus 2021; 49:E2. [PMID: 32610290 DOI: 10.3171/2020.4.focus20185] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2020] [Accepted: 04/20/2020] [Indexed: 11/06/2022]
Abstract
Brain-computer interfaces (BCIs) provide a way for the brain to interface directly with a computer. Many different brain signals can be used to control a device, varying in ease of recording, reliability, stability, temporal and spatial resolution, and noise. Electrocorticography (ECoG) electrodes provide a highly reliable signal from the human brain surface, and these signals have been used to decode movements, vision, and speech. ECoG-based BCIs are being developed to provide increased options for treatment and assistive devices for patients who have functional limitations. Decoding ECoG signals in real time provides direct feedback to the patient and can be used to control a cursor on a computer or an exoskeleton. In this review, the authors describe the current state of ECoG-based BCIs that are approaching clinical viability for restoring lost communication and motor function in patients with amyotrophic lateral sclerosis or tetraplegia. These studies provide a proof of principle and the possibility that ECoG-based BCI technology may also be useful in the future for assisting in the cortical rehabilitation of patients who have suffered a stroke.
Collapse
Affiliation(s)
- Kai J Miller
- Departments of1Neurosurgery.,2Physiology & Biomedical Engineering, and
| | - Dora Hermes
- 2Physiology & Biomedical Engineering, and.,3Neurology, Mayo Clinic, Rochester, Minnesota
| | | |
Collapse
|
29
|
Camarrone F, Branco MP, Ramsey NF, Van Hulle MM. Accurate Offline Asynchronous Detection of Individual Finger Movement From Intracranial Brain Signals Using a Novel Multiway Approach. IEEE Trans Biomed Eng 2021; 68:2176-2187. [PMID: 33186097 DOI: 10.1109/tbme.2020.3037934] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Asynchronous motor Brain Computer Interfacing (BCI) is characterized by the continuous decoding of intended muscular activity from brain signals. Such applications have gained widespread interest for enabling users to issue commands volitionally. In conventional motor BCIs features extracted from brain signals are concatenated into vector- or matrix-based (or one-/two-way) representations. Nevertheless, when accounting for the original multimodal or multiway signal structure, decoding performance has been shown to improve jointly with result interpretability. However, as multiway decoders are notorious for the extensive computational cost to train them, conventional ones are still preferred. To curb this limitation, we introduce a novel multiway classifier, called Block-Term Tensor Classifier that inherits the improved accuracy of multiway methods while providing fast training. We show that it can outperform state-of-the-art multiway and two-way Linear Discriminant Analysis classifiers in asynchronous detection of individual finger movements from intracranial recordings, an essential feature to achieve a sense of dexterity with hand prosthetics and exoskeletons.
Collapse
|
30
|
van den Boom M, Miller KJ, Gregg NM, Ojeda Valencia G, Lee KH, Richner TJ, Ramsey NF, Worrell GA, Hermes D. Typical somatomotor physiology of the hand is preserved in a patient with an amputated arm: An ECoG case study. Neuroimage Clin 2021; 31:102728. [PMID: 34182408 PMCID: PMC8253998 DOI: 10.1016/j.nicl.2021.102728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 04/17/2021] [Accepted: 05/10/2021] [Indexed: 12/03/2022]
Abstract
Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65-175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15-28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1-3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.
