scholarly journals Task-specific vision models explain task-specific areas of visual cortex

2018 ◽  
Author(s):  
Kshitij Dwivedi ◽  
Gemma Roig
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Brain Region ◽  
Computational Models ◽  
Brain Area ◽  
Relative Activity ◽  
Scene Classification ◽  
The Brain ◽  
Insight Into ◽  
Better Than

AbstractComputational models such as deep neural networks (DNN) trained for classification are often used to explain responses of the visual cortex. However, not all the areas of the visual cortex are involved in object/scene classification. For instance, scene selective occipital place area (OPA) plays a role in mapping navigational affordances. Therefore, for explaining responses of such task-specific brain area, we investigate if a model that performs a related task can serve as a better computational model than a model that performs an unrelated task. We found that DNN trained on a task (scene-parsing) related to the function (navigational affordances) of a brain region (OPA) explains its responses better than a DNN trained on a task (scene-classification) which is not explicitly related. In a subsequent analysis, we found that the DNNs that showed high correlation with a particular brain region were trained on a task that was consistent with functions of that brain region reported in previous neuroimaging studies. Our results demonstrate that the task is paramount for selecting a computational model of a brain area. Further, explaining the responses of a brain area by a diverse set of tasks has the potential to shed some light on its functions.Author summaryAreas in the human visual cortex are specialized for specific behaviors either due to supervision and interaction with the world or due to evolution. A standard way to gain insight into the function of these brain region is to design experiments related to a particular behavior, and localize the regions showing significant relative activity corresponding to that behavior. In this work, we investigate if we can figure out the function of a brain area in visual cortex using computational vision models. From our results, we find that explaining responses of a brain region using DNNs trained on a diverse set of possible vision tasks can help us gain insights into its function. The consistency of our results using DNNs with the previous neuroimaging studies suggest that the brain region may be specialized for behavior similar to the tasks for which DNNs showed a high correlation with its responses.

Vanishing White Matter Hyperintensities in CADASIL: A Case Report with Insight into Disease Mechanisms

2020 ◽  
Vol 78 (3) ◽  
pp. 907-910
Author(s):  
Eric Jouvent ◽  
Nassira Alili ◽  
Dominique Hervé ◽  
Hugues Chabriat
Keyword(s):  
White Matter ◽  
White Matter Hyperintensities ◽  
Autosomal Dominant ◽  
Brain Area ◽  
Imaging Features ◽  
Water Influx ◽  
Disease Mechanisms ◽  
The Brain ◽  
Vanishing White Matter ◽  
Insight Into

In a woman with Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL) followed for 15 years, we observed magnetic resonance imaging white matter hyperintensities that vanished in the anterior temporal poles while the brain volume decreased unexpectedly. These imaging changes were transient and detected when the patient was being treated by valproic acid for stabilizing mood disturbances. This intriguing case supports that mechanisms underlying white matter hyperintensities can vary from one brain area to another and that important modifications of water influx into the brain tissue might be involved in some imaging features of CADASIL.

scholarly journals Modelling thalamocortical circuitry shows visually induced LTP changes laminar connectivity in human visual cortex

2020 ◽  
Author(s):  
Rachael L. Sumner ◽  
Meg J. Spriggs ◽  
Alexander D. Shaw
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Evoked Potential ◽  
Long Term Potentiation ◽  
The State ◽  
Clinical Neuroscience ◽  
Non Invasive ◽  
Term Potentiation ◽  
The Brain

AbstractNeuroplasticity is essential to learning and memory in the brain; it has therefore also been implicated in numerous neurological and psychiatric disorders, making measuring the state of neuroplasticity of foremost importance to clinical neuroscience. Long-term potentiation (LTP) is a key mechanism of neuroplasticity and has been studied extensively, and invasively in non-human animals. Translation to human application largely relies on the validation of non-invasive measures of LTP. The current study provides validation for the use of a thalamocortical computational model of visual cortex for investigating and replicating interlaminar connectivity changes using non-invasive EEG recording of humans, and a commonly used visual sensory LTP paradigm. The model demonstrated remarkable accuracy recapitulating post-tetanus changes including increased excitatory connectivity from thalamus to layer IV and from layer IV to II/III. The findings also further validate visual sensory induced LTP and evoked potential modulation for measuring of the state of LTP in cortex.

