1
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Yuasa K, Groen IIA, Piantoni G, Montenegro S, Flinker A, Devore S, Devinsky O, Doyle W, Dugan P, Friedman D, Ramsey N, Petridou N, Winawer J. Precise Spatial Tuning of Visually Driven Alpha Oscillations in Human Visual Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.11.528137. [PMID: 36865223 PMCID: PMC9979988 DOI: 10.1101/2023.02.11.528137] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2023]
Abstract
Neuronal oscillations at about 10 Hz, called alpha oscillations, are often thought to arise from synchronous activity across occipital cortex, reflecting general cognitive states such as arousal and alertness. However, there is also evidence that modulation of alpha oscillations in visual cortex can be spatially specific. Here, we used intracranial electrodes in human patients to measure alpha oscillations in response to visual stimuli whose location varied systematically across the visual field. We separated the alpha oscillatory power from broadband power changes. The variation in alpha oscillatory power with stimulus position was then fit by a population receptive field (pRF) model. We find that the alpha pRFs have similar center locations to pRFs estimated from broadband power (70-180 Hz), but are several times larger. The results demonstrate that alpha suppression in human visual cortex can be precisely tuned. Finally, we show how the pattern of alpha responses can explain several features of exogenous visual attention. Significance Statement The alpha oscillation is the largest electrical signal generated by the human brain. An important question in systems neuroscience is the degree to which this oscillation reflects system-wide states and behaviors such as arousal, alertness, and attention, versus much more specific functions in the routing and processing of information. We examined alpha oscillations at high spatial precision in human patients with intracranial electrodes implanted over visual cortex. We discovered a surprisingly high spatial specificity of visually driven alpha oscillations, which we quantified with receptive field models. We further use our discoveries about properties of the alpha response to show a link between these oscillations and the spread of visual attention.
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2
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Numan T, Breedt LC, Maciel BDAPC, Kulik SD, Derks J, Schoonheim MM, Klein M, de Witt Hamer PC, Miller JJ, Gerstner ER, Stufflebeam SM, Hillebrand A, Stam CJ, Geurts JJG, Reijneveld JC, Douw L. Regional healthy brain activity, glioma occurrence and symptomatology. Brain 2022; 145:3654-3665. [PMID: 36130310 PMCID: PMC9586543 DOI: 10.1093/brain/awac180] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 04/22/2022] [Accepted: 05/04/2022] [Indexed: 11/24/2022] Open
Abstract
It is unclear why exactly gliomas show preferential occurrence in certain brain areas. Increased spiking activity around gliomas leads to faster tumour growth in animal models, while higher non-invasively measured brain activity is related to shorter survival in patients. However, it is unknown how regional intrinsic brain activity, as measured in healthy controls, relates to glioma occurrence. We first investigated whether gliomas occur more frequently in regions with intrinsically higher brain activity. Second, we explored whether intrinsic cortical activity at individual patients’ tumour locations relates to tumour and patient characteristics. Across three cross-sectional cohorts, 413 patients were included. Individual tumour masks were created. Intrinsic regional brain activity was assessed through resting-state magnetoencephalography acquired in healthy controls and source-localized to 210 cortical brain regions. Brain activity was operationalized as: (i) broadband power; and (ii) offset of the aperiodic component of the power spectrum, which both reflect neuronal spiking of the underlying neuronal population. We additionally assessed (iii) the slope of the aperiodic component of the power spectrum, which is thought to reflect the neuronal excitation/inhibition ratio. First, correlation coefficients were calculated between group-level regional glioma occurrence, as obtained by concatenating tumour masks across patients, and group-averaged regional intrinsic brain activity. Second, intrinsic brain activity at specific tumour locations was calculated by overlaying patients’ individual tumour masks with regional intrinsic brain activity of the controls and was associated with tumour and patient characteristics. As proposed, glioma preferentially occurred in brain regions characterized by higher intrinsic brain activity in controls as reflected by higher offset. Second, intrinsic brain activity at patients’ individual tumour locations differed according to glioma subtype and performance status: the most malignant isocitrate dehydrogenase-wild-type glioblastoma patients had the lowest excitation/inhibition ratio at their individual tumour locations as compared to isocitrate dehydrogenase-mutant, 1p/19q-codeleted glioma patients, while a lower excitation/inhibition ratio related to poorer Karnofsky Performance Status, particularly in codeleted glioma patients. In conclusion, gliomas more frequently occur in cortical brain regions with intrinsically higher activity levels, suggesting that more active regions are more vulnerable to glioma development. Moreover, indices of healthy, intrinsic excitation/inhibition ratio at patients’ individual tumour locations may capture both tumour biology and patients’ performance status. These findings contribute to our understanding of the complex and bidirectional relationship between normal brain functioning and glioma growth, which is at the core of the relatively new field of ‘cancer neuroscience’.
