scholarly journals The sensory representation of causally controlled objects

2019 ◽  
Author(s):  
Kelly B. Clancy ◽  
Thomas D. Mrsic-Flogel
Keyword(s):  
Visual Cortex ◽  
Sensory Feedback ◽  
Target Location ◽  
Activity Patterns ◽  
Wide Field ◽  
Dependent Manner ◽  
Intentional Control ◽  
External Objects ◽  
Machine Interface ◽  
Cortical Regions

SummaryIntentional control over external objects is informed by our sensory experience of them. To study how causal relationships are learned and effected, we devised a brain machine interface (BMI) task utilising wide-field calcium signals. Mice learned to entrain activity patterns in arbitrary pairs of cortical regions to guide a visual cursor to a target location for reward. Brain areas that were normally correlated could be rapidly reconfigured to exert control over the cursor in a sensory feedback-dependent manner. Higher visual cortex was more engaged when expert but not naïve animals controlled the cursor. Individual neurons in higher visual cortex responded more strongly to the cursor when mice controlled it than when they passively viewed it, with the greatest response boosting as the cursor approached target location. Thus, representations of causally-controlled objects are sensitive to intention and proximity to the subject’s goal, potentially strengthening sensory feedback to allow more fluent control.

scholarly journals Selective Modulation of Early Visual Cortical Activity by Movement Intention

2019 ◽  
Vol 29 (11) ◽  
pp. 4662-4678 ◽  
Author(s):  
Jason P Gallivan ◽  
Craig S Chapman ◽  
Daniel J Gale ◽  
J Randall Flanagan ◽  
Jody C Culham
Keyword(s):  
Visual Cortex ◽  
Activity Patterns ◽  
Action Planning ◽  
Cortical Activity ◽  
Target Object ◽  
Movement Planning ◽  
Dependent Manner ◽  
Related Information ◽  
Early Visual Cortex ◽  
Visual Cortical

Abstract The primate visual system contains myriad feedback projections from higher- to lower-order cortical areas, an architecture that has been implicated in the top-down modulation of early visual areas during working memory and attention. Here we tested the hypothesis that these feedback projections also modulate early visual cortical activity during the planning of visually guided actions. We show, across three separate human functional magnetic resonance imaging (fMRI) studies involving object-directed movements, that information related to the motor effector to be used (i.e., limb, eye) and action goal to be performed (i.e., grasp, reach) can be selectively decoded—prior to movement—from the retinotopic representation of the target object(s) in early visual cortex. We also find that during the planning of sequential actions involving objects in two different spatial locations, that motor-related information can be decoded from both locations in retinotopic cortex. Together, these findings indicate that movement planning selectively modulates early visual cortical activity patterns in an effector-specific, target-centric, and task-dependent manner. These findings offer a neural account of how motor-relevant target features are enhanced during action planning and suggest a possible role for early visual cortex in instituting a sensorimotor estimate of the visual consequences of movement.

scholarly journals Spatiotemporal structure of sensory-evoked and spontaneous activity revealed by mesoscale imaging in anesthetized and awake mice

2020 ◽  
Author(s):  
Navvab Afrashteh ◽  
Samsoon Inayat ◽  
Edgar Bermudez Contreras ◽  
Artur Luczak ◽  
Bruce L. McNaughton ◽  
...  
Keyword(s):  
Spontaneous Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Sensory Stimulation ◽  
Wide Field ◽  
Evoked Responses ◽  
Evoked Activity ◽  
Cortical Regions ◽  
The One ◽  
Sensory Evoked

AbstractBrain activity propagates across the cortex in diverse spatiotemporal patterns, both as a response to sensory stimulation and during spontaneous activity. Despite been extensively studied, the relationship between the characteristics of such patterns during spontaneous and evoked activity is not completely understood. To investigate this relationship, we compared visual, auditory, and tactile evoked activity patterns elicited with different stimulus strengths and spontaneous activity motifs in lightly anesthetized and awake mice using mesoscale wide-field voltage-sensitive dye and glutamate imaging respectively. The characteristics of cortical activity that we compared include amplitude, speed, direction, and complexity of propagation trajectories in spontaneous and evoked activity patterns. We found that the complexity of the propagation trajectories of spontaneous activity, quantified as their fractal dimension, is higher than the one from sensory evoked responses. Moreover, the speed and direction of propagation, are modulated by the amplitude during both, spontaneous and evoked activity. Finally, we found that spontaneous activity had similar amplitude and speed when compared to evoked activity elicited with low stimulus strengths. However, this similarity gradually decreased when the strength of stimuli eliciting evoked responses increased. Altogether, these findings are consistent with the fact that even primary sensory areas receive widespread inputs from other cortical regions, and that, during rest, the cortex tends to reactivate traces of complex, multi-sensory experiences that may have occurred in a range of different behavioural contexts.

scholarly journals Functional parcellation of mouse visual cortex using statistical techniques reveals response-dependent clustering of cortical processing areas