scholarly journals Quantification and selection of ictogenic zones in epilepsy surgery

2019 ◽  
Author(s):  
Petroula Laiou ◽  
Eleftherios Avramidis ◽  
Marinho A. Lopes ◽  
Eugenio Abela ◽  
Michael Müller ◽  
...  
Keyword(s):  
Computational Models ◽  
Brain Networks ◽  
Network Models ◽  
Combinatorial Problem ◽  
Valuable Insight ◽  
Potential Applicability ◽  
Healthy Functioning ◽  
The Brain ◽  
Insight Into ◽  
Selection Of

AbstractNetwork models of brain dynamics provide valuable insight into the healthy functioning of the brain and how this breaks down in disease. A pertinent example is the use of network models to understand seizure generation (ictogenesis) in epilepsy. Recently, computational models have emerged to aid our understanding of seizures and to predict the outcome of surgical perturbations to brain networks. Such approaches provide the opportunity to quantify the effect of removing regions of tissue from brain networks and thereby search for the optimal resection strategy.Here, we use computational models to elucidate how sets of nodes contribute to the ictogenicity of networks. In small networks we fully elucidate the ictogenicity of all possible sets of nodes and demonstrate that the distribution of ictogenicity across sets depends on network topology. However, the full elucidation is a combinatorial problem that becomes intractable for large networks. Therefore, we develop a global optimisation approach to search for minimal sets of nodes that contribute significantly to ictogenesis. We demonstrate the potential applicability of these methods in practice by identifying optimal sets of nodes to resect in networks derived from 20 individuals who underwent resective surgery for epilepsy.

scholarly journals A Connectivity-based Psychometric Prediction Framework for Brain-behavior Relationship Studies

2020 ◽  
Author(s):  
Jianxiao Wu ◽  
Simon B. Eickhoff ◽  
Felix Hoffstaedter ◽  
Kaustubh R. Patil ◽  
Holger Schwender ◽  
...  
Keyword(s):  
Brain Region ◽  
Predictive Power ◽  
Interindividual Variability ◽  
Brain Regions ◽  
Population Based ◽  
Behavior Patterns ◽  
Brain Behavior ◽  
Prediction Approach ◽  
The Brain ◽  
Insight Into

AbstractThe recent availability of population-based studies with neuroimaging and behavioral measurements opens promising perspectives to investigate the relationships between interindividual variability in brain regions’ connectivity and behavioral phenotypes. However, the multivariate nature of connectivity-based prediction model severely limits the insight into brain-behavior patterns for neuroscience. To address this issue, we propose a connectivity-based psychometric prediction framework based on individual regions’ connectivity profiles. We first illustrate two main applications: 1) single brain region’s predictive power for a range of psychometric variables 2) single psychometric variable’s predictive power variation across brain region. We compare the patterns of brain-behavior provided by these approaches to the brain-behavior relationships from activation approaches. Then, capitalizing on the increased transparency of our approach, we demonstrate how the influence of various data processing and analyses can directly influence the patterns of brain-behavior relationships. Such region-based prediction approach can hence contribute to neurobiological validity in the study of brain-behavior relationships.

scholarly journals Scaling of information in large neural populations reveals signatures of information-limiting correlations

2020 ◽  
Author(s):  
MohammadMehdi Kafashan ◽  
Anna Jaffe ◽  
Selmaan N. Chettih ◽  
Ramon Nogueira ◽  
Iñigo Arandia-Romero ◽  
...  
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Visual Stimulus ◽  
Brain Area ◽  
Correlated Noise ◽  
Neuronal Populations ◽  
Total Information ◽  
Neural Populations ◽  
The Brain ◽  
Stimulus Direction

