scholarly journals Methodology for tDCS integration with fMRI

2019 ◽  
Author(s):  
Zeinab Esmaeilpour ◽  
A. Duke Shereen ◽  
Peyman Ghobadi-Azbari ◽  
Abhishek Datta ◽  
Adam J. Woods ◽  
...  
Keyword(s):  
Behavioral Interventions ◽  
Computational Models ◽  
Brain Activity ◽  
State Level ◽  
Resting State Fmri ◽  
Underlying Mechanism ◽  
Cognitive State ◽  
Neural Activation ◽  
Imaging Data

AbstractIntegration of tDCS with fMRI holds promise for investigation the underlying mechanism of stimulation effect. There are 118 published tDCS studies (up to 1st Oct 2018) that used fMRI as a proxy measure of neural activation to answer mechanistic, predictive, and localization questions about how brain activity is modulated by tDCS. FMRI can potentially contribute as: a measure of cognitive state-level variance in baseline brain activation before tDCS; inform the design of stimulation montages that aim to target functional networks during specific tasks; and act as an outcome measure of functional response to tDCS. In this systematic review we explore methodological parameter space of tDCS integration with fMRI. Existing tDCS-fMRI literature shows little replication across these permutations; few studies used comparable study designs. Here, we use a case study with both task and resting state fMRI before and after tDCS in a cross-over design to discuss methodological confounds. We further outline how computational models of current flow should be combined with imaging data to understand sources of variability in responsiveness. Through the case study, we demonstrate how modeling and imaging methodology can be integrated for individualized analysis. Finally, we discuss the importance of conducting tDCS-fMRI with stimulation equipment certified as safe to use inside the MR scanner, and of correcting for image artifacts caused by tDCS. tDCS-fMRI can address important questions on the functional mechanisms of tDCS action (e.g. target engagement) and has the potential to support enhancement of behavioral interventions, provided studies are designed rationally.

scholarly journals External drivers of BOLD signal’s non-stationarity

2021 ◽  
Author(s):  
Arian Ashourvan ◽  
Sérgio Pequito ◽  
Maxwell Bertolero ◽  
Jason Z. Kim ◽  
Danielle S. Bassett ◽  
...  
Keyword(s):  
Computational Models ◽  
Linear Time ◽  
Brain Activity ◽  
External Input ◽  
Imaging Data ◽  
External Stimulation ◽  
Cortical Input ◽  
Linear Time Invariant ◽  
Human Connectome Project ◽  
And Task

AbstractA fundamental challenge in neuroscience is to uncover the principles governing how the brain interacts with the external environment. However, assumptions about external stimuli fundamentally constrain current computational models. We show in silico that unknown external stimulation can produce error in the estimated linear time-invariant dynamical system. To address these limitations, we propose an approach to retrieve the external (unknown) input parameters and demonstrate that the estimated system parameters during external input quiescence uncover spatiotemporal profiles of external inputs over external stimulation periods more accurately. Finally, we unveil the expected (and unexpected) sensory and task-related extra-cortical input profiles using functional magnetic resonance imaging data acquired from 96 subjects (Human Connectome Project) during the resting-state and task scans. Together, we provide evidence that this embodied brain activity model offers information about the structure and dimensionality of the BOLD signal’s external drivers and shines light on likely external sources contributing to the BOLD signal’s non-stationarity.

scholarly journals Estimation and Validation of Individualized Dynamic Brain Models with Resting State fMRI

2019 ◽  
Author(s):  
Matthew F. Singh ◽  
Todd S. Braver ◽  
Michael W. Cole ◽  
ShiNung Ching
Keyword(s):  
Human Brain ◽  
Resting State ◽  
Nonlinear Dynamical Systems ◽  
Brain Activity ◽  
Network Models ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Data Driven ◽  
Imaging Data ◽  
Individual Level

AbstractA key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

scholarly journals Reliability of single-subject neural activation patterns in speech production tasks

2019 ◽  
Author(s):  
Saul A. Frankford ◽  
Alfonso Nieto-Castañón ◽  
Jason A. Tourville ◽  
Frank H. Guenther
Keyword(s):  
Speech Production ◽  
High Reliability ◽  
Brain Activity ◽  
Intraclass Correlation ◽  
Correlation Coefficients ◽  
Speech Disorders ◽  
Single Subject ◽  
Neural Activation ◽  
Imaging Data ◽  
Activation Patterns

AbstractSpeech neuroimaging research targeting individual speakers could help elucidate differences that may be crucial to understanding speech disorders. However, this research necessitates reliable brain activation across multiple speech production sessions. In the present study, we evaluated the reliability of speech-related brain activity measured by functional magnetic resonance imaging data from twenty neuro-typical subjects who participated in two experiments involving reading aloud simple speech stimuli. Using traditional methods like the Dice and intraclass correlation coefficients, we found that most individuals displayed moderate to high reliability. We also found that a novel machine-learning subject classifier could identify these individuals by their speech activation patterns with 97% accuracy from among a dataset of seventy-five subjects. These results suggest that single-subject speech research would yield valid results and that investigations into the reliability of speech activation in people with speech disorders are warranted.