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Affiliation(s)
- Tianne Numan
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Lucas C Breedt
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Bernardo de A P C Maciel
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Shanna D Kulik
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Jolanda Derks
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Menno M Schoonheim
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands
| | - Martin Klein
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Medical Psychology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Philip C de Witt Hamer
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurosurgery, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Julie J Miller
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Elizabeth R Gerstner
- Department of Neurology, Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven M Stufflebeam
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jeroen J G Geurts
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Jaap C Reijneveld
- Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Department of Neurology, Stichting Epilepsie Instellingen Nederland, Heemstede 2103 SW, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, Amsterdam UMC location Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands.,Cancer Center Amsterdam, Imaging and Biomarkers, Brain Tumor Center Amsterdam, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Systems and Network Neuroscience, Amsterdam 1081 HV, The Netherlands.,Amsterdam Neuroscience, Brain Imaging, Amsterdam 1081 HV, The Netherlands.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
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3
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Ibarra Chaoul A, Siegel M. Cortical correlation structure of aperiodic neuronal population activity. Neuroimage 2021; 245:118672. [PMID: 34715318 PMCID: PMC8752963 DOI: 10.1016/j.neuroimage.2021.118672] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 10/12/2021] [Accepted: 10/20/2021] [Indexed: 11/02/2022] Open
Abstract
Electrophysiological population signals contain oscillatory and non-oscillatory aperiodic (1/frequency-like) components. So far research has largely focused on oscillatory activity, and only recently, interest in aperiodic population activity has gained momentum. Accordingly, while the cortical correlation structure of oscillatory population activity has been characterized, little is known about the correlation of aperiodic neuronal activity. To address this, we investigated aperiodic neuronal population activity in the human brain using resting-state magnetoencephalography (MEG). We combined source-analysis, signal orthogonalization and irregular-resampling auto-spectral analysis (IRASA) to systematically characterize the cortical distribution and correlation of aperiodic neuronal activity. We found that aperiodic population activity is robustly correlated across the cortex and that this correlation is spatially well structured. Furthermore, we found that the cortical correlation structure of aperiodic activity is similar but distinct from the correlation structure of oscillatory neuronal activity. Anterior cortical regions showed the strongest differences between oscillatory and aperiodic correlation patterns. Our results suggest that correlations of aperiodic population activity serve as robust markers of cortical network interactions. Furthermore, our results show that aperiodic and oscillatory signal components provide non-redundant information about large-scale neuronal correlations. This may reflect at least partly distinct neuronal mechanisms underlying and reflected by oscillatory and aperiodic neuronal population activity.
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Affiliation(s)
- Andrea Ibarra Chaoul
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; MEG Center, University of Tübingen, Otfried-Müller-Str. 47, Tübingen 72076, Germany; IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Otfried-Müller-Str. 27, Tübingen 72076, Germany.
| | - Markus Siegel
- Department of Neural Dynamics and Magnetoencephalography, Hertie Institute for Clinical Brain Research, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; Centre for Integrative Neuroscience, University of Tübingen, Otfried-Müller-Str. 25, Tübingen 72076, Germany; MEG Center, University of Tübingen, Otfried-Müller-Str. 47, Tübingen 72076, Germany.
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4
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Kupers ER, Benson NC, Winawer J. A visual encoding model links magnetoencephalography signals to neural synchrony in human cortex. Neuroimage 2021; 245:118655. [PMID: 34687857 PMCID: PMC8788390 DOI: 10.1016/j.neuroimage.2021.118655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Accepted: 10/11/2021] [Indexed: 01/23/2023] Open
Abstract
Synchronization of neuronal responses over large distances is hypothesized to be important for many cortical functions. However, no straightforward methods exist to estimate synchrony non-invasively in the living human brain. MEG and EEG measure the whole brain, but the sensors pool over large, overlapping cortical regions, obscuring the underlying neural synchrony. Here, we developed a model from stimulus to cortex to MEG sensors to disentangle neural synchrony from spatial pooling of the instrument. We find that synchrony across cortex has a surprisingly large and systematic effect on predicted MEG spatial topography. We then conducted visual MEG experiments and separated responses into stimulus-locked and broadband components. The stimulus-locked topography was similar to model predictions assuming synchronous neural sources, whereas the broadband topography was similar to model predictions assuming asynchronous sources. We infer that visual stimulation elicits two distinct types of neural responses, one highly synchronous and one largely asynchronous across cortex.
