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Ji Z, Song RR, Swan AR, Angeles Quinto A, Lee RR, Huang M. Magnetoencephalography Language Mapping Using Auditory Memory Retrieval and Silent Repeating Task. J Clin Neurophysiol 2024; 41:148-154. [PMID: 35512180 PMCID: PMC9633581 DOI: 10.1097/wnp.0000000000000947] [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: 11/26/2022] Open
Abstract
PURPOSE The study aims to (1) examine the spatiotemporal map of magnetoencephalography-evoked responses during an Auditory Memory Retrieval and Silent Repeating (AMRSR) task, and determine the hemispheric dominance for language, and (2) evaluate the accuracy of the AMRSR task in Wernicke and Broca area localization. METHODS In 30 patients with brain tumors and/or epilepsies, the AMRSR task was used to evoke magnetoencephalography responses. We applied Fast VEctor-based Spatial-Temporal Analyses with minimum L1-norm source imaging method to the magnetoencephalography responses for localizing the brain areas evoked by the AMRSR task. RESULTS The Fast-VEctor-based Spatial-Temporal Analysis found consistent activation in the posterior superior temporal gyrus around 300 to 500 ms, and another activation in the frontal cortex (pars opercularis and/or pars triangularis) around 600 to 900 ms, which were localized to the Wernicke area (BA 22) and Broca area (BA 44 and BA 45), respectively. The language-dominant hemispheric laterization elicited by the AMRSR task was comparable with the result from an Auditory Dichotic task result given to the same patient, with the exception that AMRSR is more sensitive on bilateral language laterization cases on finding the Wernicke and Broca areas. CONCLUSIONS For all patients who successfully finished the AMRSR task, Fast-VEctor-based Spatial-Temporal Analysis could establish accurate and robust localizations of Broca and Wernicke area and determine hemispheric dominance. For subjects with normal auditory functionality, the AMRSR paradigm evaluation showed significant promise in providing reliable assessments of cerebral language dominance and language network localization.
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Affiliation(s)
- Zhengwei Ji
- Radiology Department, University of California, San Diego, California, U.S.A
| | - Ryan R. Song
- Department of Molecular and Cell Biology, University of California, Berkeley, California, U.S.A.; and
| | - Ashley Robb Swan
- Radiology Department, University of California, San Diego, California, U.S.A
| | | | - Roland R. Lee
- Radiology Department, University of California, San Diego, California, U.S.A
- Radiology Service, San Diego VA Healthcare System, San Diego, California, U.S.A
| | - Mingxiong Huang
- Radiology Department, University of California, San Diego, California, U.S.A
- Radiology Service, San Diego VA Healthcare System, San Diego, California, U.S.A
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Lawton T, Shelley-Tremblay J, Huang MX. Case report: Neural timing deficits prevalent in developmental disorders, aging, and concussions remediated rapidly by movement discrimination exercises. Front Neurol 2023; 14:898781. [PMID: 37818220 PMCID: PMC10560731 DOI: 10.3389/fneur.2023.898781] [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: 03/17/2022] [Accepted: 09/04/2023] [Indexed: 10/12/2023] Open
Abstract
Background The substantial evidence that neural timing deficits are prevalent in developmental disorders, aging, and concussions resulting from a Traumatic Brain Injury (TBI) is presented. Objective When these timing deficits are remediated using low-level movement-discrimination training, then high-level cognitive skills, including reading, attention, processing speed, problem solving, and working memory improve rapidly and effectively. Methods In addition to the substantial evidence published previously, new evidence based on a neural correlate, MagnetoEncephalography physiological recordings, on an adult dyslexic, and neuropsychological tests on this dyslexic subject and an older adult were measured before and after 8-weeks of contrast sensitivity-based left-right movement-discrimination exercises were completed. Results The neuropsychological tests found large improvements in reading, selective and sustained attention, processing speed, working memory, and problem-solving skills, never before found after such a short period of training. Moreover, these improvements were found 4 years later for older adult. Substantial MEG signal increases in visual Motion, Attention, and Memory/Executive Control Networks were observed following training on contrast sensitivity-based left-right movement-discrimination. Improving the function of magnocells using figure/ground movement-discrimination at both low and high levels in dorsal stream: (1) improved both feedforward and feedback pathways to modulate attention by enhancing coupled theta/gamma and alpha/gamma oscillations, (2) is adaptive, and (3) incorporated cycles of feedback and reward at multiple levels. Conclusion What emerges from multiple studies is the essential role of timing deficits in the dorsal stream that are prevalent in developmental disorders like dyslexia, in aging, and following a TBI. Training visual dorsal stream function at low levels significantly improved high-level cognitive functions, including processing speed, selective and sustained attention, both auditory and visual working memory, problem solving, and reading fluency. A paradigm shift for treating cognitive impairments in developmental disorders, aging, and concussions is crucial. Remediating the neural timing deficits of low-level dorsal pathways, thereby improving both feedforward and feedback pathways, before cognitive exercises to improve specific cognitive skills provides the most rapid and effective methods to improve cognitive skills. Moreover, this adaptive training with substantial feedback shows cognitive transfer to tasks not trained on, significantly improving a person's quality of life rapidly and effectively.
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Affiliation(s)
- Teri Lawton
- Cognitive Neuroscience, Perception Dynamics Institute, Encinitas, CA, United States
| | | | - Ming-Xiong Huang
- Radiology Imaging Laboratory, Department of Radiology, University of California, San Diego, San Diego, CA, United States
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Huang MX, Harrington DL, Angeles-Quinto A, Ji Z, Robb-Swan A, Huang CW, Shen Q, Hansen H, Baumgartner J, Hernandez-Lucas J, Nichols S, Jacobus J, Song T, Lerman I, Bazhenov M, Krishnan GP, Baker DG, Rao R, Lee RR. EMG-projected MEG High-Resolution Source Imaging of Human Motor Execution: Brain-Muscle Coupling above Movement Frequencies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.23.23291825. [PMID: 37425691 PMCID: PMC10327237 DOI: 10.1101/2023.06.23.23291825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Magnetoencephalography (MEG) is a non-invasive functional imaging technique for pre-surgical mapping. However, movement-related MEG functional mapping of primary motor cortex (M1) has been challenging in presurgical patients with brain lesions and sensorimotor dysfunction due to the large numbers of trails needed to obtain adequate signal to noise. Moreover, it is not fully understood how effective the brain communication is with the muscles at frequencies above the movement frequency and its harmonics. We developed a novel Electromyography (EMG)-projected MEG source imaging technique for localizing M1 during ~1 minute recordings of left and right self-paced finger movements (~1 Hz). High-resolution MEG source images were obtained by projecting M1 activity towards the skin EMG signal without trial averaging. We studied delta (1-4 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-90 Hz) bands in 13 healthy participants (26 datasets) and two presurgical patients with sensorimotor dysfunction. In healthy participants, EMG-projected MEG accurately localized M1 with high accuracy in delta (100.0%), theta (100.0%), and beta (76.9%) bands, but not alpha (34.6%) and gamma (0.0%) bands. Except for delta, all other frequency bands were above the movement frequency and its harmonics. In both presurgical patients, M1 activity in the affected hemisphere was also accurately localized, despite highly irregular EMG movement patterns in one patient. Altogether, our EMG-projected MEG imaging approach is highly accurate and feasible for M1 mapping in presurgical patients. The results also provide insight into movement related brain-muscle coupling above the movement frequency and its harmonics.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
- Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA
| | - Deborah L. Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, CA, USA
| | - Ashley Robb-Swan
- Department of Radiology, University of California, San Diego, CA, USA
| | - Charles W. Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Qian Shen
- Department of Radiology, University of California, San Diego, CA, USA
| | - Hayden Hansen
- Department of Radiology, University of California, San Diego, CA, USA
| | - Jared Baumgartner
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | | | - Sharon Nichols
- Department of Neurosciences, University of California, San Diego, CA, USA
| | - Joanna Jacobus
- Department of Psychiatry, University of California, San Diego, CA, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, CA, USA
| | - Imanuel Lerman
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Maksim Bazhenov
- Department of Medicine, University of California, San Diego, CA, USA
| | - Giri P Krishnan
- Department of Medicine, University of California, San Diego, CA, USA
| | - Dewleen G. Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, CA, USA
- VA Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA, USA
| | - Ramesh Rao
- Department of Electrical and Computer Engineering, University of California, San Diego, CA, USA
| | - Roland R. Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
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Huang MX, Angeles-Quinto A, Robb-Swan A, De-la-Garza BG, Huang CW, Cheng CK, Hesselink JR, Bigler ED, Wilde EA, Vaida F, Troyer EA, Max JE. Assessing Pediatric Mild Traumatic Brain Injury and Its Recovery Using Resting-State Magnetoencephalography Source Magnitude Imaging and Machine Learning. J Neurotrauma 2023; 40:1112-1129. [PMID: 36884305 PMCID: PMC10259613 DOI: 10.1089/neu.2022.0220] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023] Open
Abstract
The objectives of this machine-learning (ML) resting-state magnetoencephalography (rs-MEG) study involving children with mild traumatic brain injury (mTBI) and orthopedic injury (OI) controls were to define a neural injury signature of mTBI and to delineate the pattern(s) of neural injury that determine behavioral recovery. Children ages 8-15 years with mTBI (n = 59) and OI (n = 39) from consecutive admissions to an emergency department were studied prospectively for parent-rated post-concussion symptoms (PCS) at: 1) baseline (average of 3 weeks post-injury) to measure pre-injury symptoms and also concurrent symptoms; and 2) at 3-months post-injury. rs-MEG was conducted at the baseline assessment. The ML algorithm predicted cases of mTBI versus OI with sensitivity of 95.5 ± 1.6% and specificity of 90.2 ± 2.7% at 3-weeks post-injury for the combined delta-gamma frequencies. The sensitivity and specificity were significantly better (p < 0.0001) for the combined delta-gamma frequencies compared with the delta-only and gamma-only frequencies. There were also spatial differences in rs-MEG activity between mTBI and OI groups in both delta and gamma bands in frontal and temporal lobe, as well as more widespread differences in the brain. The ML algorithm accounted for 84.5% of the variance in predicting recovery measured by PCS changes between 3 weeks and 3 months post-injury in the mTBI group, and this was significantly lower (p < 10-4) in the OI group (65.6%). Frontal lobe pole (higher) gamma activity was significantly (p < 0.001) associated with (worse) PCS recovery exclusively in the mTBI group. These findings demonstrate a neural injury signature of pediatric mTBI and patterns of mTBI-induced neural injury related to behavioral recovery.
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Affiliation(s)
- Ming-Xiong Huang
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Annemarie Angeles-Quinto
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Ashley Robb-Swan
- Department of Radiology, University of California, San Diego, California, USA
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, California, USA
| | | | - Charles W. Huang
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Chung-Kuan Cheng
- Department of Computer Science and Engineering, University of California, San Diego, California, USA
| | - John R. Hesselink
- Department of Radiology, University of California, San Diego, California, USA
| | - Erin D. Bigler
- Department of Neurology, University of Utah, Salt Lake City, Utah, USA
| | | | - Florin Vaida
- Herbert Wertheim School of Public Health, Division of Biostatistics and Bioinformatics, University of California, San Diego, California, USA
| | - Emily A. Troyer
- Department of Psychiatry, University of California, San Diego, California, USA
| | - Jeffrey E. Max
- Department of Psychiatry, University of California, San Diego, California, USA
- Department of Psychiatry, Rady Children's Hospital, San Diego, California, USA
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Bu Y, Harrington DL, Lee RR, Shen Q, Angeles-Quinto A, Ji Z, Hansen H, Hernandez-Lucas J, Baumgartner J, Song T, Nichols S, Baker D, Rao R, Lerman I, Lin T, Tu XM, Huang M. Magnetoencephalogram-based brain-computer interface for hand-gesture decoding using deep learning. Cereb Cortex 2023:7161766. [PMID: 37183188 DOI: 10.1093/cercor/bhad173] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/28/2023] [Accepted: 04/29/2023] [Indexed: 05/16/2023] Open
Abstract
Advancements in deep learning algorithms over the past decade have led to extensive developments in brain-computer interfaces (BCI). A promising imaging modality for BCI is magnetoencephalography (MEG), which is a non-invasive functional imaging technique. The present study developed a MEG sensor-based BCI neural network to decode Rock-Paper-scissors gestures (MEG-RPSnet). Unique preprocessing pipelines in tandem with convolutional neural network deep-learning models accurately classified gestures. On a single-trial basis, we found an average of 85.56% classification accuracy in 12 subjects. Our MEG-RPSnet model outperformed two state-of-the-art neural network architectures for electroencephalogram-based BCI as well as a traditional machine learning method, and demonstrated equivalent and/or better performance than machine learning methods that have employed invasive, electrocorticography-based BCI using the same task. In addition, MEG-RPSnet classification performance using an intra-subject approach outperformed a model that used a cross-subject approach. Remarkably, we also found that when using only central-parietal-occipital regional sensors or occipitotemporal regional sensors, the deep learning model achieved classification performances that were similar to the whole-brain sensor model. The MEG-RSPnet model also distinguished neuronal features of individual hand gestures with very good accuracy. Altogether, these results show that noninvasive MEG-based BCI applications hold promise for future BCI developments in hand-gesture decoding.
