1
|
Adler A, Wax M, Pantazis D. Localization of Brain Signals by Alternating Projection. Biomed Signal Process Control 2024; 90:105796. [PMID: 38249934 PMCID: PMC10795592 DOI: 10.1016/j.bspc.2023.105796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
A popular approach for modeling brain activity in MEG and EEG is based on a small set of current dipoles, where each dipole represents the combined activation of a local area of the brain. Here, we address the problem of multiple dipole localization with a novel solution called Alternating Projection (AP). The AP solution is based on minimizing the least-squares (LS) criterion by transforming the multi-dimensional optimization required for direct LS solution, to a sequential and iterative solution in which one source at a time is localized, while keeping the other sources fixed. Results from simulated, phantom, and human MEG data demonstrated the high accuracy of the AP method, with superior localization results than popular scanning methods from the multiple-signal classification (MUSIC) and beamformer families. In addition, the AP method was more robust to forward model errors resulting from head rotations and translations, as well as different cortex tessellation grids for the forward and inverse solutions, with consistently higher localization accuracy in low SNR and highly correlated sources.
Collapse
Affiliation(s)
- Amir Adler
- Braude College of Enginnering and with the McGovern Institute for Brain Research at MIT
| | | | | |
Collapse
|
2
|
Hecker L, Giri A, Pantazis D, Adler A. LOCALIZATION OF SPATIALLY EXTENDED BRAIN SOURCES BY FLEXIBLE ALTERNATING PROJECTION (FLEX-AP). bioRxiv 2023:2023.11.03.565461. [PMID: 37961131 PMCID: PMC10635117 DOI: 10.1101/2023.11.03.565461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Magnetoencephalography (MEG) and electroencephalography (EEG) are widely employed techniques for the in-vivo measurement of neural activity with exceptional temporal resolution. Modeling the neural sources underlying these signals is of high interest for both neuroscience research and pathology. The method of Alternating Projection (AP) was recently shown to outperform the well-established recursively applied and projected multiple signal classification (RAP-MUSIC) algorithm. In this work, we further enhanced AP to allow for source extent estimation, a novel approach termed flexible extent AP (FLEX-AP). We found that FLEX-AP achieves significantly lower errors for spatially coherent sources compared to AP, RAP-MUSIC, and the corresponding extension, FLEX-RAP-MUSIC. We also found an advantage for discrete dipoles under forward modeling errors encountered in real-world scenarios. Together, our results indicate that the FLEX-AP method can unify dipole fitting and distributed source imaging into a single algorithm with promising accuracy.
Collapse
Affiliation(s)
| | - Amita Giri
- McGovern Institute for Brain Research, MIT
| | | | - Amir Adler
- Braude College of Engineering
- McGovern Institute for Brain Research, MIT
| |
Collapse
|
3
|
Andrade K, Guieysse T, Medani T, Koechlin E, Pantazis D, Dubois B. The dual-path hypothesis for the emergence of anosognosia in Alzheimer's disease. Front Neurol 2023; 14:1239057. [PMID: 38020610 PMCID: PMC10654627 DOI: 10.3389/fneur.2023.1239057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Although neurocognitive models have been proposed to explain anosognosia in Alzheimer's disease (AD), the neural cascade responsible for its origin in the human brain remains unknown. Here, we build on a mechanistic dual-path hypothesis that brings error-monitoring and emotional processing systems as key elements for self-awareness, with distinct impacts on the emergence of anosognosia in AD. Proceeding from the notion of anosognosia as a dimensional syndrome, varying between a lack of concern about one's own deficits (i.e., anosodiaphoria) and a complete lack of awareness of deficits, our hypothesis states that (i) unawareness of deficits would result from primary damage to the error-monitoring system, whereas (ii) anosodiaphoria would more likely result from an imbalance between emotional processing and error-monitoring. In the first case, a synaptic failure in the error-monitoring system, in which the anterior and posterior cingulate cortices play a major role, would have a negative impact on error (or deficits) awareness, preventing patients from becoming aware of their condition. In the second case, an impairment in the emotional processing system, in which the amygdala and the orbitofrontal cortex play a major role, would prevent patients from monitoring the internal milieu for relevant errors (or deficits) and assigning appropriate value to them, thus biasing their impact on the error-monitoring system. Our hypothesis stems on two scientific premises. One comes from preliminary results in AD patients showing a synaptic failure in the error-monitoring system along with a decline of awareness for cognitive difficulties at the time of diagnosis. Another comes from the somatic marker hypothesis, which proposes that emotional signals are critical to adaptive behavior. Further exploration of these premises will be of great interest to illuminate the foundations of self-awareness and improve our knowledge of the underlying paths of anosognosia in AD and other brain disorders.
Collapse
Affiliation(s)
- Katia Andrade
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Assistance Publique-Hôpitaux de Paris (AP-HP), Sorbonne University, Pitié-Salpêtrière Hospital, Paris, France
- Frontlab, Paris Brain Institute (Institut du Cerveau, ICM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Thomas Guieysse
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Assistance Publique-Hôpitaux de Paris (AP-HP), Sorbonne University, Pitié-Salpêtrière Hospital, Paris, France
| | - Takfarinas Medani
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States
| | - Etienne Koechlin
- École Normale Supérieure, Laboratoire de Neurosciences Cognitives et Computationnelles, Paris, France
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Bruno Dubois
- Institute of Memory and Alzheimer’s Disease (IM2A), Department of Neurology, Assistance Publique-Hôpitaux de Paris (AP-HP), Sorbonne University, Pitié-Salpêtrière Hospital, Paris, France
- Frontlab, Paris Brain Institute (Institut du Cerveau, ICM), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| |
Collapse
|
4
|
Baker C, Suárez-Méndez I, Smith G, Marsh EB, Funke M, Mosher JC, Maestú F, Xu M, Pantazis D. Hyperbolic graph embedding of MEG brain networks to study brain alterations in individuals with subjective cognitive decline. bioRxiv 2023:2023.10.23.563643. [PMID: 37961615 PMCID: PMC10634754 DOI: 10.1101/2023.10.23.563643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
An expansive area of research focuses on discerning patterns of alterations in functional brain networks from the early stages of Alzheimer's disease, even at the subjective cognitive decline (SCD) stage. Here, we developed a novel hyperbolic MEG brain network embedding framework for transforming high-dimensional complex MEG brain networks into lower-dimensional hyperbolic representations. Using this model, we computed hyperbolic embeddings of the MEG brain networks of two distinct participant groups: individuals with SCD and healthy controls. We demonstrated that these embeddings preserve both local and global geometric information, presenting reduced distortion compared to rival models, even when brain networks are mapped into low-dimensional spaces. In addition, our findings showed that the hyperbolic embeddings encompass unique SCD-related information that improves the discriminatory power above and beyond that of connectivity features alone. Notably, we introduced a unique metric-the radius of the node embeddings-which effectively proxies the hierarchical organization of the brain. Using this metric, we identified subtle hierarchy organizational differences between the two participant groups, suggesting increased hierarchy in the dorsal attention, frontoparietal, and ventral attention subnetworks among the SCD group. Last, we assessed the correlation between these hierarchical variations and cognitive assessment scores, revealing associations with diminished performance across multiple cognitive evaluations in the SCD group. Overall, this study presents the first evaluation of hyperbolic embeddings of MEG brain networks, offering novel insights into brain organization, cognitive decline, and potential diagnostic avenues of Alzheimer's disease.
