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Mamashli F, Khan S, Hatamimajoumerd E, Jas M, Uluç I, Lankinen K, Obleser J, Friederici AD, Maess B, Ahveninen J. Characterizing directional dynamics of semantic prediction based on inter-regional temporal generalization. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580183. [PMID: 38405823 PMCID: PMC10888763 DOI: 10.1101/2024.02.13.580183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The event-related potential/field component N400(m) has been widely used as a neural index for semantic prediction. It has long been hypothesized that feedback information from inferior frontal areas plays a critical role in generating the N400. However, due to limitations in causal connectivity estimation, direct testing of this hypothesis has remained difficult. Here, magnetoencephalography (MEG) data was obtained during a classic N400 paradigm where the semantic predictability of a fixed target noun was manipulated in simple German sentences. To estimate causality, we implemented a novel approach based on machine learning and temporal generalization to estimate the effect of inferior frontal gyrus (IFG) on temporal areas. In this method, a support vector machine (SVM) classifier is trained on each time point of the neural activity in IFG to classify less predicted (LP) and highly predicted (HP) nouns and then tested on all time points of superior/middle temporal sub-regions activity (and vice versa, to establish spatio-temporal evidence for or against causality). The decoding accuracy was significantly above chance level when the classifier was trained on IFG activity and tested on future activity in superior and middle temporal gyrus (STG/MTG). The results present new evidence for a model predictive speech comprehension where predictive IFG activity is fed back to shape subsequent activity in STG/MTG, implying a feedback mechanism in N400 generation. In combination with the also observed strong feedforward effect from left STG/MTG to IFG, our findings provide evidence of dynamic feedback and feedforward influences between IFG and temporal areas during N400 generation.
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Affiliation(s)
- Fahimeh Mamashli
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
| | - Sheraz Khan
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
| | - Elaheh Hatamimajoumerd
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115
| | - Mainak Jas
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
| | - Işıl Uluç
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
| | - Kaisu Lankinen
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
| | - Jonas Obleser
- Department of Psychology, University of Lübeck, Lübeck 23562, Germany
| | - Angela D. Friederici
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Burkhard Maess
- MEG and Cortical Networks Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig 04103, Germany
| | - Jyrki Ahveninen
- Department of Radiology, Massachusetts General Hospital, Athinoula A. Martinos Center for Biomedical Imaging, Harvard Medical School, Boston, MA 02129
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Kim H, Seo P, Kim MJ, Huh JI, Sunwoo JS, Cha KS, Jeong E, Kim HJ, Jung KY, Kim KH. Characterization of attentional event-related potential from REM sleep behavior disorder patients based on explainable machine learning. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107496. [PMID: 36972628 DOI: 10.1016/j.cmpb.2023.107496] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 02/20/2023] [Accepted: 03/17/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Idiopathic rapid eye movement sleep behavior disorder (iRBD) is a prodromal stage of neurodegeneration and is associated with cortical dysfunction. The purpose of this study was to investigate the spatiotemporal characteristics of cortical activities underlying impaired visuospatial attention in iRBD patients using an explainable machine-learning approach. METHODS An algorithm based on a convolutional neural network (CNN) was devised to discriminate cortical current source activities of iRBD patients due to single-trial event-related potentials (ERPs), from those of normal controls. The ERPs from 16 iRBD patients and 19 age- and sex-matched normal controls were recorded while the subjects were performing visuospatial attentional task, and converted to two-dimensional images representing current source densities on flattened cortical surface. The CNN classifier was trained based on overall data, and then, a transfer learning approach was applied for the fine-tuning to each patient. RESULTS The trained classifier yielded high classification accuracy. The critical features for the classification were determined by layer-wise relevance propagation, so that the spatiotemporal characteristics of cortical activities that were most relevant to cognitive impairment in iRBD were revealed. CONCLUSIONS These results suggest that the recognized dysfunction in visuospatial attention of iRBD patients originates from neural activity impairment in relevant cortical regions and may contribute to the development of useful iRBD biomarkers based on neural activity.