Collapse
Affiliation(s)
- Max van den Boom
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA; Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - Kai J Miller
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Nicholas M Gregg
- Department of Neurology, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Gabriela Ojeda Valencia
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Kendall H Lee
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Thomas J Richner
- Department of Neurosurgery, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Nick F Ramsey
- Department of Neurology & Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
| | - Greg A Worrell
- Department of Neurology, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
| |
Collapse
|
31
|
Wang Y, Hays MA, Coogan C, Kang JY, Flinker A, Arya R, Korzeniewska A, Crone NE. Spatial-Temporal Functional Mapping Combined With Cortico-Cortical Evoked Potentials in Predicting Cortical Stimulation Results. Front Hum Neurosci 2021; 15:661976. [PMID: 33935673 PMCID: PMC8079642 DOI: 10.3389/fnhum.2021.661976] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 03/23/2021] [Indexed: 11/13/2022] Open
Abstract
Functional human brain mapping is commonly performed during invasive monitoring with intracranial electroencephalographic (iEEG) electrodes prior to resective surgery for drug resistant epilepsy. The current gold standard, electrocortical stimulation mapping (ESM), is time consuming, sometimes elicits pain, and often induces after discharges or seizures. Moreover, there is a risk of overestimating eloquent areas due to propagation of the effects of stimulation to a broader network of language cortex. Passive iEEG spatial-temporal functional mapping (STFM) has recently emerged as a potential alternative to ESM. However, investigators have observed less correspondence between STFM and ESM maps of language than between their maps of motor function. We hypothesized that incongruities between ESM and STFM of language function may arise due to propagation of the effects of ESM to cortical areas having strong effective connectivity with the site of stimulation. We evaluated five patients who underwent invasive monitoring for seizure localization, whose language areas were identified using ESM. All patients performed a battery of language tasks during passive iEEG recordings. To estimate the effective connectivity of stimulation sites with a broader network of task-activated cortical sites, we measured cortico-cortical evoked potentials (CCEPs) elicited across all recording sites by single-pulse electrical stimulation at sites where ESM was performed at other times. With the combination of high gamma power as well as CCEPs results, we trained a logistic regression model to predict ESM results at individual electrode pairs. The average accuracy of the classifier using both STFM and CCEPs results combined was 87.7%, significantly higher than the one using STFM alone (71.8%), indicating that the correspondence between STFM and ESM results is greater when effective connectivity between ESM stimulation sites and task-activated sites is taken into consideration. These findings, though based on a small number of subjects to date, provide preliminary support for the hypothesis that incongruities between ESM and STFM may arise in part from propagation of stimulation effects to a broader network of cortical language sites activated by language tasks, and suggest that more studies, with larger numbers of patients, are needed to understand the utility of both mapping techniques in clinical practice.
Collapse
Affiliation(s)
- Yujing Wang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Mark A Hays
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Christopher Coogan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Joon Y Kang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Adeen Flinker
- Department of Neurology, New York University School of Medicine, New York, NY, United States
| | - Ravindra Arya
- Comprehensive Epilepsy Center, Division of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| |
Collapse
|
32
|
Wang Y, Korzeniewska A, Usami K, Valenzuela A, Crone NE. The Dynamics of Language Network Interactions in Lexical Selection: An Intracranial EEG Study. Cereb Cortex 2021; 31:2058-2070. [PMID: 33283856 PMCID: PMC7945024 DOI: 10.1093/cercor/bhaa344] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 10/18/2020] [Accepted: 10/22/2020] [Indexed: 11/14/2022] Open
Abstract
Speaking in sentences requires selection from contextually determined lexical representations. Although posterior temporal cortex (PTC) and Broca's areas play important roles in storage and selection, respectively, of lexical representations, there has been no direct evidence for physiological interactions between these areas on time scales typical of lexical selection. Using intracranial recordings of cortical population activity indexed by high-gamma power (70-150 Hz) modulations, we studied the causal dynamics of cortical language networks while epilepsy surgery patients performed a sentence completion task in which the number of potential lexical responses was systematically varied. Prior to completion of sentences with more response possibilities, Broca's area was not only more active, but also exhibited more local network interactions with and greater top-down influences on PTC, consistent with activation of, and competition between, more lexical representations. These findings provide the most direct experimental support yet for network dynamics playing a role in lexical selection among competing alternatives during speech production.
Collapse
Affiliation(s)
- Yujing Wang
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Fischell Department of Bioengineering, University of Maryland College Park, College Park, MD 20742, USA
| | - Anna Korzeniewska
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Kiyohide Usami
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
- Department of Epilepsy, Movement Disorders and Physiology, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto 606-8507, Japan
| | - Alyssandra Valenzuela
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Nathan E Crone
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| |
Collapse
|
33
|
van den Boom MA, Miller K, Ramsey N, Hermes D. Functional MRI based simulations of ECoG grid configurations for optimal measurement of spatially distributed hand-gesture information. J Neural Eng 2021; 18. [PMID: 33418549 DOI: 10.1088/1741-2552/abda0d] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2020] [Accepted: 01/08/2021] [Indexed: 01/13/2023]
Abstract
Objective In electrocorticography (ECoG), the physical characteristics of the electrode grid determine which aspect of the neurophysiology is measured. For particular cases, the ECoG grid may be tailored to capture specific features, such as in the development and use of brain-computer-interfaces (BCI). Neural representations of hand movement are increasingly used to control ECoG based BCIs. However, it remains unclear which grid configurations are the most optimal to capture the dynamics of hand gesture information. Here, we investigate how the design and surgical placement of grids would affect the usability of ECoG measurements. Approach High resolution 7T functional MRI was used as a proxy for neural activity in ten healthy participants to simulate various grid configurations, and evaluated the performance of each configuration for decoding hand gestures. The grid configurations varied in number of electrodes, electrode distance and electrode size. Main results Optimal decoding of hand gestures occurred in grid configurations with a higher number of densely-packed, large-size, electrodes up to a grid of ~5x5 electrodes. When restricting the grid placement to a highly informative region of primary sensorimotor cortex, optimal parameters converged to about 3x3 electrodes, an inter-electrode distance of 8mm, and an electrode size of 3mm radius (performing at ~70% 3-class classification accuracy). Significance Our approach might be used to identify the most informative region, find the optimal grid configuration and assist in positioning of the grid to achieve high BCI performance for the decoding of hand-gestures prior to surgical implantation.
