scholarly journals Computational Fingerprints: Modeling Interactions Between Brain Regions as Points in a Function Space

2021 ◽  
Author(s):  
Craig Poskanzer ◽  
Stefano Anzellotti
Keyword(s):  
Function Space ◽  
Hermite Polynomials ◽  
Ground Truth ◽  
Brain Regions ◽  
Basis Functions ◽  
Statistical Dependence ◽  
Seed Region ◽  
Nonlinear Interactions ◽  
Brain Areas ◽  
Face Area

In this paper we propose a novel technique to investigate the nonlinear interactions between brain regions that captures both the strength and the type of the functional relationship. Inspired by the field of functional analysis, we propose that the relationship between activity in two different brain areas can be viewed as a point in function space, identified by coordinates along an infinite set of basis functions. Using Hermite Polynomials as basis functions, we estimate from fMRI data a truncated set of coordinates that serve as a "computational fingerprint," characterizing the interaction between two brain areas. We provide a proof of the convergence of the estimates in the limit, and we validate the method with simulations in which the ground truth is known, additionally showing that computational fingerprints detect statistical dependence also when correlations ("functional connectivity") is near zero. We then use computational fingerprints to examine the neural interactions with a seed region of choice: the Fusiform Face Area (FFA). Using k-means clustering across each voxel's computational fingerprint, we illustrate that the addition of the nonlinear basis functions allows for the discrimination of inter-regional interactions that are otherwise grouped together when only linear dependence is used. Finally, we show that regions in V5 and medial occipital and temporal lobes exhibit significant nonlinear interactions with the FFA.

scholarly journals Intersubject MVPD: Empirical Comparison of fMRI Denoising Methods for Connectivity Analysis

2018 ◽  
Author(s):  
Yichen Li ◽  
Rebecca Saxe ◽  
Stefano Anzellotti
Keyword(s):  
Brain Region ◽  
Simulated Data ◽  
Ground Truth ◽  
Real Data ◽  
Brain Regions ◽  
Fmri Data ◽  
Statistical Dependence ◽  
The Real ◽  
The Difference ◽  
Multivariate Pattern

AbstractNoise is a major challenge for the analysis of fMRI data in general and for connectivity analyses in particular. As researchers develop increasingly sophisticated tools to model statistical dependence between the fMRI signal in different brain regions, there is a risk that these models may increasingly capture artifactual relationships between regions, that are the result of noise. Thus, choosing optimal denoising methods is a crucial step to maximize the accuracy and reproducibility of connectivity models. Most comparisons between denoising methods require knowledge of the ground truth: of what is the ‘real signal’. For this reason, they are usually based on simulated fMRI data. However, simulated data may not match the statistical properties of real data, limiting the generalizability of the conclusions. In this article, we propose an approach to evaluate denoising methods using real (non-simulated) fMRI data. First, we introduce an intersubject version of multivariate pattern dependence (iMVPD) that computes the statistical dependence between a brain region in one participant, and another brain region in a different participant. iMVPD has the following advantages: 1) it is multivariate, 2) it trains and tests models on independent folds of the real fMRI data, and 3) it generates predictions that are both between subjects and between regions. Since whole-brain sources of noise are more strongly correlated within subject than between subjects, we can use the difference between standard MVPD and iMVPD as a ‘discrepancy metric’ to evaluate denoising techniques (where more effective techniques should yield smaller differences). As predicted, the difference is the greatest in the absence of denoising methods. Furthermore, a combination of removal of the global signal and CompCorr optimizes denoising (among the set of denoising options tested).

scholarly journals Measuring and Modeling Nonlinear Interactions Between Brain Regions with fMRI

2016 ◽  
Author(s):  
Stefano Anzellotti ◽  
Evelina Fedorenko ◽  
Alexander J E Kell ◽  
Alfonso Caramazza ◽  
Rebecca Saxe
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Brain Regions ◽  
Nonlinear Dependence ◽  
Statistical Dependence ◽  
Nonlinear Interactions ◽  
Analysis Framework ◽  
Standard Assumption ◽  
Neural Computations ◽  
Large Scale Networks

