scholarly journals Stimulus induced visual cortical networks are recapitulated by spontaneous local and inter-areal synchronization

2015 ◽  
Author(s):  
Christopher Lewis ◽  
Conrado Bosmann ◽  
Thilo Womelsdorf ◽  
Pascal Fries
Keyword(s):  
Phase Synchronization ◽  
Brain Activity ◽  
Brain Organization ◽  
Neuronal Synchronization ◽  
Gamma Frequency ◽  
Co Activation ◽  
Visual Areas ◽  
Multiple Levels ◽  
Visual Cortical ◽  
Signal Correlation

Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous inter-areal co-activation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across several visual areas in awake monkeys to investigate spatial patterns of local and inter-areal synchronization. We show that stimulation-induced patterns of inter-areal co-activation were reactivated in the absence of stimulation. Reactivation occurred through both, inter-areal co-fluctuation of local activity and inter-areal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of inter-areal coupling observed during stimulation, i.e. the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.

scholarly journals Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization

2016 ◽  
Vol 113 (5) ◽  
pp. E606-E615 ◽  
Author(s):  
Christopher M. Lewis ◽  
Conrado A. Bosman ◽  
Thilo Womelsdorf ◽  
Pascal Fries
Keyword(s):  
Phase Synchronization ◽  
Brain Activity ◽  
Brain Organization ◽  
Neuronal Synchronization ◽  
Gamma Frequency ◽  
Dorsal Aspect ◽  
Visual Areas ◽  
Multiple Levels ◽  
Visual Cortical ◽  
Signal Correlation

Intrinsic covariation of brain activity has been studied across many levels of brain organization. Between visual areas, neuronal activity covaries primarily among portions with similar retinotopic selectivity. We hypothesized that spontaneous interareal coactivation is subserved by neuronal synchronization. We performed simultaneous high-density electrocorticographic recordings across the dorsal aspect of several visual areas in one hemisphere in each of two awake monkeys to investigate spatial patterns of local and interareal synchronization. We show that stimulation-induced patterns of interareal coactivation were reactivated in the absence of stimulation for the visual quadrant covered. Reactivation occurred through both interareal cofluctuation of local activity and interareal phase synchronization. Furthermore, the trial-by-trial covariance of the induced responses recapitulated the pattern of interareal coupling observed during stimulation, i.e., the signal correlation. Reactivation-related synchronization showed distinct peaks in the theta, alpha, and gamma frequency bands. During passive states, this rhythmic reactivation was augmented by specific patterns of arrhythmic correspondence. These results suggest that networks of intrinsic covariation observed at multiple levels and with several recording techniques are related to synchronization and that behavioral state may affect the structure of intrinsic dynamics.

PHASE SYNCHRONIZATION OF NEURONAL NOISE IN MOUSE HIPPOCAMPAL EPILEPTIFORM DYNAMICS

2012 ◽  
Vol 23 (01) ◽  
pp. 1250033 ◽  
Author(s):  
DEMITRE SERLETIS ◽  
PETER L. CARLEN ◽  
TAUFIK A. VALIANTE ◽  
BERJ L. BARDAKJIAN
Keyword(s):  
Phase Synchronization ◽  
Brain Activity ◽  
Phase Coupling ◽  
Intact Mouse ◽  
Frequency Bands ◽  
Gamma Frequency ◽  
Nonlinear Phase ◽  
Mouse Hippocampus ◽  
Show Evidence ◽  
Interictal State

Organized brain activity is the result of dynamical, segregated neuronal signals that may be used to investigate synchronization effects using sophisticated neuroengineering techniques. Phase synchrony analysis, in particular, has emerged as a promising methodology to study transient and frequency-specific coupling effects across multi-site signals. In this study, we investigated phase synchronization in intracellular recordings of interictal and ictal epileptiform events recorded from pairs of cells in the whole (intact) mouse hippocampus. In particular, we focused our analysis on the background noise-like activity (NLA), previously reported to exhibit complex neurodynamical properties. Our results show evidence for increased linear and nonlinear phase coupling in NLA across three frequency bands [theta (4–10 Hz), beta (12–30 Hz) and gamma (30–80 Hz)] in the ictal compared to interictal state dynamics. We also present qualitative and statistical evidence for increased phase synchronization in the theta, beta and gamma frequency bands from paired recordings of ictal NLA. Overall, our results validate the use of background NLA in the neurodynamical study of epileptiform transitions and suggest that what is considered "neuronal noise" is amenable to synchronization effects in the spatiotemporal domain.

scholarly journals Inter-individual deep image reconstruction

2022 ◽  
Author(s):  
Jun Kai Ho ◽  
Tomoyasu Horikawa ◽  
Kei Majima ◽  
Yukiyasu Kamitani
Keyword(s):  
Image Reconstruction ◽  
Brain Activity ◽  
Activity Patterns ◽  
Visual Image ◽  
Training Data ◽  
Visual Features ◽  
Fine Grained ◽  
Visual Areas ◽  
Brain Activity Pattern ◽  
Visual Cortical