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Affiliation(s)
- Hyun Kim
- Department of Biomedical Engineering College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do 220-710, South Korea
| | - Pukyeong Seo
- Department of Biomedical Engineering College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do 220-710, South Korea
| | - Min Ju Kim
- Department of Biomedical Engineering College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do 220-710, South Korea
| | - Jun Il Huh
- Department of Biomedical Engineering College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do 220-710, South Korea
| | - Jun-Sang Sunwoo
- Department of Neurology, Kangbuk Samsung Hospital, Seoul, South Korea
| | - Kwang Su Cha
- Department of Neurology Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - El Jeong
- Interdisciplinary Program in Bioengineering College of Engineering, Seoul National University, Seoul, South Korea
| | - Han-Joon Kim
- Department of Neurology Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea
| | - Ki-Young Jung
- Department of Neurology Seoul National University Hospital, Seoul National University College of Medicine, 101, Daehak-ro, Jongno-gu, Seoul 03080, South Korea.
| | - Kyung Hwan Kim
- Department of Biomedical Engineering College of Health Science, Yonsei University, 234 Maeji-ri, Heungup-myun, Wonju, Gangwon-do 220-710, South Korea.
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Alterations in EEG functional connectivity in individuals with depression: A systematic review. J Affect Disord 2023; 328:287-302. [PMID: 36801418 DOI: 10.1016/j.jad.2023.01.126] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 01/22/2023] [Accepted: 01/30/2023] [Indexed: 02/19/2023]
Abstract
The brain works as an organised, network-like structure of functionally interconnected regions. Disruptions to interconnectivity in certain networks have been linked to symptoms of depression and impairments in cognition. Electroencephalography (EEG) is a low-burden tool by which differences in functional connectivity (FC) can be assessed. This systematic review aims to provide a synthesis of evidence relating to EEG FC in depression. A comprehensive electronic literature search for terms relating to depression, EEG, and FC was conducted on studies published before the end of November 2021, according to PRISMA guidelines. Studies comparing EEG measures of FC of individuals with depression to that of healthy control groups were included. Data was extracted by two independent reviewers, and the quality of EEG FC methods was assessed. Fifty-two studies assessing EEG FC in depression were identified: 36 assessed resting-state FC, and 16 assessed task-related or other (i.e., sleep) FC. Somewhat consistent findings in resting-state studies suggest for no differences between depression and control groups in EEG FC in the delta and gamma frequencies. However, while most resting-state studies noted a difference in alpha, theta, and beta, no clear conclusions could be drawn about the direction of the difference, due to considerable inconsistencies between study design and methodology. This was also true for task-related and other EEG FC. More robust research is needed to understand the true differences in EEG FC in depression. Given that the FC between brain regions drives behaviour, cognition, and emotion, characterising how FC differs in depression is essential for understanding the aetiology of depression.
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Ahveninen J, Uluç I, Raij T, Nummenmaa A, Mamashli F. Spectrotemporal content of human auditory working memory represented in functional connectivity patterns. Commun Biol 2023; 6:294. [PMID: 36941477 PMCID: PMC10027691 DOI: 10.1038/s42003-023-04675-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 03/07/2023] [Indexed: 03/23/2023] Open
Abstract
Recent research suggests that working memory (WM), the mental sketchpad underlying thinking and communication, is maintained by multiple regions throughout the brain. Whether parts of a stable WM representation could be distributed across these brain regions is, however, an open question. We addressed this question by examining the content-specificity of connectivity-pattern matrices between subparts of cortical regions-of-interest (ROI). These connectivity patterns were calculated from functional MRI obtained during a ripple-sound auditory WM task. Statistical significance was assessed by comparing the decoding results to a null distribution derived from a permutation test considering all comparable two- to four-ROI connectivity patterns. Maintained WM items could be decoded from connectivity patterns across ROIs in frontal, parietal, and superior temporal cortices. All functional connectivity patterns that were specific to maintained sound content extended from early auditory to frontoparietal cortices. Our results demonstrate that WM maintenance is supported by content-specific patterns of functional connectivity across different levels of cortical hierarchy.
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Affiliation(s)
- Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA.
- Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Işıl Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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Steele AG, Manson GA, Horner PJ, Sayenko DG, Contreras-Vidal JL. Effects of transcutaneous spinal stimulation on spatiotemporal cortical activation patterns: A proof-of-concept EEG study. J Neural Eng 2022; 19. [PMID: 35732141 DOI: 10.1088/1741-2552/ac7b4b] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 06/22/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Transcutaneous spinal cord stimulation (TSS) has been shown to be a promising non-invasive alternative to epidural spinal cord stimulation (ESS) for improving outcomes of people with spinal cord injury (SCI). However, studies on the effects of TSS on cortical activation are limited. Our objectives were to evaluate the spatiotemporal effects of TSS on brain activity, and determine changes in functional connectivity under several different stimulation conditions. As a control, we also assessed the effects of functional electrical stimulation (FES) on cortical activity. APPROACH Non-invasive scalp electroencephalography (EEG) was recorded during TSS or FES while five neurologically intact participants performed one of three lower-limb tasks while in the supine position: (1) A no contraction control task, (2) a rhythmic contraction task, or (3) a tonic contraction task. After EEG denoising and segmentation, independent components were clustered across subjects to characterize sensorimotor networks in the time and frequency domains. Independent components of the event related potentials (ERPs) were calculated for each cluster and condition. Next, a Generalized Partial Directed Coherence (gPDC) analysis was performed on each cluster to compare the functional connectivity between conditions and tasks. RESULTS Independent Component analysis of EEG during TSS resulted in three clusters identified at Brodmann areas (BA) 9, BA 6, and BA 4, which are areas associated with working memory, planning, and movement control. Lastly, we found significant (p < 0.05, adjusted for multiple comparisons) increases and decreases in functional connectivity of clusters during TSS, but not during FES when compared to the no stimulation conditions. SIGNIFICANCE The findings from this study provide evidence of how TSS recruits cortical networks during tonic and rhythmic lower limb movements. These results have implications for the development of spinal cord-based computer interfaces, and the design of neural stimulation devices for the treatment of pain and sensorimotor deficit.
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Affiliation(s)
- Alexander G Steele
- Department of Neurosurgery, Houston Methodist Research Institute, 6670 Bertner Ave, Houston, Texas, 77030-2707, UNITED STATES
| | - Gerome A Manson
- Department of Neurosurgery, Houston Methodist Research Institute, 6670 Bertner Ave, Houston, Texas, 77030-2707, UNITED STATES
| | - Philip J Horner
- Department of Neurosurgery, Houston Methodist Research Institute, 6670 Bertner Ave, Houston, Texas, 77030-2707, UNITED STATES
| | - Dimitry G Sayenko
- Department of Neurosurgery, Houston Methodist Research Institute, 6670 Bertner Ave, Houston, Texas, 77030-2707, UNITED STATES
| | - Jose L Contreras-Vidal
- Electrical and Computer Engineering, University of Houston, N308 Engineering Building I, Houston, Texas, 77204-4005, UNITED STATES
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Liu L, Ren J, Li Z, Yang C. A review of MEG dynamic brain network research. Proc Inst Mech Eng H 2022; 236:763-774. [PMID: 35465768 DOI: 10.1177/09544119221092503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The dynamic description of neural networks has attracted the attention of researchers for dynamic networks may carry more information compared with resting-state networks. As a non-invasive electrophysiological data with high temporal and spatial resolution, magnetoencephalogram (MEG) can provide rich information for the analysis of dynamic functional brain networks. In this review, the development of MEG brain network was summarized. Several analysis methods such as sliding window, Hidden Markov model, and time-frequency based methods used in MEG dynamic brain network studies were discussed. Finally, the current research about multi-modal brain network analysis and their applications with MEG neurophysiology, which are prospected to be one of the research directions in the future, were concluded.
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Affiliation(s)
- Lu Liu
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
| | - Jiechuan Ren
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Zhimei Li
- Department of Internal Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chunlan Yang
- Faculty of Environment and Life, Beijing University of Technology, Beijing, China
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Ng B, Reh RK, Mostafavi S. A practical guide to applying machine learning to infant EEG data. Dev Cogn Neurosci 2022; 54:101096. [PMID: 35334336 PMCID: PMC8943418 DOI: 10.1016/j.dcn.2022.101096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 03/07/2022] [Accepted: 03/10/2022] [Indexed: 11/08/2022] Open
Abstract
Electroencephalography (EEG) has been widely adopted by the developmental cognitive neuroscience community, but the application of machine learning (ML) in this domain lags behind adult EEG studies. Applying ML to infant data is particularly challenging due to the low number of trials, low signal-to-noise ratio, high inter-subject variability, and high inter-trial variability. Here, we provide a step-by-step tutorial on how to apply ML to classify cognitive states in infants. We describe the type of brain attributes that are widely used for EEG classification and also introduce a Riemannian geometry based approach for deriving connectivity estimates that account for inter-trial and inter-subject variability. We present pipelines for learning classifiers using trials from a single infant and from multiple infants, and demonstrate the application of these pipelines on a standard infant EEG dataset of forty 12-month-old infants collected under an auditory oddball paradigm. While we classify perceptual states induced by frequent versus rare stimuli, the presented pipelines can be easily adapted for other experimental designs and stimuli using the associated code that we have made publicly available.
