scholarly journals Learning Active Multimodal Subspaces in the Brain

2021 ◽  
Author(s):  
Ishaan Batta ◽  
Anees Abrol ◽  
Zening Fu ◽  
Vince Calhoun
Keyword(s):  
Learning Task ◽  
Subspace Learning ◽  
Brain Regions ◽  
Normal Brain ◽  
Imaging Data ◽  
Multimodal Features ◽  
Active Subspaces ◽  
Brain Imaging Data ◽  
Active Subspace ◽  
The Brain

Here we introduce a multimodal framework to identify subspaces in the human brain that are defined by collective changes in structural and functional measures and are actively linked to demographic, biological and cognitive indicators in a population. We determine the multimodal subspaces using principles of active subspace learning (ASL) and demonstrate its application on a sample learning task (biological ageing) on a Schizophrenia dataset. The proposed multimodal ASL method successfully identifies latent brain representations as subsets of brain regions and connections forming co-varying subspaces in association with biological age. We show that Schizophrenia is characterized by different subspace patterns compared to those in a cognitively normal brain. The multimodal features generated by projecting structural and functional MRI components onto these active subspaces perform better than several PCA-based transformations and equally well when compared to non-transformed features on the studied learning task. In essence, the proposed method successfully learns active brain subspaces associated with a specific brain condition but inferred from the brain imaging data along with the biological/cognitive traits of interest.

scholarly journals Probing Brain Activation Patterns by Dissociating Semantics and Syntax in Sentences

2020 ◽  
Vol 34 (05) ◽  
pp. 9201-9208
Author(s):  
Shaonan Wang ◽  
Jiajun Zhang ◽  
Nan Lin ◽  
Chengqing Zong
Keyword(s):  
Brain Regions ◽  
Feature Representation ◽  
Semantic Feature ◽  
Imaging Data ◽  
Activation Patterns ◽  
Syntactic Feature ◽  
Syntactic Information ◽  
Brain Imaging Data ◽  
The Right ◽  
The Brain

The relation between semantics and syntax and where they are represented in the neural level has been extensively debated in neurosciences. Existing methods use manually designed stimuli to distinguish semantic and syntactic information in a sentence that may not generalize beyond the experimental setting. This paper proposes an alternative framework to study the brain representation of semantics and syntax. Specifically, we embed the highly-controlled stimuli as objective functions in learning sentence representations and propose a disentangled feature representation model (DFRM) to extract semantic and syntactic information in sentences. This model can generate one semantic and one syntactic vector for each sentence. Then we associate these disentangled feature vectors with brain imaging data to explore brain representation of semantics and syntax. Results have shown that semantic feature is represented more robustly than syntactic feature across the brain including the default-mode, frontoparietal, visual networks, etc.. The brain representations of semantics and syntax are largely overlapped, but there are brain regions only sensitive to one of them. For instance, several frontal and temporal regions are specific to the semantic feature; parts of the right superior frontal and right inferior parietal gyrus are specific to the syntactic feature.

scholarly journals Exploratory Factor Analysis with Structured Residuals for Brain Imaging Data

2020 ◽  
Author(s):  
Erik-Jan van Kesteren ◽  
Rogier A. Kievit
Keyword(s):  
Factor Analysis ◽  
Dimension Reduction ◽  
Brain Imaging ◽  
Exploratory Factor Analysis ◽  
A Priori ◽  
Brain Regions ◽  
Imaging Data ◽  
Number Of Factors ◽  
Brain Imaging Data ◽  
The Brain

AbstractDimension reduction is widely used and often necessary to reduce high dimensional data to a small number of underlying variables, making subsequent analyses and their interpretation tractable. One popular technique is Exploratory Factor Analysis (EFA), used by cognitive neuroscientists to reduce measurements from a large number of brain regions to a tractable number of factors. However, dimension reduction often ignores relevant a priori knowledge about the structure of the data. For example, it is well established that the brain is highly symmetric. In this paper, we (a) show the adverse consequences of ignoring a priori structure in factor analysis, (b) propose a technique to accommodate structure in EFA using structured residuals (EFAST), and (c) apply this technique to three large and varied brain imaging datasets, demonstrating the superior fit and interpretability of our approach. We provide an R software package to enable researchers to apply EFAST to other suitable datasets.