Collapse
Affiliation(s)
- Cole Baker
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Isabel Suárez-Méndez
- Department of Experimental Psychology, Complutense University of Madrid, Madrid 28040, Spain
| | | | - Elisabeth B Marsh
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Michael Funke
- Department of Neurology, McGovern Medical School, UTHealth Houston, Houston, TX 77030, USA
| | - John C Mosher
- Department of Neurology, McGovern Medical School, UTHealth Houston, Houston, TX 77030, USA
| | - Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid, Madrid 28040, Spain
| | - Mengjia Xu
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Data Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| |
Collapse
|
5
|
Giri A, Mosher JC, Adler A, Pantazis D. An F-ratio-based method for estimating the number of active sources in MEG. Front Hum Neurosci 2023; 17:1235192. [PMID: 37780957 PMCID: PMC10537939 DOI: 10.3389/fnhum.2023.1235192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/22/2023] [Indexed: 10/03/2023] Open
Abstract
Introduction Magnetoencephalography (MEG) is a powerful technique for studying the human brain function. However, accurately estimating the number of sources that contribute to the MEG recordings remains a challenging problem due to the low signal-to-noise ratio (SNR), the presence of correlated sources, inaccuracies in head modeling, and variations in individual anatomy. Methods To address these issues, our study introduces a robust method for accurately estimating the number of active sources in the brain based on the F-ratio statistical approach, which allows for a comparison between a full model with a higher number of sources and a reduced model with fewer sources. Using this approach, we developed a formal statistical procedure that sequentially increases the number of sources in the multiple dipole localization problem until all sources are found. Results Our results revealed that the selection of thresholds plays a critical role in determining the method's overall performance, and appropriate thresholds needed to be adjusted for the number of sources and SNR levels, while they remained largely invariant to different inter-source correlations, translational modeling inaccuracies, and different cortical anatomies. By identifying optimal thresholds and validating our F-ratio-based method in simulated, real phantom, and human MEG data, we demonstrated the superiority of our F-ratio-based method over existing state-of-the-art statistical approaches, such as the Akaike Information Criterion (AIC) and Minimum Description Length (MDL). Discussion Overall, when tuned for optimal selection of thresholds, our method offers researchers a precise tool to estimate the true number of active brain sources and accurately model brain function.
Collapse
Affiliation(s)
- Amita Giri
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John C. Mosher
- Department of Neurology, McGovern Medical School, Texas Institute for Restorative Neurotechnologies, UTHealth, Houston, TX, United States
| | - Amir Adler
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Electrical Engineering, Braude College of Engineering, Karmiel, Israel
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| |
Collapse
|
6
|
Razafimahatratra S, Guieysse T, Lejeune FX, Houot M, Medani T, Dreyfus G, Klarsfeld A, Villain N, Pereira FR, La Corte V, George N, Pantazis D, Andrade K. Can a failure in the error-monitoring system explain unawareness of memory deficits in Alzheimer's disease? Cortex 2023; 166:428-440. [PMID: 37423786 DOI: 10.1016/j.cortex.2023.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 03/13/2023] [Accepted: 05/11/2023] [Indexed: 07/11/2023]
Abstract
Unawareness of memory deficits is an early manifestation in patients with Alzheimer's disease (AD), which often delays diagnosis. This intriguing behavior constitutes a form of anosognosia, whose neural mechanisms remain largely unknown. We hypothesized that anosognosia may depend on a critical synaptic failure in the error-monitoring system, which would prevent AD patients from being aware of their own memory impairment. To investigate, we measured event-related potentials (ERPs) evoked by erroneous responses during a word memory recognition task in two groups of amyloid positive individuals with only subjective memory complaints at study entry: those who progressed to AD within the five-year study period (PROG group), and those who remained cognitively normal (CTRL group). A significant reduction in the amplitude of the positivity error (Pe), an ERP related to error awareness, was observed in the PROG group at the time of AD diagnosis (vs study entry) in intra-group analysis, as well as when compared with the CTRL group in inter-group analysis, based on the last EEG acquisition for all subjects. Importantly, at the time of AD diagnosis, the PROG group exhibited clinical signs of anosognosia, overestimating their cognitive abilities, as evidenced by the discrepancy scores obtained from caregiver/informant vs participant reports on the cognitive subscale of the Healthy Aging Brain Care Monitor. To our knowledge, this is the first study to reveal the emergence of a failure in the error-monitoring system during a word memory recognition task at the early stages of AD. This finding, along with the decline of awareness for cognitive impairment observed in the PROG group, strongly suggests that a synaptic dysfunction in the error-monitoring system may be the critical neural mechanism at the origin of unawareness of deficits in AD.
Collapse
Affiliation(s)
- Solofo Razafimahatratra
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Thomas Guieysse
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - François-Xavier Lejeune
- Sorbonne Université, Paris Brain Institute (ICM Institut du Cerveau), AP-HP, INSERM, CNRS, University Hospital Pitié-Salpêtrière, Paris, France; Paris Brain Institute's Data and Analysis Core, University Hospital Pitié-Salpêtrière, Paris, France
| | - Marion Houot
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Centre of Excellence of Neurodegenerative Disease (CoEN), AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Clinical Investigation Centre, Institut du Cerveau et de la Moelle épinière (ICM), Pitié-Salpêtrière Hospital Paris, France
| | - Takfarinas Medani
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA
| | | | - André Klarsfeld
- Laboratory of Brain Plasticity, CNRS UMR 8249, ESPCI Paris - PSL, Paris, France
| | - Nicolas Villain
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Filipa Raposo Pereira
- Brain & Spine Institute, ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, F-75013, Paris, France
| | - Valentina La Corte
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Nathalie George
- Brain & Spine Institute, ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Centre MEG-EEG, F-75013, Paris, France
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Katia Andrade
- Institute of Memory and Alzheimer's Disease (IM2A), Department of Neurology, AP-HP, Pitié-Salpêtrière Hospital, Paris, France; Laboratory of Brain Plasticity, CNRS UMR 8249, ESPCI Paris - PSL, Paris, France; FrontLab, Paris Brain Institute, ICM, Pitié Salpêtrière GH, 47 Bd de l'Hôpital, 75013, Paris, France.
| |
Collapse
|
7
|
Bruffaerts R, Pongos A, Shain C, Lipkin B, Siegelman M, Wens V, Sjøgård M, Pantazis D, Blank I, Goldman S, De Tiège X, Fedorenko E. Functional identification of language-responsive channels in individual participants in MEG investigations. bioRxiv 2023:2023.03.23.533424. [PMID: 36993378 PMCID: PMC10055362 DOI: 10.1101/2023.03.23.533424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Making meaningful inferences about the functional architecture of the language system requires the ability to refer to the same neural units across individuals and studies. Traditional brain imaging approaches align and average brains together in a common space. However, lateral frontal and temporal cortex, where the language system resides, is characterized by high structural and functional inter-individual variability. This variability reduces the sensitivity and functional resolution of group-averaging analyses. This problem is compounded by the fact that language areas often lay in close proximity to regions of other large-scale networks with different functional profiles. A solution inspired by other fields of cognitive neuroscience (e.g., vision) is to identify language areas functionally in each individual brain using a 'localizer' task (e.g., a language comprehension task). This approach has proven productive in fMRI, yielding a number of discoveries about the language system, and has been successfully extended to intracranial recording investigations. Here, we apply this approach to MEG. Across two experiments (one in Dutch speakers, n=19; one in English speakers, n=23), we examined neural responses to the processing of sentences and a control condition (nonword sequences). We demonstrated that the neural response to language is spatially consistent at the individual level. The language-responsive sensors of interest were, as expected, less responsive to the nonwords condition. Clear inter-individual differences were present in the topography of the neural response to language, leading to greater sensitivity when the data were analyzed at the individual level compared to the group level. Thus, as in fMRI, functional localization yields benefits in MEG and thus opens the door to probing fine-grained distinctions in space and time in future MEG investigations of language processing.