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Miljevic A, Bailey NW, Vila-Rodriguez F, Herring SE, Fitzgerald PB. EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:546-554. [PMID: 34740847 DOI: 10.1016/j.bpsc.2021.10.017] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 10/15/2021] [Accepted: 10/19/2021] [Indexed: 10/19/2022]
Abstract
Brain connectivity can be estimated through many analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.
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Affiliation(s)
- Aleksandra Miljevic
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia.
| | - Neil W Bailey
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
| | - Fidel Vila-Rodriguez
- Non-Invasive Neurostimulation Therapies Laboratory, Dept. Psychiatry, The University of British Columbia, Vancouver, BC, Canada
| | - Sally E Herring
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
| | - Paul B Fitzgerald
- Epworth Centre for Innovation in Mental Health, Department of Psychiatry, Central Clinical School, Monash University, Epworth HealthCare, 888 Toorak Rd, Camberwell, Victoria 3124, Australia
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Mamashli F, Khan S, Hämäläinen M, Jas M, Raij T, Stufflebeam SM, Nummenmaa A, Ahveninen J. Synchronization patterns reveal neuronal coding of working memory content. Cell Rep 2021; 36:109566. [PMID: 34433024 PMCID: PMC8428113 DOI: 10.1016/j.celrep.2021.109566] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 04/26/2021] [Accepted: 07/28/2021] [Indexed: 11/24/2022] Open
Abstract
Neuronal oscillations are suggested to play an important role in auditory working memory (WM), but their contribution to content-specific representations has remained unclear. Here, we measure magnetoencephalography during a retro-cueing task with parametric ripple-sound stimuli, which are spectrotemporally similar to speech but resist non-auditory memory strategies. Using machine learning analyses, with rigorous between-subject cross-validation and non-parametric permutation testing, we show that memorized sound content is strongly represented in phase-synchronization patterns between subregions of auditory and frontoparietal cortices. These phase-synchronization patterns predict the memorized sound content steadily across the studied maintenance period. In addition to connectivity-based representations, there are indices of more local, “activity silent” representations in auditory cortices, where the decoding accuracy of WM content significantly increases after task-irrelevant “impulse stimuli.” Our results demonstrate that synchronization patterns across auditory sensory and association areas orchestrate neuronal coding of auditory WM content. This connectivity-based coding scheme could also extend beyond the auditory domain. Mamashli et al. use machine learning analyses of human magnetoencephalography (MEG) recordings to study “working memory,” maintenance of information in mind over brief periods of time. Their results show that the human brain maintains working memory content in transient functional connectivity patterns across sensory and association areas.
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Affiliation(s)
- Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Matti Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Mainak Jas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Tommi Raij
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Departments of Physical Medicine and Rehabilitation and Neurobiology, Northwestern University, 710 North Lake Shore Drive, Chicago, IL 60611, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Bldg. 149 13(th) Street, Charlestown, MA 02129, USA; Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
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Yin W, Mamashli F, Buhl DL, Khudyakov P, Volfson D, Martenyi F, Gevorkyan H, Rosen L, Simen AA. Safety, pharmacokinetics and quantitative EEG modulation of TAK-071, a novel muscarinic M1 receptor positive allosteric modulator, in healthy subjects. Br J Clin Pharmacol 2021; 88:600-612. [PMID: 34240455 PMCID: PMC9291057 DOI: 10.1111/bcp.14975] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 06/06/2021] [Accepted: 06/19/2021] [Indexed: 12/26/2022] Open
Abstract
Aims TAK‐071 is a muscarinic M1 receptor positive allosteric modulator designed to have low cooperativity with acetylcholine. This was a first‐in‐human study to evaluate the safety, pharmacokinetics, and pharmacodynamics of TAK‐071. Methods TAK‐071 was administered as single and multiple doses in a randomized, double‐blind, placebo‐controlled, parallel‐group design in healthy volunteers alone and in combination with donepezil. Laboratory, electrocardiogram (ECG) and electroencephalogram (EEG) evaluations were performed. Cerebrospinal fluid and blood samples were taken to evaluate the pharmacokinetics (PK), relative bioavailability and food effect. Results TAK‐071 was safe and well tolerated, and no deaths or serious adverse events occurred. TAK‐071 demonstrated a long mean (% coefficient of variation) half‐life of 46.3 (25.2%) to 60.5 (51.5%) hours and excellent brain penetration following oral dosing. Coadministration with donepezil had no impact on the PK of either drug. There was no food effect on systemic exposure. Quantitative EEG analysis revealed that TAK‐071 40‐80 mg increased power in the 7‐9 Hz range in the posterior electrode group with eyes open and 120‐160 mg doses increased power in the 16‐18 Hz range and reduced power in the 2‐4 Hz range in central‐posterior areas with eyes open and eyes closed. Functional connectivity was significantly reduced after TAK‐071 at high doses and was enhanced with coadministration of donepezil under the eyes‐closed condition. Conclusions PK and safety profiles of TAK‐071 were favorable, including those exceeding expected pharmacologically active doses based on preclinical data. When administered without donepezil TAK‐071 was largely free of cholinergic adverse effects. Further clinical evaluation of TAK‐071 is warranted.