AbstractIn the study of connectivity in large-scale networks of brain regions, a standard assumption is made that the statistical dependence between regions is univariate and linear. However, brain regions encode information in multivariate responses, and neural computations are nonlinear. Multivariate and nonlinear statistical dependence between regions is likely ubiquitous, but it is not captured by current methods. To fill this gap, we introduce a novel analysis framework: fMRI responses are characterized as points in multidimensional spaces, and nonlinear dependence is modeled using artificial neural networks. Converging evidence from multiple experiments shows that nonlinear dependence 1) models mappings between brain regions more accurately than linear dependence, explaining more variance in left-out data; 2) reveals functional subdivisions within cortical networks, and 3) is modulated by the task participants are performing.

In Search of the Mnemonic Traces of Concepts

2015 ◽  
Author(s):  
Andrew C. Papanicolaou
Keyword(s):  
Functional Neuroimaging ◽  
Distributed Storage ◽  
Anterior Part ◽  
Brain Regions ◽  
Fusiform Face Area ◽  
Brain Areas ◽  
Temporal Lobes ◽  
Face Area ◽  
The Brain ◽  
Parahippocampal Place Area

This chapter focuses on the search for mnemonic traces of concepts that are thought to exist in the form of neuronal circuits in the brain. It begins with a review of the evidence derived from observations of the effects of focal brain lesions suggesting that there are several brain regions specialized for recognizing objects belonging to different categories. It then considers brain areas that have been identified through functional neuroimaging, including the fusiform face area, the parahippocampal place area, and the extra-striate body area. It also examines the specialization of the anterior part of the temporal lobes, especially the left, for naming, and whether these and other brain areas contain mnemonic traces of concepts or traces of cardinal concept features. Finally, it discusses the “top-down” activation of category-specific areas and the idea of distributed storage of concept features.

scholarly journals PyMVPD: A toolbox for multivariate pattern dependence

2021 ◽  
Author(s):  
Mengting Fang ◽  
Craig Poskanzer ◽  
Stefano Anzellotti
Keyword(s):  
Linear Regression ◽  
Network Models ◽  
Brain Regions ◽  
Statistical Dependence ◽  
Linear Regression Models ◽  
Neural Network Models ◽  
Face Area ◽  
Artificial Neural ◽  
Multivariate Pattern ◽  
Artificial Neural Network Models

Cognitive tasks engage multiple brain regions. Studying how these regions interact is key to understand the neural bases of cognition. Standard approaches to model the interactions between brain regions rely on univariate statistical dependence. However, newly developed methods can capture multivariate dependence. Multivariate Pattern Dependence (MVPD) is a powerful and flexible approach that trains and tests multivariate models of the interactions between brain regions using independent data. In this article, we introduce PyMVPD: an open source toolbox for Multivariate Pattern Dependence. The toolbox includes pre-implemented linear regression models and artificial neural network models of the interactions between regions. It is designed to be easily customizable. We demonstrate example applications of PyMVPD using well-studied seed regions such as the fusiform face area (FFA) and the parahippocampal place area (PPA). Next, we compare the performance of different model architectures. Overall, artificial neural networks outperform linear regression. Importantly, the best performing architecture is region-dependent: MVPD subdivides cortex in distinct, contiguous regions whose interaction with FFA and PPA is best captured by different models.

scholarly journals Multivariate pattern connectivity

2016 ◽  
Author(s):  
Stefano Anzellotti ◽  
Alfonso Caramazza ◽  
Rebecca Saxe
Keyword(s):  
Functional Connectivity ◽  
Cognitive Task ◽  
Brain Regions ◽  
Seed Region ◽  
Fusiform Face Area ◽  
Face Area ◽  
The Right ◽  
Multivariate Pattern ◽  
Differential Connectivity ◽  
Over Time