AbstractHow is information distributed across large neuronal populations within a given brain area? One possibility is that information is distributed roughly evenly across neurons, so that total information scales linearly with the number of recorded neurons. Alternatively, the neural code might be highly redundant, meaning that total information saturates. Here we investigated how information about the direction of a moving visual stimulus is distributed across hundreds of simultaneously recorded neurons in mouse primary visual cortex (V1). We found that information scales sublinearly, due to the presence of correlated noise in these populations. Using recent theoretical advances, we compartmentalized noise correlations into information-limiting and nonlimiting components, and then extrapolated to predict how information grows when neural populations are even larger. We predict that tens of thousands of neurons are required to encode 95% of the information about visual stimulus direction, a number much smaller than the number of neurons in V1. Overall, these findings suggest that the brain uses a widely distributed, but nonetheless redundant code that supports recovering most information from smaller subpopulations.

scholarly journals Deciphering how specialized interneuron-specific cell types contribute to circuit function

2020 ◽  
Author(s):  
Alexandre Guet-McCreight ◽  
Frances K Skinner
Keyword(s):  
Computational Models ◽  
Brain Area ◽  
Pyramidal Cells ◽  
Cell Types ◽  
Specific Cell ◽  
Cell Type ◽  
Inhibitory Cell ◽  
Cell Firing ◽  
The Brain

AbstractThe wide diversity of inhibitory cells across the brain makes them fit to contribute to network dynamics in specialized fashions. However, the contributions of a particular inhibitory cell type in a behaving animal is challenging to decipher as one needs to both record cellular activities and identify the cell type being recorded. Thus, using computational modeling to explore cell-specific contributions so as to predict and hypothesize functional contributions is desirable. Here we examine potential contributions of interneuron-specific 3 (I-S3) cells - a type of inhibitory interneuron found in CA1 hippocampus that only targets other inhibitory interneurons - during simulated theta rhythms. We use previously developed multi-compartment models of oriens lacunosum-moleculare (OLM) cells, the main target of I-S3 cells, and explore how I-S3 cell inputs during in vitro and in vivo scenarios contribute to theta. We find that I-S3 cells suppress OLM cell spiking, rather than engender its spiking via post-inhibitory rebound mechanisms. To elicit recruitment similar to experiment, the inclusion of disinhibited pyramidal cell inputs is necessary, suggesting that I-S3 cell firing can broaden the window for disinhibiting pyramidal cells. Using in vivo virtual networks, we show that I-S3 cells can contribute to a sharpening of OLM cell recruitment at theta frequencies. Further, a shifting of the timing of I-S3 cell spiking due to external modulation can shift the timing of the OLM cell firing and thus disinhibitory windows. We thus propose a specialized contribution of I-S3 cells to create temporally precise coordination of modulation pathways.Significance StatementHow information is processed across different brain structures is an important question that relates to the different functions that the brain performs. In this work we use computational models that focus on a particular inhibitory cell type that only inhibits other inhibitory cell types – the I-S3 cell in the hippocampus. We show that this cell type is able to broaden the window for disinhibition of excitatory cells. We further illustrate that this broadening presents itself as a mechanism for input pathway switching and modulation over the timing of inhibitory cell spiking. Overall, this work contributes to our knowledge of how coordination between sensory and memory consolidation information is attained in a brain area that is involved in memory formation.

Computational neurostimulation

2021 ◽  
Author(s):  
Ainslie Johnstone ◽  
James J. Bonaiuto ◽  
Sven Bestmann
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Motor Cortex ◽  
Magnetic Stimulation ◽  
Computational Models ◽  
Primary Motor Cortex ◽  
Health And Disease ◽  
Small Footprint ◽  
The Impact ◽  
The Brain ◽  
Insight Into

Computational neurostimulation is the use of biologically grounded computational models to investigate the mechanism of action of brain stimulation and predict the impact of transcranial magnetic stimulation (TMS) on behavior in health and disease. Computational models are now widespread, and their success is incontrovertible, yet they have left a rather small footprint on the field of TMS. We highlight and discuss recent advances in models of primary motor cortex TMS, the brain region for which most models have been developed. These models provide insight into the putative, but unobservable, mechanisms through which TMS influences physiology, and help predicting the effects of different TMS applications. We discuss how these advances in computational neurostimulation provide opportunities for mechanistically understanding and predicting the impact of TMS on behavior.