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Affiliation(s)
- Wei Yin
- Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA
| | - Fahimeh Mamashli
- Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA.,Signal Insights, LLC, Cambridge, MA, USA
| | - Derek L Buhl
- Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA
| | | | | | | | - Hakop Gevorkyan
- California Clinical Trials Medical Group, in affiliation with Parexel International, Glendale, CA, USA
| | - Laura Rosen
- Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA
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Mamashli F, Kozhemiako N, Khan S, Nunes AS, McGuiggan NM, Losh A, Joseph RM, Ahveninen J, Doesburg SM, Hämäläinen MS, Kenet T. Children with autism spectrum disorder show altered functional connectivity and abnormal maturation trajectories in response to inverted faces. Autism Res 2021; 14:1101-1114. [PMID: 33709531 DOI: 10.1002/aur.2497] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 02/08/2021] [Indexed: 12/21/2022]
Abstract
The processing of information conveyed by faces is a critical component of social communication. While the neurophysiology of processing upright faces has been studied extensively in autism spectrum disorder (ASD), less is known about the neurophysiological abnormalities associated with processing inverted faces in ASD. We used magnetoencephalography (MEG) to study both long-range and local functional connectivity, with the latter assessed using local cross-frequency coupling, in response to inverted faces stimuli, in 7-18 years old individuals with ASD and age and IQ matched typically developing (TD) individuals. We found abnormally reduced coupling between the phase of the alpha rhythm and the amplitude of the gamma rhythm in the fusiform face area (FFA) in response to inverted faces, as well as reduced long-range functional connectivity between the FFA and the inferior frontal gyrus (IFG) in response to inverted faces in the ASD group. These group differences were absent in response to upright faces. The magnitude of functional connectivity between the FFA and the IFG was significantly correlated with the severity of ASD, and FFA-IFG long-range functional connectivity increased with age in TD group, but not in the ASD group. Our findings suggest that both local and long-range functional connectivity are abnormally reduced in children with ASD when processing inverted faces, and that the pattern of abnormalities associated with the processing of inverted faces differs from the pattern of upright faces in ASD, likely due to the presumed greater reliance on top-down regulations necessary for efficient processing of inverted faces. LAY SUMMARY: We found alterations in the neurophysiological responses to inverted faces in children with ASD, that were not reflected in the evoked responses, and were not observed in the responses to upright faces. These alterations included reduced local functional connectivity in the fusiform face area (FFA), and decreased long-range alpha-band modulated functional connectivity between the FFA and the left IFG. The magnitude of long-range functional connectivity between the FFA and the inferior frontal gyrus was correlated with the severity of ASD.