The sensory cortex is characterized by general organizational principles such as topography and hierarchy. However, measured brain activity given identical input exhibits substantially different patterns across individuals. While anatomical and functional alignment methods have been proposed in functional magnetic resonance imaging (fMRI) studies, it remains unclear whether and how hierarchical and fine-grained representations can be converted between individuals while preserving the encoded perceptual contents. In this study, we evaluated machine learning models called neural code converters that predict one's brain activity pattern (target) from another's (source) given the same stimulus by the decoding of hierarchical visual features and the reconstruction of perceived images. The training data for converters consisted of fMRI data obtained with identical sets of natural images presented to pairs of individuals. Converters were trained using the whole visual cortical voxels from V1 through the ventral object areas, without explicit labels of visual areas. We decoded the converted brain activity patterns into hierarchical visual features of a deep neural network (DNN) using decoders pre-trained on the target brain and then reconstructed images via the decoded features. Without explicit information about visual cortical hierarchy, the converters automatically learned the correspondence between the visual areas of the same levels. DNN feature decoding at each layer showed higher decoding accuracies from corresponding levels of visual areas, indicating that hierarchical representations were preserved after conversion. The viewed images were faithfully reconstructed with recognizable silhouettes of objects even with relatively small amounts of data for converter training. The conversion also allows pooling data across multiple individuals, leading to stably high reconstruction accuracy compared to those converted between individuals. These results demonstrate that the conversion learns hierarchical correspondence and preserves the fine-grained representations of visual features, enabling visual image reconstruction using decoders trained on other individuals.

scholarly journals In your phase: neural phase synchronisation underlies visual imagery of faces

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Andrés Canales-Johnson ◽  
Renzo C. Lanfranco ◽  
Juan Pablo Morales ◽  
David Martínez-Pernía ◽  
Joaquín Valdés ◽  
...  
Keyword(s):  
Long Range ◽  
Visual Imagery ◽  
Brain Activity ◽  
Neural Connectivity ◽  
Frequency Range ◽  
Subjective Impression ◽  
Gamma Frequency ◽  
Gamma Range ◽  
Theta Frequency ◽  
Socially Relevant

AbstractMental imagery is the process through which we retrieve and recombine information from our memory to elicit the subjective impression of “seeing with the mind’s eye”. In the social domain, we imagine other individuals while recalling our encounters with them or modelling alternative social interactions in future. Many studies using imaging and neurophysiological techniques have shown several similarities in brain activity between visual imagery and visual perception, and have identified frontoparietal, occipital and temporal neural components of visual imagery. However, the neural connectivity between these regions during visual imagery of socially relevant stimuli has not been studied. Here we used electroencephalography to investigate neural connectivity and its dynamics between frontal, parietal, occipital and temporal electrodes during visual imagery of faces. We found that voluntary visual imagery of faces is associated with long-range phase synchronisation in the gamma frequency range between frontoparietal electrode pairs and between occipitoparietal electrode pairs. In contrast, no effect of imagery was observed in the connectivity between occipitotemporal electrode pairs. Gamma range synchronisation between occipitoparietal electrode pairs predicted subjective ratings of the contour definition of imagined faces. Furthermore, we found that visual imagery of faces is associated with an increase of short-range frontal synchronisation in the theta frequency range, which temporally preceded the long-range increase in the gamma synchronisation. We speculate that the local frontal synchrony in the theta frequency range might be associated with an effortful top-down mnemonic reactivation of faces. In contrast, the long-range connectivity in the gamma frequency range along the fronto-parieto-occipital axis might be related to the endogenous binding and subjective clarity of facial visual features.

Neural correlates of motion processing through echolocation, source hearing, and vision in blind echolocation experts and sighted echolocation novices

2014 ◽  
Vol 111 (1) ◽  
pp. 112-127 ◽  
Author(s):  
L. Thaler ◽  
J. L. Milne ◽  
S. R. Arnott ◽  
D. Kish ◽  
M. A. Goodale
Keyword(s):  
Visual Motion ◽  
Brain Activity ◽  
Region Of Interest ◽  
Occipital Cortex ◽  
Neural Substrates ◽  
Motion Processing ◽  
Planum Temporale ◽  
Show Evidence ◽  
Visual Cortical ◽  
Source Motion