2021 ◽  
Vol 17 (2) ◽  
pp. e1008548
Author(s):  
Mari Ganesh Kumar ◽  
Ming Hu ◽  
Aadhirai Ramanujan ◽  
Mriganka Sur ◽  
Hema A. Murthy
Keyword(s):  
Visual Cortex ◽  
Resting State ◽  
Activity Patterns ◽  
Visual Stimuli ◽  
Machine Learning Techniques ◽  
Wide Field ◽  
Neuronal Responses ◽  
Visual Areas ◽  
Supervised Classifiers ◽  
Using Data

The visual cortex of the mouse brain can be divided into ten or more areas that each contain complete or partial retinotopic maps of the contralateral visual field. It is generally assumed that these areas represent discrete processing regions. In contrast to the conventional input-output characterizations of neuronal responses to standard visual stimuli, here we asked whether six of the core visual areas have responses that are functionally distinct from each other for a given visual stimulus set, by applying machine learning techniques to distinguish the areas based on their activity patterns. Visual areas defined by retinotopic mapping were examined using supervised classifiers applied to responses elicited by a range of stimuli. Using two distinct datasets obtained using wide-field and two-photon imaging, we show that the area labels predicted by the classifiers were highly consistent with the labels obtained using retinotopy. Furthermore, the classifiers were able to model the boundaries of visual areas using resting state cortical responses obtained without any overt stimulus, in both datasets. With the wide-field dataset, clustering neuronal responses using a constrained semi-supervised classifier showed graceful degradation of accuracy. The results suggest that responses from visual cortical areas can be classified effectively using data-driven models. These responses likely reflect unique circuits within each area that give rise to activity with stronger intra-areal than inter-areal correlations, and their responses to controlled visual stimuli across trials drive higher areal classification accuracy than resting state responses.

A Preliminary Study of Abnormal Centrality of Cortical Regions and Subsystems in Whole Brain Functional Connectivity of Autism Spectrum Disorder Boys

2021 ◽  
pp. 155005942110262
Author(s):  
Bo Chen
Keyword(s):  
Autism Spectrum Disorder ◽  
Activity Patterns ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Eigenvector Centrality ◽  
Information Process ◽  
Whole Brain ◽  
Functional Connections ◽  
Cortical Regions ◽  
Prefrontal Lobe

The abnormal cortices of autism spectrum disorder (ASD) brains are uncertain. However, the pathological alterations of ASD brains are distributed throughout interconnected cortical systems. Functional connections (FCs) methodology identifies cooperation and separation characteristics of information process in macroscopic cortical activity patterns under the context of network neuroscience. Embracing the graph theory concepts, this paper introduces eigenvector centrality index (EC score) ground on the FCs, and further develops a new framework for researching the dysfunctional cortex of ASD in holism significance. The important process is to uncover noticeable regions and subsystems endowed with antagonistic stance in EC-scores of 26 ASD boys and 28 matched healthy controls (HCs). For whole brain regional EC scores of ASD boys, orbitofrontal superior medial cortex, insula R, posterior cingulate gyrus L, and cerebellum 9 L are endowed with different EC scores significantly. In the brain subsystems level, EC scores of DMN, prefrontal lobe, and cerebellum are aberrant in the ASD boys. Generally, the EC scores display widespread distribution of diseased regions in ASD brains. Meanwhile, the discovered regions and subsystems, such as MPFC, AMYG, INS, prefrontal lobe, and DMN, are engaged in social processing. Meanwhile, the CBCL externalizing problem scores are associated with EC scores.

scholarly journals Using Brain Activity Patterns to Differentiate Real and Virtual Attended Targets during Augmented Reality Scenarios

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 226
Author(s):  
Lisa-Marie Vortmann ◽  
Leonid Schwenke ◽  
Felix Putze
Keyword(s):  
Augmented Reality ◽  
Eye Tracking ◽  
Brain Activity ◽  
Activity Patterns ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Neural Net ◽  
Dependent Manner ◽  
Tracking Data ◽  
Average Accuracy

Augmented reality is the fusion of virtual components and our real surroundings. The simultaneous visibility of generated and natural objects often requires users to direct their selective attention to a specific target that is either real or virtual. In this study, we investigated whether this target is real or virtual by using machine learning techniques to classify electroencephalographic (EEG) and eye tracking data collected in augmented reality scenarios. A shallow convolutional neural net classified 3 second EEG data windows from 20 participants in a person-dependent manner with an average accuracy above 70% if the testing data and training data came from different trials. This accuracy could be significantly increased to 77% using a multimodal late fusion approach that included the recorded eye tracking data. Person-independent EEG classification was possible above chance level for 6 out of 20 participants. Thus, the reliability of such a brain–computer interface is high enough for it to be treated as a useful input mechanism for augmented reality applications.

scholarly journals Preparatory Activity in Posterior Temporal Cortex Causally Contributes to Object Detection in Scenes