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Affiliation(s)
- Fahimeh Mamashli
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Nataliia Kozhemiako
- Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Sheraz Khan
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Adonay S Nunes
- Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA.,Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Nicole M McGuiggan
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA
| | - Ainsley Losh
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Robert M Joseph
- Department of Anatomy and Neurobiology, Boston University, Boston, Massachusetts, USA
| | - Jyrki Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Vancouver, British Columbia, Canada.,Behavioral and Cognitive Neuroscience Institute, Simon Fraser University, Vancouver, British Columbia, Canada
| | - Matti S Hämäläinen
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Radiology, MGH, Harvard Medical School, Boston, Massachusetts, USA
| | - Tal Kenet
- Athinoula A. Martinos Center for Biomedical Imaging, MGH/HST, Charlestown, Massachusetts, USA.,Department of Neurology, MGH, Harvard Medical School, Boston, Massachusetts, USA
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12
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High-dimensional brain-wide functional connectivity mapping in magnetoencephalography. J Neurosci Methods 2020; 348:108991. [PMID: 33181166 DOI: 10.1016/j.jneumeth.2020.108991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/06/2020] [Accepted: 10/22/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. NEW METHOD We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis. RESULTS We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease. CONCLUSIONS Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
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13
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Ronconi L, Vitale A, Federici A, Pini E, Molteni M, Casartelli L. Altered neural oscillations and connectivity in the beta band underlie detail-oriented visual processing in autism. Neuroimage Clin 2020; 28:102484. [PMID: 33395975 PMCID: PMC7663221 DOI: 10.1016/j.nicl.2020.102484] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 08/11/2020] [Accepted: 10/22/2020] [Indexed: 11/21/2022]
Abstract
Sensory and perceptual anomalies may have a major impact on basic cognitive and social skills in humans. Autism Spectrum Disorder (ASD) represents a special perspective to explore this relationship, being characterized by both these features. The present study employed electroencephalography (EEG) to test whether detail-oriented visual perception, a recognized hallmark of ASD, is associated with altered neural oscillations and functional connectivity in the beta frequency band, considering its role in feedback and top-down reentrant signalling in the typical population. Using a visual crowding task, where participants had to discriminate a peripheral target letter surrounded by flankers at different distances, we found that detail-oriented processing in children with ASD, as compared to typically developing peers, could be attributed to anomalous oscillatory activity in the beta band (15-30 Hz), while no differences emerged in the alpha band (8-12 Hz). Altered beta oscillatory response reflected in turn atypical functional connectivity between occipital areas, where the initial stimulus analysis is accomplished, and infero-temporal regions, where objects identity is extracted. Such atypical beta connectivity predicted both ASD symptomatology and their detail-oriented processing. Overall, these results might be explained by an altered feedback connectivity within the visual system, with potential cascade effects in visual scene parsing and higher order functions.
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Affiliation(s)
- Luca Ronconi
- Faculty of Psychology, Vita-Salute San Raffaele University, Milan, Italy; IRCCS San Raffaele Scientific Institute, Milan, Italy; Theoretical and Cognitive Neuroscience Unit, Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy.
| | - Andrea Vitale
- Theoretical and Cognitive Neuroscience Unit, Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Alessandra Federici
- Theoretical and Cognitive Neuroscience Unit, Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; IMT School of Advanced Studies Lucca, Lucca, Italy
| | - Elisa Pini
- Theoretical and Cognitive Neuroscience Unit, Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Psychology, University of Milano-Bicocca, Milano, Italy
| | - Massimo Molteni
- Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy.
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Child Psychopathology Department, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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14
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Mamashli F, Huang S, Khan S, Hämäläinen MS, Ahlfors SP, Ahveninen J. Distinct Regional Oscillatory Connectivity Patterns During Auditory Target and Novelty Processing. Brain Topogr 2020; 33:477-488. [PMID: 32441009 DOI: 10.1007/s10548-020-00776-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 05/12/2020] [Indexed: 11/26/2022]
Abstract
Auditory attention allows us to focus on relevant target sounds in the acoustic environment while maintaining the capability to orient to unpredictable (novel) sound changes. An open question is whether orienting to expected vs. unexpected auditory events are governed by anatomically distinct attention pathways, respectively, or by differing communication patterns within a common system. To address this question, we applied a recently developed PeSCAR analysis method to evaluate spectrotemporal functional connectivity patterns across subregions of broader cortical regions of interest (ROIs) to analyze magnetoencephalography data obtained during a cued auditory attention task. Subjects were instructed to detect a predictable harmonic target sound embedded among standard tones in one ear and to ignore the standard tones and occasional unpredictable novel sounds presented in the opposite ear. Phase coherence of estimated source activity was calculated between subregions of superior temporal, frontal, inferior parietal, and superior parietal cortex ROIs. Functional connectivity was stronger in response to target than novel stimuli between left superior temporal and left parietal ROIs and between left frontal and right parietal ROIs, with the largest effects observed in the beta band (15-35 Hz). In contrast, functional connectivity was stronger in response to novel than target stimuli in inter-hemispheric connections between left and right frontal ROIs, observed in early time windows in the alpha band (8-12 Hz). Our findings suggest that auditory processing of expected target vs. unexpected novel sounds involves different spatially, temporally, and spectrally distributed oscillatory connectivity patterns across temporal, parietal, and frontal areas.
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Affiliation(s)
- Fahimeh Mamashli
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
| | - Samantha Huang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Sheraz Khan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Matti S Hämäläinen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Seppo P Ahlfors
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Jyrki Ahveninen
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
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