Perivascular (Virchow–Robin) spaces

2021 ◽  
pp. 86-89
Keyword(s):  
Blood Vessels ◽  
Shape Change ◽  
Lymphatic Drainage ◽  
Brain Regions ◽  
Mechanical Trauma ◽  
Normal Brain ◽  
Perivascular Spaces ◽  
Mr Images ◽  
Lenticulostriate Artery ◽  
The Brain

Perivascular spaces; also known as the Virchow-Robin Spaces, they are pleurally lined, interstitial fluid-filled areas that surround certain blood vessels in various organs, especially the perforating arteries in the brain, with an immunological function. Dilated perivascular spaces are divided into three types. The first of these is on the lenticulostriate artery, the second is in the cortex following the path of the medullary artery, and the third is in the midbrain. Perivascular spaces can be detected as areas of dilatation on MR images. Although a limited number of perivascular spaces can be seen in a normal brain, the increase in the number of these spaces has been associated with the incidence of various neurodegenerative diseases. Different theories have been suggested about the tendency of the perivascular spaces to expand. Current theories include mechanical trauma due to cerebrospinal fluid pulsing, elongation of penetrating blood vessels, unusual vascular permeability, and increased fluid exudation. In addition, the brain tissue atrophy that occurs with aging; It is thought to contribute to the widening of perivascular spaces by causing shrinkage of arteries, altered arterial wall permeability, obstruction of lymphatic drainage pathways and vascular demyelination. It is assumed that the clinical significance of the dilation tendencies of the perivascular spaces is based on shape change rather than size. These spaces have been mostly observed in brain regions such as corpus callosum, cingulate gyrus, dentate nucleus, substantia nigra and various arterial basins including lenticulostriate artery and mesencephalothalamic artery. In conclusion, when sections are taken on MR imaging, it is possible that perivascular spaces may be confused with microvascular diseases and some neurodegenerative changes. In addition, perivascular spaces can be seen without pathological significance. Therefore, it would be appropriate to investigate the etiological relationship by evaluating the radiological findings and clinical picture together.

scholarly journals A Computational Network Control Theory Analysis of Depression Symptoms

2018 ◽  
Vol 1 ◽  
Author(s):  
Yoed N. Kenett ◽  
Roger E. Beaty ◽  
John D. Medaglia
Keyword(s):  
Control Theory ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Brain Network ◽  
Network Control ◽  
Depression Symptoms ◽  
Theory Approach ◽  
Imaging Data ◽  
The Brain ◽  
Subclinical Depression

AbstractRumination and impaired inhibition are considered core characteristics of depression. However, the neurocognitive mechanisms that contribute to these atypical cognitive processes remain unclear. To address this question, we apply a computational network control theory approach to structural brain imaging data acquired via diffusion tensor imaging in a large sample of participants, to examine how network control theory relates to individual differences in subclinical depression. Recent application of this theory at the neural level is built on a model of brain dynamics, which mathematically models patterns of inter-region activity propagated along the structure of an underlying network. The strength of this approach is its ability to characterize the potential role of each brain region in regulating whole-brain network function based on its anatomical fingerprint and a simplified model of node dynamics. We find that subclinical depression is negatively related to higher integration abilities in the right anterior insula, replicating and extending previous studies implicating atypical switching between the default mode and Executive Control Networks in depression. We also find that subclinical depression is related to the ability to “drive” the brain system into easy to reach neural states in several brain regions, including the bilateral lingual gyrus and lateral occipital gyrus. These findings highlight brain regions less known in their role in depression, and clarify their roles in driving the brain into different neural states related to depression symptoms.

scholarly journals Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits

2020 ◽  
Author(s):  
Jacob Billings ◽  
Manish Saggar ◽  
Shella Keilholz ◽  
Giovanni Petri
Keyword(s):  
Functional Connectivity ◽  
Brain Function ◽  
Persistent Homology ◽  
Brain Regions ◽  
Topological Data Analysis ◽  
Time Varying ◽  
Imaging Data ◽  
Experimental Conditions ◽  
Common Time ◽  
Brain Imaging Data