Collapse
Affiliation(s)
- Rose Bruffaerts
- Computational Neurology, Experimental Neurobiology Unit (ENU), Department of Biomedical Sciences, University of Antwerp, Belgium; Department of Neurosciences, KU Leuven, Belgium
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alvince Pongos
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Bioengineering, UC Berkeley-UCSF, San Francisco, CA, USA
| | - Cory Shain
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benjamin Lipkin
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew Siegelman
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Psychology, Columbia University, New York, NY, USA
| | - Vincent Wens
- Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - Martin Sjøgård
- Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Idan Blank
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Psychology, University of California Los Angeles, CA, USA
| | - Serge Goldman
- Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - Xavier De Tiège
- Department of Functional Neuroimaging, Service of Nuclear Medicine, CUB Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - Evelina Fedorenko
- Brain and Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA, USA
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
8
|
Capone M, Sirohiwal A, Aschi M, Pantazis D, Daidone I. Alternative Fast and Slow Charge‐Separation Pathways in Photosystem II. Angew Chem Int Ed Engl 2023. [DOI: 10.1002/ange.202216276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Affiliation(s)
- Matteo Capone
- University of L'Aquila Department of Physical and Chemical Sciences: Universita degli Studi dell'Aquila Dipartimento di Scienze Fisiche e Chimiche Department of Physical and Chemical Sciences 67100 ITALY
| | - Abhishek Sirohiwal
- Stockholm University: Stockholms Universitet Department of Biochemistry and Biophysics SWEDEN
| | - Massimiliano Aschi
- University of L'Aquila Department of Physical and Chemical Sciences: Universita degli Studi dell'Aquila Dipartimento di Scienze Fisiche e Chimiche Department of Physical and Chemical Sciences ITALY
| | - Dimitrios Pantazis
- Max-Planck-Institut fur Kohlenforschung Institut fur Kohlenforschung GERMANY
| | - Isabella Daidone
- University of L'Aquila Physical and Chemical Sciences Via Vetoio Coppito 1 67010 L'Aquila ITALY
| |
Collapse
|
9
|
Hong ES, Kim HS, Hong SK, Pantazis D, Min BK. Deep learning-based electroencephalic diagnosis of tinnitus symptom. Front Hum Neurosci 2023; 17:1126938. [PMID: 37206311 PMCID: PMC10189886 DOI: 10.3389/fnhum.2023.1126938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Accepted: 04/11/2023] [Indexed: 05/21/2023] Open
Abstract
Tinnitus is a neuropathological phenomenon caused by the recognition of external sound that does not actually exist. Existing diagnostic methods for tinnitus are rather subjective and complicated medical examination procedures. The present study aimed to diagnose tinnitus using deep learning analysis of electroencephalographic (EEG) signals while patients performed auditory cognitive tasks. We found that, during an active oddball task, patients with tinnitus could be identified with an area under the curve of 0.886 through a deep learning model (EEGNet) using EEG signals. Furthermore, using broadband (0.5 to 50 Hz) EEG signals, an analysis of the EEGNet convolutional kernel feature maps revealed that alpha activity might play a crucial role in identifying patients with tinnitus. A subsequent time-frequency analysis of the EEG signals indicated that the tinnitus group had significantly reduced pre-stimulus alpha activity compared with the healthy group. These differences were observed in both the active and passive oddball tasks. Only the target stimuli during the active oddball task yielded significantly higher evoked theta activity in the healthy group compared with the tinnitus group. Our findings suggest that task-relevant EEG features can be considered as a neural signature of tinnitus symptoms and support the feasibility of EEG-based deep-learning approach for the diagnosis of tinnitus.
Collapse
Affiliation(s)
- Eul-Seok Hong
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Medical Center, Seoul, Republic of Korea
| | - Sung Kwang Hong
- Department of Otolaryngology, Hallym University College of Medicine, Anyang, Republic of Korea
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
- Institute of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
- *Correspondence: Byoung-Kyong Min,
| |
Collapse
|
10
|
Guieysse T, Lamothe R, Houot M, Razafimahatratra S, Medani T, Lejeune FX, Dreyfus G, Klarsfeld A, Pantazis D, Koechlin E, Andrade K. Detecting Anosognosia from the Prodromal Stage of Alzheimer's Disease. J Alzheimers Dis 2023; 95:1723-1733. [PMID: 37718816 PMCID: PMC10578267 DOI: 10.3233/jad-230552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/05/2023] [Indexed: 09/19/2023]
Abstract
BACKGROUND Though not originally developed for this purpose, the Healthy Aging Brain Care Monitor (HABC-M) seems a valuable instrument for assessing anosognosia in Alzheimer's disease (AD). OBJECTIVES Our study aimed at 1) investigating the validity of the HABC-M (31 items), and its cognitive, psychological, and functional subscales, in discriminating AD patients from controls; 2) exploring whether the HABC-M discrepancy scores between the self-reports of patients/controls in these different domains and the respective ratings provided by their caregivers/informants correlate with an online measure of self-awareness; 3) determining whether the caregiver burden level, also derived from the HABC-M, could add additional support for detecting anosognosia. METHODS The HABC-M was administered to 30 AD patients and 30 healthy controls, and to their caregivers/informants. A measure of online awareness was established from subjects' estimation of their performances in a computerized experiment. RESULTS The HABC-M discrepancy scores distinguished AD patients from controls. The cognitive subscale discriminated the two groups from the prodromal AD stage, with an AUC of 0.88 [95% CI: 0.78;0.97]. Adding the caregiver burden level raised it to 0.94 [0.86;0.99]. Significant correlations between the HABC-M and online discrepancy scores were observed in the patients group, providing convergent validity of these methods. CONCLUSIONS The cognitive HABC-M (six items) can detect anosognosia across the AD spectrum. The caregiver burden (four items) may corroborate the suspicion of anosognosia. The short-hybrid scale, built from these 10 items instead of the usual 31, showed the highest sensitivity for detecting anosognosia from the prodromal AD stage, which may further help with timely diagnosis.
Collapse
Affiliation(s)
- Thomas Guieysse
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Roxane Lamothe
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Marion Houot
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Centre of Excellence of Neurodegenerative Disease (CoEN), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Solofo Razafimahatratra
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
| | - Takfarinas Medani
- Signal & Image Processing Institute, University of Southern California, Los Angeles, CA, USA
| | - François-Xavier Lejeune
- Paris Brain Institute (Institut du Cerveau, ICM), Data Analysis Core, INSERM, CNRS, Assistance Publique-Hôpitaux de Paris (AP-HP), Sorbonne Université, Pitié-Salpêtrière University Hospital, Paris, France
| | | | - André Klarsfeld
- Laboratory of Brain Plasticity, CNRS UMR 8249, ESPCI Paris - PSL, Paris, France
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Etienne Koechlin
- Laboratoire de Neurosciences Cognitives et Computationnelles, École Normale Supérieure, Paris, France
| | - Katia Andrade
- Department of Neurology, Institute of Memory and Alzheimer’s Disease (IM2A), AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Laboratory of Brain Plasticity, CNRS UMR 8249, ESPCI Paris - PSL, Paris, France
- FrontLab, Paris Brain Institute, ICM, Pitié Salpêtrière GH, Paris, France
| |
Collapse
|
11
|
Zhang W, Pantazis D. Optimizing the rate of visual stimulus presentation in MEG experiments. J Vis 2022. [DOI: 10.1167/jov.22.14.3050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Weijia Zhang
- Fu Foundation School of Engineering and Applied Sciences, Columbia University, New York, NY, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| |
Collapse
|
12
|
Kooij B, Varava P, Fadaei-Tirani F, Scopelliti R, Pantazis D, Van Trieste G, Powers D, Severin K. Copper Complexes with Diazoolefin Ligands and their Photochemical Conversion into Alkenylidene Complexes. Angew Chem Int Ed Engl 2022. [DOI: 10.1002/ange.202214899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Bastiaan Kooij
- EPFL: Ecole Polytechnique Federale de Lausanne Chemistry SWITZERLAND
| | - Paul Varava
- EPFL: Ecole Polytechnique Federale de Lausanne Chemistry SWITZERLAND
| | | | | | - Dimitrios Pantazis
- Max-Planck-Institut für Kohlenforschung: Max-Planck-Institut fur Kohlenforschung Chemistry GERMANY
| | | | | | - Kay Severin
- Swiss Federal Institute of Technology Lausanne EPFL Department of Chemical Sciences and Engineering EPFL - BCH 1015 Lausanne SWITZERLAND
| |
Collapse
|
13
|
Smith CP, Fullerton E, Walton L, Funnell E, Pantazis D, Lugo H. The validity and reliability of wearable devices for the measurement of vertical oscillation for running. PLoS One 2022; 17:e0277810. [PMID: 36395290 PMCID: PMC9671438 DOI: 10.1371/journal.pone.0277810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 11/03/2022] [Indexed: 11/18/2022] Open
Abstract
Wearable devices are a popular training tool to measure biomechanical performance indicators during running, including vertical oscillation (VO). VO is a contributing factor in running economy and injury risk, therefore VO feedback can have a positive impact on running performance. The validity and reliability of the VO measurements from wearable devices is crucial for them to be an effective training tool. The aims of this study were to test the validity and reliability of VO measurements from wearable devices against video analysis of a single trunk marker. Four wearable devices were compared: the INCUS NOVA, Garmin Heart Rate Monitor-Pro (HRM), Garmin Running Dynamics Pod (RDP), and Stryd Running Power Meter Footpod (Footpod). Fifteen participants completed treadmill running at five different self-selected speeds for one minute at each speed. Each speed interval was completed twice. VO was recorded simultaneously by video and the wearables devices. There was significant effect of measurement method on VO (p < 0.001), with the NOVA and Footpod underestimating VO compared to video analysis, while the HRM and RDP overestimated. Although there were significant differences in the average VO values, all devices were significantly correlated with the video analysis (R > = 0.51, p < 0.001). Significant agreement between repeated VO measurements for all devices, revealed the devices to be reliable (ICC > = 0.948, p < 0.001). There was also significant agreement for VO measurements between each device and the video analysis (ICC > = 0.731, p < = 0.001), therefore validating the devices for VO measurement during running. These results demonstrate that wearable devices are valid and reliable tools to detect changes in VO during running. However, VO measurements varied significantly between the different wearables tested and this should be considered when comparing VO values between devices.