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Affiliation(s)
- Yifeng Bu
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Deborah L Harrington
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Roland R Lee
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Qian Shen
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Annemarie Angeles-Quinto
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Zhengwei Ji
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Hayden Hansen
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
| | | | - Jared Baumgartner
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Tao Song
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Sharon Nichols
- Department of Neurosciences, University of California San Diego, La Jolla, CA 92093, USA
| | - Dewleen Baker
- VA Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA 92093, USA
| | - Ramesh Rao
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Imanuel Lerman
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- VA Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Anesthesiology, University of California San Diego, La Jolla, CA 92093, USA
| | - Tuo Lin
- Division of Biostatistics and Bioinformatics, University of California, San Diego, CA 92093, USA
| | - Xin Ming Tu
- Division of Biostatistics and Bioinformatics, University of California, San Diego, CA 92093, USA
| | - Mingxiong Huang
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA
- Radiology, Research Services, VA, San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Radiology, University of California San Diego, La Jolla, CA 92093, USA
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Edgar JC, Berman JI, Liu S, Chen YH, Huang M, Brodkin ES, Roberts TPL, Bloy L. Two mechanisms facilitate regional independence between brain regions based on an examination of alpha-band activity in healthy control adult males. Int J Psychophysiol 2022; 178:51-59. [PMID: 35718287 PMCID: PMC10155819 DOI: 10.1016/j.ijpsycho.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 04/26/2022] [Accepted: 06/10/2022] [Indexed: 11/29/2022]
Abstract
BACKGROUND At rest, 8 to 12 Hz alpha rhythms are the dominant rhythm in the brain, with a common peak alpha frequency (PAF = the frequency at which alpha generators show maximum power) observed across brain regions. Although a common PAF across brain regions should result in high between-region connectivity, especially connectivity measures assessing the phase-similarity between alpha generators, high inter-regional alpha connectivity has not been observed. The present study was conducted as an initial step toward identifying mechanisms that allow brain regions to maintain functional independence in the presence of a common PAF. METHODS MEG data were obtained from 16 healthy control male adults (mean age = 24 years; range 21 to 30 years). A task requiring participants to alternate between a 10 s eyes-closed condition and a 5 s eyes-open condition was used to drive parietal-occipital alpha generators, with the 10 s eyes-closed condition eliciting large-amplitude alpha activity and thus providing alpha measures with good signal-to-noise ratio for source localization. Alpha source-space measures were obtained using Vector-based Spatial-Temporal Analysis using L1-minimum-norm. In each participant, the four strongest parietal-occipital alpha generators were identified. Connectivity between sources was assessed via a measure of phase-based connectivity called inter-site phase clustering (ISPC). RESULTS Intra-class correlations (ICC) showed very high similarity in the average PAF (=computed using all eyes-closed data) between the four alpha sources (ICC single measure = 0.88, p < 0.001). Despite a common average PAF, across participants, significant ISPC was often observed no more than that expected by chance. Examination of the alpha time course data indicated that low ISPC was often due to instantaneous changes in alpha phase (phase slips). ISPC analyses removing data with phase slips indicated that low ISPC was also due to slight continuous changes in the alpha frequency, with frequency drift more likely in non-significant than significant ISPC trials. CONCLUSIONS The present exploratory effort suggested two processes underlying the lack of observed inter-regional alpha phase coherence that may help maintain regional functional independence even in the presence of a common PAF. In particular, although the alpha generators were observed to oscillate at the same rate on average, across time each alpha generator oscillated a little slower or faster, and about every one and a half seconds an alpha generator abruptly lost the beat. Because of this, functional independence among alpha generators (and thus brain regions) was the rule rather than the exception. Studies replicating these processes that allow brain regions to maintain functional independence, using different source localization methods and in different conditions (e.g., a true resting state), are warranted. IMPACT STATEMENT Using source localization to measure parietal-occipital alpha generator activity, two properties that limit between-region alpha functional connectivity are proposed. In particular, a model of alpha generator activity is offered where via transient phase slips occurring approximately every 1.5 s, as well as slight non-stationarity in the alpha frequency, brain regions retain a common alpha frequency while also maintaining regional identity and presumably functionality. Findings also suggest novel markers for use in studies examining changes in alpha activity across maturation as well as in studies examining alpha activity in patient populations where alpha abnormalities have been reported.
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Affiliation(s)
| | | | - Song Liu
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yu-Han Chen
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Mingxiong Huang
- The University of California San Diego, Department of Radiology, San Diego, CA, USA; San Diego VA Healthcare System, Department of Radiology, San Diego, CA, USA
| | - Edward S Brodkin
- Department of Psychiatry, Center for Neurobiology and Behavior, Perelman School of Medicine at the University of Pennsylvania, Translational Research Laboratory, Philadelphia, PA, USA
| | | | - Luke Bloy
- The Children's Hospital of Philadelphia, Philadelphia, PA, USA
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Hauk O, Stenroos M, Treder MS. Towards an objective evaluation of EEG/MEG source estimation methods - The linear approach. Neuroimage 2022; 255:119177. [PMID: 35390459 DOI: 10.1016/j.neuroimage.2022.119177] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 11/18/2022] Open
Abstract
The spatial resolution of EEG/MEG source estimates, often described in terms of source leakage in the context of the inverse problem, poses constraints on the inferences that can be drawn from EEG/MEG source estimation results. Software packages for EEG/MEG data analysis offer a large choice of source estimation methods but few tools to experimental researchers for methods evaluation and comparison. Here, we describe a framework and tools for objective and intuitive resolution analysis of EEG/MEG source estimation based on linear systems analysis, and apply those to the most widely used distributed source estimation methods such as L2-minimum-norm estimation (L2-MNE) and linearly constrained minimum variance (LCMV) beamformers. Within this framework it is possible to define resolution metrics that define meaningful aspects of source estimation results (such as localization accuracy in terms of peak localization error, PLE, and spatial extent in terms of spatial deviation, SD) that are relevant to the task at hand and can easily be visualized. At the core of this framework is the resolution matrix, which describes the potential leakage from and into point sources (point-spread and cross-talk functions, or PSFs and CTFs, respectively). Importantly, for linear methods these functions allow generalizations to multiple sources or complex source distributions. This paper provides a tutorial-style introduction into linear EEG/MEG source estimation and resolution analysis aimed at experimental (rather than methods-oriented) researchers. We used this framework to demonstrate how L2-MNE-type as well as LCMV beamforming methods can be evaluated in practice using software tools that have only recently become available for routine use. Our novel methods comparison includes PLE and SD for a larger number of methods than in similar previous studies, such as unweighted, depth-weighted and normalized L2-MNE methods (including dSPM, sLORETA, eLORETA) and two LCMV beamformers. The results demonstrate that some methods can achieve low and even zero PLE for PSFs. However, their SD as well as both PLE and SD for CTFs are far less optimal for all methods, in particular for deep cortical areas. We hope that our paper will encourage EEG/MEG researchers to apply this approach to their own tasks at hand.
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Affiliation(s)
- Olaf Hauk
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF, UK.
| | - Matti Stenroos
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Matthias S Treder
- School of Computer Sciences and Informatics, Cardiff University, Cardiff, UK
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Hadriche A, Behy I, Necibi A, Kachouri A, Amar CB, Jmail N. Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:6406362. [PMID: 34992674 PMCID: PMC8727131 DOI: 10.1155/2021/6406362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 11/03/2021] [Accepted: 11/05/2021] [Indexed: 11/17/2022]
Abstract
Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique's robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.
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Affiliation(s)
- Abir Hadriche
- REGIM Lab, ENIS, Sfax University, Tunisia
- Digital Research Center of Sfax, Tunisia
| | | | | | | | | | - Nawel Jmail
- MIRACL Lab, Sfax University, Tunisia
- LETI Lab, ENIS, Sfax University, Tunisia
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Alberto GE, Stapleton-Kotloski JR, Klorig DC, Rogers ER, Constantinidis C, Daunais JB, Godwin DW. MEG source imaging detects optogenetically-induced activity in cortical and subcortical networks. Nat Commun 2021; 12:5259. [PMID: 34489452 PMCID: PMC8421372 DOI: 10.1038/s41467-021-25481-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 08/09/2021] [Indexed: 02/07/2023] Open
Abstract
Magnetoencephalography measures neuromagnetic activity with high temporal, and theoretically, high spatial resolution. We developed an experimental platform combining MEG-compatible optogenetic techniques in nonhuman primates for use as a functional brain-mapping platform. Here we show localization of optogenetically evoked signals to known sources in the superficial arcuate sulcus of cortex and in CA3 of hippocampus at a resolution of 750 µm3. We detect activation in subcortical, thalamic, and extended temporal structures, conforming to known anatomical and functional brain networks associated with the respective sites of stimulation. This demonstrates that high-resolution localization of experimentally produced deep sources is possible within an intact brain. This approach is suitable for exploring causal relationships between discrete brain regions through precise optogenetic control and simultaneous whole brain MEG recording with high-resolution magnetic source imaging (MSI).
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Affiliation(s)
- Gregory E. Alberto
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine; Department of Neurobiology and Anatomy, Winston-Salem, NC USA
| | - Jennifer R. Stapleton-Kotloski
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine Department of Neurology, Winston-Salem, NC USA ,grid.509341.aResearch and Education Department, W.G. (Bill) Hefner Veterans Affairs Medical Center, Salisbury, NC USA
| | - David C. Klorig
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine; Department of Neurobiology and Anatomy, Winston-Salem, NC USA
| | - Emily R. Rogers
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine; Department of Neurobiology and Anatomy, Winston-Salem, NC USA
| | - Christos Constantinidis
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine; Department of Neurobiology and Anatomy, Winston-Salem, NC USA
| | - James B. Daunais
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine Department of Physiology and Pharmacology, Winston-Salem, NC USA
| | - Dwayne W. Godwin
- grid.241167.70000 0001 2185 3318Wake Forest School of Medicine; Department of Neurobiology and Anatomy, Winston-Salem, NC USA ,grid.241167.70000 0001 2185 3318Wake Forest School of Medicine Department of Neurology, Winston-Salem, NC USA ,grid.509341.aResearch and Education Department, W.G. (Bill) Hefner Veterans Affairs Medical Center, Salisbury, NC USA ,grid.241167.70000 0001 2185 3318Wake Forest School of Medicine Department of Physiology and Pharmacology, Winston-Salem, NC USA
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10
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Feasibility of Reconstructing Source Functional Connectivity with Low-Density EEG. Brain Topogr 2021; 34:709-719. [PMID: 34415477 PMCID: PMC8556201 DOI: 10.1007/s10548-021-00866-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 08/02/2021] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Functional connectivity (FC) is increasingly used as target for neuromodulation and enhancement of performance. A reliable assessment of FC with electroencephalography (EEG) currently requires a laboratory environment with high-density montages and a long preparation time. This study investigated the feasibility of reconstructing source FC with a low-density EEG montage towards a usage in real life applications. METHODS Source FC was reconstructed with inverse solutions and quantified as node degree of absolute imaginary coherence in alpha frequencies. We used simulated coherent point sources as well as two real datasets to investigate the impact of electrode density (19 vs. 128 electrodes) and usage of template vs. individual MRI-based head models on localization accuracy. In addition, we checked whether low-density EEG is able to capture inter-individual variations in coherence strength. RESULTS In numerical simulations as well as real data, a reduction of the number of electrodes led to less reliable reconstructions of coherent sources and of coupling strength. Yet, when comparing different approaches to reconstructing FC from 19 electrodes, source FC obtained with beamformers outperformed sensor FC, FC computed after independent component analysis, and source FC obtained with sLORETA. In particular, only source FC based on beamformers was able to capture neural correlates of motor behavior. CONCLUSION Reconstructions of FC from low-density EEG is challenging, but may be feasible when using source reconstructions with beamformers.