We have shown in previous research (Thaler L, Arnott SR, Goodale MA. PLoS One 6: e20162, 2011) that motion processing through echolocation activates temporal-occipital cortex in blind echolocation experts. Here we investigated how neural substrates of echo-motion are related to neural substrates of auditory source-motion and visual-motion. Three blind echolocation experts and twelve sighted echolocation novices underwent functional MRI scanning while they listened to binaural recordings of moving or stationary echolocation or auditory source sounds located either in left or right space. Sighted participants' brain activity was also measured while they viewed moving or stationary visual stimuli. For each of the three modalities separately (echo, source, vision), we then identified motion-sensitive areas in temporal-occipital cortex and in the planum temporale. We then used a region of interest (ROI) analysis to investigate cross-modal responses, as well as laterality effects. In both sighted novices and blind experts, we found that temporal-occipital source-motion ROIs did not respond to echo-motion, and echo-motion ROIs did not respond to source-motion. This double-dissociation was absent in planum temporale ROIs. Furthermore, temporal-occipital echo-motion ROIs in blind, but not sighted, participants showed evidence for contralateral motion preference. Temporal-occipital source-motion ROIs did not show evidence for contralateral preference in either blind or sighted participants. Our data suggest a functional segregation of processing of auditory source-motion and echo-motion in human temporal-occipital cortex. Furthermore, the data suggest that the echo-motion response in blind experts may represent a reorganization rather than exaggeration of response observed in sighted novices. There is the possibility that this reorganization involves the recruitment of “visual” cortical areas.

scholarly journals Rest-task Modulation of fMRI-derived Global Signal Topography is Mediated by Transient Co-activation Patterns

2019 ◽  
Author(s):  
Jianfeng Zhang ◽  
Zirui Huang ◽  
Shankar Tumati ◽  
Georg Northoff
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Peak Period ◽  
Physiological Noise ◽  
Activation Patterns ◽  
Global Signal ◽  
Co Activation ◽  
Human Connectome Project ◽  
Partial Dissociation ◽  
Signal Correlation

AbstractRecent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.

scholarly journals Brain hierarchy score: Which deep neural networks are hierarchically brain-like?

2020 ◽  
Author(s):  
Soma Nonaka ◽  
Kei Majima ◽  
Shuntaro C. Aoki ◽  
Yukiyasu Kamitani
Keyword(s):  
Neural Networks ◽  
Image Recognition ◽  
High Performance ◽  
Deep Neural Networks ◽  
Recognition Performance ◽  
Brain Activity ◽  
Spatial Integration ◽  
Hierarchical Processing ◽  
Visual Areas ◽  
The Brain

SummaryAchievement of human-level image recognition by deep neural networks (DNNs) has spurred interest in whether and how DNNs are brain-like. Both DNNs and the visual cortex perform hierarchical processing, and correspondence has been shown between hierarchical visual areas and DNN layers in representing visual features. Here, we propose the brain hierarchy (BH) score as a metric to quantify the degree of hierarchical correspondence based on the decoding of individual DNN unit activations from human brain activity. We find that BH scores for 29 pretrained DNNs with varying architectures are negatively correlated with image recognition performance, indicating that recently developed high-performance DNNs are not necessarily brain-like. Experimental manipulations of DNN models suggest that relatively simple feedforward architecture with broad spatial integration is critical to brain-like hierarchy. Our method provides new ways for designing DNNs and understanding the brain in consideration of their representational homology.

scholarly journals Altered effective connectivity in migraine patients during emotional stimuli: a multi-frequency magnetoencephalography study

2022 ◽  
Vol 23 (1) ◽  
Author(s):  
Jing Ren ◽  
Qun Yao ◽  
Minjie Tian ◽  
Feng Li ◽  
Yueqiu Chen ◽  
...  
Keyword(s):  
Brain Activity ◽  
Effective Connectivity ◽  
Temporal Cortex ◽  
Primary Headache ◽  
Clustering Coefficient ◽  
Frequency Range ◽  
Emotional Stimuli ◽  
Emotional Modulation ◽  
Gamma Frequency ◽  
Wide Range

Abstract Background Migraine is a common and disabling primary headache, which is associated with a wide range of psychiatric comorbidities. However, the mechanisms of emotion processing in migraine are not fully understood yet. The present study aimed to investigate the neural network during neutral, positive, and negative emotional stimuli in the migraine patients. Methods A total of 24 migraine patients and 24 age- and sex-matching healthy controls were enrolled in this study. Neuromagnetic brain activity was recorded using a whole-head magnetoencephalography (MEG) system upon exposure to human facial expression stimuli. MEG data were analyzed in multi-frequency ranges from 1 to 100 Hz. Results The migraine patients exhibited a significant enhancement in the effective connectivity from the prefrontal lobe to the temporal cortex during the negative emotional stimuli in the gamma frequency (30–90 Hz). Graph theory analysis revealed that the migraine patients had an increased degree and clustering coefficient of connectivity in the delta frequency range (1–4 Hz) upon exposure to positive emotional stimuli and an increased degree of connectivity in the delta frequency range (1–4 Hz) upon exposure to negative emotional stimuli. Clinical correlation analysis showed that the history, attack frequency, duration, and neuropsychological scales of the migraine patients had a negative correlation with the network parameters in certain frequency ranges. Conclusions The results suggested that the individuals with migraine showed deviant effective connectivity in viewing the human facial expressions in multi-frequencies. The prefrontal-temporal pathway might be related to the altered negative emotional modulation in migraine. These findings suggested that migraine might be characterized by more universal altered cerebral processing of negative stimuli. Since the significant result in this study was frequency-specific, more independent replicative studies are needed to confirm these results, and to elucidate the neurocircuitry underlying the association between migraine and emotional conditions.

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