Collapse
Affiliation(s)
- Craig P. Smith
- INCUS Performance Ltd., Loughborough, United Kingdom
- * E-mail:
| | | | - Liam Walton
- INCUS Performance Ltd., Loughborough, United Kingdom
| | | | | | - Heinz Lugo
- INCUS Performance Ltd., Loughborough, United Kingdom
| |
Collapse
|
14
|
Bonetti L, Brattico E, Bruzzone SEP, Donati G, Deco G, Pantazis D, Vuust P, Kringelbach ML. Brain recognition of previously learned versus novel temporal sequences: a differential simultaneous processing. Cereb Cortex 2022; 33:5524-5537. [PMID: 36346308 PMCID: PMC10152090 DOI: 10.1093/cercor/bhac439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 10/12/2022] [Accepted: 12/13/2022] [Indexed: 11/09/2022] Open
Abstract
Abstract
Memory for sequences is a central topic in neuroscience, and decades of studies have investigated the neural mechanisms underlying the coding of a wide array of sequences extended over time. Yet, little is known on the brain mechanisms underlying the recognition of previously memorized versus novel temporal sequences. Moreover, the differential brain processing of single items in an auditory temporal sequence compared to the whole superordinate sequence is not fully understood. In this magnetoencephalography (MEG) study, the items of the temporal sequence were independently linked to local and rapid (2–8 Hz) brain processing, while the whole sequence was associated with concurrent global and slower (0.1–1 Hz) processing involving a widespread network of sequentially active brain regions. Notably, the recognition of previously memorized temporal sequences was associated to stronger activity in the slow brain processing, while the novel sequences required a greater involvement of the faster brain processing. Overall, the results expand on well-known information flow from lower- to higher order brain regions. In fact, they reveal the differential involvement of slow and faster whole brain processing to recognize previously learned versus novel temporal information.
Collapse
Affiliation(s)
- L Bonetti
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg , Universitetsbyen 3, 8000, Aarhus C , Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford , Stoke place 7, OX39BX, Oxford , UK
- University of Oxford Department of Psychiatry, , Oxford, UK
- University of Bologna Department of Psychology, , Italy
| | - E Brattico
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg , Universitetsbyen 3, 8000, Aarhus C , Denmark
- University of Bari Aldo Moro Department of Education, Psychology, Communication, , Italy
| | - S E P Bruzzone
- Center for Music in the Brain (MIB) , Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Universitetsbyen 3, 8000, Aarhus C , Denmark
- Copenhagen University Hospital Rigshospitalet Neurobiology Research Unit (NRU), , Inge Lehmanns Vej 6, 2100, Copenhagen , Denmark
- Faculty of Health and Medical Sciences, University of Copenhagen , Blegdamsvej 3B, 2200, Copenhagen , Denmark
| | - G Donati
- University of Bologna Department of Psychology, , Italy
| | - G Deco
- Center for Brain and Cognition, Universitat Pompeu Fabra Computational and Theoretical Neuroscience Group, , Edifici Merce Rodereda, C/ de Ramon Trias Fargas, 25, 08018 Barcelona , Spain
| | - D Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology (MIT) , 77 Massachusetts Ave, Cambridge, MA 02139 , USA
| | - P Vuust
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg , Universitetsbyen 3, 8000, Aarhus C , Denmark
| | - M L Kringelbach
- Center for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg , Universitetsbyen 3, 8000, Aarhus C , Denmark
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford , Stoke place 7, OX39BX, Oxford , UK
- University of Oxford Department of Psychiatry, , Oxford, UK
| |
Collapse
|
15
|
Beach SD, Ozernov-Palchik O, May SC, Centanni TM, Perrachione TK, Pantazis D, Gabrieli JDE. The Neural Representation of a Repeated Standard Stimulus in Dyslexia. Front Hum Neurosci 2022; 16:823627. [PMID: 35634200 PMCID: PMC9133793 DOI: 10.3389/fnhum.2022.823627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 04/19/2022] [Indexed: 11/13/2022] Open
Abstract
The neural representation of a repeated stimulus is the standard against which a deviant stimulus is measured in the brain, giving rise to the well-known mismatch response. It has been suggested that individuals with dyslexia have poor implicit memory for recently repeated stimuli, such as the train of standards in an oddball paradigm. Here, we examined how the neural representation of a standard emerges over repetitions, asking whether there is less sensitivity to repetition and/or less accrual of "standardness" over successive repetitions in dyslexia. We recorded magnetoencephalography (MEG) as adults with and without dyslexia were passively exposed to speech syllables in a roving-oddball design. We performed time-resolved multivariate decoding of the MEG sensor data to identify the neural signature of standard vs. deviant trials, independent of stimulus differences. This "multivariate mismatch" was equally robust and had a similar time course in the two groups. In both groups, standards generated by as few as two repetitions were distinct from deviants, indicating normal sensitivity to repetition in dyslexia. However, only in the control group did standards become increasingly different from deviants with repetition. These results suggest that many of the mechanisms that give rise to neural adaptation as well as mismatch responses are intact in dyslexia, with the possible exception of a putatively predictive mechanism that successively integrates recent sensory information into feedforward processing.
Collapse
Affiliation(s)
- Sara D. Beach
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, United States
| | - Ola Ozernov-Palchik
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sidney C. May
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tracy M. Centanni
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tyler K. Perrachione
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, United States
- Department of Speech, Language and Hearing Sciences, Boston University, Boston, MA, United States
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John D. E. Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, United States
| |
Collapse
|
16
|
Anilkumar A, Ash P, Chakravarty AR, Comba P, DeBeer S, Dey A, Draksharapu A, Goswami D, Itoh S, Karlin K, Lakshmi KV, Mazumdar S, Pantazis D, Parker D, Que L, Rajaraman G, Rath SP, Sastri C, Sen Gupta S, Solomon EI. Electron transfer, spectroscopy and theory: general discussion. Faraday Discuss 2022; 234:245-263. [PMID: 35510729 DOI: 10.1039/d2fd90013k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
17
|
Ash P, Chakravarty AR, Comba P, Dey A, Goswami D, Jäger CM, Karlin K, Kundu S, La Gatta S, López Domene R, Maréchal JD, Mazumdar S, Mugesh G, Pantazis D, Pordea A, Sadler PJ, Schünemann V, Sen Gupta S, Shoji O, Solomon EI, Walton P, Wolny JA. Natural and artificial enzymes and medicinal aspects: general discussion. Faraday Discuss 2022; 234:367-387. [PMID: 35510879 DOI: 10.1039/d2fd90014a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
18
|
Rosenthal IA, Singh SR, Hermann KL, Pantazis D, Conway BR. Color Space Geometry Uncovered with Magnetoencephalography. Curr Biol 2022; 32:1670-1674. [PMID: 35413249 DOI: 10.1016/j.cub.2022.03.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
19
|
Centanni TM, Beach SD, Ozernov-Palchik O, May S, Pantazis D, Gabrieli JDE. Categorical perception and influence of attention on neural consistency in response to speech sounds in adults with dyslexia. Ann Dyslexia 2022; 72:56-78. [PMID: 34495457 PMCID: PMC8901776 DOI: 10.1007/s11881-021-00241-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 07/21/2021] [Indexed: 06/13/2023]
Abstract
Developmental dyslexia is a common neurodevelopmental disorder that is associated with alterations in the behavioral and neural processing of speech sounds, but the scope and nature of that association is uncertain. It has been proposed that more variable auditory processing could underlie some of the core deficits in this disorder. In the current study, magnetoencephalography (MEG) data were acquired from adults with and without dyslexia while they passively listened to or actively categorized tokens from a /ba/-/da/ consonant continuum. We observed no significant group difference in active categorical perception of this continuum in either of our two behavioral assessments. During passive listening, adults with dyslexia exhibited neural responses that were as consistent as those of typically reading adults in six cortical regions associated with auditory perception, language, and reading. However, they exhibited significantly less consistency in the left supramarginal gyrus, where greater inconsistency correlated significantly with worse decoding skills in the group with dyslexia. The group difference in the left supramarginal gyrus was evident only when neural data were binned with a high temporal resolution and was only significant during the passive condition. Interestingly, consistency significantly improved in both groups during active categorization versus passive listening. These findings suggest that adults with dyslexia exhibit typical levels of neural consistency in response to speech sounds with the exception of the left supramarginal gyrus and that this consistency increases during active versus passive perception of speech sounds similarly in the two groups.