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11
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Huang MX, Huang CW, Harrington DL, Nichols S, Robb-Swan A, Angeles-Quinto A, Le L, Rimmele C, Drake A, Song T, Huang JW, Clifford R, Ji Z, Cheng CK, Lerman I, Yurgil KA, Lee RR, Baker DG. Marked Increases in Resting-State MEG Gamma-Band Activity in Combat-Related Mild Traumatic Brain Injury. Cereb Cortex 2021; 30:283-295. [PMID: 31041986 DOI: 10.1093/cercor/bhz087] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2019] [Revised: 03/29/2019] [Accepted: 04/01/2019] [Indexed: 01/08/2023] Open
Abstract
Combat-related mild traumatic brain injury (mTBI) is a leading cause of sustained impairments in military service members and veterans. Recent animal studies show that GABA-ergic parvalbumin-positive interneurons are susceptible to brain injury, with damage causing abnormal increases in spontaneous gamma-band (30-80 Hz) activity. We investigated spontaneous gamma activity in individuals with mTBI using high-resolution resting-state magnetoencephalography source imaging. Participants included 25 symptomatic individuals with chronic combat-related blast mTBI and 35 healthy controls with similar combat experiences. Compared with controls, gamma activity was markedly elevated in mTBI participants throughout frontal, parietal, temporal, and occipital cortices, whereas gamma activity was reduced in ventromedial prefrontal cortex. Across groups, greater gamma activity correlated with poorer performances on tests of executive functioning and visuospatial processing. Many neurocognitive associations, however, were partly driven by the higher incidence of mTBI participants with both higher gamma activity and poorer cognition, suggesting that expansive upregulation of gamma has negative repercussions for cognition particularly in mTBI. This is the first human study to demonstrate abnormal resting-state gamma activity in mTBI. These novel findings suggest the possibility that abnormal gamma activities may be a proxy for GABA-ergic interneuron dysfunction and a promising neuroimaging marker of insidious mild head injuries.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Charles W Huang
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Deborah L Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Sharon Nichols
- Department of Neuroscience, University of California, San Diego, CA, USA
| | - Ashley Robb-Swan
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Annemarie Angeles-Quinto
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Lu Le
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, CA, USA
| | - Carl Rimmele
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, CA, USA
| | - Angela Drake
- Cedar Sinai Medical Group Chronic Pain Program, Beverly Hills, CA, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, CA, USA
| | - Jeffrey W Huang
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Royce Clifford
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, CA, USA
| | - Chung-Kuan Cheng
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
| | - Imanuel Lerman
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Kate A Yurgil
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA.,Department of Psychological Sciences, Loyola University, New Orleans, LA, USA
| | - Roland R Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Dewleen G Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
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12
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Hämäläinen M, Huang M, Bowyer SM. Magnetoencephalography Signal Processing, Forward Modeling, Magnetoencephalography Inverse Source Imaging, and Coherence Analysis. Neuroimaging Clin N Am 2021; 30:125-143. [PMID: 32336402 DOI: 10.1016/j.nic.2020.02.001] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Magnetoencephalography (MEG) is a noninvasive functional imaging technique for the brain. MEG directly measures the magnetic signal due to neuronal activation in gray matter with high spatial localization accuracy. The first part of this article covers the overall concepts of MEG and the forward and inverse modeling techniques. It is followed by examples of analyzing evoked and resting-state MEG signals using a high-resolution MEG source imaging technique. Next, different techniques for connectivity and network analysis are reviewed with examples showing connectivity estimates from resting-state and epileptic activity.
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Affiliation(s)
- Matti Hämäläinen
- Department of Radiology, Athinoula A. Martinos Center, Massachusetts General Hospital, 149 13th Street, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA, USA
| | - Mingxiong Huang
- Department of Radiology, UCSD Radiology Imaging Lab, University of California, San Diego, 3510 Dunhill Street, San Diego, CA 92121, USA
| | - Susan M Bowyer
- Department of Neurology, MEG Lab, Henry Ford Hospital, 2799 West Grand Boulevard, CFP 079, Detroit, MI 48202, USA; Wayne State University School of Medicine, Detroit, MI, USA; Department of Physics, Oakland University, Rochester, MI, USA.
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13
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Huang MX, Huang CW, Harrington DL, Robb-Swan A, Angeles-Quinto A, Nichols S, Huang JW, Le L, Rimmele C, Matthews S, Drake A, Song T, Ji Z, Cheng CK, Shen Q, Foote E, Lerman I, Yurgil KA, Hansen HB, Naviaux RK, Dynes R, Baker DG, Lee RR. Resting-state magnetoencephalography source magnitude imaging with deep-learning neural network for classification of symptomatic combat-related mild traumatic brain injury. Hum Brain Mapp 2021; 42:1987-2004. [PMID: 33449442 PMCID: PMC8046098 DOI: 10.1002/hbm.25340] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 11/16/2020] [Accepted: 12/23/2020] [Indexed: 12/20/2022] Open
Abstract
Combat‐related mild traumatic brain injury (cmTBI) is a leading cause of sustained physical, cognitive, emotional, and behavioral disabilities in Veterans and active‐duty military personnel. Accurate diagnosis of cmTBI is challenging since the symptom spectrum is broad and conventional neuroimaging techniques are insensitive to the underlying neuropathology. The present study developed a novel deep‐learning neural network method, 3D‐MEGNET, and applied it to resting‐state magnetoencephalography (rs‐MEG) source‐magnitude imaging data from 59 symptomatic cmTBI individuals and 42 combat‐deployed healthy controls (HCs). Analytic models of individual frequency bands and all bands together were tested. The All‐frequency model, which combined delta‐theta (1–7 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–80 Hz) frequency bands, outperformed models based on individual bands. The optimized 3D‐MEGNET method distinguished cmTBI individuals from HCs with excellent sensitivity (99.9 ± 0.38%) and specificity (98.9 ± 1.54%). Receiver‐operator‐characteristic curve analysis showed that diagnostic accuracy was 0.99. The gamma and delta‐theta band models outperformed alpha and beta band models. Among cmTBI individuals, but not controls, hyper delta‐theta and gamma‐band activity correlated with lower performance on neuropsychological tests, whereas hypo alpha and beta‐band activity also correlated with lower neuropsychological test performance. This study provides an integrated framework for condensing large source‐imaging variable sets into optimal combinations of regions and frequencies with high diagnostic accuracy and cognitive relevance in cmTBI. The all‐frequency model offered more discriminative power than each frequency‐band model alone. This approach offers an effective path for optimal characterization of behaviorally relevant neuroimaging features in neurological and psychiatric disorders.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Radiology, University of California, San Diego, California, USA
| | - Charles W Huang
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Deborah L Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Radiology, University of California, San Diego, California, USA
| | - Ashley Robb-Swan
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Radiology, University of California, San Diego, California, USA
| | - Annemarie Angeles-Quinto
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Radiology, University of California, San Diego, California, USA
| | - Sharon Nichols
- Department of Neurosciences, University of California, San Diego, California, USA
| | - Jeffrey W Huang
- Department of Computer Science, Columbia University, New York, New York, USA
| | - Lu Le
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, California, USA
| | - Carl Rimmele
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, California, USA
| | - Scott Matthews
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, California, USA
| | - Angela Drake
- Cedar Sinai Medical Group Chronic Pain Program, Beverly Hills, California, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, California, USA
| | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, California, USA
| | - Chung-Kuan Cheng
- Department of Computer Science and Engineering, University of California, San Diego, California, USA
| | - Qian Shen
- Department of Radiology, University of California, San Diego, California, USA
| | - Ericka Foote
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Imanuel Lerman
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Kate A Yurgil
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Psychological Sciences, Loyola University New Orleans, Louisiana, USA
| | - Hayden B Hansen
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA
| | - Robert K Naviaux
- Department of Medicine, University of California, San Diego, California, USA.,Department of Pediatrics, University of California, San Diego, California, USA.,Department of Pathology, University of California, San Diego, California, USA
| | - Robert Dynes
- Department of Physics, University of California, San Diego, California, USA
| | - Dewleen G Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, California, USA.,Department of Psychiatry, University of California, San Diego, California, USA
| | - Roland R Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California, USA.,Department of Radiology, University of California, San Diego, California, USA
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14
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Lu L, Sheng J, Liu Z, Gao JH. Neural representations of imagined speech revealed by frequency-tagged magnetoencephalography responses. Neuroimage 2021; 229:117724. [PMID: 33421593 DOI: 10.1016/j.neuroimage.2021.117724] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 12/25/2020] [Accepted: 01/03/2021] [Indexed: 10/22/2022] Open
Abstract
Speech mental imagery is a quasi-perceptual experience that occurs in the absence of real speech stimulation. How imagined speech with higher-order structures such as words, phrases and sentences is rapidly organized and internally constructed remains elusive. To address this issue, subjects were tasked with imagining and perceiving poems along with a sequence of reference sounds with a presentation rate of 4 Hz while magnetoencephalography (MEG) recording was conducted. Giving that a sentence in a traditional Chinese poem is five syllables, a sentential rhythm was generated at a distinctive frequency of 0.8 Hz. Using the frequency tagging we concurrently tracked the neural processing timescale to the top-down generation of rhythmic constructs embedded in speech mental imagery and the bottom-up sensory-driven activity that were precisely tagged at the sentence-level rate of 0.8 Hz and a stimulus-level rate of 4 Hz, respectively. We found similar neural responses induced by the internal construction of sentences from syllables with both imagined and perceived poems and further revealed shared and distinct cohorts of cortical areas corresponding to the sentence-level rhythm in imagery and perception. This study supports the view of a common mechanism between imagery and perception by illustrating the neural representations of higher-order rhythmic structures embedded in imagined and perceived speech.
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Affiliation(s)
- Lingxi Lu
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871 China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871 China; Center for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, 100083 China
| | - Jingwei Sheng
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871 China; Beijing Quanmag Healthcare, Beijing, 100195 China
| | - Zhaowei Liu
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871 China; Center for Excellence in Brain Science and Intelligence Technology (Institute of Neuroscience), Chinese Academy of Science, Shanghai, 200031 China
| | - Jia-Hong Gao
- PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, 100871 China; Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871 China; Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, 100871, China.
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15
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Zheng L, Sheng J, Cen Z, Teng P, Wang J, Wang Q, Lee RR, Luan G, Huang M, Gao JH. Enhanced Fast-VESTAL for Magnetoencephalography Source Imaging: From Theory to Clinical Application in Epilepsy. IEEE Trans Biomed Eng 2020; 68:793-806. [PMID: 32790623 DOI: 10.1109/tbme.2020.3016468] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
A novel magnetoencephalography source imaging approach called Fast Vector-based Spatio-Temporal Analysis (Fast-VESTAL) has been successfully applied in creating source images from evoked and resting-state data from both healthy subjects and individuals with neurological and/or psychiatric disorders, but its reconstructed source images may show false-positive activations, especially under low signal-to-noise ratio conditions. Here, to effectively reduce false-positive artifacts, we introduced an enhanced Fast-VESTAL (eFast-VESTAL) approach that adopts generalized second-order cone programming. We compared the spatiotemporal characteristics of the eFast-VESTAL approach to those of the popular distributed source approaches (e.g., the minimum L2-norm/ mixed-norm methods) using computer simulations and auditory experiments. More importantly, we applied eFast-VESTAL to the presurgical evaluation of epilepsy. Our results demonstrated that eFast-VESTAL exhibited a lower dipole localization error and/or a higher correlation coefficient (CC) between the estimated source time series and ground truth under various conditions of source waveforms. Experimentally, eFast-VESTAL displayed more focal activation maps and a higher CC between the raw and predicted sensor data in response to auditory stimulation. Notably, eFast-VESTAL was the most accurate method for noninvasively detecting the epileptic zones determined using more invasive stereo-electroencephalography in the comparison.