AbstractWhen we perform a cognitive task, multiple brain regions are engaged. Understanding how these regions interact is a fundamental step to uncover the neural bases of behavior. Most research on the interactions between brain regions has focused on the univariate responses in the regions. However, fine grained patterns of response encode important information, as shown by multivariate pattern analysis. In the present article, we introduce and apply multivariate pattern connectivity (MVPC): a technique to study the dependence between brain regions in humans in terms of the multivariate relations between their patterns of responses. MVPC characterizes the responses in each brain region as trajectories in region-specific multidimensional spaces, and models the multivariate relationship between these trajectories. Considering the fusiform face area (FFA) as a seed region, we used searchlight-based MVPC to reveal interactions between regions undetected by univariate functional connectivity analyses. MVPC (but not functional connectivity) identified significant interactions between right FFA and the right anterior temporal lobe, the right superior temporal sulcus, and the dorsal visual stream. Additionally, MVPC outperformed univariate connectivity in its ability to explain independent variance in the responses of individual voxels. In the end, MVPC uncovered different connectivity profiles associated with different representational subspaces of FFA: the first principal component of FFA shows differential connectivity with occipital and parietal regions implicated in the processing of low-level properties of faces, while the second and third components show differential connectivity with anterior temporal regions implicated in the processing of invariant representations of face identity.Author SummaryHuman behavior is supported by systems of brain regions that exchange infor-mation to complete a task. This exchange of information between brain regions leads to statistical relationships between their responses over time. Most likely, these relationships do not link only the mean responses in two brain regions, but also their finer spatial patterns. Analyzing finer response patterns has been a key advance in the study of responses within individual regions, and can be leveraged to study between-region interactions. To capture the overall statistical relationship between two brain regions, we need to describe each region’s responses with respect to dimensions that best account for the variation in that region over time. These dimensions can be different from region to region. We introduce an approach in which each region’s responses are characterized in terms of region-specific dimensions that best account for its responses, and the relationships between regions are modeled with multivariate linear models. We demonstrate that this approach provides a better account of the data as compared to standard functional connectivity, and we use it to discover multiple dimensions within the fusiform face area that have different connectivity profiles with the rest of the brain.

Engaging regularly with fiction influences connectivity in cortical areas for language and mentalizing

2017 ◽  
Author(s):  
Roel M. Willems ◽  
Franziska Hartung
Keyword(s):  
Functional Connectivity ◽  
Time Course ◽  
Language Skills ◽  
Brain Regions ◽  
Brain Areas ◽  
Daily Lives ◽  
Cortical Areas ◽  
Social Abilities ◽  
Behavioral Evidence ◽  
Amount Of Reading

Behavioral evidence suggests that engaging with fiction is positively correlated with social abilities. The rationale behind this link is that engaging with fictional narratives offers a ‘training modus’ for mentalizing and empathizing. We investigated the influence of the amount of reading that participants report doing in their daily lives, on connections between brain areas while they listened to literary narratives. Participants (N=57) listened to two literary narratives while brain activation was measured with fMRI. We computed time-course correlations between brain regions, and compared the correlation values from listening to narratives to listening to reversed speech. The between-region correlations were then related to the amount of fiction that participants read in their daily lives. Our results show that amount of fiction reading is related to functional connectivity in areas known to be involved in language and mentalizing. This suggests that reading fiction influences social cognition as well as language skills.

scholarly journals Genetic and Neuroimaging Approaches to Understanding Post-Traumatic Stress Disorder

2020 ◽  
Vol 21 (12) ◽  
pp. 4503
Author(s):  
Sabah Nisar ◽  
Ajaz A. Bhat ◽  
Sheema Hashem ◽  
Najeeb Syed ◽  
Santosh K. Yadav ◽  
...  
Keyword(s):  
Post Traumatic Stress Disorder ◽  
Traumatic Stress ◽  
Stress Disorder ◽  
Memory Function ◽  
Deep Understanding ◽  
Brain Regions ◽  
Post Traumatic Stress ◽  
Brain Areas ◽  
Social Burden ◽  
Post Traumatic