Collapse
Affiliation(s)
- T M Centanni
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Psychology, Texas Christian University, Fort Worth, TX, USA.
| | - S D Beach
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - O Ozernov-Palchik
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - S May
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Boston College, Boston, MA, USA
| | - D Pantazis
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - J D E Gabrieli
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
20
|
Hermann KL, Singh SR, Rosenthal IA, Pantazis D, Conway BR. Temporal dynamics of the neural representation of hue and luminance polarity. Nat Commun 2022; 13:661. [PMID: 35115511 PMCID: PMC8814185 DOI: 10.1038/s41467-022-28249-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 01/12/2022] [Indexed: 11/09/2022] Open
Abstract
Hue and luminance contrast are basic visual features. Here we use multivariate analyses of magnetoencephalography data to investigate the timing of the neural computations that extract them, and whether they depend on common neural circuits. We show that hue and luminance-contrast polarity can be decoded from MEG data and, with lower accuracy, both features can be decoded across changes in the other feature. These results are consistent with the existence of both common and separable neural mechanisms. The decoding time course is earlier and more temporally precise for luminance polarity than hue, a result that does not depend on task, suggesting that luminance contrast is an updating signal that separates visual events. Meanwhile, cross-temporal generalization is slightly greater for representations of hue compared to luminance polarity, providing a neural correlate of the preeminence of hue in perceptual grouping and memory. Finally, decoding of luminance polarity varies depending on the hues used to obtain training and testing data. The pattern of results is consistent with observations that luminance contrast is mediated by both L-M and S cone sub-cortical mechanisms.
Collapse
Affiliation(s)
- Katherine L Hermann
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Department of Psychology, Stanford University, Stanford, CA, 94305, USA
| | - Shridhar R Singh
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
| | - Isabelle A Rosenthal
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, 91125, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bevil R Conway
- Laboratory of Sensorimotor Research, National Eye Institute, Bethesda, MD, 20892, USA.
- National Institute of Mental Health, Bethesda, MD, 20892, USA.
| |
Collapse
|
21
|
Cuesta P, Ochoa-Urrea M, Funke M, Hasan O, Zhu P, Marcos A, López ME, Schulz PE, Lhatoo S, Pantazis D, Mosher JC, Maestu F. OUP accepted manuscript. Brain Commun 2022; 4:fcac012. [PMID: 35282163 PMCID: PMC8914494 DOI: 10.1093/braincomms/fcac012] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2021] [Revised: 11/29/2021] [Accepted: 02/01/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Pablo Cuesta
- Department of Radiology, Rehabilitation and Physiotherapy, Complutense University of Madrid, Madrid, Spain
- Correspondence to: Pablo Cuesta Prieto, Associate professor Department of Radiology, Rehabilitation and Physiotherapy, Medicine School Complutense University of Madrid Plaza, Ramón y Cajal, s/n. Ciudad Universitaria 28040 Madrid, Spain E-mail:
| | - Manuela Ochoa-Urrea
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Michael Funke
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Omar Hasan
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Ping Zhu
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
- Vivian L. Smith Department of Neurosurgery, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Alberto Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Spain
| | - Maria Eugenia López
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| | - Paul E. Schulz
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Samden Lhatoo
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, USA
| | - John C. Mosher
- Department of Neurology, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Texas Institute for Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Fernando Maestu
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
- Department of Experimental Psychology, Cognitive Processes and Speech Therapy, Complutense University of Madrid, Madrid, Spain
| |
Collapse
|
22
|
Razafimahatratra S, Guieysse T, Medani T, Lejeune F, Houot M, George N, La Corte V, Klarsfeld A, Dreyfus G, Pantazis D, Dubois B, Andrade K. Why don’t Alzheimer’s disease patients know that they forget? Alzheimers Dement 2021. [DOI: 10.1002/alz.053526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | - Thomas Guieysse
- Institute of Memory and Alzheimer’s Disease Salpêtrière Hospital Paris France
| | | | | | - Marion Houot
- Brain and Spine Institute Salpêtrière Hospital Paris France
| | - Nathalie George
- Institut du Cerveau et de la Moelle Epiniere ICM INSERM U 1127 CNRS UMR 7225 Sorbonne Universite Centre MEG‐EEG Paris France
| | - Valentina La Corte
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A) Départment de Neurologie Paris France
| | - André Klarsfeld
- Laboratoire Plasticité du Cerveau CNRS UMR 8249 ESPCI Paris‐PSL Paris France
| | - Gérard Dreyfus
- Laboratoire Plasticité du Cerveau CNRS UMR 8249 ESPCI Paris‐PSL Paris France
| | | | - Bruno Dubois
- Institute of Memory and Alzheimer's Disease (IM2A) Department of Neurology Pitié‐Salpêtrière Hospital AP‐HP Boulevard de l'hôpital Paris, France Paris F‐75013 France
- Brain & Spine Institute (ICM) INSERM U 1127 CNRS UMR 7225 Boulevard de l'hôpital Paris, France Paris F‐75013 France
| | - Katia Andrade
- Institute of Memory and Alzheimer’s Disease Salpêtrière Hospital Paris France
| |
Collapse
|
23
|
Lydic K, Pantazis D, Kanwisher N. Can MEG Source Localization Reveal the Time Course of Processing in the FFA, PPA, and EBA? J Vis 2021. [DOI: 10.1167/jov.21.9.2758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
|
24
|
Min BK, Kim HS, Ko W, Ahn MH, Suk HI, Pantazis D, Knight RT. Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex. Neuroimage 2021; 237:118165. [PMID: 34000400 PMCID: PMC8344402 DOI: 10.1016/j.neuroimage.2021.118165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/30/2021] [Accepted: 05/11/2021] [Indexed: 11/16/2022] Open
Abstract
The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized linear discriminant analysis to human scalp EEG data and were able to distinguish a mental-rotation task versus a color-perception task with 87% decoding accuracy. Dorsal and ventral areas in lateral PFC provided the dominant features dissociating the two tasks. Our findings show that EEG can reliably decode two independent task states from PFC and emphasize the PFC dorsal/ventral functional specificity in processing the where rotation task versus the what color task.
Collapse
Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Korea.
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Institute of Life Science, Asan Medical Center, Seoul 05505, Korea
| | - Wonjun Ko
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea
| | - Min-Hee Ahn
- Laboratory of Brain and Cognitive Science for Convergence Medicine, College of Medicine, Hallym University, Anyang 14068, Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Korea
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Robert T Knight
- Department of Psychology, Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA 94720, USA
| |
Collapse
|
25
|
Abstract
We present a deep learning solution to the problem of localization of magnetoencephalography (MEG) brain signals. The proposed deep model architectures are tuned to single and multiple time point MEG data, and can estimate varying numbers of dipole sources. Results from simulated MEG data on the cortical surface of a real human subject demonstrated improvements against the popular RAP-MUSIC localization algorithm in specific scenarios with varying SNR levels, inter-source correlation values, and number of sources. Importantly, the deep learning models had robust performance to forward model errors resulting from head translation and rotation and a significant reduction in computation time, to a fraction of 1 ms, paving the way to real-time MEG source localization.