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16
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Huang MX, Nichols S, Robb-Swan A, Angeles-Quinto A, Harrington DL, Drake A, Huang CW, Song T, Diwakar M, Risbrough VB, Matthews S, Clifford R, Cheng CK, Huang JW, Sinha A, Yurgil KA, Ji Z, Lerman I, Lee RR, Baker DG. MEG Working Memory N-Back Task Reveals Functional Deficits in Combat-Related Mild Traumatic Brain Injury. Cereb Cortex 2020; 29:1953-1968. [PMID: 29668852 DOI: 10.1093/cercor/bhy075] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 03/11/2018] [Accepted: 03/13/2018] [Indexed: 12/24/2022] Open
Abstract
Combat-related mild traumatic brain injury (mTBI) is a leading cause of sustained cognitive impairment in military service members and Veterans. However, the mechanism of persistent cognitive deficits including working memory (WM) dysfunction is not fully understood in mTBI. Few studies of WM deficits in mTBI have taken advantage of the temporal and frequency resolution afforded by electromagnetic measurements. Using magnetoencephalography (MEG) and an N-back WM task, we investigated functional abnormalities in combat-related mTBI. Study participants included 25 symptomatic active-duty service members or Veterans with combat-related mTBI and 20 healthy controls with similar combat experiences. MEG source-magnitude images were obtained for alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and low-frequency (1-7 Hz) bands. Compared with healthy combat controls, mTBI participants showed increased MEG signals across frequency bands in frontal pole (FP), ventromedial prefrontal cortex, orbitofrontal cortex (OFC), and anterior dorsolateral prefrontal cortex (dlPFC), but decreased MEG signals in anterior cingulate cortex. Hyperactivations in FP, OFC, and anterior dlPFC were associated with slower reaction times. MEG activations in lateral FP also negatively correlated with performance on tests of letter sequencing, verbal fluency, and digit symbol coding. The profound hyperactivations from FP suggest that FP is particularly vulnerable to combat-related mTBI.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Sharon Nichols
- Department of Neuroscience, University of California, San Diego, CA, USA
| | - Ashley Robb-Swan
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Deborah L Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Angela Drake
- Cedar Sinai Medical Group Chronic Pain Program, Beverly Hills, CA, USA
| | - Charles W Huang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, CA, USA
| | - Mithun Diwakar
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA
| | - Victoria B Risbrough
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Scott Matthews
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, CA, USA
| | - Royce Clifford
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Chung-Kuan Cheng
- Department of Computer Science and Engineering, University of California, San Diego, CA, USA
| | | | - Anusha Sinha
- California Institute of Technology, Pasadena, CA, USA
| | - Kate A Yurgil
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA.,Loyola University New Orleans, LA, USA
| | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, CA, USA
| | - Imanuel Lerman
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Roland R Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Radiology, University of California, San Diego, CA, USA
| | - Dewleen G Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA.,Department of Psychiatry, University of California, San Diego, CA, USA.,VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
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17
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Shu S, Qin L, Yin Y, Han M, Cui W, Gao JH. Cortical electrophysiological evidence for individual-specific temporal organization of brain functional networks. Hum Brain Mapp 2020; 41:2160-2172. [PMID: 31961469 PMCID: PMC7267903 DOI: 10.1002/hbm.24937] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2019] [Revised: 01/02/2020] [Accepted: 01/13/2020] [Indexed: 12/21/2022] Open
Abstract
The human brain has been demonstrated to rapidly and continuously form and dissolve networks on a subsecond timescale, offering effective and essential substrates for cognitive processes. Understanding how the dynamic organization of brain functional networks on a subsecond level varies across individuals is, therefore, of great interest for personalized neuroscience. However, it remains unclear whether features of such rapid network organization are reliably unique and stable in single subjects and, therefore, can be used in characterizing individual networks. Here, we used two sets of 5‐min magnetoencephalography (MEG) resting data from 39 healthy subjects over two consecutive days and modeled the spontaneous brain activity as recurring networks fast shifting between each other in a coordinated manner. MEG cortical maps were obtained through source reconstruction using the beamformer method and subjects' temporal structure of recurring networks was obtained via the Hidden Markov Model. Individual organization of dynamic brain activity was quantified with the features of the network‐switching pattern (i.e., transition probability and mean interval time) and the time‐allocation mode (i.e., fractional occupancy and mean lifetime). Using these features, we were able to identify subjects from the group with significant accuracies (~40% on average in 0.5–48 Hz). Notably, the default mode network displayed a distinguishable pattern, being the least frequently visited network with the longest duration for each visit. Together, we provide initial evidence suggesting that the rapid dynamic temporal organization of brain networks achieved in electrophysiology is intrinsic and subject specific.
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Affiliation(s)
- Su Shu
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Lang Qin
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Department of Linguistics, The University of Hong Kong, Hong Kong, China
| | - Yayan Yin
- Department of Radiology, Xuanwu Hospital of Capital Medical University, Beijing, China
| | - Meizhen Han
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Wei Cui
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,Center for Biomedical Engineering, University of Science and Technology of China, Hefei, Anhui, China
| | - Jia-Hong Gao
- Beijing City Key Lab for Medical Physics and Engineering, Institution of Heavy Ion Physics, School of Physics, Peking University, Beijing, China.,Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China.,McGovern Institute for Brain Research, Peking University, Beijing, China
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18
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Bowyer SM, Pang EW, Huang M, Papanicolaou AC, Lee RR. Presurgical Functional Mapping with Magnetoencephalography. Neuroimaging Clin N Am 2020; 30:159-174. [DOI: 10.1016/j.nic.2020.02.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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19
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Huang M, Lewine JD, Lee RR. Magnetoencephalography for Mild Traumatic Brain Injury and Posttraumatic Stress Disorder. Neuroimaging Clin N Am 2020; 30:175-192. [DOI: 10.1016/j.nic.2020.02.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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20
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Green HL, Edgar JC, Matsuzaki J, Roberts TPL. Magnetoencephalography Research in Pediatric Autism Spectrum Disorder. Neuroimaging Clin N Am 2020; 30:193-203. [PMID: 32336406 PMCID: PMC7216756 DOI: 10.1016/j.nic.2020.01.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Magnetoencephalography (MEG) research indicates differences in neural brain measures in children with autism spectrum disorder (ASD) compared to typically developing (TD) children. As reviewed here, resting-state MEG exams are of interest as well as MEG paradigms that assess neural function across domains (e.g., auditory, resting state). To date, MEG research has primarily focused on group-level differences. Research is needed to explore whether MEG measures can predict, at the individual level, ASD diagnosis, prognosis (future severity), and response to therapy.
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Affiliation(s)
- Heather L Green
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, USA.
| | - J Christopher Edgar
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | - Junko Matsuzaki
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, USA
| | - Timothy P L Roberts
- Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Boulevard, Philadelphia, PA 19104, USA
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21
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Edgar JC. Identifying electrophysiological markers of autism spectrum disorder and schizophrenia against a backdrop of normal brain development. Psychiatry Clin Neurosci 2020; 74:1-11. [PMID: 31472015 PMCID: PMC10150852 DOI: 10.1111/pcn.12927] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 08/26/2019] [Accepted: 08/27/2019] [Indexed: 01/25/2023]
Abstract
An examination of electroencephalographic and magnetoencephalographic studies demonstrates how age-related changes in brain neural function temporally constrain their use as diagnostic markers. A first example shows that, given maturational changes in the resting-state peak alpha frequency in typically developing children but not in children who have autism spectrum disorder (ASD), group differences in alpha-band activity characterize only a subset of children who have ASD. A second example, auditory encoding processes in schizophrenia, shows that the complication of normal age-related brain changes on detecting and interpreting group differences in neural activity is not specific to children. MRI studies reporting group differences in the rate of brain maturation demonstrate that a group difference in brain maturation may be a concern for all diagnostic brain markers. Attention to brain maturation is needed whether one takes a DSM-5 or a Research Domain Criteria approach to research. For example, although there is interest in cross-diagnostic studies comparing brain measures in ASD and schizophrenia, such studies are difficult given that measures are obtained in one group well after and in the other much closer to the onset of symptoms. In addition, given differences in brain activity among infants, toddlers, children, adolescents, and younger and older adults, creating tasks and research designs that produce interpretable findings across the life span and yet allow for development is difficult at best. To conclude, brain imaging findings show an effect of brain maturation on diagnostic markers separate from (and potentially difficult to distinguish from) effects of disease processes. Available research with large samples already provides direction about the age range(s) when diagnostic markers are most robust and informative.
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Affiliation(s)
- J Christopher Edgar
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, USA
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22
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Huang MX, Robb Swan A, Angeles Quinto A, Huang JW, De-la-Garza BG, Huang CW, Hesselink JR, Bigler ED, Wilde EA, Max JE. Resting-State Magnetoencephalography Source Imaging Pilot Study in Children with Mild Traumatic Brain Injury. J Neurotrauma 2019; 37:994-1001. [PMID: 31724480 DOI: 10.1089/neu.2019.6417] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Mild traumatic brain injury (mTBI) accounts for the vast majority of all pediatric TBI. An important minority of children who have suffered an mTBI have enduring cognitive and emotional symptoms. However, the mechanisms of chronic symptoms in children with pediatric mTBI are not fully understood. This is in part due to the limited sensitivity of conventional neuroimaging technologies. The present study examined resting-state magnetoencephalography (rs-MEG) source images in 12 children who had mTBI and 12 age-matched control children. The rs-MEG exams were performed in children with mTBI 6 months after injury when they reported no clinically significant post-injury psychiatric changes and few if any somatic sensorimotor symptoms but did report cognitive symptoms. MEG source magnitude images were obtained for different frequency bands in alpha (8-12 Hz), beta (15-30 Hz), gamma (30-90 Hz), and low-frequency (1-7 Hz) bands. In contrast to the control participants, rs-MEG source imaging in the children with mTBI showed: 1) hyperactivity from the bilateral insular cortices in alpha, beta, and low-frequency bands, from the left amygdala in alpha band, and from the left precuneus in beta band; 2) hypoactivity from the bilateral dorsolateral prefrontal cortices (dlPFC) in alpha and beta bands, from the ventromedial prefrontal cortex (vmPFC) in beta band, from the ventrolateral prefrontal cortex (vlPFC) in gamma band, from the anterior cingulate cortex (ACC) in alpha band, and from the right precuneus in alpha band. The present study showed that MEG source imaging technique revealed abnormalities in the resting-state electromagnetic signals from the children with mTBI.
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Affiliation(s)
- Ming-Xiong Huang
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Ashley Robb Swan
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Annemarie Angeles Quinto
- Department of Radiology, University of California, San Diego, California.,Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, California
| | - Jeffrey W Huang
- Department of Computer Sciences, Columbia University, New York, New York
| | | | - Charles W Huang
- Department of Bioengineering, Stanford University, Stanford, California
| | - John R Hesselink
- Department of Radiology, University of California, San Diego, California
| | - Erin D Bigler
- Department of Neurology, University of Utah, Salt Lake City, Utah
| | | | - Jeffrey E Max
- Department of Psychiatry, University of California, San Diego, California.,Department of Psychiatry, Rady Children's Hospital, San Diego, California
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23
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Thrower SL, Kandala SK, Fuentes D, Stefan W, Sowko N, Huang M, Mathieu K, Hazle JD. A compressed sensing approach to immobilized nanoparticle localization for superparamagnetic relaxometry. Phys Med Biol 2019; 64:194001. [PMID: 31422952 DOI: 10.1088/1361-6560/ab3c06] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Superparamagnetic relaxometry (SPMR) exploits the unique magnetic properties of targeted superparamagnetic iron oxide nanoparticles (SPIOs) to detect small numbers of cancer cells. Reconstruction of the spatial distribution of cancer-bound nanoparticles requires solving an ill-posed inverse problem. The current method, multiple source analysis (MSA), uses a least-squares fit to determine the strength and location of a pre-determined number of magnetic dipoles. In this proof-of-concept study, we propose the application of a sparsity averaged reweighting algorithm (SARA) for volumetric reconstruction of immobilized nanoparticle distributions. We first calibrate the parameters that define the location of the sensors in the forward model of measurement physics. Using this optimized model, we evaluated the performance of the algorithms on various configurations of single and multiple point-source phantoms. We investigated the effect of the data fidelity parameter, voxel size, and iterative reweighting on the reconstruction produced by SARA. We found that the calibrated physics model can predict the detected field values within 5% of the measured data. When only a single source was present, both algorithms were able to detect as little as 0.5 µg of immobilized particles. However, when two sources were measured simultaneously, MSA failed to detect sources containing as much as 10 µg of particles, while SARA detected all of the sources containing at least 5 µg of particles. We show that a suitable data fidelity parameter can be selected objectively, and the total magnitude and location of a point source reconstructed by SARA is not sensitive to voxel size. Detection and localization of multiple small clusters of nanoparticles is a crucial step in SPMR-based diagnostic applications. Our algorithm overcomes the need to know the number of dipoles before reconstruction and improves the sensitivity of the reconstruction when multiple sources are present.
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Affiliation(s)
- S L Thrower
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States of America. Author to whom any correspondence should be addressed
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24
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Spadoni AD, Huang M, Simmons AN. Emerging Approaches to Neurocircuits in PTSD and TBI: Imaging the Interplay of Neural and Emotional Trauma. Curr Top Behav Neurosci 2019; 38:163-192. [PMID: 29285732 PMCID: PMC8896198 DOI: 10.1007/7854_2017_35] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) commonly co-occur in general and military populations and have a number of overlapping symptoms. While research suggests that TBI is risk factor for PTSD and that PTSD may mediate TBI-related outcomes, the mechanisms of these relationships are not well understood. Neuroimaging may help elucidate patterns of neurocircuitry both specific and common to PTSD and TBI and thus help define the nature of their interaction, refine diagnostic classification, and may potentially yield opportunities for targeted treatments. In this review, we provide a summary of some of the most common and the most innovative neuroimaging approaches used to characterize the neural circuits associated with PTSD, TBI, and their comorbidity. We summarize the state of the science for each disorder and describe the few studies that have explicitly attempted to characterize the neural substrates of their shared and dissociable influence. While some promising targets in the medial frontal lobes exist, there is not currently a comprehensive understanding of the neurocircuitry mediating the interaction of PTSD and TBI. Future studies should exploit innovative neuroimaging approaches and longitudinal designs to specifically target the neural mechanisms driving PTSD-TBI-related outcomes.
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Affiliation(s)
- Andrea D Spadoni
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA.