Collapse
Affiliation(s)
- Max Alexander van den Boom
- Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St. SW, Rochester, Minnesota, 55905, UNITED STATES
| | - Kai Miller
- Department of Neurosurgery, Mayo Clinic, 200 1st St SW Floor 8, Rochester, Minnesota, 55905, UNITED STATES
| | - Nick Ramsey
- Neurology and Neurosurgery, University Medical Centre Utrecht Brain Centre, Heidelberglaan 100, Utrecht, Utrecht, 3584 CX, NETHERLANDS
| | - Dora Hermes
- Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St. SW, Rochester, Minnesota, 55905, UNITED STATES
| |
Collapse
|
34
|
Berezutskaya J, Baratin C, Freudenburg ZV, Ramsey NF. High-density intracranial recordings reveal a distinct site in anterior dorsal precentral cortex that tracks perceived speech. Hum Brain Mapp 2020; 41:4587-4609. [PMID: 32744403 PMCID: PMC7555065 DOI: 10.1002/hbm.25144] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 06/23/2020] [Accepted: 07/06/2020] [Indexed: 01/15/2023] Open
Abstract
Various brain regions are implicated in speech processing, and the specific function of some of them is better understood than others. In particular, involvement of the dorsal precentral cortex (dPCC) in speech perception remains debated, and attribution of the function of this region is more or less restricted to motor processing. In this study, we investigated high-density intracranial responses to speech fragments of a feature film, aiming to determine whether dPCC is engaged in perception of continuous speech. Our findings show that dPCC exhibited preference to speech over other tested sounds. Moreover, the identified area was involved in tracking of speech auditory properties including speech spectral envelope, its rhythmic phrasal pattern and pitch contour. DPCC also showed the ability to filter out noise from the perceived speech. Comparing these results to data from motor experiments showed that the identified region had a distinct location in dPCC, anterior to the hand motor area and superior to the mouth articulator region. The present findings uncovered with high-density intracranial recordings help elucidate the functional specialization of PCC and demonstrate the unique role of its anterior dorsal region in continuous speech perception.
Collapse
Affiliation(s)
- Julia Berezutskaya
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
- Donders Institute for Brain, Cognition and BehaviourRadboud UniversityNijmegenThe Netherlands
| | - Clarissa Baratin
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
- Université Grenoble AlpesGrenoble Institut des NeurosciencesGrenobleFrance
| | - Zachary V. Freudenburg
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Nicolas F. Ramsey
- Brain Center, Department of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| |
Collapse
|
35
|
Adaptive analysis of cortical plasticity with fMRI in full face and arm transplants. Brain Imaging Behav 2020; 15:1788-1801. [DOI: 10.1007/s11682-020-00374-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
36
|
Kolasinski J, Dima DC, Mehler DMA, Stephenson A, Valadan S, Kusmia S, Rossiter HE. Spatially and Temporally Distinct Encoding of Muscle and Kinematic Information in Rostral and Caudal Primary Motor Cortex. Cereb Cortex Commun 2020; 1:tgaa009. [PMID: 32864612 PMCID: PMC7446240 DOI: 10.1093/texcom/tgaa009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 03/24/2020] [Accepted: 03/25/2020] [Indexed: 12/02/2022] Open
Abstract
The organizing principle of human motor cortex does not follow an anatomical body map, but rather a distributed representational structure in which motor primitives are combined to produce motor outputs. Electrophysiological recordings in primates and human imaging data suggest that M1 encodes kinematic features of movements, such as joint position and velocity. However, M1 exhibits well-documented sensory responses to cutaneous and proprioceptive stimuli, raising questions regarding the origins of kinematic motor representations: are they relevant in top-down motor control, or are they an epiphenomenon of bottom-up sensory feedback during movement? Here we provide evidence for spatially and temporally distinct encoding of kinematic and muscle information in human M1 during the production of a wide variety of naturalistic hand movements. Using a powerful combination of high-field functional magnetic resonance imaging and magnetoencephalography, a spatial and temporal multivariate representational similarity analysis revealed encoding of kinematic information in more caudal regions of M1, over 200 ms before movement onset. In contrast, patterns of muscle activity were encoded in more rostral motor regions much later after movements began. We provide compelling evidence that top-down control of dexterous movement engages kinematic representations in caudal regions of M1 prior to movement production.
