scholarly journals Gradients of connectivity as graph fourier bases of brain activity

2021 ◽  
pp. 1-15
Author(s):  
Giulia Lioi ◽  
Vincent Gripon ◽  
Abdelbasset Brahim ◽  
François Rousseau ◽  
Nicolas Farrugia
Keyword(s):  
Signal Processing ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Complex Structure ◽  
Brain Networks ◽  
Graph Signal Processing ◽  
As Graph ◽  
Functional Relevance ◽  
And Function

The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity “coupled to” an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.

Error-related Persistence of Motor Activity in Resting-state Networks

2018 ◽  
Vol 30 (12) ◽  
pp. 1883-1901 ◽  
Author(s):  
Nicolò F. Bernardi ◽  
Floris T. Van Vugt ◽  
Ricardo Ruy Valle-Mena ◽  
Shahabeddin Vahdat ◽  
David J. Ostry
Keyword(s):  
Motor Learning ◽  
Resting State ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Neural Activation ◽  
Resting State Functional Connectivity ◽  
Movement Training ◽  
Movement Error ◽  
Human Participants

The relationship between neural activation during movement training and the plastic changes that survive beyond movement execution is not well understood. Here we ask whether the changes in resting-state functional connectivity observed following motor learning overlap with the brain networks that track movement error during training. Human participants learned to trace an arched trajectory using a computer mouse in an MRI scanner. Motor performance was quantified on each trial as the maximum distance from the prescribed arc. During learning, two brain networks were observed, one showing increased activations for larger movement error, comprising the cerebellum, parietal, visual, somatosensory, and cortical motor areas, and the other being more activated for movements with lower error, comprising the ventral putamen and the OFC. After learning, changes in brain connectivity at rest were found predominantly in areas that had shown increased activation for larger error during task, specifically the cerebellum and its connections with motor, visual, and somatosensory cortex. The findings indicate that, although both errors and accurate movements are important during the active stage of motor learning, the changes in brain activity observed at rest primarily reflect networks that process errors. This suggests that error-related networks are represented in the initial stages of motor memory formation.

scholarly journals Spatial and temporal autocorrelation weave human brain networks

2021 ◽  
Author(s):  
Maxwell Shinn ◽  
Amber Hu ◽  
Laurel Turner ◽  
Stephanie Noble ◽  
Sophie Achard ◽  
...  
Keyword(s):  
Network Topology ◽  
Healthy Aging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Temporal Autocorrelation ◽  
Activity Networks ◽  
Graph Properties ◽  
Test Retest Reliability

Correlations are a basic object of analysis across neuroscience, but multivariate patterns of correlations can be difficult to interpret. For example, correlations are fundamental to understanding timeseries derived from resting-state functional magnetic resonance imaging (rs-fMRI), a proxy of brain activity. Networks constructed from regional correlations in rs-fMRI timeseries are often interpreted as brain connectivity, yet the links between brain networks and neurobiology have until now been largely speculative. Here, we show that the topology of rs-fMRI brain networks is caused by the spatial and temporal autocorrelation of the timeseries used to construct them. Spatial and temporal autocorrelation show high test-retest reliability, and are correlated with popular measures of network topology. A generative model of spatially and temporally autocorrelated timeseries exhibits similar network topology to brain networks, and when fit to individual subjects, it captures near the reliability limit of subject and regional variation. We demonstrate why spatial and temporal autocorrelation induce network structure, and highlight their ability to link graph properties to neurobiology during healthy aging. These results offer a reductionistic account of brain network complexity, explaining characteristic patterns in brain networks using timeseries statistics.

scholarly journals The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality

2019 ◽  
Vol 30 (4) ◽  
pp. 2019-2029 ◽  
Author(s):  
Eloise A Stark ◽  
Joana Cabral ◽  
Madelon M E Riem ◽  
Marinus H Van IJzendoorn ◽  
Alan Stein ◽  
...  
Keyword(s):  
Functional Neuroimaging ◽  
Brain Connectivity ◽  
Brain Activity ◽  
Brain Networks ◽  
Brain Network ◽  
Adult Brain ◽  
Significant Activity ◽  
Nulliparous Women ◽  
Emotion And Memory ◽  
Underlying Mechanisms