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA.
| | - Mingxiong Huang
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, La Jolla, CA, USA
| | - Alan N Simmons
- Research Service, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
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25
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Edgar JC, Dipiero M, McBride E, Green HL, Berman J, Ku M, Liu S, Blaskey L, Kuschner E, Airey M, Ross JL, Bloy L, Kim M, Koppers S, Gaetz W, Schultz RT, Roberts TPL. Abnormal maturation of the resting-state peak alpha frequency in children with autism spectrum disorder. Hum Brain Mapp 2019; 40:3288-3298. [PMID: 30977235 DOI: 10.1002/hbm.24598] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 03/25/2019] [Accepted: 04/02/2019] [Indexed: 12/15/2022] Open
Abstract
Age-related changes in resting-state (RS) neural rhythms in typically developing children (TDC) but not children with autism spectrum disorder (ASD) suggest that RS measures may be of clinical use in ASD only for certain ages. The study examined this issue via assessing RS peak alpha frequency (PAF), a measure previous studies, have indicated as abnormal in ASD. RS magnetoencephalographic (MEG) data were obtained from 141 TDC (6.13-17.70 years) and 204 ASD (6.07-17.93 years). A source model with 15 regional sources projected the raw MEG surface data into brain source space. PAF was identified in each participant from the source showing the largest amplitude alpha activity (7-13 Hz). Given sex differences in PAF in TDC (females > males) and relatively few females in both groups, group comparisons were conducted examining only male TDC (N = 121) and ASD (N = 183). Regressions showed significant group slope differences, with an age-related increase in PAF in TDC (R2 = 0.32) but not ASD (R2 = 0.01). Analyses examining male children below or above 10-years-old (median split) indicated group effects only in the younger TDC (8.90 Hz) and ASD (9.84 Hz; Cohen's d = 1.05). In the older ASD, a higher nonverbal IQ was associated with a higher PAF. In the younger TDC, a faster speed of processing was associated with a higher PAF. PAF as a marker for ASD depends on age, with a RS alpha marker of more interest in younger versus older children with ASD. Associations between PAF and cognitive ability were also found to be age and group specific.
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Affiliation(s)
- J Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Marissa Dipiero
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Emma McBride
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Heather L Green
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jeffrey Berman
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Matthew Ku
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Song Liu
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Lisa Blaskey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.,Center for Autism Research, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Emily Kuschner
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Center for Autism Research, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Megan Airey
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Judith L Ross
- Thomas Jefferson University, Department of Pediatrics, Philadelphia, Pennsylvania
| | - Luke Bloy
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Mina Kim
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Simon Koppers
- RWTH Aachen University, Institute of Imaging and Computer Vision, Aachen, Germany
| | - William Gaetz
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Robert T Schultz
- Center for Autism Research, Department of Pediatrics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Timothy P L Roberts
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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26
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Lawton T, Huang MX. Dynamic cognitive remediation for a Traumatic Brain Injury (TBI) significantly improves attention, working memory, processing speed, and reading fluency. Restor Neurol Neurosci 2019; 37:71-86. [PMID: 30741708 PMCID: PMC6481540 DOI: 10.3233/rnn-180856] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
BACKGROUND In the U.S. 3.8 million people have a Traumatic Brain Injury (TBI) each year. Rapid brain training exercises to improve cognitive function after a mild TBI are needed. OBJECTIVE This study determines whether cognitive remediation by discriminating the direction a test pattern moves relative to a stationary background (movement figure-ground discrimination) improves the vision and cognitive deficits that result from a TBI, providing a paradigm shift in treatment methods. METHODS Movement-discrimination neurotraining was used to remediate low-level visual timing deficits in the dorsal stream to determine whether it improved high-level cognitive functions, such as processing speed, reading fluency, and the executive control functions of attention and working memory in four men with a TBI between the ages of 15-68. Standardized tests, as well as Magnetoencephalography (MEG) brain imaging, were administered at the beginning and end of 8-16 weeks of intervention training to evaluate improvements in cognitive skills. RESULTS Movement-discrimination cognitive neurotraining remediated both low-level visual timing deficits and high-level cognitive functioning, including selective and sustained attention, reading fluency, processing speed, and working memory for all TBI patients we studied. MEG brain imaging, using the Fast-VESTAL procedure, showed that this movement-discrimination training improved time-locked activity in the dorsal stream, attention, and executive control networks. CONCLUSIONS Remediating visual timing deficits in the dorsal stream revealed the causal role of visual movement discrimination training in improving high-level cognitive functions such as focusing and switching attention, working memory, processing speed, and reading. This study found that movement-discrimination training was very rapid and effective in remediating cognitive deficits, providing a new approach that is very beneficial for treating a mild TBI.
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Affiliation(s)
- Teri Lawton
- Department of Cognitive Neuroscience, Perception Dynamics Institute, Encinitas, CA, USA
| | - Ming-Xiong Huang
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, Radiology Imaging Laboratory, University of California at San Diego, San Diego, CA, USA
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27
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Liu K, Yu ZL, Wu W, Gu Z, Zhang J, Cen L, Nagarajan S, Li Y. Bayesian Electromagnetic Spatio-Temporal Imaging of Extended Sources Based on Matrix Factorization. IEEE Trans Biomed Eng 2019; 66:2457-2469. [PMID: 30605088 DOI: 10.1109/tbme.2018.2890291] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Accurate estimation of the locations and extents of neural sources from electroencephalography and magnetoencephalography (E/MEG) is challenging, especially for deep and highly correlated neural activities. In this study, we proposed a new fully data-driven source imaging method, source imaging based on spatio-temporal basis function (SI-STBF), which is built upon a Bayesian framework, to address this issue. The SI-STBF is based on the factorization of a source matrix as a product of a sparse coding matrix and a temporal basis function (TBF) matrix, which includes a few TBFs. The prior of the TBF is set in the empirical Bayesian manner. Similarly, for the spatial constraint, the SI-STBF assumes the prior covariance of the coding matrix as a weighted sum of several spatial covariance components. Both the TBFs and the coding matrix are learned from E/MEG simultaneously through variational Bayesian inference. To enable inference on high-resolution source space, we derived a scalable algorithm using convex analysis. The performance of the SI-STBF was assessed using both simulated and experimental E/MEG recordings. Compared with L2-norm constrained methods, the SI-STBF is superior in reconstructing extended sources with less spatial diffusion and less localization error. By virtue of the spatio-temporal factorization of source matrix, the SI-STBF also produces more accurate estimations than spatial-only constraint method for high correlated and deep sources.
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28
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Sheffield A, Ahn S, Alagapan S, Fröhlich F. Modulating neural oscillations by transcranial static magnetic field stimulation of the dorsolateral prefrontal cortex: A crossover, double-blind, sham-controlled pilot study. Eur J Neurosci 2018; 49:250-262. [PMID: 30380175 DOI: 10.1111/ejn.14232] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Revised: 10/02/2018] [Accepted: 10/16/2018] [Indexed: 12/23/2022]
Abstract
Transcranial static magnetic field stimulation (tSMS) is a novel non-invasive brain stimulation technique that has been shown to locally increase alpha power in the parietal and occipital cortex. We investigated if tSMS locally increased alpha power in the left or right prefrontal cortex, as the balance of left/right prefrontal alpha power (frontal alpha asymmetry) has been linked to emotional processing and mood disorders. Therefore, altering frontal alpha asymmetry with tSMS may serve as a novel treatment to psychiatric diseases. We performed a crossover, double-blind, sham-controlled pilot study to assess the effects of prefrontal tSMS on neural oscillations. Twenty-four right-handed healthy participants were recruited and received left dorsolateral prefrontal cortex (DLPFC) tSMS, right DLPFC tSMS, and sham tSMS in a randomized order. Electroencephalography data were collected before (2 min eyes-closed, 2 min eyes-open), during (10 min eyes-open), and after (2 min eyes-open) stimulation. In contrast with our hypothesis, neither left nor right tSMS locally increased frontal alpha power. However, alpha power increased in occipital cortex during left DLPFC tSMS. Right DLPFC tSMS increased post-stimulation fronto-parietal theta power, indicating possible relevance to memory and cognition. Left and right DLPFC tSMS increased post-stimulation left hemisphere beta power, indicating possible changes to motor behavior. Left DLPFC tSMS also increased post-stimulation right frontal beta power, demonstrating complex network effects that may be relevant to aggressive behavior. We concluded that DLPFC tSMS modulated the network oscillations in regions distant from the location of stimulation and that tSMS has region specific effects on neural oscillations.
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Affiliation(s)
- Alec Sheffield
- Neuroscience Program, Colorado College, Colorado Springs, Colorado.,Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sangtae Ahn
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Sankaraleengam Alagapan
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Flavio Fröhlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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29
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Variation sparse source imaging based on conditional mean for electromagnetic extended sources. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.06.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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30
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Chhabria S, Blaha KA, Della Rossa F, Sorrentino F. Targeted synchronization in an externally driven population of mechanical oscillators. CHAOS (WOODBURY, N.Y.) 2018; 28:111102. [PMID: 30501207 DOI: 10.1063/1.5052652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Accepted: 11/02/2018] [Indexed: 06/09/2023]
Abstract
We experimentally investigate the synchronization of driven metronomes using a servo motor to impose external control. We show that a driven metronome will only synchronize in a narrow range near its own frequency; when we introduce coupling between metronomes, we can widen the range of frequencies over which a metronome will synchronize to the external input. Using these features, we design a signal to synchronize a population of dissimilar metronomes; separately we design a signal to selectively synchronize a subpopulation of metronomes within a heterogeneous population.
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Affiliation(s)
- Sumit Chhabria
- Department of Mechanical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Karen A Blaha
- Department of Mechanical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Fabio Della Rossa
- Department of Mechanical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
| | - Francesco Sorrentino
- Department of Mechanical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, USA
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31
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Robust EEG/MEG Based Functional Connectivity with the Envelope of the Imaginary Coherence: Sensor Space Analysis. Brain Topogr 2018; 31:895-916. [PMID: 29546509 PMCID: PMC6182573 DOI: 10.1007/s10548-018-0640-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Accepted: 02/26/2018] [Indexed: 10/25/2022]
Abstract
The brain's functional connectivity (FC) estimated at sensor level from electromagnetic (EEG/MEG) signals can provide quick and useful information towards understanding cognition and brain disorders. Volume conduction (VC) is a fundamental issue in FC analysis due to the effects of instantaneous correlations. FC methods based on the imaginary part of the coherence (iCOH) of any two signals are readily robust to VC effects, but neglecting the real part of the coherence leads to negligible FC when the processes are truly connected but with zero or π-phase (modulus 2π) interaction. We ameliorate this issue by proposing a novel method that implements an envelope of the imaginary coherence (EIC) to approximate the coherence estimate of supposedly active underlying sources. We compare EIC with state-of-the-art FC measures that included lagged coherence, iCOH, phase lag index (PLI) and weighted PLI (wPLI), using bivariate autoregressive and stochastic neural mass models. Additionally, we create realistic simulations where three and five regions were mapped on a template cortical surface and synthetic MEG signals were obtained after computing the electromagnetic leadfield. With this simulation and comparison study, we also demonstrate the feasibility of sensor FC analysis using receiver operating curve analysis whilst varying the signal's noise level. However, these results should be interpreted with caution given the known limitations of the sensor-based FC approach. Overall, we found that EIC and iCOH demonstrate superior results with most accurate FC maps. As they complement each other in different scenarios, that will be important to study normal and diseased brain activity.
