scholarly journals 4488 Neural Network of the Cognitive Model of Reading

2020 ◽  
Vol 4 (s1) ◽  
pp. 140-141
Author(s):  
Joseph Posner ◽  
Vivian Dickens ◽  
Andrew DeMarco ◽  
Sarah Snider ◽  
Peter Turkeltaub ◽  
...  
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Cognitive Processing ◽  
Word Reading ◽  
Cognitive Model ◽  
Network Connectivity ◽  
Cognitive Models ◽  
Neuronal Interactions ◽  
Language Areas ◽  
The Brain

OBJECTIVES/GOALS: A particularly debilitating consequence of stroke is alexia, an acquired impairment in reading. Cognitive models aim to characterize how information is processed based on behavioral data. If we can concurrently characterize how neural networks process that information, we can enhance the models to reflect the neuronal interactions that drive them. METHODS/STUDY POPULATION: There will be 10 unimpaired adult readers. Two functional localizer tasks, deigned to consistently activate robust language areas, identify the regions of interest that process the cognitive reading functions (orthography, phonology, semantics). Another task, designed for this experiment, analyses the reading-related functional-connectivity between these areas by presenting words classified along the attributes of frequency, concreteness, and regularity, which utilize specific cognitive routes, and a visual control. Connectivity is analyzed during word reading overall vs. a control condition to determine overall reading-related connectivity, and while reading words that have high vs. low attribute values, to determine if cognitive processing routes bias the neural reading network connectivity. RESULTS/ANTICIPATED RESULTS: The localizer analysis is expected to result in the activation of canonical reading areas. The degree of functional connectivity observed between these regions is expected to depend on the degree to which each cognitive route is utilized to read a given word. After orthographic, phonologic, and semantic areas have been identified, the connectivity analysis should show that there is high correlation between all three types of areas during reading compared to the control condition. Then the frequency, regularity, and concreteness of the words being read should alter the reliance on the pathways between these area types. This would support the hypothesized pattern of connectivity as predicted by the cognitive reading routes. Otherwise, it will show how the neural reading network differs from the cognitive model. DISCUSSION/SIGNIFICANCE OF IMPACT: The results will determine the relationship between the cognitive reading model and the neural reading network. Cognitive models show what processes occur in the brain, but neural networks show how these processes occur. By relating these components, we obtain a more complete view of reading in the brain, which can inform future alexia treatments.

Semantic and Formal Representation of Cognitive Models for the Metacognitive Architecture CARINA

2021 ◽  
pp. 54-72
Author(s):  
Alba J. Jerónimo ◽  
María P. Barrera ◽  
Manuel F. Caro ◽  
Adán A. Gómez
Keyword(s):  
Knowledge Representation ◽  
Semantic Memory ◽  
Cognitive Model ◽  
Semantic Knowledge ◽  
Cognitive Models ◽  
Formal Representation ◽  
Object Properties ◽  
Internal Information ◽  
Processing Mechanisms ◽  
The Brain

A cognitive model is a computational model of internal information processing mechanisms of the brain for the purposes of comprehension and prediction. CARINA metacognitive architecture runs cognitive models. However, CARINA does not currently have mechanisms to store and learn from cognitive models executed in the past. Semantic knowledge representation is a field of study which concentrates on using formal symbols to a collection of propositions, objects, object properties, and relations among objects. In CARINA Beliefs are a form of represent the semantic knowledge. The aim of this chapter is to formally describe a CARINA-based cognitive model through of denotational mathematics and to represent these models using a technique of semantic knowledge representation called beliefs. All the knowledge received by CARINA is stored in the semantic memory in the form of beliefs. Thus, a cognitive model represented through beliefs will be ready to be stored in semantic memory of the metacognitive architecture CARINA. Finally, an illustrative example is presented.

scholarly journals Characterization of postsurgical functional connectivity changes in temporal lobe epilepsy

2020 ◽  
Vol 133 (2) ◽  
pp. 392-402 ◽  
Author(s):  
Victoria L. Morgan ◽  
Baxter P. Rogers ◽  
Hernán F. J. González ◽  
Sarah E. Goodale ◽  
Dario J. Englot
Keyword(s):  
Functional Connectivity ◽  
Temporal Lobe ◽  
Network Connectivity ◽  
Temporal Lobectomy ◽  
Seizure Outcome ◽  
Seizure Recurrence ◽  
Female Age ◽  
Selective Amygdalohippocampectomy ◽  
The Brain