Functional connectivity (FC) and its time-varying analogue (TVFC) leverage brain imaging data to interpret brain function as patterns of coordinating activity among brain regions. While many questions remain regarding the organizing principles through which brain function emerges from multi-regional interactions, advances in the mathematics of Topological Data Analysis (TDA) may provide new insights into the brain’s spontaneous self-organization. One tool from TDA, “persistent homology”, observes the occurrence and the persistence of n-dimensional holes presented in the metric space over a dataset. The occurrence of n-dimensional holes within the TVFC point cloud may denote conserved and preferred routes of information flow among brain regions. In the present study, we compare the use of persistence homology versus more traditional TVFC metrics at the task of segmenting brain states that differ across a common time-series of experimental conditions. We find that the structures identified by persistence homology more accurately segment the stimuli, more accurately segment volunteer performance during experimentally defined tasks, and generalize better across volunteers. Finally, we present empirical and theoretical observations that interpret brain function as a topological space defined by cyclic and interlinked motifs among distributed brain regions, especially, the attention networks.

Supercomputing in the Study and Stimulation of the Brain

2021 ◽  
pp. 290-300
Author(s):  
Laura Dipietro ◽  
Seth Elkin-Frankston ◽  
Ciro Ramos-Estebanez ◽  
Timothy Wagner
Keyword(s):  
High Performance ◽  
Neurological Diseases ◽  
Data Sets ◽  
Imaging Data ◽  
Building Simulations ◽  
History Of ◽  
Brain Imaging Data ◽  
Evolution Of Science ◽  
Computational Systems ◽  
The Brain

The history of neuroscience has tracked with the evolution of science and technology. Today, neuroscience's trajectory is heavily dependent on computational systems and the availability of high-performance computing (HPC), which are becoming indispensable for building simulations of the brain, coping with high computational demands of analysis of brain imaging data sets, and developing treatments for neurological diseases. This chapter will briefly review the current and potential future use of supercomputers in neuroscience.

scholarly journals High-density EEG mobile brain/body imaging data recorded during a challenging auditory gait pacing task

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Johanna Wagner ◽  
Ramon Martinez-Cancino ◽  
Arnaud Delorme ◽  
Scott Makeig ◽  
Teodoro Solis-Escalante ◽  
...  
Keyword(s):  
Pressure Sensors ◽  
Step Length ◽  
High Density ◽  
Imaging Data ◽  
Joint Angles ◽  
Body Imaging ◽  
Cortical Dynamics ◽  
Brain Imaging Data ◽  
Auditory Cueing ◽  
The Brain

Abstract In this report we present a mobile brain/body imaging (MoBI) dataset that allows study of source-resolved cortical dynamics supporting coordinated gait movements in a rhythmic auditory cueing paradigm. Use of an auditory pacing stimulus stream has been recommended to identify deficits and treat gait impairments in neurologic populations. Here, the rhythmic cueing paradigm required healthy young participants to walk on a treadmill (constant speed) while attempting to maintain step synchrony with an auditory pacing stream and to adapt their step length and rate to unanticipated shifts in tempo of the pacing stimuli (e.g., sudden shifts to a faster or slower tempo). High-density electroencephalography (EEG, 108 channels), surface electromyography (EMG, bilateral tibialis anterior), pressure sensors on the heel (to register timing of heel strikes), and goniometers (knee, hip, and ankle joint angles) were concurrently recorded in 20 participants. The data is provided in the Brain Imaging Data Structure (BIDS) format to promote data sharing and reuse, and allow the inclusion of the data into fully automated data analysis workflows.

scholarly journals Integrating the Brain Imaging Data Structure (BIDS) standard into C-PAC