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32
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Huang MX, Swan AR, Quinto AA, Matthews S, Harrington DL, Nichols S, Bruder BJ, Snook CC, Huang CW, Baker DG, Lee RR. A pilot treatment study for mild traumatic brain injury: Neuroimaging changes detected by MEG after low-intensity pulse-based transcranial electrical stimulation. Brain Inj 2017; 31:1951-1963. [DOI: 10.1080/02699052.2017.1363409] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Ashley Robb Swan
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Annemarie Angeles Quinto
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Scott Matthews
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- ASPIRE Center, VASDHS Residential Rehabilitation Treatment Program, San Diego, CA, USA
| | - Deborah L. Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
| | - Sharon Nichols
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | | | | | - Charles W. Huang
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
| | - Dewleen G. Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Roland R. Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
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33
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Huang MX, Anderson B, Huang CW, Kunde GJ, Vreeland EC, Huang JW, Matlashov AN, Karaulanov T, Nettles CP, Gomez A, Minser K, Weldon C, Paciotti G, Harsh M, Lee RR, Flynn ER. Development of advanced signal processing and source imaging methods for superparamagnetic relaxometry. Phys Med Biol 2017; 62:734-757. [PMID: 28072579 PMCID: PMC5797703 DOI: 10.1088/1361-6560/aa553b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Superparamagnetic relaxometry (SPMR) is a highly sensitive technique for the in vivo detection of tumor cells and may improve early stage detection of cancers. SPMR employs superparamagnetic iron oxide nanoparticles (SPION). After a brief magnetizing pulse is used to align the SPION, SPMR measures the time decay of SPION using super-conducting quantum interference device (SQUID) sensors. Substantial research has been carried out in developing the SQUID hardware and in improving the properties of the SPION. However, little research has been done in the pre-processing of sensor signals and post-processing source modeling in SPMR. In the present study, we illustrate new pre-processing tools that were developed to: (1) remove trials contaminated with artifacts, (2) evaluate and ensure that a single decay process associated with bounded SPION exists in the data, (3) automatically detect and correct flux jumps, and (4) accurately fit the sensor signals with different decay models. Furthermore, we developed an automated approach based on multi-start dipole imaging technique to obtain the locations and magnitudes of multiple magnetic sources, without initial guesses from the users. A regularization process was implemented to solve the ambiguity issue related to the SPMR source variables. A procedure based on reduced chi-square cost-function was introduced to objectively obtain the adequate number of dipoles that describe the data. The new pre-processing tools and multi-start source imaging approach have been successfully evaluated using phantom data. In conclusion, these tools and multi-start source modeling approach substantially enhance the accuracy and sensitivity in detecting and localizing sources from the SPMR signals. Furthermore, multi-start approach with regularization provided robust and accurate solutions for a poor SNR condition similar to the SPMR detection sensitivity in the order of 1000 cells. We believe such algorithms will help establishing the industrial standards for SPMR when applying the technique in pre-clinical and clinical settings.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Charles W. Huang
- Department of Bioengineering, University of California, San Diego, CA, USA
| | - Gerd J. Kunde
- Imagion Biosystems, Albuquerque, NM, USA
- Los Alamos National Laboratory, Los Alamos, NM, USA
| | | | | | - Andrei N. Matlashov
- Imagion Biosystems, Albuquerque, NM, USA
- Los Alamos National Laboratory, Los Alamos, NM, USA
| | - Todor Karaulanov
- Imagion Biosystems, Albuquerque, NM, USA
- Los Alamos National Laboratory, Los Alamos, NM, USA
| | | | | | | | | | | | | | - Roland R. Lee
- Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
- Department of Radiology, University of California, San Diego, CA, USA
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Huang MX, Harrington DL, Robb Swan A, Angeles Quinto A, Nichols S, Drake A, Song T, Diwakar M, Huang CW, Risbrough VB, Dale A, Bartsch H, Matthews S, Huang JW, Lee RR, Baker DG. Resting-State Magnetoencephalography Reveals Different Patterns of Aberrant Functional Connectivity in Combat-Related Mild Traumatic Brain Injury. J Neurotrauma 2016; 34:1412-1426. [PMID: 27762653 DOI: 10.1089/neu.2016.4581] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Blast mild traumatic brain injury (mTBI) is a leading cause of sustained impairment in military service members and veterans. However, the mechanism of persistent disability is not fully understood. The present study investigated disturbances in brain functioning in mTBI participants using a source-imaging-based approach to analyze functional connectivity (FC) from resting-state magnetoencephalography (rs-MEG). Study participants included 26 active-duty service members or veterans who had blast mTBI with persistent post-concussive symptoms, and 22 healthy control active-duty service members or veterans. The source time courses from regions of interest (ROIs) were used to compute ROI to whole-brain (ROI-global) FC for different frequency bands using two different measures: 1) time-lagged cross-correlation and 2) phase-lock synchrony. Compared with the controls, blast mTBI participants showed increased ROI-global FC in beta, gamma, and low-frequency bands, but not in the alpha band. Sources of abnormally increased FC included the: 1) prefrontal cortex (right ventromedial prefrontal cortex [vmPFC], right rostral anterior cingulate cortex [rACC]), and left ventrolateral and dorsolateral prefrontal cortex; 2) medial temporal lobe (bilateral parahippocampus, hippocampus, and amygdala); and 3) right putamen and cerebellum. In contrast, the blast mTBI group also showed decreased FC of the right frontal pole. Group differences were highly consistent across the two different FC measures. FC of the left ventrolateral prefrontal cortex correlated with executive functioning and processing speed in mTBI participants. Altogether, our findings of increased and decreased regionalpatterns of FC suggest that disturbances in intrinsic brain connectivity may be the result of multiple mechanisms, and are associated with cognitive sequelae of the injury.
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Affiliation(s)
- Ming-Xiong Huang
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Deborah L Harrington
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Ashley Robb Swan
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Annemarie Angeles Quinto
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Sharon Nichols
- 3 Department of Neuroscience, University of California , San Diego, California
| | | | - Tao Song
- 2 Department of Radiology, University of California , San Diego, California
| | - Mithun Diwakar
- 2 Department of Radiology, University of California , San Diego, California
| | - Charles W Huang
- 5 Department of Bioengineering, University of California , San Diego, California
| | - Victoria B Risbrough
- 6 Department of Psychiatry, University of California , San Diego, California.,7 VA Center of Excellence for Stress and Mental Health , San Diego, California
| | - Anders Dale
- 2 Department of Radiology, University of California , San Diego, California
| | - Hauke Bartsch
- 2 Department of Radiology, University of California , San Diego, California
| | - Scott Matthews
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,6 Department of Psychiatry, University of California , San Diego, California.,8 Aspire Center , VASDHS Residential Rehabilitation Treatment Program, San Diego, California
| | | | - Roland R Lee
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,2 Department of Radiology, University of California , San Diego, California
| | - Dewleen G Baker
- 1 Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System , San Diego, California.,6 Department of Psychiatry, University of California , San Diego, California.,7 VA Center of Excellence for Stress and Mental Health , San Diego, California
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Strohmeier D, Bekhti Y, Haueisen J, Gramfort A. The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal MEG/EEG Source Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2218-2228. [PMID: 27093548 PMCID: PMC5533305 DOI: 10.1109/tmi.2016.2553445] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatial sparsity of the neuronal activation is a common assumption. It is often taken into account using convex constraints based on the l1-norm. The resulting source estimates are however biased in amplitude and often suboptimal in terms of source selection due to high correlations in the forward model. In this work, we demonstrate that an inverse solver based on a block-separable penalty with a Frobenius norm per block and a l0.5-quasinorm over blocks addresses both of these issues. For solving the resulting non-convex optimization problem, we propose the iterative reweighted Mixed Norm Estimate (irMxNE), an optimization scheme based on iterative reweighted convex surrogate optimization problems, which are solved efficiently using a block coordinate descent scheme and an active set strategy. We compare the proposed sparse imaging method to the dSPM and the RAP-MUSIC approach based on two MEG data sets. We provide empirical evidence based on simulations and analysis of MEG data that the proposed method improves on the standard Mixed Norm Estimate (MxNE) in terms of amplitude bias, support recovery, and stability.
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Datko M, Gougelet R, Huang MX, Pineda JA. Resting State Functional Connectivity MRI among Spectral MEG Current Sources in Children on the Autism Spectrum. Front Neurosci 2016; 10:258. [PMID: 27375419 PMCID: PMC4899470 DOI: 10.3389/fnins.2016.00258] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2016] [Accepted: 05/23/2016] [Indexed: 12/05/2022] Open
Abstract
Social and communicative impairments are among the core symptoms of autism spectrum disorders (ASD), and a great deal of evidence supports the notion that these impairments are associated with aberrant functioning and connectivity of various cortical networks. The present study explored the links between sources of MEG amplitude in various frequency bands and functional connectivity MRI in the resting state. The goal of combining these modalities was to use sources of neural oscillatory activity, measured with MEG, as functionally relevant seed regions for a more traditional pairwise fMRI connectivity analysis. We performed a seed-based connectivity analysis on resting state fMRI data, using seed regions derived from frequency-specific amplitude sources in resting state MEG data in the same nine subjects with ASD (10–17 years of age). We then compared fMRI connectivity among these MEG-source-derived regions between participants with autism and typically developing, age-matched controls. We used a source modeling technique designed for MEG data to detect significant amplitude sources in six frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), low gamma (30–60 Hz), and high gamma (60–120 Hz). MEG-derived source maps for each participant were co-registered in standard MNI space, and group-level source maps were obtained for each frequency. For each frequency band, the 10 largest clusters resulting from these t-tests were used as regions of interest (ROIs) for the fMRI functional connectivity analysis. Pairwise BOLD signal correlations were obtained between each pair of these ROIs for each frequency band. Each pairwise correlation was compared between the ASD and TD groups using t-tests. We also constrained these pairwise correlations to known network structures, resulting in a follow-up set of correlation matrices specific to each network we considered. Frequency-specific MEG sources had distinct patterns of fMRI resting state functional connectivity in the ASD group, but perhaps the most significant was a finding of hypoconnectivity between many sources of low and high gamma activity. These novel findings suggest that in ASD there are differences in functionally defined networks as shown in previous fMRI studies, as well as between sets of regions defined by magnetoencephalographic neural oscillatory activity.
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Affiliation(s)
- Michael Datko
- Cognitive Science, University of California San DiegoLa Jolla, CA, USA; Neurosciences, University of California San DiegoLa Jolla, CA, USA
| | - Robert Gougelet
- Cognitive Science, University of California San Diego La Jolla, CA, USA
| | - Ming-Xiong Huang
- Department of Radiology, University of California San Diego La Jolla, CA, USA
| | - Jaime A Pineda
- Cognitive Science, University of California San DiegoLa Jolla, CA, USA; Neurosciences, University of California San DiegoLa Jolla, CA, USA
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Huang CW, Huang MX, Ji Z, Swan AR, Angeles AM, Song T, Huang JW, Lee RR. High-resolution MEG source imaging approach to accurately localize Broca’s area in patients with brain tumor or epilepsy. Clin Neurophysiol 2016; 127:2308-16. [DOI: 10.1016/j.clinph.2016.02.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2015] [Revised: 12/15/2015] [Accepted: 02/09/2016] [Indexed: 11/28/2022]
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Kida T, Tanaka E, Kakigi R. Multi-Dimensional Dynamics of Human Electromagnetic Brain Activity. Front Hum Neurosci 2016; 9:713. [PMID: 26834608 PMCID: PMC4717327 DOI: 10.3389/fnhum.2015.00713] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2015] [Accepted: 12/21/2015] [Indexed: 12/21/2022] Open
Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) are invaluable neuroscientific tools for unveiling human neural dynamics in three dimensions (space, time, and frequency), which are associated with a wide variety of perceptions, cognition, and actions. MEG/EEG also provides different categories of neuronal indices including activity magnitude, connectivity, and network properties along the three dimensions. In the last 20 years, interest has increased in inter-regional connectivity and complex network properties assessed by various sophisticated scientific analyses. We herein review the definition, computation, short history, and pros and cons of connectivity and complex network (graph-theory) analyses applied to MEG/EEG signals. We briefly describe recent developments in source reconstruction algorithms essential for source-space connectivity and network analyses. Furthermore, we discuss a relatively novel approach used in MEG/EEG studies to examine the complex dynamics represented by human brain activity. The correct and effective use of these neuronal metrics provides a new insight into the multi-dimensional dynamics of the neural representations of various functions in the complex human brain.
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Affiliation(s)
- Tetsuo Kida
- Department of Integrative Physiology, National Institute for Physiological SciencesOkazaki, Japan
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Huang M, Risling M, Baker DG. The role of biomarkers and MEG-based imaging markers in the diagnosis of post-traumatic stress disorder and blast-induced mild traumatic brain injury. Psychoneuroendocrinology 2016; 63:398-409. [PMID: 25769625 DOI: 10.1016/j.psyneuen.2015.02.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2014] [Revised: 02/13/2015] [Accepted: 02/15/2015] [Indexed: 12/23/2022]
Abstract
BACKGROUND Pervasive use of improvised explosive devices (IEDs), rocket-propelled grenades, and land mines in the recent conflicts in Iraq and Afghanistan has brought traumatic brain injury (TBI) and its impact on health outcomes into public awareness. Blast injuries have been deemed signature wounds of these wars. War-related TBI is not new, having become prevalent during WWI and remaining medically relevant in WWII and beyond. Medicine's past attempts to accurately diagnose and disentangle the pathophysiology of war-related TBI parallels current lines of inquiry and highlights limitations in methodology and attribution of symptom etiology, be it organic, psychological, or behavioral. New approaches and biomarkers are needed. PRECLINICAL Serological biomarkers and biomarkers of injury obtained with imaging techniques represent cornerstones in the translation between experimental data and clinical observations. Experimental models for blast related TBI and PTSD can generate critical data on injury threshold, for example for white matter injury from acceleration. Carefully verified and validated models can be evaluated with gene expression arrays and proteomics to identify new candidates for serological biomarkers. Such models can also be analyzed with diffusion MRI and microscopy in order to identify criteria for detection of diffuse white matter injuries, such as DAI (diffuse axonal injury). The experimental models can also be analyzed with focus on injury outcome in brain stem regions, such as locus coeruleus or nucleus raphe magnus that can be involved in response to anxiety changes. CLINICAL Mild (and some moderate) TBI can be difficult to diagnose because the injuries are often not detectable on conventional MRI or CT. There is accumulating evidence that injured brain tissues in TBI patients generate abnormal low-frequency magnetic activity (ALFMA, peaked at 1-4Hz) that can be measured and localized by magnetoencephalography (MEG). MEG imaging detects TBI abnormalities at the rates of 87% for the mild TBI, group (blast-induced plus non-blast causes) and 100% for the moderate group. Among the mild TBI patients, the rates of abnormalities are 96% and 77% for the blast and non-blast TBI groups, respectively. There is emerging evidence based on fMRI and MEG studies showing hyper-activity in the amygdala and hypo-activity in pre-frontal cortex in individuals with PTSD. MEG signal may serve as a sensitive imaging marker for mTBI, distinguishable from abnormalities generated in association with PTSD. More work is needed to fully describe physiological mechanisms of post-concussive symptoms.