Collapse
Affiliation(s)
- James Kolasinski
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Diana C Dima
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - David M A Mehler
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Alice Stephenson
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Sara Valadan
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Slawomir Kusmia
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Holly E Rossiter
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, CF24 4HQ, UK
| |
Collapse
|
37
|
Abstract
Intracranial electroencephalography (iEEG) is measured from electrodes placed in or on the brain. These measurements have an excellent signal-to-noise ratio and iEEG signals have often been used to decode brain activity or drive brain-computer interfaces (BCIs). iEEG recordings are typically done for seizure monitoring in epilepsy patients who have these electrodes placed for a clinical purpose: to localize both brain regions that are essential for function and others where seizures start. Brain regions not involved in epilepsy are thought to function normally and provide a unique opportunity to learn about human neurophysiology. Intracranial electrodes measure the aggregate activity of large neuronal populations and recorded signals contain many features. Different features are extracted by analyzing these signals in the time and frequency domain. The time domain may reveal an evoked potential at a particular time after the onset of an event. Decomposition into the frequency domain may show narrowband peaks in the spectrum at specific frequencies or broadband signal changes that span a wide range of frequencies. Broadband power increases are generally observed when a brain region is active while most other features are highly specific to brain regions, inputs, and tasks. Here we describe the spatiotemporal dynamics of several iEEG signals that have often been used to decode brain activity and drive BCIs.
Collapse
|
38
|
Huber L, Finn ES, Handwerker DA, Bönstrup M, Glen DR, Kashyap S, Ivanov D, Petridou N, Marrett S, Goense J, Poser BA, Bandettini PA. Sub-millimeter fMRI reveals multiple topographical digit representations that form action maps in human motor cortex. Neuroimage 2020; 208:116463. [DOI: 10.1016/j.neuroimage.2019.116463] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 11/10/2019] [Accepted: 12/11/2019] [Indexed: 12/31/2022] Open
|
39
|
Malekian V, Nasiraei-Moghaddam A, Akhavan A, Hossein-Zadeh GA. Efficient de-noising of high-resolution fMRI using local and sub-band information. J Neurosci Methods 2020; 331:108497. [PMID: 31698001 DOI: 10.1016/j.jneumeth.2019.108497] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2019] [Revised: 09/24/2019] [Accepted: 10/30/2019] [Indexed: 11/16/2022]
Abstract
BACKGROUND High-resolution fMRI, useful for accurate brain mapping, suffers from low functional sensitivity at a reasonable acquisition time. Conventional smoothing techniques although reduce the noise and boost the sensitivity, but degrade the spatial resolution of fMRI. NEW METHODS We propose a novel spatial de-noising technique to increase sensitivity while preserving the boundaries of active regions in the high-resolution fMRI. A modified version of PCA that utilizes adjacent voxels information (LPCA) is first suggested for de-noising. This technique is then further empowered by its application to wavelet sub-bands (WLPCA). RESULTS Proposed techniques were assessed on both simulated and experimental data. Identifiablity index was calculated for evaluation of the denoising on the simulated data. Maximum and mean z-scores along with LAE and SSIM were reported on experimental data for two presented techniques as well as Guassian smoothing. WLPCA outperformed other techniques in Identifiablity index, for simulation, and in preserving maximum z-score, for experimental study. COMPARISON WITH EXISTING METHODS The presented technique was developed to simultaneously suppress the noise and preserve the boundaries of active areas against leakage. For first aim, its achievable mean z-score was compared to conventional Gaussian. For second aim, its maximum z-score was compared to that of no-smoothing. While Gaussian and no-smoothing can work fine with only one measure, WLPCA was able to improve both measures concurrently. CONCLUSIONS The local PCA based methods, and in particular WLPCA, is an effective noise reduction step that preserves the spatial resolution by preventing activity leakage of high-resolution fMRI data.