GigaScience ◽  
2016 ◽  
Vol 5 (suppl_1) ◽  
Author(s):  
Daniel Clark ◽  
Krzysztof J. Gorgolewski ◽  
R. Cameron Craddock
Keyword(s):  
Data Structure ◽  
Brain Imaging ◽  
Imaging Data ◽  
Brain Imaging Data ◽  
The Brain

scholarly journals An Autism Spectrum Disorder Adaptive Identification Based on the Elimination of Brain Connections: A Proof of Long-Range Underconnectivity

2021 ◽  
Author(s):  
Fatima zahra Benabdallah ◽  
Ahmed Drissi El Maliani ◽  
Dounia Lotfi ◽  
Rachid Jennane ◽  
Mohammed El hassouni
Keyword(s):  
Autism Spectrum Disorder ◽  
Functional Connectivity ◽  
Long Range ◽  
Data Exchange ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Imaging Data ◽  
Brain Imaging Data

Abstract Autism spectrum disorder (ASD) is theoretically characterized by alterations in functional connectivity between brain regions. Many works presented approaches to determine informative patterns that help to predict autism from typical development. However, most of the proposed pipelines are not specifically designed for the autism problem, i.e they do not corroborate with autism theories about functional connectivity. In this paper, we propose a framework that takes into account the properties of local connectivity and long range under-connectivity in the autistic brain. The originality of the proposed approach is to adopt elimination as a technique in order to well emerge the autistic brain connectivity alterations, and show how they contribute to differentiate ASD from controls. Experimental results conducted on the large multi-site Autism Brain Imaging Data Exchange (ABIDE) show that our approach provides accurate prediction up to 70% and succeeds to prove the existence of deficits in the long-range connectivity in the ASD subjects brains.

scholarly journals The Emerging Role of the Interplay Among Astrocytes, Microglia, and Neurons in the Hippocampus in Health and Disease

2021 ◽  
Vol 13 ◽  
Author(s):  
Daniele Lana ◽  
Filippo Ugolini ◽  
Daniele Nosi ◽  
Gary L. Wenk ◽  
Maria Grazia Giovannini
Keyword(s):  
Acute Inflammation ◽  
Brain Aging ◽  
Morphological Changes ◽  
Brain Regions ◽  
Stratum Radiatum ◽  
Low Grade ◽  
Normal Brain ◽  
Chronic Inflammatory Response ◽  
Health And Disease ◽  
The Brain

For over a century, neurons have been considered the basic functional units of the brain while glia only elements of support. Activation of glia has been long regarded detrimental for survival of neurons but more it appears that this is not the case in all circumstances. In this review, we report and discuss the recent literature on the alterations of astrocytes and microglia during inflammaging, the low-grade, slow, chronic inflammatory response that characterizes normal brain aging, and in acute inflammation. Becoming reactive, astrocytes and microglia undergo transcriptional, functional, and morphological changes that transform them into cells with different properties and functions, such as A1 and A2 astrocytes, and M1 and M2 microglia. This classification of microglia and astrocytes in two different, all-or-none states seems too simplistic, and does not correspond to the diverse variety of phenotypes so far found in the brain. Different interactions occur among the many cell populations of the central nervous system in health and disease conditions. Such interactions give rise to networks of morphological and functional reciprocal reliance and dependency. Alterations affecting one cell population reverberate to the others, favoring or dysregulating their activities. In the last part of this review, we present the modifications of the interplay between neurons and glia in rat models of brain aging and acute inflammation, focusing on the differences between CA1 and CA3 areas of the hippocampus, one of the brain regions most susceptible to different insults. With triple labeling fluorescent immunohistochemistry and confocal microscopy (TIC), it is possible to evaluate and compare quantitatively the morphological and functional alterations of the components of the neuron-astrocyte-microglia triad. In the contiguous and interconnected regions of rat hippocampus, CA1 and CA3 Stratum Radiatum, astrocytes and microglia show a different, finely regulated, and region-specific reactivity, demonstrating that glia responses vary in a significant manner from area to area. It will be of great interest to verify whether these differential reactivities of glia explain the diverse vulnerability of the hippocampal areas to aging or to different damaging insults, and particularly the higher sensitivity of CA1 pyramidal neurons to inflammatory stimuli.

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