scholarly journals Beyond Consensus: Embracing Heterogeneity in Curated Neuroimaging Meta-Analysis

2017 ◽  
Author(s):  
Gia H. Ngo ◽  
Simon B. Eickhoff ◽  
Minh Nguyen ◽  
Gunes Sevinc ◽  
Peter T. Fox ◽  
...  
Keyword(s):  
Mental Disorder ◽  
Topic Model ◽  
Meta Analysis ◽  
Brain Regions ◽  
Small Scale ◽  
Functional Domain ◽  
Seed Region ◽  
Activation Patterns ◽  
Co Activation ◽  
Meta Analyses

AbstractCoordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore, ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here, we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. Overall, the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

scholarly journals A Meta-analysis of Functional Neuroimaging Studies of Self- and Other Judgments Reveals a Spatial Gradient for Mentalizing in Medial Prefrontal Cortex

2012 ◽  
Vol 24 (8) ◽  
pp. 1742-1752 ◽  
Author(s):  
Bryan T. Denny ◽  
Hedy Kober ◽  
Tor D. Wager ◽  
Kevin N. Ochsner
Keyword(s):  
Functional Organization ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Brain Regions ◽  
The Self ◽  
Spatial Gradient ◽  
Self And Other ◽  
Neuroimaging Data ◽  
Medial Pfc ◽  
Meta Analyses

The distinction between processes used to perceive and understand the self and others has received considerable attention in psychology and neuroscience. Brain findings highlight a role for various regions, in particular the medial PFC (mPFC), in supporting judgments about both the self and others. We performed a meta-analysis of 107 neuroimaging studies of self- and other-related judgments using multilevel kernel density analysis [Kober, H., & Wager, T. D. Meta-analyses of neuroimaging data. Wiley Interdisciplinary Reviews, 1, 293–300, 2010]. We sought to determine what brain regions are reliably involved in each judgment type and, in particular, what the spatial and functional organization of mPFC is with respect to them. Relative to nonmentalizing judgments, both self- and other judgments were associated with activity in mPFC, ranging from ventral to dorsal extents, as well as common activation of the left TPJ and posterior cingulate. A direct comparison between self- and other judgments revealed that ventral mPFC as well as left ventrolateral PFC and left insula were more frequently activated by self-related judgments, whereas dorsal mPFC, in addition to bilateral TPJ and cuneus, was more frequently activated by other-related judgments. Logistic regression analyses revealed that ventral and dorsal mPFC lay at opposite ends of a functional gradient: The z coordinates reported in individual studies predicted whether the study involved self- or other-related judgments, which were associated with increasingly ventral or dorsal portions of mPFC, respectively. These results argue for a distributed rather than localizationist account of mPFC organization and support an emerging view on the functional heterogeneity of mPFC.

scholarly journals A "global" network dysfunction in the mirror neuron system in autism is modulated by task complexity and age: A meta-analysis of neuroimaging studies

2020 ◽  
Author(s):  
Mei Yan Melody Chan ◽  
Yvonne M.Y. Han
Keyword(s):  
Mirror Neurons ◽  
Task Complexity ◽  
Mirror Neuron System ◽  
Meta Analysis ◽  
Mirror Neuron ◽  
Brain Regions ◽  
Local Network ◽  
Activation Patterns ◽  
Neuron System ◽  
The Brain