Collapse
Affiliation(s)
- Vahid Malekian
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Abbas Nasiraei-Moghaddam
- Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
| | - Amir Akhavan
- Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
| | - Gholam-Ali Hossein-Zadeh
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran; School of Electrical and Computer Engineering, University College of Engineering, University of Tehran, Tehran, Iran
| |
Collapse
|
40
|
Abstract
Human brain function research has evolved dramatically in the last decades. In this chapter the role of modern methods of recording brain activity in understanding human brain function is explained. Current knowledge of brain function relevant to brain-computer interface (BCI) research is detailed, with an emphasis on the motor system which provides an exceptional level of detail to decoding of intended or attempted movements in paralyzed beneficiaries of BCI technology and translation to computer-mediated actions. BCI technologies that stand to benefit the most of the detailed organization of the human cortex are, and for the foreseeable future are likely to be, reliant on intracranial electrodes. These evolving technologies are expected to enable severely paralyzed people to regain the faculty of movement and speech in the coming decades.
Collapse
Affiliation(s)
- Nick F Ramsey
- Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| |
Collapse
|
41
|
Salari E, Freudenburg ZV, Branco MP, Aarnoutse EJ, Vansteensel MJ, Ramsey NF. Classification of Articulator Movements and Movement Direction from Sensorimotor Cortex Activity. Sci Rep 2019; 9:14165. [PMID: 31578420 PMCID: PMC6775133 DOI: 10.1038/s41598-019-50834-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Accepted: 09/11/2019] [Indexed: 12/21/2022] Open
Abstract
For people suffering from severe paralysis, communication can be difficult or nearly impossible. Technology systems called brain-computer interfaces (BCIs) are being developed to assist these people with communication by using their brain activity to control a computer without any muscle activity. To benefit the development of BCIs that employ neural activity related to speech, we investigated if neural activity patterns related to different articulator movements can be distinguished from each other. We recorded with electrocorticography (ECoG), the neural activity related to different articulator movements in 4 epilepsy patients and classified which articulator participants moved based on the sensorimotor cortex activity patterns. The same was done for different movement directions of a single articulator, the tongue. In both experiments highly accurate classification was obtained, on average 92% for different articulators and 85% for different tongue directions. Furthermore, the data show that only a small part of the sensorimotor cortex is needed for classification (ca. 1 cm2). We show that recordings from small parts of the sensorimotor cortex contain information about different articulator movements which might be used for BCI control. Our results are of interest for BCI systems that aim to decode neural activity related to (actual or attempted) movements from a contained cortical area.
Collapse
Affiliation(s)
- E Salari
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z V Freudenburg
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M P Branco
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E J Aarnoutse
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M J Vansteensel
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N F Ramsey
- UMC Utrecht Brain Center, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands.
| |
Collapse
|
42
|
Branco MP, de Boer LM, Ramsey NF, Vansteensel MJ. Encoding of kinetic and kinematic movement parameters in the sensorimotor cortex: A Brain-Computer Interface perspective. Eur J Neurosci 2019; 50:2755-2772. [PMID: 30633413 PMCID: PMC6625947 DOI: 10.1111/ejn.14342] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 11/30/2018] [Accepted: 01/07/2019] [Indexed: 01/23/2023]
Abstract
For severely paralyzed people, Brain-Computer Interfaces (BCIs) can potentially replace lost motor output and provide a brain-based control signal for augmentative and alternative communication devices or neuroprosthetics. Many BCIs focus on neuronal signals acquired from the hand area of the sensorimotor cortex, employing changes in the patterns of neuronal firing or spectral power associated with one or more types of hand movement. Hand and finger movement can be described by two groups of movement features, namely kinematics (spatial and motion aspects) and kinetics (muscles and forces). Despite extensive primate and human research, it is not fully understood how these features are represented in the SMC and how they lead to the appropriate movement. Yet, the available information may provide insight into which features are most suitable for BCI control. To that purpose, the current paper provides an in-depth review on the movement features encoded in the SMC. Even though there is no consensus on how exactly the SMC generates movement, we conclude that some parameters are well represented in the SMC and can be accurately used for BCI control with discrete as well as continuous feedback. However, the vast evidence also suggests that movement should be interpreted as a combination of multiple parameters rather than isolated ones, pleading for further exploration of sensorimotor control models for accurate BCI control.