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Affiliation(s)
- Mingxiong Huang
- Radiology Services, VA San Diego Healthcare System, San Diego, CA, USA; Research Services, VA San Diego Healthcare System, San Diego, CA, USA; Department of Radiology, University of California, San Diego, CA, USA.
| | - Mårten Risling
- Karolinska Institutet, Department of Neuroscience, Stockholm, Sweden
| | - Dewleen G Baker
- Veterans Affairs Center for Excellence in Stress and Mental Health (CESAMH), San Diego, CA, USA; University of California San Diego, Department of Psychiatry, La Jolla, USA
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40
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Ghanbari Y, Bloy L, Tunc B, Shankar V, Roberts TPL, Edgar JC, Schultz RT, Verma R. On characterizing population commonalities and subject variations in brain networks. Med Image Anal 2015; 38:215-229. [PMID: 26674972 DOI: 10.1016/j.media.2015.10.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 06/25/2015] [Accepted: 10/21/2015] [Indexed: 12/30/2022]
Abstract
Brain networks based on resting state connectivity as well as inter-regional anatomical pathways obtained using diffusion imaging have provided insight into pathology and development. Such work has underscored the need for methods that can extract sub-networks that can accurately capture the connectivity patterns of the underlying population while simultaneously describing the variation of sub-networks at the subject level. We have designed a multi-layer graph clustering method that extracts clusters of nodes, called 'network hubs', which display higher levels of connectivity within the cluster than to the rest of the brain. The method determines an atlas of network hubs that describes the population, as well as weights that characterize subject-wise variation in terms of within- and between-hub connectivity. This lowers the dimensionality of brain networks, thereby providing a representation amenable to statistical analyses. The applicability of the proposed technique is demonstrated by extracting an atlas of network hubs for a population of typically developing controls (TDCs) as well as children with autism spectrum disorder (ASD), and using the structural and functional networks of a population to determine the subject-level variation of these hubs and their inter-connectivity. These hubs are then used to compare ASD and TDCs. Our method is generalizable to any population whose connectivity (structural or functional) can be captured via non-negative network graphs.
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Affiliation(s)
- Yasser Ghanbari
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States .
| | - Luke Bloy
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States ; Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Birkan Tunc
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Varsha Shankar
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
| | - Timothy P L Roberts
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - J Christopher Edgar
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Robert T Schultz
- Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States
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Liljeström M, Stevenson C, Kujala J, Salmelin R. Task- and stimulus-related cortical networks in language production: Exploring similarity of MEG- and fMRI-derived functional connectivity. Neuroimage 2015; 120:75-87. [DOI: 10.1016/j.neuroimage.2015.07.017] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2014] [Revised: 06/28/2015] [Accepted: 07/07/2015] [Indexed: 11/30/2022] Open
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Huang M, Lee RR. Magnetoencephalography (MEG) Slow-Wave Imaging for Diagnosing Non-acute Mild Traumatic Brain Injury. CURRENT RADIOLOGY REPORTS 2015. [DOI: 10.1007/s40134-015-0121-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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43
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Wens V, Marty B, Mary A, Bourguignon M, Op de Beeck M, Goldman S, Van Bogaert P, Peigneux P, De Tiège X. A geometric correction scheme for spatial leakage effects in MEG/EEG seed-based functional connectivity mapping. Hum Brain Mapp 2015; 36:4604-21. [PMID: 26331630 DOI: 10.1002/hbm.22943] [Citation(s) in RCA: 71] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2014] [Revised: 08/04/2015] [Accepted: 08/04/2015] [Indexed: 11/11/2022] Open
Abstract
Spatial leakage effects are particularly confounding for seed-based investigations of brain networks using source-level electroencephalography (EEG) or magnetoencephalography (MEG). Various methods designed to avoid this issue have been introduced but are limited to particular assumptions about its temporal characteristics. Here, we investigate the usefulness of a model-based geometric correction scheme (GCS) to suppress spatial leakage emanating from the seed location. We analyze its properties theoretically and then assess potential advantages and limitations with simulated and experimental MEG data (resting state and auditory-motor task). To do so, we apply Minimum Norm Estimation (MNE) for source reconstruction and use variation of error parameters, statistical gauging of spatial leakage correction and comparison with signal orthogonalization. Results show that the GCS has a local (i.e., near the seed) effect only, in line with the geometry of MNE spatial leakage, and is able to map spatially all types of brain interactions, including linear correlations eliminated after signal orthogonalization. Furthermore, it is robust against the introduction of forward model errors. On the other hand, the GCS can be affected by local overcorrection effects and seed mislocation. These issues arise with signal orthogonalization too, although significantly less extensively, so the two approaches complement each other. The GCS thus appears to be a valuable addition to the spatial leakage correction toolkits for seed-based FC analyses in source-projected MEG/EEG data.
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Affiliation(s)
- Vincent Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
| | - Brice Marty
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
| | - Alison Mary
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, and UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Mathieu Bourguignon
- Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto NeuroImaging, School of Science, Aalto University, FI-00076 AALTO, Espoo, Finland
| | - Marc Op de Beeck
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
| | - Serge Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
| | - Patrick Van Bogaert
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
| | - Philippe Peigneux
- UR2NF - Neuropsychology and Functional Neuroimaging Research Unit at CRCN - Centre de Recherches Cognition et Neurosciences, and UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - Xavier De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau, UNI - ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,ULB - Hôpital Erasme, Magnetoencephalography Unit, Brussels, Belgium
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Robb Swan A, Nichols S, Drake A, Angeles A, Diwakar M, Song T, Lee RR, Huang MX. Magnetoencephalography Slow-Wave Detection in Patients with Mild Traumatic Brain Injury and Ongoing Symptoms Correlated with Long-Term Neuropsychological Outcome. J Neurotrauma 2015; 32:1510-21. [PMID: 25808909 DOI: 10.1089/neu.2014.3654] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Mild traumatic brain injury (mTBI) is common in the United States, accounting for as many as 75-80% of all TBIs. It is recognized as a significant public health concern, but there are ongoing controversies regarding the etiology of persistent symptoms post-mTBI. This constellation of nonspecific symptoms is referred to as postconcussive syndrome (PCS). The present study combined results from magnetoencephalography (MEG) and cognitive assessment to examine group differences and relationships between brain activity and cognitive performance in 31 military and civilian individuals with a history of mTBI+PCS and 33 matched healthy control subjects. An operator-free analysis was used for MEG data to increase reliability of the technique. Subjects completed a comprehensive neuropsychological assessment, and measures of abnormal slow-wave activity from MEG were collected. Results demonstrated significant group differences on measures of executive functioning and processing speed. In addition, significant correlations between slow-wave activity on MEG and patterns of cognitive functioning were found in cortical areas, consistent with cognitive impairments on exams. Results provide more objective evidence that there may be subtle changes to the neurobiological integrity of the brain that can be detected by MEG. Further, these findings suggest that these abnormalities are associated with cognitive outcomes and may account, at least in part, for long-term PCS in those who have sustained an mTBI.
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Affiliation(s)
- Ashley Robb Swan
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Sharon Nichols
- 4 Department of Neuroscience, University of California , San Diego, San Diego, California
| | - Angela Drake
- 5 Department of Community Health, National University , San Diego, California
| | - AnneMarie Angeles
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Mithun Diwakar
- 3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Tao Song
- 3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Roland R Lee
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,2 Radiology Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
| | - Ming-Xiong Huang
- 1 Research Services, VA San Diego Healthcare System , San Diego, California.,2 Radiology Services, VA San Diego Healthcare System , San Diego, California.,3 Department of Radiology, University of California , San Diego, San Diego, California
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45
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Reis C, Wang Y, Akyol O, Ho WM, Ii RA, Stier G, Martin R, Zhang JH. What's New in Traumatic Brain Injury: Update on Tracking, Monitoring and Treatment. Int J Mol Sci 2015; 16:11903-65. [PMID: 26016501 PMCID: PMC4490422 DOI: 10.3390/ijms160611903] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2015] [Revised: 05/04/2015] [Accepted: 05/06/2015] [Indexed: 12/11/2022] Open
Abstract
Traumatic brain injury (TBI), defined as an alteration in brain functions caused by an external force, is responsible for high morbidity and mortality around the world. It is important to identify and treat TBI victims as early as possible. Tracking and monitoring TBI with neuroimaging technologies, including functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), positron emission tomography (PET), and high definition fiber tracking (HDFT) show increasing sensitivity and specificity. Classical electrophysiological monitoring, together with newly established brain-on-chip, cerebral microdialysis techniques, both benefit TBI. First generation molecular biomarkers, based on genomic and proteomic changes following TBI, have proven effective and economical. It is conceivable that TBI-specific biomarkers will be developed with the combination of systems biology and bioinformation strategies. Advances in treatment of TBI include stem cell-based and nanotechnology-based therapy, physical and pharmaceutical interventions and also new use in TBI for approved drugs which all present favorable promise in preventing and reversing TBI.
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Affiliation(s)
- Cesar Reis
- Department of Anesthesiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA.
| | - Yuechun Wang
- Department of Physiology and Pharmacology, Loma Linda University School of Medicine, 11041 Campus Street, Risley Hall, Room 219, Loma Linda, CA 92354, USA.
- Department of Physiology, School of Medicine, University of Jinan, Guangzhou 250012, China.
| | - Onat Akyol
- Department of Physiology and Pharmacology, Loma Linda University School of Medicine, 11041 Campus Street, Risley Hall, Room 219, Loma Linda, CA 92354, USA.
| | - Wing Mann Ho
- Department of Physiology and Pharmacology, Loma Linda University School of Medicine, 11041 Campus Street, Risley Hall, Room 219, Loma Linda, CA 92354, USA.
- Department of Neurosurgery, University Hospital Innsbruck, Tyrol 6020, Austria.
| | - Richard Applegate Ii
- Department of Anesthesiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA.
| | - Gary Stier
- Department of Anesthesiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA.
| | - Robert Martin
- Department of Anesthesiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA.
| | - John H Zhang
- Department of Anesthesiology, Loma Linda University Medical Center, Loma Linda, CA 92354, USA.
- Department of Physiology and Pharmacology, Loma Linda University School of Medicine, 11041 Campus Street, Risley Hall, Room 219, Loma Linda, CA 92354, USA.
- Department of Neurosurgery, Loma Linda University School of Medicine, Loma Linda, CA 92354, USA.
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Diwakar M, Harrington DL, Maruta J, Ghajar J, El-Gabalawy F, Muzzatti L, Corbetta M, Huang MX, Lee RR. Filling in the gaps: Anticipatory control of eye movements in chronic mild traumatic brain injury. NEUROIMAGE-CLINICAL 2015; 8:210-23. [PMID: 26106545 PMCID: PMC4473731 DOI: 10.1016/j.nicl.2015.04.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Revised: 04/10/2015] [Accepted: 04/12/2015] [Indexed: 01/18/2023]
Abstract
A barrier in the diagnosis of mild traumatic brain injury (mTBI) stems from the lack of measures that are adequately sensitive in detecting mild head injuries. MRI and CT are typically negative in mTBI patients with persistent symptoms of post-concussive syndrome (PCS), and characteristic difficulties in sustaining attention often go undetected on neuropsychological testing, which can be insensitive to momentary lapses in concentration. Conversely, visual tracking strongly depends on sustained attention over time and is impaired in chronic mTBI patients, especially when tracking an occluded target. This finding suggests deficient internal anticipatory control in mTBI, the neural underpinnings of which are poorly understood. The present study investigated the neuronal bases for deficient anticipatory control during visual tracking in 25 chronic mTBI patients with persistent PCS symptoms and 25 healthy control subjects. The task was performed while undergoing magnetoencephalography (MEG), which allowed us to examine whether neural dysfunction associated with anticipatory control deficits was due to altered alpha, beta, and/or gamma activity. Neuropsychological examinations characterized cognition in both groups. During MEG recordings, subjects tracked a predictably moving target that was either continuously visible or randomly occluded (gap condition). MEG source-imaging analyses tested for group differences in alpha, beta, and gamma frequency bands. The results showed executive functioning, information processing speed, and verbal memory deficits in the mTBI group. Visual tracking was impaired in the mTBI group only in the gap condition. Patients showed greater error than controls before and during target occlusion, and were slower to resynchronize with the target when it reappeared. Impaired tracking concurred with abnormal beta activity, which was suppressed in the parietal cortex, especially the right hemisphere, and enhanced in left caudate and frontal–temporal areas. Regional beta-amplitude demonstrated high classification accuracy (92%) compared to eye-tracking (65%) and neuropsychological variables (80%). These findings show that deficient internal anticipatory control in mTBI is associated with altered beta activity, which is remarkably sensitive given the heterogeneity of injuries. Neuropsychological test performance was impaired in mTBI patients. Visual tracking was impaired in the gap task, where targets were randomly occluded. Impaired visual tracking concurred with abnormal MEG beta activity. Beta was suppressed in parietal and enhanced in caudate and frontal–temporal areas. Regional MEG beta-amplitude demonstrated high classification accuracy (92%).