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Affiliation(s)
- Eline R Kupers
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Department of Psychology, Stanford University, Stanford, CA 94305, United States.
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; eSciences Institute, University of Washington, Seattle, WA 98195, United States
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States
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5
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Kupers ER, Edadan A, Benson NC, Zuiderbaan W, de Jong MC, Dumoulin SO, Winawer J. A population receptive field model of the magnetoencephalography response. Neuroimage 2021; 244:118554. [PMID: 34509622 PMCID: PMC8631249 DOI: 10.1016/j.neuroimage.2021.118554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 07/16/2021] [Accepted: 09/02/2021] [Indexed: 12/23/2022] Open
Abstract
Computational models which predict the neurophysiological response from experimental stimuli have played an important role in human neuroimaging. One type of computational model, the population receptive field (pRF), has been used to describe cortical responses at the millimeter scale using functional magnetic resonance imaging (fMRI) and electrocorticography (ECoG). However, pRF models are not widely used for non-invasive electromagnetic field measurements (EEG/MEG), because individual sensors pool responses originating from several centimeter of cortex, containing neural populations with widely varying spatial tuning. Here, we introduce a forward-modeling approach in which pRFs estimated from fMRI data are used to predict MEG sensor responses. Subjects viewed contrast-reversing bar stimuli sweeping across the visual field in separate fMRI and MEG sessions. Individual subject's pRFs were modeled on the cortical surface at the millimeter scale using the fMRI data. We then predicted cortical time series and projected these predictions to MEG sensors using a biophysical MEG forward model, accounting for the pooling across cortex. We compared the predicted MEG responses to observed visually evoked steady-state responses measured in the MEG session. We found that pRF parameters estimated by fMRI could explain a substantial fraction of the variance in steady-state MEG sensor responses (up to 60% in individual sensors). Control analyses in which we artificially perturbed either pRF size or pRF position reduced MEG prediction accuracy, indicating that MEG data are sensitive to pRF properties derived from fMRI. Our model provides a quantitative approach to link fMRI and MEG measurements, thereby enabling advances in our understanding of spatiotemporal dynamics in human visual field maps.
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Affiliation(s)
- Eline R Kupers
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Department of Psychology, Stanford University, Stanford, CA 94305, United States.
| | - Akhil Edadan
- Spinoza Center for Neuroimaging, Amsterdam 1105 BK, the Netherlands; Department of Experimental Psychology, Utrecht University, Utrecht 3584 CS, the Netherlands
| | - Noah C Benson
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States; Sciences Institute, University of Washington, Seattle, WA 98195, United States
| | | | - Maartje C de Jong
- Spinoza Center for Neuroimaging, Amsterdam 1105 BK, the Netherlands; Department of Psychology, University of Amsterdam, Amsterdam 1001 NK, the Netherlands; Amsterdam Brain and Cognition (ABC), University of Amsterdam, Amsterdam 1001 NK, the Netherlands
| | - Serge O Dumoulin
- Spinoza Center for Neuroimaging, Amsterdam 1105 BK, the Netherlands; Department of Experimental Psychology, Utrecht University, Utrecht 3584 CS, the Netherlands; Department of Experimental and Applied Psychology, VU University, Amsterdam 1081 BT, the Netherlands
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY 10003, United States; Center for Neural Science, New York University, New York, NY 10003, United States
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6
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Wienke C, Bartsch MV, Vogelgesang L, Reichert C, Hinrichs H, Heinze HJ, Dürschmid S. Mind-wandering Is Accompanied by Both Local Sleep and Enhanced Processes of Spatial Attention Allocation. Cereb Cortex Commun 2021; 2:tgab001. [PMID: 34296151 PMCID: PMC8153027 DOI: 10.1093/texcom/tgab001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 12/28/2020] [Accepted: 12/30/2020] [Indexed: 11/30/2022] Open
Abstract
Mind-wandering (MW) is a subjective, cognitive phenomenon, in which thoughts move away from the task toward an internal train of thoughts, possibly during phases of neuronal sleep-like activity (local sleep, LS). MW decreases cortical processing of external stimuli and is assumed to decouple attention from the external world. Here, we directly tested how indicators of LS, cortical processing, and attentional selection change in a pop-out visual search task during phases of MW. Participants’ brain activity was recorded using magnetoencephalography, MW was assessed via self-report using randomly interspersed probes. As expected, the performance decreased under MW. Consistent with the occurrence of LS, MW was accompanied by a decrease in high-frequency activity (HFA, 80–150 Hz) and an increase in slow wave activity (SWA, 1–6 Hz). In contrast, visual attentional selection as indexed by the N2pc component was enhanced during MW with the N2pc amplitude being directly linked to participants’ performance. This observation clearly contradicts accounts of attentional decoupling that would predict a decrease in attention-related responses to external stimuli during MW. Together, our results suggest that MW occurs during phases of LS with processes of attentional target selection being upregulated, potentially to compensate for the mental distraction during MW.