Post-traumatic stress disorder (PTSD) is a highly disabling condition, increasingly recognized as both a disorder of mental health and social burden, but also as an anxiety disorder characterized by fear, stress, and negative alterations in mood. PTSD is associated with structural, metabolic, and molecular changes in several brain regions and the neural circuitry. Brain areas implicated in the traumatic stress response include the amygdala, hippocampus, and prefrontal cortex, which play an essential role in memory function. Abnormalities in these brain areas are hypothesized to underlie symptoms of PTSD and other stress-related psychiatric disorders. Conventional methods of studying PTSD have proven to be insufficient for diagnosis, measurement of treatment efficacy, and monitoring disease progression, and currently, there is no diagnostic biomarker available for PTSD. A deep understanding of cutting-edge neuroimaging genetic approaches is necessary for the development of novel therapeutics and biomarkers to better diagnose and treat the disorder. A current goal is to understand the gene pathways that are associated with PTSD, and how those genes act on the fear/stress circuitry to mediate risk vs. resilience for PTSD. This review article explains the rationale and practical utility of neuroimaging genetics in PTSD and how the resulting information can aid the diagnosis and clinical management of patients with PTSD.

scholarly journals A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

2020 ◽  
Author(s):  
Judy A. Prasad ◽  
Aishwarya H. Balwani ◽  
Erik C. Johnson ◽  
Joseph D. Miano ◽  
Vandana Sampathkumar ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Subcortical Structures ◽  
Neural Structure ◽  
Brain Areas ◽  
X Ray ◽  
Anatomical Imaging ◽  
Brain Pathways ◽  
Critical Aspects

AbstractNeural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.

scholarly journals Local Anesthesia at ST36 to Reveal Responding Brain Areas todeqi

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Ling-min Jin ◽  
Cai-juan Qin ◽  
Lei Lan ◽  
Jin-bo Sun ◽  
Fang Zeng ◽  
...  
Keyword(s):  
Local Anesthesia ◽  
Primary Motor Cortex ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Manual Acupuncture ◽  
Brain Areas ◽  
Potential Approach ◽  
Brain Responses ◽  
Set Up ◽  
Significant Activation

Background.Development of non-deqicontrol is still a challenge. This study aims to set up a potential approach to non-deqicontrol by using lidocaine anesthesia at ST36.Methods.Forty healthy volunteers were recruited and they received two fMRI scans. One was accompanied with manual acupuncture at ST36 (DQ group), and another was associated with both local anesthesia and manual acupuncture at the same acupoint (LA group).Results.Comparing to DQ group, more than 90 percentdeqisensations were reduced by local anesthesia in LA group. The mainly activated regions in DQ group were bilateral IFG, S1, primary motor cortex, IPL, thalamus, insula, claustrum, cingulate gyrus, putamen, superior temporal gyrus, and cerebellum. Surprisingly only cerebellum showed significant activation in LA group. Compared to the two groups, bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC were remarkably activated.Conclusions.Local anesthesia at ST36 is able to block most of thedeqifeelings and inhibit brain responses todeqi, which would be developed into a potential approach for non-deqicontrol. Bilateral S1, insula, ipsilateral IFG, IPL, claustrum, and contralateral ACC might be the key brain regions responding todeqi.

Heterogeneity in spontaneous sleep arousals: positive and negative links with early amyloid-beta and cognition

2021 ◽  
Author(s):  
Daphne Chylinski ◽  
Maxime Van Egroo ◽  
Justinas Narbutas ◽  
Ekaterina Koshmanova ◽  
Christian Berthomier ◽  
...  
Keyword(s):  
Amyloid Beta ◽  
Recent Literature ◽  
Sleep Stage ◽  
Brain Regions ◽  
Sleep Disruption ◽  
Sleep Spindles ◽  
Older Individuals ◽  
Brain Areas ◽  
Stage Transition ◽  
Negative Links

Abstract Recent literature is pointing towards a tight relationship between sleep quality and amyloid-beta (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD). Sleep arousals are considered to induce sleep disruption, and though their heterogeneity has been suggested, their correlates remain to be established. We classified arousals in sleep of 100 healthy older individuals according to their association with muscular tone increase (E+/E-) and sleep stage transition (T+/T-), and show differences in EEG oscillatory compositions across arousal types. We found that T + E- arousals, which interrupt sleep stability, were positively correlated with Aβ burden in brain regions earliest affected by AD neuropathology. By contrast, more prevalent T-E + arousals, upholding sleep continuity, were associated with lower cortical Aβ burden, and better cognition. We provide empirical evidence that spontaneous arousals are diverse and differently associated with brain integrity and cognition. Sleep arousals may offer opportunities to transiently synchronise distant brain areas, akin to sleep spindles.

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