Abstract Background Impaired imitation has been found to be an important factor contributing to social communication deficits in individuals with autism spectrum disorder (ASD). It has been hypothesized that the neural correlates of imitation, the mirror neuron system (MNS), are dysfunctional in ASD, resulting in imitation impairment as one of the key behavioral manifestations in ASD. Previous MNS studies produced inconsistent results, leaving the debate of whether mirror neurons are “broken” in ASD unresolved.Methods This meta-analysis aimed to explore the differences in MNS activation patterns between typically developing (TD) and ASD individuals when they observe/imitate biological motions with/without emotional components. Effect-size signed differential mapping (ES-SDM) was adopted to synthesize the available fMRI data. Results The MNS is dysfunctional in ASD; not only the brain regions containing mirror neurons were affected, the brain regions supporting MNS functioning were also impaired. Second, MNS dysfunction in ASD is modulated by task complexity; differential activation patterns during the presentation of “cold” and “hot” stimuli might be a result of atypical functional connectivity in ASD. Third, MNS dysfunction in ASD individuals is modulated by age. MNS regions were found to show delayed maturation; abnormal lateralization development in some of the brain regions also contributed to the atypical development of the MNS in ASD. Limitations We have attempted to include a comprehensive set of original data for this analysis. However, whole brain analysis data were not obtainable from some of the published papers, these studies could not be included as a result. Moreover, the results indicating the age effect on MNS in ASD could only be generalized to individuals aged 11-37, as MNS activation remains unstudied for populations beyond this age range. Also, the ES-SDM linear regression modelling might not be ideal to illustrate the associations between age and MNS activation; the meta-regression results should be treated with caution. Conclusion There is a “global” rather than a “local” network dysfunction, which may underlie the imitation impairments in individuals with ASD. Task complexity and age modulate the functioning of the MNS, which may explain the previous peculiar results contributing to the unresolved “broken mirror neuron” debate.

scholarly journals The Effects of Tryptamine Psychedelics in the Brain: A meta-Analysis of Functional and Review of Molecular Imaging Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
João Castelhano ◽  
Gisela Lima ◽  
Marta Teixeira ◽  
Carla Soares ◽  
Marta Pais ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Theory Of Mind ◽  
Molecular Mimicry ◽  
Meta Analysis ◽  
Temporal Cortex ◽  
Brain Regions ◽  
Imaging Studies ◽  
Activation Patterns ◽  
Therapeutic Benefits ◽  
The Brain

There is an increasing interest in the neural effects of psychoactive drugs, in particular tryptamine psychedelics, which has been incremented by the proposal that they have potential therapeutic benefits, based on their molecular mimicry of serotonin. It is widely believed that they act mainly through 5HT2A receptors but their effects on neural activation of distinct brain systems are not fully understood. We performed a quantitative meta-analysis of brain imaging studies to investigate the effects of substances within this class (e.g., LSD, Psilocybin, DMT, Ayahuasca) in the brain from a molecular and functional point of view. We investigated the question whether the changes in activation patterns and connectivity map into regions with larger 5HT1A/5HT2A receptor binding, as expected from indolaemine hallucinogens (in spite of the often reported emphasis only on 5HT2AR). We did indeed find that regions with changed connectivity and/or activation patterns match regions with high density of 5HT2A receptors, namely visual BA19, visual fusiform regions in BA37, dorsal anterior and posterior cingulate cortex, medial prefrontal cortex, and regions involved in theory of mind such as the surpramarginal gyrus, and temporal cortex (rich in 5HT1A receptors). However, we also found relevant patterns in other brain regions such as dorsolateral prefrontal cortex. Moreover, many of the above-mentioned regions also have a significant density of both 5HT1A/5HT2A receptors, and available PET studies on the effects of psychedelics on receptor occupancy are still quite scarce, precluding a metanalytic approach. Finally, we found a robust neuromodulatory effect in the right amygdala. In sum, the available evidence points towards strong neuromodulatory effects of tryptamine psychedelics in key brain regions involved in mental imagery, theory of mind and affective regulation, pointing to potential therapeutic applications of this class of substances.

scholarly journals The Relationship between BOLD and Neural Activity Arises from Temporally Sparse Events

2019 ◽  
Author(s):  
Xiaodi Zhang ◽  
Wen-Ju Pan ◽  
Shella Dawn Keilholz
Keyword(s):  
Neural Activity ◽  
Field Potential ◽  
High Amplitude ◽  
Brain Regions ◽  
Functional Networks ◽  
Time Varying ◽  
Bold Signal ◽  
Activation Patterns ◽  
Amplitude Data ◽  
Co Activation