scholarly journals A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

2017 ◽  
Author(s):  
Jeremy R. Manning ◽  
Xia Zhu ◽  
Theodore L. Willke ◽  
Rajesh Ranganath ◽  
Kimberly Stachenfeld ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Brain Activity ◽  
Probabilistic Approach ◽  
Covariance Structure ◽  
Fmri Data ◽  
Cognitive State ◽  
Bayesian Hierarchical ◽  
Connectivity Patterns ◽  
Hierarchical Matrix

AbstractRecent research shows that the covariance structure of functional magnetic resonance imaging (fMRI) data - commonly described as functional connectivity - can change as a function of the participant’s cognitive state (for review see [35]). Here we present a Bayesian hierarchical matrix factorization model, termed hierarchical topographic factor analysis (HTFA), for efficiently discovering full-brain networks in large multi-subject neuroimaging datasets. HTFA approximates each subject’s network by first re-representing each brain image in terms of the activities of a set of localized nodes, and then computing the covariance of the activity time series of these nodes. The number of nodes, along with their locations, sizes, and activities (over time) are learned from the data. Because the number of nodes is typically substantially smaller than the number of fMRI voxels, HTFA can be orders of magnitude more efficient than traditional voxel-based functional connectivity approaches. In one case study, we show that HTFA recovers the known connectivity patterns underlying a collection of synthetic datasets. In a second case study, we illustrate how HTFA may be used to discover dynamic full-brain activity and connectivity patterns in real fMRI data, collected as participants listened to a story. In a third case study, we carried out a similar series of analyses on fMRI data collected as participants viewed an episode of a television show. In these latter case studies, we found that the HTFA-derived activity and connectivity patterns can be used to reliably decode which moments in the story or show the participants were experiencing. Further, we found that these two classes of patterns contained partially non-overlapping information, such that decoders trained on combinations of activity-based and dynamic connectivity-based features performed better than decoders trained on activity or connectivity patterns alone. We replicated this latter result with two additional (previously developed) methods for efficiently characterizing full-brain activity and connectivity patterns.

scholarly journals Interactions between sleep disturbances and Alzheimer’s disease on brain function: a preliminary study combining the static and dynamic functional MRI

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaicheng Li ◽  
Xiao Luo ◽  
Qingze Zeng ◽  
Yerfan Jiaerken ◽  
Shuyue Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Risk Factor ◽  
Sleep Disturbance ◽  
Brain Function ◽  
Sleep Disturbances ◽  
Brain Activity ◽  
Vascular Risk Factor ◽  
Underlying Mechanism ◽  
Amyloid Pet

AbstractThough sleep disturbance constitutes the risk factor for Alzheimer’s disease (AD), the underlying mechanism is still unclear. This study aims to explore the interaction between sleep disturbances and AD on brain function. We included 192 normal controls, 111 mild cognitive impairment (MCI), and 30 AD patients, with either poor or normal sleep (PS, NS, respectively). To explore the strength and stability of brain activity, we used static amplitude of low-frequency fluctuation (sALFF) and dynamic ALFF (dALFF) variance. Further, we examined white matter hyperintensities (WMH) and amyloid PET deposition, representing the vascular risk factor and AD-related hallmark, respectively. We observed that sleep disturbance significantly interacted with disease severity, exposing distinct effects on sALFF and dALFF variance. Interestingly, PS groups showed the dALFF variance trajectory of initially increased, then decreased and finally increased along the AD spectrum, while showing the opposite trajectory of sALFF. Further correlation analysis showed that the WMH burden correlates with dALFF variance in PS groups. Conclusively, our study suggested that sleep disturbance interacts with AD severity, expressing as effects of compensatory in MCI and de-compensatory in AD, respectively. Further, vascular impairment might act as important pathogenesis underlying the interaction effect between sleep and AD.

scholarly journals Jumping over baselines with new methods to predict activation maps from resting-state fMRI

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Lacosse ◽  
Klaus Scheffler ◽  
Gabriele Lohmann ◽  
Georg Martius
Keyword(s):  
Resting State ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Behavioral Differences ◽  
Large Variability ◽  
Single Vertex ◽  
Spontaneous Brain Activity ◽  
New Methods ◽  
Group Averaging ◽  
Proposed Modification