Collapse
Affiliation(s)
- Mariana P. Branco
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | | | - Nick F. Ramsey
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Mariska J. Vansteensel
- Brain Center Rudolf MagnusDepartment of Neurology and NeurosurgeryUniversity Medical Center UtrechtUtrechtThe Netherlands
| |
Collapse
|
43
|
van den Boom MA, Vansteensel MJ, Koppeschaar MI, Raemaekers MAH, Ramsey NF. Towards an intuitive communication-BCI: decoding visually imagined characters from the early visual cortex using high-field fMRI. Biomed Phys Eng Express 2019; 5. [PMID: 32983573 DOI: 10.1088/2057-1976/ab302c] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Brain-computer interfaces aim to provide people with paralysis with the possibility to use their neural signals to control devices. For communication, most BCIs are based on the selection of letters from a (digital) letter board to spell words and sentences. Visual mental imagery of letters could offer a new, fast and intuitive way to spell in a BCI-communication solution. Here we provide a proof of concept for the decoding of visually imagined characters from the early visual cortex using 7 Tesla functional MRI. Sixteen healthy participants visually imagined three different characters for 3, 5 and 7 s in a slow event-related design. Using single-trial classification, we were able to decode the characters with an average accuracy of 54%, which is significantly above chance level (33%). Furthermore, the imagined characters were classifiable shortly after cue onset and remained classifiable with prolonged imagery. These properties, combined with the cortical location of the early visual cortex and its decodable activity, encourage further research on intracranial interfacing using surface electrodes to bring us closer to such a visual imagery based BCI communication solution.
Collapse
Affiliation(s)
- Max A van den Boom
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Mariska J Vansteensel
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Melissa I Koppeschaar
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Matthijs A H Raemaekers
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Nick F Ramsey
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
44
|
Hamidian S, Vachha B, Jenabi M, Karimi S, Young RJ, Holodny AI, Peck KK. Resting-State Functional Magnetic Resonance Imaging and Probabilistic Diffusion Tensor Imaging Demonstrate That the Greatest Functional and Structural Connectivity in the Hand Motor Homunculus Occurs in the Area of the Thumb. Brain Connect 2019; 8:371-379. [PMID: 29987948 DOI: 10.1089/brain.2018.0589] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
The primary hand motor region is classically believed to be in the "hand knob" area in the precentral gyrus (PCG). However, hand motor task-based activation is often localized outside this area. The purpose of this study is to investigate the structural and functional connectivity driven by different seed locations corresponding to the little, index, and thumb in the PCG using probabilistic diffusion tractography (PDT) and resting-state functional magnetic resonance imaging (rfMRI). Twelve healthy subjects had three regions of interest (ROIs) placed in the left PCG: lateral to the hand knob (thumb area), within the hand knob (index finger area), and medial to the hand knob (little finger area). Connectivity maps were generated using PDT and rfMRI. Individual and group level analyses were performed. Results show that the greatest hand motor connectivity between both hemispheres was obtained using the ROI positioned just lateral to the hand knob in the PCG (the thumb area). The number of connected voxels in the PCG between the two hemispheres was greatest in the lateral-most ROI (the thumb area): 279 compared with 13 for the medial-most ROI and 9 for the central hand knob ROI. Similarly, the highest white matter connectivity between the two hemispheres resulted from the ROI placed in the lateral portion of PCG (p < 0.003). The maximal functional and structural connectivity of the hand motor area between hemispheres occurs in the thumb area, located laterally at the "hand knob." Thus, this location appears maximal for rfMRI and PDT seeding of the motor area.
Collapse
Affiliation(s)
- Shaminta Hamidian
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Behroze Vachha
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Mehrnaz Jenabi
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Sasan Karimi
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Robert J Young
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Andrei I Holodny
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York
| | - Kyung K Peck
- 1 Department of Radiology, Memorial Sloan-Kettering Cancer Center , New York, New York.,2 Department of Medical Physics, Memorial Sloan-Kettering Cancer Center , New York, New York
| |
Collapse
|
45
|
Salari E, Freudenburg ZV, Vansteensel MJ, Ramsey NF. Spatial-Temporal Dynamics of the Sensorimotor Cortex: Sustained and Transient Activity. IEEE Trans Neural Syst Rehabil Eng 2019; 26:1084-1092. [PMID: 29752244 DOI: 10.1109/tnsre.2018.2821058] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
How the sensorimotor cortex is organized with respect to controlling different features of movement is unclear. One unresolved question concerns the relation between the duration of an action and the duration of the associated neuronal activity change in the sensorimotor cortex. Using subdural electrocorticography electrodes, we investigated in five subjects, whether high frequency band (HFB; 75-135 Hz) power changes have a transient or sustained relation to speech duration, during pronunciation of the Dutch /i/ vowel with different durations. We showed that the neuronal activity patterns recorded from the sensorimotor cortex can be directly related to action duration in some locations, whereas in other locations, during the same action, neuronal activity is transient, with a peak in HFB activity at movement onset and/or offset. This data sheds light on the neural underpinnings of motor actions and we discuss the possible mechanisms underlying these different response types.