OBJECTIVESeizure outcome after mesial temporal lobe epilepsy (mTLE) surgery is complex and diverse, even across patients with homogeneous presurgical clinical profiles. The authors hypothesized that this is due in part to variations in network connectivity across the brain before and after surgery. Although presurgical network connectivity has been previously characterized in these patients, the objective of this study was to characterize presurgical to postsurgical functional network connectivity changes across the brain after mTLE surgery.METHODSTwenty patients with drug-refractory unilateral mTLE (5 left side, 10 female, age 39.3 ± 13.5 years) who underwent either selective amygdalohippocampectomy (n = 13) or temporal lobectomy (n = 7) were included in the study. Presurgical and postsurgical (36.6 ± 14.3 months after surgery) functional connectivity (FC) was measured with 3-T MRI and compared with findings in age-matched healthy controls (n = 44, 21 female, age 39.3 ± 14.3 years). Postsurgical connectivity changes were then related to seizure outcome, type of surgery, and presurgical disease parameters.RESULTSThe results demonstrated significant decreases of FC from control group values across the brain after surgery that were not present before surgery, including many contralateral hippocampal connections distal to the surgical site. Postsurgical impairment of contralateral precuneus to ipsilateral occipital connectivity was associated with seizure recurrence. Presurgical impairment of the contralateral precuneus to contralateral temporal lobe connectivity was associated with those who underwent selective amygdalohippocampectomy compared to those who had temporal lobectomy. Finally, changes in thalamic connectivity after surgery were linearly related to duration of epilepsy and frequency of consciousness-impairing seizures prior to surgery.CONCLUSIONSThe widespread contralateral hippocampal FC changes after surgery may be a reflection of an ongoing epileptogenic progression that has been altered by the surgery, rather than a direct result of the surgery itself. This network evolution may contribute to long-term seizure outcome. Therefore, the combination of presurgical network mapping with the understanding of the dynamic effects of surgery on the networks may ultimately be used to create predictors of the likelihood of long-term seizure recurrence in individual patients after mTLE surgery.

scholarly journals Effects of Cognitive Training in Mild Cognitive Impairmentmeasured by Resting State Functional Imaging

2020 ◽  
Vol 10 (11) ◽  
pp. 175
Author(s):  
Seungho Kim ◽  
Eunhee Park ◽  
Hyunsil Cha ◽  
Jae-Chang Jung ◽  
Tae-Du Jung ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Training Program ◽  
Cognitive Training ◽  
Resting State ◽  
Cognitive Abilities ◽  
Motor Coordination ◽  
Network Connectivity ◽  
Functional Magnetic Resonance ◽  
Function Test ◽  
The Brain

Mild cognitive impairment (MCI) is defined as an intermediate state of cognitive alteration between normal aging and dementia. In this study, we performed a functional network connectivity analysis using resting-state functional magnetic resonance imaging to investigate the association between changes in functional connectivity in the brain and the improvement in cognitive abilities after cognitive training. A computerized cognitive training program was used to improve the abilities of fifteen participants with MCI. The cognitive training program (Comcog), which consists of three weekly sessions totaling 90 min, was conducted with all participants over six weeks. The cognitive abilities before (pre-Comcog) and after (post-Comcog) the cognitive training process were measured using a neurocognitive function test. After the Comcog, the participants enhanced their visual and verbal memories, attention, and visuo-motor coordination. The functional connectivity between cingulo-opercular (CON) and default mode (DMN) showed significant improvements after Comcog training. Therefore, our study suggests that cognitive training may improve the cognitive abilities of participants. This improvement was associated with an increase in the functional connectivity between DMN and CON. The increase in functional connectivity after cognitive training was specifically associated with overall cognitive functions, including executive, memory, decision-making, and motivational functions.

scholarly journals TMIC-46. GLIOMA-INDUCED SYNAPTOGENESIS IS ENRICHED WITHIN FUNCTIONAL CONNECTIVITY NETWORK HUBS AND INFLUENCES LANGUAGE PROCESSING IN ADULT IDH WT GLIOBLASTOMA

2019 ◽  
Vol 21 (Supplement_6) ◽  
pp. vi257-vi258
Author(s):  
Saritha Krishna ◽  
Sofia Kakaizada ◽  
Claudia Valdivia ◽  
Kyounghee Seo ◽  
David Raleigh ◽  
...  
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Task Performance ◽  
Language Processing ◽  
Hippocampal Neurons ◽  
Neuronal Excitability ◽  
Glioma Cells ◽  
Network Connectivity ◽  
Marker Expression ◽  
Neuron Interactions