AbstractCognitive fMRI research primarily relies on task-averaged responses over many subjects to describe general principles of brain function. Nonetheless, there exists a large variability between subjects that is also reflected in spontaneous brain activity as measured by resting state fMRI (rsfMRI). Leveraging this fact, several recent studies have therefore aimed at predicting task activation from rsfMRI using various machine learning methods within a growing literature on ‘connectome fingerprinting’. In reviewing these results, we found lack of an evaluation against robust baselines that reliably supports a novelty of predictions for this task. On closer examination to reported methods, we found most underperform against trivial baseline model performances based on massive group averaging when whole-cortex prediction is considered. Here we present a modification to published methods that remedies this problem to large extent. Our proposed modification is based on a single-vertex approach that replaces commonly used brain parcellations. We further provide a summary of this model evaluation by characterizing empirical properties of where prediction for this task appears possible, explaining why some predictions largely fail for certain targets. Finally, with these empirical observations we investigate whether individual prediction scores explain individual behavioral differences in a task.

scholarly journals Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bo-yong Park ◽  
Seok-Jun Hong ◽  
Sofie L. Valk ◽  
Casey Paquola ◽  
Oualid Benkarim ◽  
...  
Keyword(s):  
Data Exchange ◽  
Computational Models ◽  
Simulation Models ◽  
Autism Diagnostic Observation Schedule ◽  
Supervised Machine Learning ◽  
Global Network ◽  
Imaging Data ◽  
Phenotypic Data ◽  
Severity Scores ◽  
High Level

AbstractThe pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.

Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

2021 ◽  
pp. 1-29
Author(s):  
Kangyu Jin ◽  
Zhe Shen ◽  
Guoxun Feng ◽  
Zhiyong Zhao ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Resting State ◽  
Parietal Lobe ◽  
Brain Activity ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Middle Temporal Gyrus ◽  
Support Vector ◽  
Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

scholarly journals Abnormal interhemispheric functional connectivity in patients with strabismic amblyopia: a resting-state fMRI study using voxel-mirrored homotopic connectivity

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Shuang Zhang ◽  
Gui-Ping Gao ◽  
Wen-Qing Shi ◽  
Biao Li ◽  
Qi Lin ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Receiver Operating Characteristic ◽  
Operating Characteristic ◽  
Brain Activity ◽  
Characteristic Curve ◽  
Clinical Manifestations ◽  
Resting State Fmri ◽  
Diagnostic Value ◽  
Functional Connections ◽  
Receiver Operating

Abstract Background Previous studies have demonstrated that strabismus amblyopia can result in markedly brain function alterations. However, the differences in spontaneous brain activities of strabismus amblyopia (SA) patients still remain unclear. Therefore, the current study intended to employthe voxel-mirrored homotopic connectivity (VMHC) method to investigate the intrinsic brain activity changes in SA patients. Purpose To investigate the changes in cerebral hemispheric functional connections in patients with SA and their relationship with clinical manifestations using the VMHC method. Material and methods In the present study, a total of 17 patients with SA (eight males and nine females) and 17 age- and weight-matched healthy control (HC) groups were enrolled. Based on the VMHC method, all subjects were examined by functional magnetic resonance imaging. The functional interaction between cerebral hemispheres was directly evaluated. The Pearson’s correlation test was used to analyze the clinical features of patients with SA. In addition, their mean VMHC signal values and the receiver operating characteristic curve were used to distinguish patients with SA and HC groups. Results Compared with HC group, patients with SA had higher VMHC values in bilateral cingulum ant, caudate, hippocampus, and cerebellum crus 1. Moreover, the VMHC values of some regions were positively correlated with some clinical manifestations. In addition, receiver operating characteristic curves presented higher diagnostic value in these areas. Conclusion SA subjects showed abnormal brain interhemispheric functional connectivity in visual pathways, which might give some instructive information for understanding the neurological mechanisms of SA patients.

scholarly journals Computational Modeling of Music Cognition: A Case Study on Model Selection

2006 ◽  
Vol 23 (5) ◽  
pp. 365-376 ◽  
Author(s):  
Henkjan Honing
Keyword(s):  
Model Selection ◽  
Strong Evidence ◽  
Goodness Of Fit ◽  
Computational Models ◽  
Recent Literature ◽  
Music Cognition ◽  
Kinematic Model ◽  
Music Performance ◽  
Limited Range

While the most common way of evaluating a computational model is to see whether it shows a good fit with the empirical data, recent literature on theory testing and model selection criticizes the assumption that this is actually strong evidence for the validity of a model. This article presents a case study from music cognition (modeling the ritardandi in music performance) and compares two families of computational models (kinematic and perceptual) using three different model selection criteria: goodness-of-fit, model simplicity, and the degree of surprise in the predictions. In the light of what counts as strong evidence for a model’s validity—namely that it makes limited range, nonsmooth, and relatively surprising predictions—the perception-based model is preferred over the kinematic model.

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