Collapse
|
46
|
Salari E, Freudenburg ZV, Vansteensel MJ, Ramsey NF. The influence of prior pronunciations on sensorimotor cortex activity patterns during vowel production. J Neural Eng 2018; 15:066025. [PMID: 30238924 PMCID: PMC6240458 DOI: 10.1088/1741-2552/aae329] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE In recent years, brain-computer interface (BCI) systems have been investigated for their potential as a communication device to assist people with severe paralysis. Decoding speech sensorimotor cortex activity is a promising avenue for the generation of BCI control signals, but is complicated by variability in neural patterns, leading to suboptimal decoding. We investigated whether neural pattern variability associated with sound pronunciation can be explained by prior pronunciations and determined to what extent prior speech affects BCI decoding accuracy. APPROACH Neural patterns in speech motor areas were evaluated with electrocorticography in five epilepsy patients, who performed a simple speech task that involved pronunciation of the /i/ sound, preceded by either silence, the /a/ sound or the /u/ sound. MAIN RESULTS The neural pattern related to the /i/ sound depends on previous sounds and is therefore associated with multiple distinct sensorimotor patterns, which is likely to reflect differences in the movements towards this sound. We also show that these patterns still contain a commonality that is distinct from the other vowel sounds (/a/ and /u/). Classification accuracies for the decoding of different sounds do increase, however, when the multiple patterns for the /i/ sound are taken into account. Simply including multiple forms of the /i/ vowel in the training set for the creation of a single /i/ model performs as well as training individual models for each /i/ variation. SIGNIFICANCE Our results are of interest for the development of BCIs that aim to decode speech sounds from the sensorimotor cortex, since they argue that a multitude of cortical activity patterns associated with speech movements can be reduced to a basis set of models which reflect meaningful language units (vowels), yet it is important to account for the variety of neural patterns associated with a single sound in the training process.
Collapse
Affiliation(s)
- E Salari
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, Netherlands
| | | | | | | |
Collapse
|
47
|
Barisano G, Sepehrband F, Ma S, Jann K, Cabeen R, Wang DJ, Toga AW, Law M. Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high field. Br J Radiol 2018; 92:20180492. [PMID: 30359093 DOI: 10.1259/bjr.20180492] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
In recent years, ultra-high field MRI (7 T and above) has received more interest for clinical imaging. Indeed, a number of studies have shown the benefits from the application of this powerful tool not only for research purposes, but also in realms of improved diagnostics and patient management. The increased signal-to-noise ratio and higher spatial resolution compared with conventional and high-field clinical scanners allow imaging of small anatomical detail and subtle pathological findings. Furthermore, greater spectral resolution achieved at ultra-high field allows the resolution of metabolites for MR spectroscopic imaging. All these advantages have a significant impact on many neurological diseases, including multiple sclerosis, cerebrovascular disease, brain tumors, epilepsy and neurodegenerative diseases, in part because the pathology can be subtle and lesions small in these diseases, therefore having higher signal and resolution will help lesion detection. In this review, we discuss the main clinical neurological applications and some technical challenges which remain with ultra-high field MRI.
Collapse
Affiliation(s)
- Giuseppe Barisano
- 1 Department of Radiology, Keck Medical Center of University of Southern California , Los Angeles, CA , USA.,2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Farshid Sepehrband
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Samantha Ma
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Kay Jann
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Ryan Cabeen
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Danny J Wang
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Arthur W Toga
- 2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| | - Meng Law
- 1 Department of Radiology, Keck Medical Center of University of Southern California , Los Angeles, CA , USA.,2 Stevens Neuroimaging and Informatics Institute, University of Southern California , Los Angeles, CA , USA
| |
Collapse
|
48
|
Yoo PE, Oxley TJ, John SE, Opie NL, Ordidge RJ, O'Brien TJ, Hagan MA, Wong YT, Moffat BA. Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI. Sci Rep 2018; 8:15556. [PMID: 30349004 PMCID: PMC6197258 DOI: 10.1038/s41598-018-33839-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Accepted: 10/03/2018] [Indexed: 01/09/2023] Open
Abstract
Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant's decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant's decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.