Abstract INTRODUCTION Little is known about the mechanisms by which gliomas integrate into functional neural networks and influence complex cognitive processes such as language. Glioma-neuron interactions are bidirectional, with increased neuronal activity promoting tumor growth and the latter in turn influencing neuronal excitability and synaptic connections. It remains unknown whether glioma-neuron interactions play a role in maintaining long-range neural networks subserving cognition in humans. We test the hypothesis that glioma-neuron interactions (“synaptogenic glioma cells”) are enriched within intratumoral high functional connectivity (FC) network hubs, thereby influencing language processing via release of synaptogenic factors into the tumor microenvironment. METHODS We employed magnetoencephalography imaginary coherence measures to identify intratumoral high (HFC) and low (LFC) functional connectivity network hubs in newly diagnosed glioblastoma patients. Primary patient samples and cultures from HFC and LFC sites were assessed for pre and post-synaptic marker expression (IF), cocultured with murine hippocampal neurons, and induced neuron organoids. ECOG Field recordings were performed on HFC/LFC tumors. Secreted proteins were measured from patient serum and LFC/HFC culture supernatant. Language assessments were performed to correlate task performance with FC measures. RESULTS Primary patient samples from HFC regions are enriched for glioblastoma cells with a synaptogenic profile as characterized by pre- and post-synaptic marker expression at both tissue and cellular level (coculture with mouse hippocampal neuron and organoid models). RNA sequencing and proteomic analyses from HFC samples revealed a neurogenic signature including thrombospondin 1 (TSP1). Overexpression of TSP1 in LFC primary patient cultures rescues the synaptogenic and proliferative phenotype. Importantly, we found a linear relationship between intratumoral HFC with patient serum TSP1 (ELISA) with a further correlation with language task performance. CONCLUSION An enriched population of synaptogenic glioma cells are organized within intratumoral high network connectivity regions. Glioma-induced neuronal synaptogenesis contributes to the microenvironment in support of network connectivity through secretion of TSP1.

scholarly journals Predicting Cortical Signatures of Consciousness using Dynamic Functional Connectivity Graph-Convolutional Neural Networks

2020 ◽  
Author(s):  
A. Grigis ◽  
J. Tasserie ◽  
V. Frouin ◽  
B. Jarraya ◽  
L. Uhrig
Keyword(s):  
Neural Networks ◽  
Functional Connectivity ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Supervised Machine Learning ◽  
Brain State ◽  
Level Of Consciousness ◽  
Clinical Consequences ◽  
Brain States ◽  
The Brain

AbstractDecoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. Using clustering algorithms, we previously demonstrated that resting-state functional MRI (rsfMRI) data can be split into several clusters also called “brain states” corresponding to “functional configurations” of the brain. Here, we propose to use a supervised machine learning method based on artificial neural networks to predict functional brain states across levels of consciousness from rsfMRI. Because it is key to consider the topology of brain regions used to build the dynamical functional connectivity matrices describing the brain state at a given time, we applied BrainNetCNN, a graph-convolutional neural network (CNN), to predict the brain states in awake and anesthetized non-human primate rsfMRI data. BrainNetCNN achieved a high prediction accuracy that lies in [0.674, 0.765] depending on the experimental settings. We propose to derive the set of connections found to be important for predicting a brain state, reflecting the level of consciousness. The results demonstrate that deep learning methods can be used not only to predict brain states but also to provide additional insight on cortical signatures of consciousness with potential clinical consequences for the monitoring of anesthesia and the diagnosis of disorders of consciousness.

scholarly journals Tools of the trade: Estimating time-varying connectivity patterns from fMRI data

2020 ◽  
Author(s):  
Armin Iraji ◽  
Ashkan Faghiri ◽  
Noah Lewis ◽  
Zening Fu ◽  
Srinivas Rachakonda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Extended Period ◽  
Brain Disorders ◽  
Dynamic Nature ◽  
Domain Experts ◽  
Connectivity Patterns ◽  
Analytical Approaches ◽  
Dynamic Metrics ◽  
The Brain

Given the dynamic nature of the brain, there has always been a motivation to move beyond “static” functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity (dFNC) at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

scholarly journals Challenging the notion of a task-negative network: default mode network involvement under varying levels of cognitive effort

2021 ◽  
Author(s):  
Sarah Weber ◽  
Andre Aleman ◽  
Kenneth Hugdahl
Keyword(s):  
Cognitive Processing ◽  
Default Mode Network ◽  
Word Reading ◽  
Block Design ◽  
Network Connectivity ◽  
Cognitive Effort ◽  
Attention And Executive Control ◽  
Cognitive Demands ◽  
Default Mode ◽  
High Effort