Collapse
Affiliation(s)
- Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Amir Adler
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Electrical Engineering Department, Braude College of Engineering, Karmiel 2161002, Israel
| |
Collapse
|
26
|
Xu M, Sanz DL, Garces P, Maestu F, Li Q, Pantazis D. A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks. IEEE Trans Biomed Eng 2021; 68:1579-1588. [PMID: 33400645 PMCID: PMC8162933 DOI: 10.1109/tbme.2021.3049199] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
Collapse
|
27
|
Beach SD, Ozernov-Palchik O, May SC, Centanni TM, Gabrieli JDE, Pantazis D. Neural Decoding Reveals Concurrent Phonemic and Subphonemic Representations of Speech Across Tasks. Neurobiol Lang (Camb) 2021; 2:254-279. [PMID: 34396148 PMCID: PMC8360503 DOI: 10.1162/nol_a_00034] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 02/21/2021] [Indexed: 06/13/2023]
Abstract
Robust and efficient speech perception relies on the interpretation of acoustically variable phoneme realizations, yet prior neuroimaging studies are inconclusive regarding the degree to which subphonemic detail is maintained over time as categorical representations arise. It is also unknown whether this depends on the demands of the listening task. We addressed these questions by using neural decoding to quantify the (dis)similarity of brain response patterns evoked during two different tasks. We recorded magnetoencephalography (MEG) as adult participants heard isolated, randomized tokens from a /ba/-/da/ speech continuum. In the passive task, their attention was diverted. In the active task, they categorized each token as ba or da. We found that linear classifiers successfully decoded ba vs. da perception from the MEG data. Data from the left hemisphere were sufficient to decode the percept early in the trial, while the right hemisphere was necessary but not sufficient for decoding at later time points. We also decoded stimulus representations and found that they were maintained longer in the active task than in the passive task; however, these representations did not pattern more like discrete phonemes when an active categorical response was required. Instead, in both tasks, early phonemic patterns gave way to a representation of stimulus ambiguity that coincided in time with reliable percept decoding. Our results suggest that the categorization process does not require the loss of subphonemic detail, and that the neural representation of isolated speech sounds includes concurrent phonemic and subphonemic information.
Collapse
Affiliation(s)
- Sara D. Beach
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA, USA
| | - Ola Ozernov-Palchik
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sidney C. May
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Lynch School of Education and Human Development, Boston College, Chestnut Hill, MA, USA
| | - Tracy M. Centanni
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Psychology, Texas Christian University, Fort Worth, TX, USA
| | - John D. E. Gabrieli
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| |
Collapse
|
28
|
Tu Y, Pantazis D, Wilson G, Khan S, Ahlfors S, Kong J. How expectations of pain elicited by consciously and unconsciously perceived cues unfold over time. Neuroimage 2021; 235:117985. [PMID: 33762214 PMCID: PMC8248481 DOI: 10.1016/j.neuroimage.2021.117985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/10/2021] [Accepted: 03/14/2021] [Indexed: 11/30/2022] Open
Abstract
Expectation can shape the perception of pain within a fraction of time, but little is known about how perceived expectation unfolds over time and modulates pain perception. Here, we combine magnetoencephalography (MEG) and machine learning approaches to track the neural dynamics of expectations of pain in healthy participants with both sexes. We found that the expectation of pain, as conditioned by facial cues, can be decoded from MEG as early as 150 ms and up to 1100 ms after cue onset, but decoding expectation elicited by unconsciously perceived cues requires more time and decays faster compared to consciously perceived ones. Also, results from temporal generalization suggest that neural dynamics of decoding cue-based expectation were predominately sustained during cue presentation but transient after cue presentation. Finally, although decoding expectation elicited by consciously perceived cues were based on a series of time-restricted brain regions during cue presentation, decoding relied on the medial prefrontal cortex and anterior cingulate cortex after cue presentation for both consciously and unconsciously perceived cues. These findings reveal the conscious and unconscious processing of expectation during pain anticipation and may shed light on enhancing clinical care by demonstrating the impact of expectation cues.
Collapse
Affiliation(s)
- Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Dimitrios Pantazis
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Seppo Ahlfors
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
| |
Collapse
|
29
|
Rosenthal IA, Singh SR, Hermann KL, Pantazis D, Conway BR. Color Space Geometry Uncovered with Magnetoencephalography. Curr Biol 2021; 31:1127-1128. [PMID: 33689711 DOI: 10.1016/j.cub.2021.02.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
30
|
Rosenthal IA, Singh SR, Hermann KL, Pantazis D, Conway BR. Color Space Geometry Uncovered with Magnetoencephalography. Curr Biol 2021; 31:515-526.e5. [PMID: 33202253 PMCID: PMC7878424 DOI: 10.1016/j.cub.2020.10.062] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/21/2020] [Accepted: 10/21/2020] [Indexed: 02/07/2023]
Abstract
The geometry that describes the relationship among colors, and the neural mechanisms that support color vision, are unsettled. Here, we use multivariate analyses of measurements of brain activity obtained with magnetoencephalography to reverse-engineer a geometry of the neural representation of color space. The analyses depend upon determining similarity relationships among the spatial patterns of neural responses to different colors and assessing how these relationships change in time. We evaluate the approach by relating the results to universal patterns in color naming. Two prominent patterns of color naming could be accounted for by the decoding results: the greater precision in naming warm colors compared to cool colors evident by an interaction of hue and lightness, and the preeminence among colors of reddish hues. Additional experiments showed that classifiers trained on responses to color words could decode color from data obtained using colored stimuli, but only at relatively long delays after stimulus onset. These results provide evidence that perceptual representations can give rise to semantic representations, but not the reverse. Taken together, the results uncover a dynamic geometry that provides neural correlates for color appearance and generates new hypotheses about the structure of color space.
Collapse
Affiliation(s)
- Isabelle A Rosenthal
- Laboratory of Sensorimotor Research, National Eye Institute, Building 49, NIH Main Campus, Bethesda, MD 20892, USA
| | - Shridhar R Singh
- Laboratory of Sensorimotor Research, National Eye Institute, Building 49, NIH Main Campus, Bethesda, MD 20892, USA
| | - Katherine L Hermann
- Laboratory of Sensorimotor Research, National Eye Institute, Building 49, NIH Main Campus, Bethesda, MD 20892, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, 524 Main Street, Cambridge, MA 02139, USA
| | - Bevil R Conway
- Laboratory of Sensorimotor Research, National Eye Institute, Building 49, NIH Main Campus, Bethesda, MD 20892, USA; National Institute of Mental Health, Bethesda, MD 20892, USA.
| |
Collapse
|
31
|
Xu M, Wang Z, Zhang H, Sanz DL, Garces P, Maestú F, Wang H, Li Q, Pantazis D. A new stochastic graph embedding method for Alzheimer’s disease early‐stage prediction and intervention evaluation. Alzheimers Dement 2020. [DOI: 10.1002/alz.047329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Affiliation(s)
- Mengjia Xu
- Massachusetts Institute of Technology Cambridge MA USA
| | - Zhijiang Wang
- Peking University Institute of Mental Health (Sixth Hospital) Beijing China
| | - Haifeng Zhang
- Peking University Institute of Mental Health (Sixth Hospital) Beijing China
| | - David Lopez Sanz
- Center for Biomedical Technology Polytechnic University Pozuelo de Alarcon Spain
| | | | | | - Huali Wang
- National Clinical Research Center for Mental Disorders Beijing China
| | | | | |
Collapse
|
32
|
Sethi Y, Pantazis D. Decoding visual categorical information in MEG using a large and diverse number of naturalistic image stimuli. J Vis 2020. [DOI: 10.1167/jov.20.11.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yuvraj Sethi
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
| |
Collapse
|
33
|
Min BK, Kim HS, Pinotsis DA, Pantazis D. Thalamocortical inhibitory dynamics support conscious perception. Neuroimage 2020; 220:117066. [PMID: 32565278 DOI: 10.1016/j.neuroimage.2020.117066] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2020] [Revised: 05/25/2020] [Accepted: 06/14/2020] [Indexed: 11/28/2022] Open
Abstract
Whether thalamocortical interactions play a decisive role in conscious perception remains an open question. We presented rapid red/green color flickering stimuli, which induced the mental perception of either an illusory orange color or non-fused red and green colors. Using magnetoencephalography, we observed 6-Hz thalamic activity associated with thalamocortical inhibitory coupling only during the conscious perception of the illusory orange color. This sustained thalamic disinhibition was temporally coupled with higher visual cortical activation during the conscious perception of the orange color, providing neurophysiological evidence of the role of thalamocortical synchronization in conscious awareness of mental representation. Bayesian model comparison consistently supported the thalamocortical model in conscious perception. Taken together, experimental and theoretical evidence established the thalamocortical inhibitory network as a gateway to conscious mental representations.