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Affiliation(s)
- Christian Wienke
- Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Mandy V Bartsch
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany
| | - Lena Vogelgesang
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany
| | - Christoph Reichert
- Forschungscampus STIMULATE, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany.,CBBS - center of behavioral brain sciences, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany.,Forschungscampus STIMULATE, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany.,CBBS - center of behavioral brain sciences, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany.,Forschungscampus STIMULATE, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany.,CBBS - center of behavioral brain sciences, Otto-von-Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Leipziger Str. 44, 39120 Magdeburg, Germany
| | - Stefan Dürschmid
- Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, 39120 Magdeburg, Germany.,Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, 39118 Magdeburg, Germany
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7
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Neural markers of suppression in impaired binocular vision. Neuroimage 2021; 230:117780. [PMID: 33503479 PMCID: PMC8063178 DOI: 10.1016/j.neuroimage.2021.117780] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Revised: 12/29/2020] [Accepted: 01/17/2021] [Indexed: 11/26/2022] Open
Abstract
Even after conventional patching treatment, individuals with a history of amblyopia typically lack good stereo vision. This is often attributed to atypical suppression between the eyes, yet the specific mechanism is still unclear. Guided by computational models of binocular vision, we tested explicit predictions about how neural responses to contrast might differ in individuals with impaired binocular vision. Participants with a history of amblyopia (N = 25), and control participants with typical visual development (N = 19) took part in the study. Neural responses to different combinations of contrast in the left and right eyes, were measured using both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Stimuli were sinusoidal gratings with a spatial frequency of 3c/deg, flickering at 4 Hz. In the fMRI experiment, we also ran population receptive field and retinotopic mapping sequences, and a phase-encoded localiser stimulus, to identify voxels in primary visual cortex (V1) sensitive to the main stimulus. Neural responses in both modalities increased monotonically with stimulus contrast. When measured with EEG, responses were attenuated in the weaker eye, consistent with a fixed tonic suppression of that eye. When measured with fMRI, a low contrast stimulus in the weaker eye substantially reduced the response to a high contrast stimulus in the stronger eye. This effect was stronger than when the stimulus-eye pairings were reversed, consistent with unbalanced dynamic suppression between the eyes. Measuring neural responses using different methods leads to different conclusions about visual differences in individuals with impaired binocular vision. Both of the atypical suppression effects may relate to binocular perceptual deficits, e.g. in stereopsis, and we anticipate that these measures could be informative for monitoring the progress of treatments aimed at recovering binocular vision.
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8
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Dürschmid S, Reichert C, Hinrichs H, Heinze HJ, Kirsch HE, Knight RT, Deouell LY. Direct Evidence for Prediction Signals in Frontal Cortex Independent of Prediction Error. Cereb Cortex 2020; 29:4530-4538. [PMID: 30590422 DOI: 10.1093/cercor/bhy331] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 11/27/2018] [Accepted: 11/29/2018] [Indexed: 12/13/2022] Open
Abstract
Predictive coding (PC) has been suggested as one of the main mechanisms used by brains to interact with complex environments. PC theories posit top-down prediction signals, which are compared with actual outcomes, yielding in turn prediction error (PE) signals, which are used, bottom-up, to modify the ensuing predictions. However, disentangling prediction from PE signals has been challenging. Critically, while many studies found indirect evidence for PC in the form of PE signals, direct evidence for the prediction signal is mostly lacking. Here, we provide clear evidence, obtained from intracranial cortical recordings in human surgical patients, that the human lateral prefrontal cortex evinces prediction signals while anticipating an event. Patients listened to task-irrelevant sequences of repetitive tones including infrequent predictable or unpredictable pitch deviants. The broadband high-frequency amplitude (HFA) was decreased prior to the onset of expected relative to unexpected deviants in the frontal cortex only, and its amplitude was sensitive to the increasing likelihood of deviants following longer trains of standards in the unpredictable condition. Single-trial HFA predicted deviations and correlated with poststimulus response to deviations. These results provide direct evidence for frontal cortex prediction signals independent of PE signals.