Everyday cognitive functioning is characterized by constant alternations between different modes of information processing, driven by fluctuations in environmental demands. At the neural level, this is realized through corresponding dynamic shifts in functional activation and network connectivity. A distinction is often made between the Default Mode Network (DMN) as a task-negative network that is upregulated in the absence of cognitive demands, and task-positive networks that are upregulated when cognitive demands such as attention and executive control are present. Such networks have been labelled the Extrinsic Mode Network (EMN). We investigated changes in brain activation and functional network connectivity during repeated alternations between levels of cognitive effort. Using fMRI and a block-design Stroop paradigm, participants switched back and forth between periods of no effort (resting), low effort (word reading, automatic processing) and high effort (color naming, cognitive control). Results showed expected EMN-activation for task versus rest, and likewise expected DMN-activation for rest versus task. The DMN was also more strongly activated during low effort contrasted with high effort, suggesting a gradual up- and down-regulation of the DMN, depending on the level of demand. The often reported anti-correlation between DMN and EMN was only present during periods of low effort, indicating intermittent contributions of both networks. These results challenge the traditional view of the DMN as solely a task-negative network. Instead, the present results suggest that both EMN and DMN may contribute to low-effort cognitive processing. In contrast, periods of resting and high effort are dominated by the DMN and EMN, respectively.

scholarly journals Handedness, language areas and neuropsychiatric diseases: insights from brain imaging and genetics

Brain ◽  
2019 ◽  
Vol 142 (10) ◽  
pp. 2938-2947 ◽  
Author(s):  
Akira Wiberg ◽  
Michael Ng ◽  
Yasser Al Omran ◽  
Fidel Alfaro-Almagro ◽  
Paul McCarthy ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Imaging ◽  
Genetic Basis ◽  
Left Handedness ◽  
Neuropsychiatric Diseases ◽  
Brain Architecture ◽  
Genomic Regions ◽  
Language Areas ◽  
The Brain

The brain signature and genetic basis of handedness are unclear. Wiberg et al. show that left-handers have higher functional connectivity between language networks, and identify four genomic regions associated with handedness. Variants within these regions appear, by influencing brain architecture, to predispose both to left-handedness and to certain neuropsychiatric diseases.

EEG-based functional networks evoked by acupuncture at ST 36: A data-driven thresholding study

2017 ◽  
Vol 31 (26) ◽  
pp. 1750187 ◽  
Author(s):  
Huiyan Li ◽  
Jiang Wang ◽  
Guosheng Yi ◽  
Bin Deng ◽  
Hexi Zhou
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Data Driven ◽  
Functional Network ◽  
Eeg Signals ◽  
Long Distance ◽  
Alternative Approach ◽  
Connected Set ◽  
Data Driven Approach ◽  
The Brain

This paper investigates how acupuncture at ST 36 modulates the brain functional network. 20 channel EEG signals from 15 healthy subjects are respectively recorded before, during and after acupuncture. The correlation between two EEG channels is calculated by using Pearson’s coefficient. A data-driven approach is applied to determine the threshold, which is performed by considering the connected set, connected edge and network connectivity. Based on such thresholding approach, the functional network in each acupuncture period is built with graph theory, and the associated functional connectivity is determined. We show that acupuncturing at ST 36 increases the connectivity of the EEG-based functional network, especially for the long distance ones between two hemispheres. The properties of the functional network in five EEG sub-bands are also characterized. It is found that the delta and gamma bands are affected more obviously by acupuncture than the other sub-bands. These findings highlight the modulatory effects of acupuncture on the EEG-based functional connectivity, which is helpful for us to understand how it participates in the cortical or subcortical activities. Further, the data-driven threshold provides an alternative approach to infer the functional connectivity under other physiological conditions.

scholarly journals Tools of the trade: estimating time-varying connectivity patterns from fMRI data

2020 ◽  
Author(s):  
Armin Iraji ◽  
Ashkan Faghiri ◽  
Noah Lewis ◽  
Zening Fu ◽  
Srinivas Rachakonda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Extended Period ◽  
Brain Disorders ◽  
Dynamic Nature ◽  
Domain Experts ◽  
Connectivity Patterns ◽  
Analytical Approaches ◽  
Dynamic Metrics ◽  
The Brain

Abstract Given the dynamic nature of the brain, there has always been a motivation to move beyond ‘static’ functional connectivity, which characterizes functional interactions over an extended period of time. Progress in data acquisition and advances in analytical neuroimaging methods now allow us to assess the whole brain’s dynamic functional connectivity (dFC) and its network-based analog, dynamic functional network connectivity at the macroscale (mm) using fMRI. This has resulted in the rapid growth of analytical approaches, some of which are very complex, requiring technical expertise that could daunt researchers and neuroscientists. Meanwhile, making real progress toward understanding the association between brain dynamism and brain disorders can only be achieved through research conducted by domain experts, such as neuroscientists and psychiatrists. This article aims to provide a gentle introduction to the application of dFC. We first explain what dFC is and the circumstances under which it can be used. Next, we review two major categories of analytical approaches to capture dFC. We discuss caveats and considerations in dFC analysis. Finally, we walk readers through an openly accessible toolbox to capture dFC properties and briefly review some of the dynamic metrics calculated using this toolbox.

Sign in / Sign up

Export Citation Format

Share Document