Collapse
Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Hyun Seok Kim
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea
| | - Dimitris A Pinotsis
- Center for Mathematical Neuroscience and Psychology, Department of Psychology, City-University of London, London, EC1V 0HB, UK; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| |
Collapse
|
34
|
Gandhi TK, Tsourides K, Singhal N, Cardinaux A, Jamal W, Pantazis D, Kjelgaard M, Sinha P. Autonomic and Electrophysiological Evidence for Reduced Auditory Habituation in Autism. J Autism Dev Disord 2020; 51:2218-2228. [PMID: 32926307 DOI: 10.1007/s10803-020-04636-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
It is estimated that nearly 90% of children on the autism spectrum exhibit sensory atypicalities. What aspects of sensory processing are affected in autism? Although sensory processing can be studied along multiple dimensions, two of the most basic ones involve examining instantaneous sensory responses and how the responses change over time. These correspond to the dimensions of 'sensitivity' and 'habituation'. Results thus far have indicated that autistic individuals do not differ systematically from controls in sensory acuity/sensitivity. However, data from studies of habituation have been equivocal. We have studied habituation in autism using two measures: galvanic skin response (GSR) and magneto-encephalography (MEG). We report data from two independent studies. The first study, was conducted with 13 autistic and 13 age-matched neurotypical young adults and used GSR to assess response to an extended metronomic sequence. The second study involved 24 participants (12 with an ASD diagnosis), different from those in study 1, spanning the pre-adolescent to young adult age range, and used MEG. Both studies reveal consistent patterns of reduced habituation in autistic participants. These results suggest that autism, through mechanisms that are yet to be elucidated, compromises a fundamental aspect of sensory processing, at least in the auditory domain. We discuss the implications for understanding sensory hypersensitivities, a hallmark phenotypic feature of autism, recently proposed theoretical accounts, and potential relevance for early detection of risk for autism.
Collapse
Affiliation(s)
- Tapan K Gandhi
- Department of Electrical Engineering, India Institute of Technology, New Delhi, 110016, India.
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Kleovoulos Tsourides
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Nidhi Singhal
- Open Doors School, Action for Autism, New Delhi, 110 054, India
| | - Annie Cardinaux
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Wasifa Jamal
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dimitrios Pantazis
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Margaret Kjelgaard
- Communication Sciences and Disorders, Bridgewater State University, Bridgewater, MA, 02325, USA
| | - Pawan Sinha
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
35
|
Min BK, Hämäläinen MS, Pantazis D. New Cognitive Neurotechnology Facilitates Studies of Cortical-Subcortical Interactions. Trends Biotechnol 2020; 38:952-962. [PMID: 32278504 PMCID: PMC7442676 DOI: 10.1016/j.tibtech.2020.03.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Revised: 03/06/2020] [Accepted: 03/06/2020] [Indexed: 11/26/2022]
Abstract
Most of the studies employing neuroimaging have focused on cortical and subcortical signals individually to obtain neurophysiological signatures of cognitive functions. However, understanding the dynamic communication between the cortex and subcortical structures is essential for unraveling the neural correlates of cognition. In this quest, magnetoencephalography (MEG) and electroencephalography (EEG) are the methods of choice because they are noninvasive electrophysiological recording techniques with high temporal resolution. Sophisticated MEG/EEG source estimation techniques and network analysis methods, developed recently, can provide a more comprehensive understanding of the neurophysiological mechanisms of fundamental cognitive processes. Used together with noninvasive modulation of cortical-subcortical communication, these approaches may open up new possibilities for expanding the repertoire of noninvasive cognitive neurotechnology.
Collapse
Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea; McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Harvard Medical School, Boston, MA 02115, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| |
Collapse
|
36
|
Xu M, Wang Z, Zhang H, Pantazis D, Wang H, Li Q. A new Graph Gaussian embedding method for analyzing the effects of cognitive training. PLoS Comput Biol 2020; 16:e1008186. [PMID: 32941425 PMCID: PMC7524000 DOI: 10.1371/journal.pcbi.1008186] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 09/29/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022] Open
Abstract
Identifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer's disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a quantitative method for functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration. An important finding of our study is the ability of the new method to capture subtle changes for individual patients before and after short-term intervention. More broadly, the MG2G method can be used in studying multiple brain disorders and injuries, e.g., in Parkinson's disease or traumatic brain injury (TBI), and hence it will be useful to the wider neuroscience community.
Collapse
Affiliation(s)
- Mengjia Xu
- Division of Applied Mathematics, Brown University, Providence, Rhode Island, United States of America
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Zhijiang Wang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
- Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
- Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| | - Haifeng Zhang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
- Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Huali Wang
- Peking University Institute of Mental Health (Sixth Hospital), Beijing, China
- National Clinical Research Center for Mental Disorders & Key Laboratory of Mental Health, Ministry of Health, Peking University, Beijing, China
- Beijing Municipal Key Laboratory for Translational Research on Diagnosis and Treatment of Dementia, Beijing, China
| | - Quanzheng Li
- Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, United States of America
| |
Collapse
|
37
|
Isik L, Mynick A, Pantazis D, Kanwisher N. The speed of human social interaction perception. Neuroimage 2020; 215:116844. [DOI: 10.1016/j.neuroimage.2020.116844] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 02/27/2020] [Accepted: 04/08/2020] [Indexed: 11/17/2022] Open
|
38
|
Affiliation(s)
- Isabelle Rosenthal
- National Eye Institute
- present address: California Institute of Technology
| | | | | | | | | |
Collapse
|
39
|
Bao H, Bagherzadeh Y, Sanz DL, Garces P, Maestu F, Li Q, Pantazis D. P3-369: EVALUATION OF FUNCTIONAL CONNECTIVITY MEASURES FOR MEG NETWORK-BASED BIOMARKERS IN ALZHEIMER'S DISEASE. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.3402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- Han Bao
- Massachusetts Institute of Technology; Cambridge MA USA
- Massachusetts General Hospital; Boston MA USA
| | | | | | | | | | | | | |
Collapse
|
40
|
Abstract
Some scenes are more memorable than others: they cement in minds with consistencies across observers and time scales. While memory mechanisms are traditionally associated with the end stages of perception, recent behavioral studies suggest that the features driving these memorability effects are extracted early on, and in an automatic fashion. This raises the question: is the neural signal of memorability detectable during early perceptual encoding phases of visual processing? Using the high temporal resolution of magnetoencephalography (MEG), during a rapid serial visual presentation (RSVP) task, we traced the neural temporal signature of memorability across the brain. We found an early and prolonged memorability related signal under a challenging ultra-rapid viewing condition, across a network of regions in both dorsal and ventral streams. This enhanced encoding could be the key to successful storage and recognition.
Collapse
Affiliation(s)
- Yalda Mohsenzadeh
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA, USA.
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Caitlin Mullin
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aude Oliva
- Computer Science and Artificial Intelligence Lab., Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
| |
Collapse
|
41
|
Abstract
Within a fraction of a second of viewing a face, we have already determined its gender, age and identity. A full understanding of this remarkable feat will require a characterization of the computational steps it entails, along with the representations extracted at each. Here, we used magnetoencephalography (MEG) to measure the time course of neural responses to faces, thereby addressing two fundamental questions about how face processing unfolds over time. First, using representational similarity analysis, we found that facial gender and age information emerged before identity information, suggesting a coarse-to-fine processing of face dimensions. Second, identity and gender representations of familiar faces were enhanced very early on, suggesting that the behavioral benefit for familiar faces results from tuning of early feed-forward processing mechanisms. These findings start to reveal the time course of face processing in humans, and provide powerful new constraints on computational theories of face perception.
Collapse
Affiliation(s)
- Katharina Dobs
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- The Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Leyla Isik
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- The Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Dimitrios Pantazis
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Nancy Kanwisher
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- McGovern Institute of Brain Research, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- The Center for Brains, Minds, and Machines, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| |
Collapse
|
42
|
Tadel F, Bock E, Niso G, Mosher JC, Cousineau M, Pantazis D, Leahy RM, Baillet S. MEG/EEG Group Analysis With Brainstorm. Front Neurosci 2019; 13:76. [PMID: 30804744 PMCID: PMC6378958 DOI: 10.3389/fnins.2019.00076] [Citation(s) in RCA: 84] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 01/23/2019] [Indexed: 11/24/2022] Open
Abstract
Brainstorm is a free, open-source Matlab and Java application for multimodal electrophysiology data analytics and source imaging [primarily MEG, EEG and depth recordings, and integration with MRI and functional near infrared spectroscopy (fNIRS)]. We also provide a free, platform-independent executable version to users without a commercial Matlab license. Brainstorm has a rich and intuitive graphical user interface, which facilitates learning and augments productivity for a wider range of neuroscience users with little or no knowledge of scientific coding and scripting. Yet, it can also be used as a powerful scripting tool for reproducible and shareable batch processing of (large) data volumes. This article describes these Brainstorm interactive and scripted features via illustration through the complete analysis of group data from 16 participants in a MEG vision study.
