The Location Reliability of the Resting-State fMRI FC of Emotional Regions Towards rTMS Therapy
AbstractResting-state magnetic resonance imaging (RS-fMRI) studies indicated that the repetitive transcranial magnetic stimulation (rTMS) exerts antidepression effect through the functional connectivity (FC) of the DLPFC with the subgenual anterior cingulate cortex (sgACC), pregneual ACC (pgACC), or nucleus accumbens (NAc). It is proposed that the FC-guided individualized precise stimulation on the DLPFC would be more effective. The current study systematically investigated the reliability of the RS-fMRI FC location as well as the FC strength with multiple potential factors. We aimed to provide a stable stimulation target ...
Source: Neuroinformatics - May 24, 2022 Category: Neuroscience Source Type: research
Editorial
(Source: Neuroinformatics)
Source: Neuroinformatics - May 11, 2022 Category: Neuroscience Source Type: research
Validation Through Collaboration: Encouraging Team Efforts to Ensure Internal and External Validity of Computational Models of Biochemical Pathways
AbstractComputational modelling of biochemical reaction pathways is an increasingly important part of neuroscience research. In order to be useful, computational models need to be valid in two senses: First, they need to be consistent with experimental data and able to make testable predictions (external validity). Second, they need to be internally consistent and independently reproducible (internal validity). Here, we discuss both types of validity and provide a brief overview of tools and technologies used to ensure they are met. We also suggest the introduction of new collaborative technologies to ensure model validity...
Source: Neuroinformatics - May 11, 2022 Category: Neuroscience Source Type: research
Complexity of Brain Dynamics as a Correlate of Consciousness in Anaesthetized Monkeys
In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observati...
Source: Neuroinformatics - May 5, 2022 Category: Neuroscience Source Type: research
Cortical Representation of Touch in Silico
AbstractWith its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents’. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here w e introduce an open-source, biologically inspired, computationally efficient network model of the somatosensory cortex’s granular and supragranular layers after reconstructing the barrel cortex in soma resolution. Comparisons of the network activity to empirical observations showed that the in ...
Source: Neuroinformatics - April 29, 2022 Category: Neuroscience Source Type: research
Separating Neural Oscillations from Aperiodic 1/f Activity: Challenges and Recommendations
AbstractElectrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law\(P\propto 1/{f}^{\beta }\) and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent\(\beta\). For investigation of either part, how...
Source: Neuroinformatics - April 7, 2022 Category: Neuroscience Source Type: research
Decentralized Brain Age Estimation Using MRI Data
AbstractRecent studies have demonstrated that neuroimaging data can be used to estimate biological brain age, as it captures information about the neuroanatomical and functional changes the brain undergoes during development and the aging process. However, researchers often have limited access to neuroimaging data because of its challenging and expensive acquisition process, thereby limiting the effectiveness of the predictive model. Decentralized models provide a way to build more accurate and generalizable prediction models, bypassing the traditional data-sharing methodology. In this work, we propose a decentralized meth...
Source: Neuroinformatics - April 5, 2022 Category: Neuroscience Source Type: research
A Computational Neural Model for Mapping Degenerate Neural Architectures
In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy as...
Source: Neuroinformatics - March 29, 2022 Category: Neuroscience Source Type: research
How Machine Learning is Powering Neuroimaging to Improve Brain Health
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, “Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application”, co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning appr...
Source: Neuroinformatics - March 28, 2022 Category: Neuroscience Source Type: research
Estimating the Parameters of the Epileptor Model for Epileptic Seizure Suppression
AbstractEpilepsy is one of the most common brain disorders worldwide, affecting millions of people every year. Given the partially successful existing treatments for epileptiform activity suppression, dynamic mathematical models have been proposed with the purpose of better understanding the factors that might trigger an epileptic seizure and how to mitigate it, among which Epileptor stands out, due to its relative simplicity and consistency with experimental observations. Recent studies using this model have provided evidence that establishing a feedback-based control approach is possible. However, for this strategy to wo...
Source: Neuroinformatics - March 18, 2022 Category: Neuroscience Source Type: research
Real-time and Recursive Estimators for Functional MRI Quality Assessment
AbstractReal-time quality assessment (rtQA) of functional magnetic resonance imaging (fMRI) based on blood oxygen level-dependent (BOLD) signal changes is critical for neuroimaging research and clinical applications. The losses of BOLD sensitivity because of different types of technical and physiological noise remain major sources of fMRI artifacts. Due to difficulty of subjective visual perception of image distortions during data acquisitions, a comprehensive automatic rtQA is needed. To facilitate rapid rtQA of fMRI data, we applied real-time and recursive quality assessment methods to whole-brain fMRI volumes, as well a...
Source: Neuroinformatics - March 17, 2022 Category: Neuroscience Source Type: research
Pitfalls and Recommended Strategies and Metrics for Suppressing Motion Artifacts in Functional MRI
AbstractIn resting-state functional magnetic resonance imaging (rs-fMRI), artefactual signals arising from subject motion can dwarf and obfuscate the neuronal activity signal. Typical motion correction approaches involve the generation of nuisance regressors, which are timeseries of non-brain signals regressed out of the fMRI timeseries to yield putatively artifact-free data. Recent work suggests that concatenating all regressors into a single regression model is more effective than the sequential application of individual regressors, which may reintroduce previously removed artifacts. This work compares 18 motion correcti...
Source: Neuroinformatics - March 15, 2022 Category: Neuroscience Source Type: research
Classification of Contrasting Discrete Emotional States Indicated by EEG Based Graph Theoretical Network Measures
AbstractThe present study shows new findings that reveal the high association between emotional arousal and neuro-functional brain connectivity measures. For this purpose, contrasting discrete emotional states (happiness vs sadness, amusement vs disgust, calmness vs excitement, calmness vs anger, fear vs anger) are classified by using Support Vector Machines (SVMs) driven by Graph Theoretical segregation (clustering coefficients, transitivity, modularity) and integration (global efficiency, local efficiency) measures of the brain network. Emotional EEG data mediated by short duration video film clips is downloaded from pub...
Source: Neuroinformatics - March 14, 2022 Category: Neuroscience Source Type: research
ERP Analysis Using a Multi-Channel Matching Pursuit Algorithm
In this study, we propose a new algorithm for analysing event-related components observed in EEG signals in psychological experiments. We investigate its capabilities and limitations. The algorithm is based on multivariate matching pursuit and clustering. It is aimed to find patterns in EEG signals which are similar across different experimental conditions, but it allows for variations in amplitude and slight variability in topography. The method proved to yield expected results in numerical simulations. For the real data coming from an emotional categorisation task experiment, we obtained two indications. First, the metho...
Source: Neuroinformatics - March 14, 2022 Category: Neuroscience Source Type: research
The Neuron Phenotype Ontology: A FAIR Approach to Proposing and Classifying Neuronal Types
AbstractThe challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many ...
Source: Neuroinformatics - March 10, 2022 Category: Neuroscience Source Type: research