2015 ◽  
Vol 27 (11) ◽  
pp. 2117-2125 ◽  
Author(s):  
Reshanne R. Reeder ◽  
Francesca Perini ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Object Detection ◽  
Activity Patterns ◽  
Temporal Cortex ◽  
Relevant Information ◽  
Visual Selective Attention ◽  
Object Categories ◽  
Preparatory Attention ◽  
Preparatory Activity ◽  
Early Visual Cortex

Theories of visual selective attention propose that top–down preparatory attention signals mediate the selection of task-relevant information in cluttered scenes. Neuroimaging and electrophysiology studies have provided correlative evidence for this hypothesis, finding increased activity in target-selective neural populations in visual cortex in the period between a search cue and target onset. In this study, we used online TMS to test whether preparatory neural activity in visual cortex is causally involved in naturalistic object detection. In two experiments, participants detected the presence of object categories (cars, people) in a diverse set of photographs of real-world scenes. TMS was applied over a region in posterior temporal cortex identified by fMRI as carrying category-specific preparatory activity patterns. Results showed that TMS applied over posterior temporal cortex before scene onset (−200 and −100 msec) impaired the detection of object categories in subsequently presented scenes, relative to vertex and early visual cortex stimulation. This effect was specific to category level detection and was related to the type of attentional template participants adopted, with the strongest effects observed in participants adopting category level templates. These results provide evidence for a causal role of preparatory attention in mediating the detection of objects in cluttered daily-life environments.

scholarly journals Perceptual grouping and inverse fMRI activity patterns in human visual cortex

10.1167/8.7.2 ◽  
2008 ◽  
Vol 8 (7) ◽  
pp. 2 ◽  
Author(s):  
Fang Fang ◽  
Daniel Kersten ◽  
Scott O. Murray
Keyword(s):  
Visual Cortex ◽  
Perceptual Grouping ◽  
Activity Patterns ◽  
Human Visual Cortex

scholarly journals Scale-Free Coupled Dynamics in Brain Networks Captured by Bivariate Focus-Based Multifractal Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Orestis Stylianou ◽  
Frigyes Samuel Racz ◽  
Andras Eke ◽  
Peter Mukli
Keyword(s):  
Resting State ◽  
Functional Coupling ◽  
Dependent Manner ◽  
Coupled Dynamics ◽  
Scale Free ◽  
Neural Processes ◽  
Eeg Recordings ◽  
Cortical Regions ◽  
The Brain

While most connectivity studies investigate functional connectivity (FC) in a scale-dependent manner, coupled neural processes may also exhibit broadband dynamics, manifesting as power-law scaling of their measures of interdependence. Here we introduce the bivariate focus-based multifractal (BFMF) analysis as a robust tool for capturing such scale-free relations and use resting-state electroencephalography (EEG) recordings of 12 subjects to demonstrate its performance in reconstructing physiological networks. BFMF was employed to characterize broadband FC between 62 cortical regions in a pairwise manner, with all investigated connections being tested for true bivariate multifractality. EEG channels were also grouped to represent the activity of six resting-state networks (RSNs) in the brain, thus allowing for the analysis of within- and between- RSNs connectivity, separately. Most connections featured true bivariate multifractality, which could be attributed to the genuine scale-free coupling of neural dynamics. Bivariate multifractality showed a characteristic topology over the cortex that was highly concordant among subjects. Long-term autocorrelation was higher in within-RSNs, while the degree of multifractality was generally found stronger in between-RSNs connections. These results offer statistical evidence of the bivariate multifractal nature of functional coupling in the brain and validate BFMF as a robust method to capture such scale-independent coupled dynamics.

scholarly journals Transformation from independent to integrative coding of multi-object arrangements in human visual cortex

2017 ◽  
Author(s):  
Daniel Kaiser ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Activity Patterns ◽  
Spatial Arrangement ◽  
Living Room ◽  
Natural Scenes ◽  
Response Patterns ◽  
Object Representations ◽  
Individual Object ◽  
Object Coding

AbstractTo optimize processing, the human visual system utilizes regularities present in naturalistic visual input. One of these regularities is the relative position of objects in a scene (e.g., a sofa in front of a television), with behavioral research showing that regularly positioned objects are easier to perceive and to remember. Here we use fMRI to test how positional regularities are encoded in the visual system. Participants viewed pairs of objects that formed minimalistic two-object scenes (e.g., a “living room” consisting of a sofa and television) presented in their regularly experienced spatial arrangement or in an irregular arrangement (with interchanged positions). Additionally, single objects were presented centrally and in isolation. Multi-voxel activity patterns evoked by the object pairs were modeled as the average of the response patterns evoked by the two single objects forming the pair. In two experiments, this approximation in object-selective cortex was significantly less accurate for the regularly than the irregularly positioned pairs, indicating integration of individual object representations. More detailed analysis revealed a transition from independent to integrative coding along the posterior-anterior axis of the visual cortex, with the independent component (but not the integrative component) being almost perfectly predicted by object selectivity across the visual hierarchy. These results reveal a transitional stage between individual object and multi-object coding in visual cortex, providing a possible neural correlate of efficient processing of regularly positioned objects in natural scenes.

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