Resting state functional magnetic resonance (rs-fMRI) imaging offers insights into how different brain regions are connected into functional networks. It was recently shown that networks that are almost identical to the ones created from conventional correlation analysis can be obtained from a subset of high-amplitude data, suggesting that the functional networks may be driven by instantaneous co-activations of multiple brain regions rather than ongoing oscillatory processes. The rs-fMRI studies, however, rely on the blood oxygen level dependent (BOLD) signal, which is only indirectly sensitive to neural activity through neurovascular coupling. To provide more direct evidence that the neuronal co-activation events produce the time-varying network patterns seen in rs-fMRI studies, we examined the simultaneous rs-fMRI and local field potential (LFP) recordings in rats performed in our lab over the past several years. We developed complementary analysis methods that focus on either the temporal or spatial domain, and found evidence that the interaction between LFP and BOLD may be driven by instantaneous co-activation events as well. BOLD maps triggered on high-amplitude LFP events resemble co-activation patterns created from rs-fMRI data alone, though the co-activation time points are defined differently in the two cases. Moreover, only LFP events that fall into the highest or lowest thirds of the amplitude distribution result in a BOLD signal that can be distinguished from noise. These findings provide evidence of an electrophysiological basis for the time-varying co-activation patterns observed in previous studies.

scholarly journals Resting-State Co-activation Patterns as Promising Candidates for Prediction of Alzheimer’s Disease in Aged Mice

2021 ◽  
Vol 14 ◽  
Author(s):  
Mohit H. Adhikari ◽  
Michaël E. Belloy ◽  
Annemie Van der Linden ◽  
Georgios A. Keliris ◽  
Marleen Verhoye
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Neurodegenerative Disorder ◽  
Amyloid Β ◽  
Occurrence Rate ◽  
Seed Region ◽  
Periodic Patterns ◽  
Activation Patterns ◽  
Co Activation

Alzheimer’s disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-β (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting-state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task-positive resting-state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (a mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed RSNs and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and eight wild type littermates at 18 months of age. Subsequently, we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly co-activated and co-deactivated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level-dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near-perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate in the development of a diagnostic, and potentially, prognostic RS-fMRI biomarker of AD.

scholarly journals The neural bases of program comprehension: a coordinate-based fMRI meta-analysis

2021 ◽  
Author(s):  
Yoshiharu Ikutani ◽  
Takeshi D. Itoh ◽  
Takatomi Kubo
Keyword(s):  
Brain Activity ◽  
Meta Analysis ◽  
Inferior Frontal Gyrus ◽  
Posterior Part ◽  
Program Comprehension ◽  
Brain Regions ◽  
Middle Temporal Gyrus ◽  
Activation Patterns ◽  
Neural Bases ◽  
Kernel Density Analysis

AbstractThe understanding of brain activity during program comprehension have advanced thanks to noninvasive neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). However, individual neuroimaging studies of program comprehension often provided inconsistent results and made it difficult to identify the neural bases. To identify the essential brain regions, this study performed a small meta-analysis on recent fMRI studies of program comprehension using multilevel kernel density analysis (MKDA). Our analysis identified a set of brain regions consistently activated in various program comprehension tasks. These regions consisted of three clusters, each of which centered at the left inferior frontal gyrus pars triangularis (IFG Tri), posterior part of middle temporal gyrus (pMTG), and right middle frontal gyrus (MFG). Additionally, subsequent analyses revealed relationships among the activation patterns in the previous studies and multiple cognitive functions. These findings suggest that program comprehension mainly recycles the language-related networks and partially employs other domain-general resources in the human brain.

scholarly journals 'Wanting' versus 'Needing' related value: an fMRI meta-analysis