Collapse
Affiliation(s)
- Peter E Yoo
- Department of Anatomy and Neuroscience, The University of Melbourne, VIC, Australia. .,Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, VIC, Australia. .,The Florey Institute of Neuroscience and Mental Health, VIC, Australia.
| | - Thomas J Oxley
- Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, VIC, Australia.,The Florey Institute of Neuroscience and Mental Health, VIC, Australia
| | - Sam E John
- Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, VIC, Australia.,The Florey Institute of Neuroscience and Mental Health, VIC, Australia
| | - Nicholas L Opie
- Vascular Bionics Laboratory, Melbourne Brain Centre, Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, VIC, Australia.,The Florey Institute of Neuroscience and Mental Health, VIC, Australia
| | - Roger J Ordidge
- Department of Anatomy and Neuroscience, The University of Melbourne, VIC, Australia
| | - Terence J O'Brien
- The Departments of Neuroscience, The Central Clinical School, Monash University, VIC, Australia.,The Department of Neurology, the Alfred Hospital, Melbourne, VIC, Australia
| | - Maureen A Hagan
- Department of Physiology, Monash University, VIC, Australia.,Biomedicine Discovery Institute, Monash University, VIC, Australia
| | - Yan T Wong
- Department of Physiology, Monash University, VIC, Australia.,Biomedicine Discovery Institute, Monash University, VIC, Australia.,Department of Electrical and Computer Systems Engineering, Monash University, VIC, Australia
| | - Bradford A Moffat
- Department of Anatomy and Neuroscience, The University of Melbourne, VIC, Australia
| |
Collapse
|
49
|
Salari E, Freudenburg ZV, Vansteensel MJ, Ramsey NF. Repeated Vowel Production Affects Features of Neural Activity in Sensorimotor Cortex. Brain Topogr 2018; 32:97-110. [PMID: 30238309 PMCID: PMC6326960 DOI: 10.1007/s10548-018-0673-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 08/15/2018] [Indexed: 11/20/2022]
Abstract
The sensorimotor cortex is responsible for the generation of movements and interest in the ability to use this area for decoding speech by brain–computer interfaces has increased recently. Speech decoding is challenging however, since the relationship between neural activity and motor actions is not completely understood. Non-linearity between neural activity and movement has been found for instance for simple finger movements. Despite equal motor output, neural activity amplitudes are affected by preceding movements and the time between movements. It is unknown if neural activity is also affected by preceding motor actions during speech. We addressed this issue, using electrocorticographic high frequency band (HFB; 75–135 Hz) power changes in the sensorimotor cortex during discrete vowel generation. Three subjects with temporarily implanted electrode grids produced the /i/ vowel at repetition rates of 1, 1.33 and 1.66 Hz. For every repetition, the HFB power amplitude was determined. During the first utterance, most electrodes showed a large HFB power peak, which decreased for subsequent utterances. This result could not be explained by differences in performance. With increasing duration between utterances, more electrodes showed an equal response to all repetitions, suggesting that the duration between vowel productions influences the effect of previous productions on sensorimotor cortex activity. Our findings correspond with previous studies for finger movements and bear relevance for the development of brain-computer interfaces that employ speech decoding based on brain signals, in that past utterances will need to be taken into account for these systems to work accurately.
Collapse
Affiliation(s)
- E Salari
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Z V Freudenburg
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M J Vansteensel
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands
| | - N F Ramsey
- Brain Center Rudolf Magnus, Department of Neurology and Neurosurgery, University Medical Center Utrecht, Utrecht, The Netherlands. .,University Medical Center Utrecht, Room G03 1.24, Heidelberglaan 100, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands.
| |
Collapse
|
50
|
Zafar A, Hong KS. Neuronal Activation Detection Using Vector Phase Analysis with Dual Threshold Circles: A Functional Near-Infrared Spectroscopy Study. Int J Neural Syst 2018; 28:1850031. [PMID: 30045647 DOI: 10.1142/s0129065718500314] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes ( Δ HbO) of functional near-infrared spectroscopy (fNIRS) is developed. The vector phase diagram displays the trajectories of Δ HbO and deoxy-hemoglobin changes ( Δ HbR), as orthogonal components, in the Δ HbO- Δ HbR polar coordinates. To determine the occurrence of an initial dip, dual threshold circles (an inner circle from the resting state, an outer circle from the peak values of the initial dip and the main HR) are incorporated into the phase diagram for making decisions. The proposed scheme is then applied to a brain-computer interface scheme, and its performance is evaluated in classifying two finger tapping tasks (right-hand thumb and little finger) from the left motor cortex. Three gamma functions are used to model the initial dip, the main HR, and the undershoot in generating the designed HR function. In classifying two tapping tasks, the signal mean and signal minimum values during 0-2.5 s, as features of initial dip, are used. The linear discriminant analysis was utilized as a classifier. The experimental results show that the active brain locations of the two tasks were quite distinctive ( p < 0.05 ), and moreover, spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s. Also, the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.
Collapse
Affiliation(s)
- Amad Zafar
- 1 School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
| | - Keum-Shik Hong
- 1 School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
| |
Collapse
|