scholarly journals A Neurodynamic Model of Saliency Prediction in V1

2021 ◽  
pp. 1-36
Author(s):  
David Berga ◽  
Xavier Otazu
Keyword(s):  
Visual Cortex ◽  
Computational Model ◽  
Primary Visual Cortex ◽  
Visual Processing ◽  
Computational Models ◽  
Data Sets ◽  
Biologically Inspired ◽  
Visual Processes ◽  
Plausible Model ◽  
Definition Of

Lateral connections in the primary visual cortex (V1) have long been hypothesized to be responsible for several visual processing mechanisms such as brightness induction, chromatic induction, visual discomfort, and bottom-up visual attention (also named saliency). Many computational models have been developed to independently predict these and other visual processes, but no computational model has been able to reproduce all of them simultaneously. In this work, we show that a biologically plausible computational model of lateral interactions of V1 is able to simultaneously predict saliency and all the aforementioned visual processes. Our model's architecture (NSWAM) is based on Penacchio's neurodynamic model of lateral connections of V1. It is defined as a network of firing rate neurons, sensitive to visual features such as brightness, color, orientation, and scale. We tested NSWAM saliency predictions using images from several eye tracking data sets. We show that the accuracy of predictions obtained by our architecture, using shuffled metrics, is similar to other state-of-the-art computational methods, particularly with synthetic images (CAT2000-Pattern and SID4VAM) that mainly contain low-level features. Moreover, we outperform other biologically inspired saliency models that are specifically designed to exclusively reproduce saliency. We show that our biologically plausible model of lateral connections can simultaneously explain different visual processes present in V1 (without applying any type of training or optimization and keeping the same parameterization for all the visual processes). This can be useful for the definition of a unified architecture of the primary visual cortex.

scholarly journals Contemporary insight into diffuse axonal injury

Folia Medica ◽  
2021 ◽  
Vol 63 (2) ◽  
pp. 163-170
Author(s):  
Polina Angelova ◽  
Ivo Kehayov ◽  
Atanas Davarski ◽  
Borislav Kitov
Keyword(s):  
Brain Area ◽  
Axonal Injury ◽  
Diffuse Axonal Injury ◽  
Final Diagnosis ◽  
Current Review ◽  
Brain Site ◽  
Injured Brain ◽  
Grade Ii ◽  
The Brain ◽  
Insight Into

Diffuse axonal injury (DAI) is present in approximately 50% of the cases with severe traumatic brain injury. It is one of the leading causes of morbidity and mortality among children and young individuals worldwide. Generally, DAI occurs as a result of high-velocity accidents. Typically, it presents with loss of consciousness for at least 6 hours and neurological deficit dependent on the brain area that is affected by the injury. The final diagnosis is confirmed by neuroimaging studies such as computed tomography and magnetic resonance imaging. According to the injured brain site, DAI is classified into three grades: Grade I–DAI with axonal lesions in the cerebral hemispheres; Grade II–DAI with focal axonal lesions in the corpus callosum; Grade III–DAI with focal or multiple axonal lesions in the brainstem. Each of the three grades is associated with different outcome.Due to the high disability and mortality rate, DAI represents an important medical, personal and social problem. The aim of the current review is to address the unsolved issues connected with the pathogenesis, diagnostics, treatment and outcome of the diffuse axonal injury.

Game Theory and the brain

2011 ◽  
Vol 30 (06) ◽  
pp. 411-418
Author(s):  
M. van 't Wout
Keyword(s):  
Brain Area ◽  
Neural Responses ◽  
Neural Basis ◽  
Causal Inferences ◽  
The Social ◽  
Virtual Lesion ◽  
Lesion Studies ◽  
The Brain ◽  
Insight Into

SummaryWhat is the neural basis of why you trust one person, but not the other? How do emotions in response to unfairness guide our interactions? What are the neural responses associated with cooperation? The field of social neuroeconomics aims to answer these and other questions to better understand the neural circuitry of decision-making in social interactions. In addition to neuroimaging studies, which can give insight into a possible correlation between brain activation and a cognitive process, (virtual) lesion studies allow drawing causal inferences about the role of a particular brain area in the social decisionmaking process. This paper will review some of the more recent findings on the neural basis of fairness, trust and cooperation identified using neuroimaging or lesion studies.

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