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Affiliation(s)
- Stefan Dürschmid
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg, Germany
| | - Christoph Reichert
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, Magdeburg, Germany.,CBBS-Center of Behavioral Brain Sciences, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany
| | - Hermann Hinrichs
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg, Germany.,Stereotactic Neurosurgery, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Leipziger Str. 44, Magdeburg, Germany.,Forschungscampus STIMULATE, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany.,CBBS-Center of Behavioral Brain Sciences, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany
| | - Hans-Jochen Heinze
- Department of Behavioral Neurology, Leibniz Institute for Neurobiology, Brenneckestr. 6, Magdeburg, Germany.,Department of Neurology, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg, Germany.,Stereotactic Neurosurgery, Otto-von-Guericke University, Leipziger Str. 44, Magdeburg, Germany.,German Center for Neurodegenerative Diseases (DZNE), Leipziger Str. 44, Magdeburg, Germany.,Forschungscampus STIMULATE, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany.,CBBS-Center of Behavioral Brain Sciences, Otto-von-Guericke University, Universitätsplatz 2, Magdeburg, Germany
| | - Heidi E Kirsch
- Department of Neurology, University of California, 400 Parnassus Avenue, San Francisco, CA, USA
| | - Robert T Knight
- Helen Wills Neuroscience Institute and Department of Psychology, University of California, Berkeley, CA, USA
| | - Leon Y Deouell
- Edmond and Lily Safra Center for Brain Sciences and Department of Psychology, The Hebrew University of Jerusalem, Mount Scopus, Jerusalem, Israel
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9
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Itthipuripat S, Sprague TC, Serences JT. Functional MRI and EEG Index Complementary Attentional Modulations. J Neurosci 2019; 39:6162-6179. [PMID: 31127004 PMCID: PMC6668200 DOI: 10.1523/jneurosci.2519-18.2019] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 04/12/2019] [Accepted: 05/16/2019] [Indexed: 02/07/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) are two noninvasive methods commonly used to study neural mechanisms supporting visual attention in humans. Studies using these tools, which have complementary spatial and temporal resolutions, implicitly assume they index similar underlying neural modulations related to external stimulus and internal attentional manipulations. Accordingly, they are often used interchangeably for constraining understanding about the impact of bottom-up and top-down factors on neural modulations. To test this core assumption, we simultaneously manipulated bottom-up sensory inputs by varying stimulus contrast and top-down cognitive modulations by changing the focus of spatial attention. Each of the male and female subjects participated in both fMRI and EEG sessions performing the same experimental paradigm. We found categorically different patterns of attentional modulation on fMRI activity in early visual cortex and early stimulus-evoked potentials measured via EEG (e.g., the P1 component and steady-state visually-evoked potentials): fMRI activation scaled additively with attention, whereas evoked EEG components scaled multiplicatively with attention. However, across longer time scales, a contralateral negative-going potential and oscillatory EEG signals in the alpha band revealed additive attentional modulation patterns like those observed with fMRI. These results challenge prior assumptions that fMRI and early stimulus-evoked potentials measured with EEG can be interchangeably used to index the same neural mechanisms of attentional modulations at different spatiotemporal scales. Instead, fMRI measures of attentional modulations are more closely linked with later EEG components and alpha-band oscillations. Considered together, hemodynamic and electrophysiological signals can jointly constrain understanding of the neural mechanisms supporting cognition.SIGNIFICANCE STATEMENT fMRI and EEG have been used as tools to measure the location and timing of attentional modulations in visual cortex and are often used interchangeably for constraining computational models under the assumption that they index similar underlying neural processes. However, by varying attentional and stimulus parameters, we found differential patterns of attentional modulations of fMRI activity in early visual cortex and commonly used stimulus-evoked potentials measured via EEG. Instead, across longer time scales, a contralateral negative-going potential and EEG oscillations in the alpha band exhibited attentional modulations similar to those observed with fMRI. Together, these results suggest that different physiological processes assayed by these complementary techniques must be jointly considered when making inferences about the neural underpinnings of cognitive operations.
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Affiliation(s)
- Sirawaj Itthipuripat
- Neurosciences Graduate Program,
- Learning Institute
- Futuristic Research in Enigmatic Aesthetics Knowledge Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, 10140, Thailand
- Department of Psychology, Center for Integrative and Cognitive Neuroscience, and Interdisciplinary Program in Neuroscience, Vanderbilt University, Nashville, Tennessee 37235, and
| | - Thomas C Sprague
- Neurosciences Graduate Program,
- Department of Psychological and Brain Sciences, University of California, Santa Barbara, Santa Barbara, California 93106-9660
| | - John T Serences
- Neurosciences Graduate Program
- Department of Psychology
- Kavli Foundation for the Brain and Mind, University of California, San Diego, La Jolla, California 92093
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