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Affiliation(s)
- Mithun Diwakar
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Deborah L Harrington
- Department of Radiology, University of California, San Diego, San Diego, CA, USA ; Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Jun Maruta
- Brain Trauma Foundation, New York, NY, USA
| | - Jamshid Ghajar
- Brain Trauma Foundation, New York, NY, USA ; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Fady El-Gabalawy
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | - Laura Muzzatti
- Department of Radiology, University of California, San Diego, San Diego, CA, USA
| | | | - Ming-Xiong Huang
- Department of Radiology, University of California, San Diego, San Diego, CA, USA ; Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Roland R Lee
- Department of Radiology, University of California, San Diego, San Diego, CA, USA ; Radiology and Research Services, VA San Diego Healthcare System, San Diego, CA, USA
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47
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Edgar JC, Heiken K, Chen YH, Herrington JD, Chow V, Liu S, Bloy L, Huang M, Pandey J, Cannon KM, Qasmieh S, Levy SE, Schultz RT, Roberts TPL. Resting-state alpha in autism spectrum disorder and alpha associations with thalamic volume. J Autism Dev Disord 2015; 45:795-804. [PMID: 25231288 PMCID: PMC6102716 DOI: 10.1007/s10803-014-2236-1] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Alpha circuits (8-12 Hz), necessary for basic and complex brain processes, are abnormal in autism spectrum disorder (ASD). The present study obtained estimates of resting-state (RS) alpha activity in children with ASD and examined associations between alpha activity, age, and clinical symptoms. Given that the thalamus modulates cortical RS alpha rhythms, associations between thalamic structure and alpha activity were examined. RS magnetoencephalography was obtained from 47 typically-developing children (TDC) and 41 children with ASD. RS alpha activity was measured using distributed source localization. Left and right thalamic volume measurements were also obtained. In both groups, the strongest alpha activity was observed in Calcarine Sulcus regions. In Calcarine regions, only TDC showed the expected association between age and alpha peak frequency. ASD had more alpha activity than TDC in regions bordering the Central Sulcus as well as parietal association cortices. In ASD, whereas greater left Central Sulcus relative alpha activity was associated with higher Social Responsiveness Scale (SRS) scores, greater Calcarine region relative alpha activity was associated with lower SRS scores. Although thalamic volume group differences were not observed, relationships between thalamic volume and Calcarine alpha power were unique to TDC. The present study also identified a failure to shift peak alpha frequency as a function of age in primary alpha-generating areas in children with ASD. Findings suggested that increased RS alpha activity in primary motor and somatosensory as well as parietal multimodal areas-with increased alpha thought to reflect greater inhibition-might impair the ability to identify or interpret social cues. Finally, to our knowledge, this is the first study to report associations between thalamic volume and alpha power, an association observed only in TDC. The lack of thalamic and alpha associations in ASD suggests thalamic contributions to RS alpha abnormalities in ASD.
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Affiliation(s)
- J Christopher Edgar
- Lurie Family Foundations MEG Imaging Center, Department of Radiology, The Children's Hospital of Philadelphia, 34th and Civic Center Blvd, Wood Building, Suite 2115, Philadelphia, PA, 10104, USA,
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48
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Berman JI, Liu S, Bloy L, Blaskey L, Roberts TPL, Edgar JC. Alpha-to-gamma phase-amplitude coupling methods and application to autism spectrum disorder. Brain Connect 2014; 5:80-90. [PMID: 25109843 DOI: 10.1089/brain.2014.0242] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Adult studies have shown that a basic property of resting-state (RS) brain activity is the coupling of posterior alpha oscillations (alpha phase) to posterior gamma oscillations (gamma amplitude). The present study examined whether this basic RS process is present in children. Given reports of abnormal parietal-occipital RS alpha in children with autism spectrum disorder (ASD), the present study examined whether RS alpha-to-gamma phase-amplitude coupling (PAC) is disrupted in ASD. Simulations presented in this study showed limitations with traditional PAC analyses. In particular, to avoid false-positive PAC findings, simulations showed the need to use a unilateral passband to filter the upper frequency band as well as the need for longer epochs of data. For the human study, eyes-closed RS magnetoencephalography data were analyzed from 25 children with ASD and 18 typically developing (TD) children with at least 60 sec of artifact-free data. Source modeling provided continuous time course data at a midline parietal-occipital source for PAC analyses. Greater alpha-to-gamma PAC was observed in ASD than TD (p<0.005). Although children with ASD had higher PAC values, in both groups gamma activity increased at the peak of the alpha oscillation. In addition, an association between alpha power and alpha-to-gamma PAC was observed in both groups, although this relationship was stronger in ASD than TD (p<0.05). Present results demonstrated that although alpha-to-gamma PAC is present in children, this basic RS process is abnormal in children with ASD. Finally, simulations and the human data highlighted the need to consider the interplay between alpha power, epoch length, and choice of signal processing methods on PAC estimates.
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Affiliation(s)
- Jeffrey I Berman
- 1 Department of Radiology, Lurie Family Foundations MEG Imaging Center, The Children's Hospital of Philadelphia , Philadelphia, Pennsylvania
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49
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Huang MX, Yurgil KA, Robb A, Angeles A, Diwakar M, Risbrough VB, Nichols SL, McLay R, Theilmann RJ, Song T, Huang CW, Lee RR, Baker DG. Voxel-wise resting-state MEG source magnitude imaging study reveals neurocircuitry abnormality in active-duty service members and veterans with PTSD. NEUROIMAGE-CLINICAL 2014; 5:408-19. [PMID: 25180160 PMCID: PMC4145534 DOI: 10.1016/j.nicl.2014.08.004] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2014] [Revised: 07/25/2014] [Accepted: 08/02/2014] [Indexed: 11/25/2022]
Abstract
Post-traumatic stress disorder (PTSD) is a leading cause of sustained impairment, distress, and poor quality of life in military personnel, veterans, and civilians. Indirect functional neuroimaging studies using PET or fMRI with fear-related stimuli support a PTSD neurocircuitry model that includes amygdala, hippocampus, and ventromedial prefrontal cortex (vmPFC). However, it is not clear if this model can fully account for PTSD abnormalities detected directly by electromagnetic-based source imaging techniques in resting-state. The present study examined resting-state magnetoencephalography (MEG) signals in 25 active-duty service members and veterans with PTSD and 30 healthy volunteers. In contrast to the healthy volunteers, individuals with PTSD showed: 1) hyperactivity from amygdala, hippocampus, posterolateral orbitofrontal cortex (OFC), dorsomedial prefrontal cortex (dmPFC), and insular cortex in high-frequency (i.e., beta, gamma, and high-gamma) bands; 2) hypoactivity from vmPFC, Frontal Pole (FP), and dorsolateral prefrontal cortex (dlPFC) in high-frequency bands; 3) extensive hypoactivity from dlPFC, FP, anterior temporal lobes, precuneous cortex, and sensorimotor cortex in alpha and low-frequency bands; and 4) in individuals with PTSD, MEG activity in the left amygdala and posterolateral OFC correlated positively with PTSD symptom scores, whereas MEG activity in vmPFC and precuneous correlated negatively with symptom score. The present study showed that MEG source imaging technique revealed new abnormalities in the resting-state electromagnetic signals from the PTSD neurocircuitry. Particularly, posterolateral OFC and precuneous may play important roles in the PTSD neurocircuitry model. Resting-state MEG detects abnormal electromagnetic activity in PTSD neurocircuitry PTSD showed hyperactivity in amygdala, hippocampus, and orbitofrontal cortex PTSD showed hypoactivity in vmPFC, frontal pole, and dlPFC PTSD symptom score correlated with MEG activity
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Kate A Yurgil
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Ashley Robb
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Annemarie Angeles
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Mithun Diwakar
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Victoria B Risbrough
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA ; Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - Sharon L Nichols
- Department of Neurosciences, University of California San Diego, San Diego, CA, USA
| | - Robert McLay
- Naval Medical Center San Diego, San Diego, CA, USA
| | - Rebecca J Theilmann
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Tao Song
- Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Charles W Huang
- Department of Bioengineering, University of California San Diego, San Diego, CA, USA
| | - Roland R Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Radiology, University of California San Diego, San Diego, CA, USA
| | - Dewleen G Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA ; Department of Psychiatry, University of California San Diego, San Diego, CA, USA
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50
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Huang MX, Nichols S, Baker DG, Robb A, Angeles A, Yurgil KA, Drake A, Levy M, Song T, McLay R, Theilmann RJ, Diwakar M, Risbrough VB, Ji Z, Huang CW, Chang DG, Harrington DL, Muzzatti L, Canive JM, Christopher Edgar J, Chen YH, Lee RR. Single-subject-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mild traumatic brain injury. NEUROIMAGE-CLINICAL 2014; 5:109-19. [PMID: 25009772 PMCID: PMC4087185 DOI: 10.1016/j.nicl.2014.06.004] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2014] [Revised: 06/10/2014] [Accepted: 06/11/2014] [Indexed: 01/13/2023]
Abstract
Traumatic brain injury (TBI) is a leading cause of sustained impairment in military and civilian populations. However, mild TBI (mTBI) can be difficult to detect using conventional MRI or CT. Injured brain tissues in mTBI patients generate abnormal slow-waves (1–4 Hz) that can be measured and localized by resting-state magnetoencephalography (MEG). In this study, we develop a voxel-based whole-brain MEG slow-wave imaging approach for detecting abnormality in patients with mTBI on a single-subject basis. A normative database of resting-state MEG source magnitude images (1–4 Hz) from 79 healthy control subjects was established for all brain voxels. The high-resolution MEG source magnitude images were obtained by our recent Fast-VESTAL method. In 84 mTBI patients with persistent post-concussive symptoms (36 from blasts, and 48 from non-blast causes), our method detected abnormalities at the positive detection rates of 84.5%, 86.1%, and 83.3% for the combined (blast-induced plus with non-blast causes), blast, and non-blast mTBI groups, respectively. We found that prefrontal, posterior parietal, inferior temporal, hippocampus, and cerebella areas were particularly vulnerable to head trauma. The result also showed that MEG slow-wave generation in prefrontal areas positively correlated with personality change, trouble concentrating, affective lability, and depression symptoms. Discussion is provided regarding the neuronal mechanisms of MEG slow-wave generation due to deafferentation caused by axonal injury and/or blockages/limitations of cholinergic transmission in TBI. This study provides an effective way for using MEG slow-wave source imaging to localize affected areas and supports MEG as a tool for assisting the diagnosis of mTBI. A voxel-based whole-brain MEG slow-wave source imaging method for mild TBI. The new approach showed 84.5% positive detection rate in 84 mild TBI patients. The new approach detected loci of injury in mild TBI patients on a single-subject basis. MEG slow-wave source imaging revealed brain areas vulnerable to mild TBI. MEG slow-wave generations correlated with mild TBI symptoms.
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Affiliation(s)
- Ming-Xiong Huang
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Radiology, University of California, San Diego, CA, USA
| | - Sharon Nichols
- Department of Neuroscience, University of California, San Diego, CA, USA
| | - Dewleen G Baker
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Psychiatry, University of California, San Diego, CA, USA ; VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Ashley Robb
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Annemarie Angeles
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | - Kate A Yurgil
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA
| | | | - Michael Levy
- Rady Children's Hospital San Diego, University of California, San Diego, CA, USA
| | - Tao Song
- Department of Radiology, University of California, San Diego, CA, USA
| | - Robert McLay
- Naval Medical Center San Diego, San Diego, CA, USA
| | | | - Mithun Diwakar
- Department of Radiology, University of California, San Diego, CA, USA
| | - Victoria B Risbrough
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Psychiatry, University of California, San Diego, CA, USA ; VA Center of Excellence for Stress and Mental Health, San Diego, CA, USA
| | - Zhengwei Ji
- Department of Radiology, University of California, San Diego, CA, USA
| | | | - Douglas G Chang
- Department of Orthopaedics, University of California, San Diego, CA, USA
| | - Deborah L Harrington
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Radiology, University of California, San Diego, CA, USA
| | - Laura Muzzatti
- Department of Radiology, University of California, San Diego, CA, USA
| | - Jose M Canive
- Psychiatry Research, New Mexico VA Healthcare System, Albuquerque, NM, USA ; Departments of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, USA
| | - J Christopher Edgar
- The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA
| | - Yu-Han Chen
- Departments of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, USA ; The Children's Hospital of Philadelphia and University of Pennsylvania, Philadelphia, PA, USA
| | - Roland R Lee
- Radiology, Research, and Psychiatry Services, VA San Diego Healthcare System, San Diego, CA, USA ; Department of Radiology, University of California, San Diego, CA, USA
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