2021 ◽  
Author(s):  
Juvenal Bosulu ◽  
Max-Antoine Allaire ◽  
Laurence Tremblay-Grénier ◽  
Yi Luo ◽  
Simon Eickhoff ◽  
...  
Keyword(s):  
Ventral Tegmental Area ◽  
Meta Analysis ◽  
Brain Activation ◽  
Activation Patterns ◽  
Lack Of Control ◽  
Ventral Tegmental ◽  
Meta Analyses ◽  
The Brain

Consumption and its excesses are sometimes explained by imbalance of need or lack of control over "wanting". "Wanting" assigns value to cues that predict rewards, whereas "needing" assigns value to biologically significant stimuli that one is deprived of. Here we aimed at studying how the brain activation patterns related to value of wanted stimuli differs from that of needed stimuli using ALE neuroimaging meta-analysis approaches. We used the perception of a cue predicting a reward for "wanting" related value and the perception of food stimuli in a hungry state as a model for "needing" related value. We carried out separate, contrasts, and conjunction meta- analyses to identify differences and similarities between "wanting" and "needing" values. Our overall results for "wanting" related value show consistent activation of the ventral tegmental area, striatum and pallidum, regions that both activate behaviour and direct choice; while for "needing" related value we found an overall consistent activation of the middle insula and to some extent the caudal-ventral putamen, regions that only direct choice. Our study suggests that "wanting" has more control on consumption, and a needed stimuli must become wanted in order to be pursued.

scholarly journals Resting-state Co-activation Patterns as Accurate Predictors of Alzheimer’s Disease in Aged Mice

2020 ◽  
Author(s):  
Mohit H. Adhikari ◽  
Michaël E. Belloy ◽  
Annemie Van der Linden ◽  
Georgios A. Keliris ◽  
Marleen Verhoye
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Neurodegenerative Disorder ◽  
Occurrence Rate ◽  
Seed Region ◽  
Resting State Networks ◽  
Periodic Patterns ◽  
Activation Patterns ◽  
Co Activation

AbstractAlzheimer’s disease (AD), a neurodegenerative disorder marked by accumulation of extracellular amyloid-beta (Aβ) plaques leads to progressive loss of memory and cognitive function. Resting state fMRI (RS-fMRI) studies have provided links between these two observations in terms of disruption of default mode and task positive resting state networks (RSNs). Important insights underlying these disruptions were recently obtained by investigating dynamic fluctuations in RS-fMRI signals in old TG2576 mice (mouse model of amyloidosis) using a set of quasi-periodic patterns (QPP). QPPs represent repeating spatiotemporal patterns of neural activity of predefined temporal length. In this article, we used an alternative methodology of co-activation patterns (CAPs) that represent instantaneous and transient brain configurations that are likely contributors to the emergence of commonly observed resting state networks (RSNs) and QPPs. We followed a recently published approach for obtaining CAPs that divided all time frames, instead of those corresponding to supra-threshold activations of a seed region as done traditionally, to extract CAPs from RS-fMRI recordings in 10 TG2576 female mice and 8 wild type littermates at 18 months of age. Subsequently we matched the CAPs from the two groups using the Hungarian method and compared the temporal (duration, occurrence rate) and the spatial (lateralization of significantly activated voxels) properties of matched CAPs. We found robust differences in the spatial components of matched CAPs. Finally, we used supervised learning to train a classifier using either the temporal or the spatial component of CAPs to distinguish the transgenic mice from the WT. We found that while duration and occurrence rates of all CAPs performed the classification with significantly higher accuracy than the chance-level, blood oxygen level dependent (BOLD) signals of significantly activated voxels from individual CAPs turned out to be a significantly better predictive feature demonstrating a near perfect classification accuracy. Our results demonstrate resting-state co-activation patterns are a promising candidate for a diagnostic, and potentially, prognostic biomarker of Alzheimer’s disease.

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