Collapse
Affiliation(s)
- François Tadel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,INSERM U1216 Grenoble Institut des Neurosciences (GIN), Grenoble, France.,Grenoble Institut des Neurosciences, Université Grenoble Alpes, Grenoble, France
| | - Elizabeth Bock
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Guiomar Niso
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Biomedical Image Technologies, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - John C Mosher
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, United States
| | - Martin Cousineau
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA, United States
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| |
Collapse
|
43
|
Centanni TM, Pantazis D, Truong DT, Gruen JR, Gabrieli JDE, Hogan TP. Increased variability of stimulus-driven cortical responses is associated with genetic variability in children with and without dyslexia. Dev Cogn Neurosci 2018; 34:7-17. [PMID: 29894888 PMCID: PMC6969288 DOI: 10.1016/j.dcn.2018.05.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 05/14/2018] [Accepted: 05/24/2018] [Indexed: 12/17/2022] Open
Abstract
Individuals with dyslexia exhibit increased brainstem variability in response to sound. It is unknown as to whether increased variability extends to neocortical regions associated with audition and reading, extends to visual stimuli, and whether increased variability characterizes all children with dyslexia or, instead, a specific subset of children. We evaluated the consistency of stimulus-evoked neural responses in children with (N = 20) or without dyslexia (N = 12) as measured by magnetoencephalography (MEG). Approximately half of the children with dyslexia had significantly higher levels of variability in cortical responses to both auditory and visual stimuli in multiple nodes of the reading network. There was a significant and positive relationship between the number of risk alleles at rs6935076 in the dyslexia-susceptibility gene KIAA0319 and the degree of neural variability in primary auditory cortex across all participants. This gene has been linked with neural variability in rodents and in typical readers. These findings indicate that unstable representations of auditory and visual stimuli in auditory and other reading-related neocortical regions are present in a subset of children with dyslexia and support the link between the gene KIAA0319 and the auditory neural variability across children with or without dyslexia.
Collapse
Affiliation(s)
- T M Centanni
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Psychology, Texas Christian University, Fort Worth, TX, USA.
| | - D Pantazis
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - D T Truong
- Departments of Pediatrics and Genetics, Yale University, New Haven, CT, USA
| | - J R Gruen
- Departments of Pediatrics and Genetics, Yale University, New Haven, CT, USA
| | - J D E Gabrieli
- McGovern Institute for Brain Research and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - T P Hogan
- Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA, USA
| |
Collapse
|
44
|
Dobs K, Isik L, Pantazis D, Kanwisher N. Rapid decoding of face identity, familiarity, gender and age. J Vis 2018. [DOI: 10.1167/18.10.1081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
|
45
|
Mullin C, Mohsenzadeh Y, Pantazis D, Oliva A. The Genesis of Visual Memory through Strong Perceptual Representations: Tracking the Spatio-Temporal Neural Trace of Memorability. J Vis 2018. [DOI: 10.1167/18.10.368] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Caitlin Mullin
- Computer Science and Artificial Intelligence Laboratory, MIT
| | - Yalda Mohsenzadeh
- Computer Science and Artificial Intelligence Laboratory, MITMcGovern Institute for Brain Research, MIT
| | | | - Aude Oliva
- Computer Science and Artificial Intelligence Laboratory, MIT
| |
Collapse
|
46
|
Mohsenzadeh Y, Mullin C, Zhou B, Pantazis D, Oliva A. Spatiotemporal dynamics of categorical representations in the human brain and deep convolutional neural networks. J Vis 2018. [DOI: 10.1167/18.10.400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yalda Mohsenzadeh
- Computer Science and Artificial Intelligence Laboratory, MITMcGovern Institute for Brain Research, MIT
| | - Caitlin Mullin
- Computer Science and Artificial Intelligence Laboratory, MIT
| | - Bolei Zhou
- Computer Science and Artificial Intelligence Laboratory, MIT
| | | | - Aude Oliva
- Computer Science and Artificial Intelligence Laboratory, MIT
| |
Collapse
|
47
|
Teng S, Cichy R, Pantazis D, Oliva A. Tracking tactile braille brain responses in space and time. J Vis 2018. [DOI: 10.1167/18.10.1225] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Santani Teng
- CSAIL, Massachusetts Institute of TechnologySmith-Kettlewell Eye Research Institute
| | - Radoslaw Cichy
- Dept. of Education and Psychology, Free University of Berlin
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
| | - Aude Oliva
- CSAIL, Massachusetts Institute of Technology
| |
Collapse
|
48
|
Mohsenzadeh Y, Qin S, Cichy RM, Pantazis D. Ultra-Rapid serial visual presentation reveals dynamics of feedforward and feedback processes in the ventral visual pathway. eLife 2018; 7:e36329. [PMID: 29927384 PMCID: PMC6029845 DOI: 10.7554/elife.36329] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 06/16/2018] [Indexed: 11/19/2022] Open
Abstract
Human visual recognition activates a dense network of overlapping feedforward and recurrent neuronal processes, making it hard to disentangle processing in the feedforward from the feedback direction. Here, we used ultra-rapid serial visual presentation to suppress sustained activity that blurs the boundaries of processing steps, enabling us to resolve two distinct stages of processing with MEG multivariate pattern classification. The first processing stage was the rapid activation cascade of the bottom-up sweep, which terminated early as visual stimuli were presented at progressively faster rates. The second stage was the emergence of categorical information with peak latency that shifted later in time with progressively faster stimulus presentations, indexing time-consuming recurrent processing. Using MEG-fMRI fusion with representational similarity, we localized recurrent signals in early visual cortex. Together, our findings segregated an initial bottom-up sweep from subsequent feedback processing, and revealed the neural signature of increased recurrent processing demands for challenging viewing conditions.
Collapse
Affiliation(s)
- Yalda Mohsenzadeh
- McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeUnited States
| | - Sheng Qin
- McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeUnited States
| | | | - Dimitrios Pantazis
- McGovern Institute for Brain ResearchMassachusetts Institute of TechnologyCambridgeUnited States
| |
Collapse
|
49
|
Khaligh-Razavi SM, Cichy RM, Pantazis D, Oliva A. Tracking the Spatiotemporal Neural Dynamics of Real-world Object Size and Animacy in the Human Brain. J Cogn Neurosci 2018; 30:1559-1576. [PMID: 29877767 DOI: 10.1162/jocn_a_01290] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Animacy and real-world size are properties that describe any object and thus bring basic order into our perception of the visual world. Here, we investigated how the human brain processes real-world size and animacy. For this, we applied representational similarity to fMRI and MEG data to yield a view of brain activity with high spatial and temporal resolutions, respectively. Analysis of fMRI data revealed that a distributed and partly overlapping set of cortical regions extending from occipital to ventral and medial temporal cortex represented animacy and real-world size. Within this set, parahippocampal cortex stood out as the region representing animacy and size stronger than most other regions. Further analysis of the detailed representational format revealed differences among regions involved in processing animacy. Analysis of MEG data revealed overlapping temporal dynamics of animacy and real-world size processing starting at around 150 msec and provided the first neuromagnetic signature of real-world object size processing. Finally, to investigate the neural dynamics of size and animacy processing simultaneously in space and time, we combined MEG and fMRI with a novel extension of MEG-fMRI fusion by representational similarity. This analysis revealed partly overlapping and distributed spatiotemporal dynamics, with parahippocampal cortex singled out as a region that represented size and animacy persistently when other regions did not. Furthermore, the analysis highlighted the role of early visual cortex in representing real-world size. A control analysis revealed that the neural dynamics of processing animacy and size were distinct from the neural dynamics of processing low-level visual features. Together, our results provide a detailed spatiotemporal view of animacy and size processing in the human brain.
Collapse
|
50
|
Chacon-Castano J, Rathbone DR, Hoffman R, Pantazis D, Yang J, Hornberger E, Hanumara NC. Music and the brain - design of an MEG compatible piano. Annu Int Conf IEEE Eng Med Biol Soc 2017; 2017:521-524. [PMID: 29059924 DOI: 10.1109/embc.2017.8036876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Magnetoencephalography (MEG) neuroimaging has been used to study subjects' responses when listening to music, but research into the effects of playing music has been limited by the lack of MEG compatible instruments that can operate in a magnetically shielded environment without creating electromagnetic interference. This paper describes the design and preliminary testing of an MEG compatible piano keyboard with 25 full size keys that employs a novel 3-state optical encoder design and electronics to provide realistic velocity-controlled volume modulation. This instrument will allow researchers to study musical performance on a finer timescale than fMRI and enable a range of MEG studies.
Collapse
|