Abstract The perception of infant emotionality, one aspect of temperament, starts to form in infancy, yet the underlying mechanisms of how infant emotionality affects adult neural dynamics remain unclear. We used a social reward task with probabilistic visual and auditory feedback (infant laughter or crying) to train 47 nulliparous women to perceive the emotional style of six different infants. Using functional neuroimaging, we subsequently measured brain activity while participants were tested on the learned emotionality of the six infants. We characterized the elicited patterns of dynamic functional brain connectivity using Leading Eigenvector Dynamics Analysis and found significant activity in a brain network linking the orbitofrontal cortex with the amygdala and hippocampus, where the probability of occurrence significantly correlated with the valence of the learned infant emotional disposition. In other words, seeing infants with neutral face expressions after having interacted and learned their various degrees of positive and negative emotional dispositions proportionally increased the activity in a brain network previously shown to be involved in pleasure, emotion, and memory. These findings provide novel neuroimaging insights into how the perception of happy versus sad infant emotionality shapes adult brain networks.

Multimodal Imaging in Psychiatry: The Electroencephalogram as a Complement to Other Modalities

CNS Spectrums ◽  
1999 ◽  
Vol 4 (8) ◽  
pp. 44-57
Author(s):  
Dean F. Salisbury ◽  
Brian F. O'Donnell ◽  
Paul G. Nestor ◽  
Martha E. Shenton ◽  
Robert W. McCarley
Keyword(s):  
Multimodal Imaging ◽  
Imaging Techniques ◽  
Brain Activity ◽  
Multimodal Approach ◽  
Resonance Imaging ◽  
Anatomy And Physiology ◽  
Levels Of Detail ◽  
Underlying Causes ◽  
And Function ◽  
The Brain

ABSTRACTThe use of different imaging modalities provides the clinician and researcher with different views of anatomy and physiology at unprecedented levels of detail. Multimodal imaging allows for noninvasive measurement of structure and function in humans during complex behavior, and thus provides information about the inner workings of the brain previously unavailable. The present paper examines the various imaging techniques available, and describes their application to the clinic—in the case of epilepsy—and to research—in the case of schizophrenia. Because the electroen-cephalogram has a dynamic response in milliseconds, it provides the best temporal sensitivity of functional measures of brain activity. When coupled with high-resolution magnetic resonance imaging measures of brain structure, this multimodal approach provides a powerful tool for understanding brain activity. Clinically, the use of multimodal imaging has provided greater precision in localization of the epileptogenic focus. For researchers attempting to determine the underlying causes of schizophrenia, the use of multimodal imaging has helped lead the field away from a specific lesion view to a more distributed system abnormality view of this disorder.

Collagen-the Ultrastructural Element of the Bone Matrix

2018 ◽  
Vol 69 (7) ◽  
pp. 1706-1709
Author(s):  
Nicoleta Dumitru ◽  
Andra Cocolos ◽  
Andra Caragheorgheopol ◽  
Constantin Dumitrache ◽  
Ovidiu Gabriel Bratu ◽  
...  
Keyword(s):  
Type I Collagen ◽  
Bone Microarchitecture ◽  
Complex Structure ◽  
Bone Matrix ◽  
Organic Matrix ◽  
Bone Diseases ◽  
Bone Collagen ◽  
Type I ◽  
New Therapeutic Approaches ◽  
And Function

There is an increased interest and more studies highlight the fact that bone strength depends not only on bone tissue quantity, but also on its quality, which is characterized by the geometry and shape of bones, trabecular bone microarchitecture, mineral content, organic matrix and bone turnover. Fibrillar type I collagen is the major organic component of bone matrix, providing form and a stable template for mineralization. The biomedical importance of collagen as a biomaterial for medical and cosmetic purposes and the improvement of the molecular, cellular biology and analytical technologies, led to increasing interest in establishing the structure of this protein and in setting of the relationships between sequence, structure, and function. Bone collagen crosslinking chemistry and its molecular packing structure are considered to be distinct features. This unique post-translational modifications provide to the fibrillar collagen matrices properties such as tensile strength and viscoelasticity. Understanding the complex structure of bone type I collagen as well as the dynamic nature of bone tissues will help to manage new therapeutic approaches to bone diseases.

Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

2020 ◽  
Vol 13 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Mohammad Khalid Pandit
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Sagittal Plane ◽  
Early Stage ◽  
Imaging Techniques ◽  
Complex Structure ◽  
Magnetic Resonance Images ◽  
Absolute Difference ◽  
Brain Hemispheres ◽  
The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

scholarly journals Graphon Filters: Graph Signal Processing in the Limit

2021 ◽  
Vol 69 ◽  
pp. 1740-1754
Author(s):  
Matthew W. Morency ◽  
Geert Leus
Keyword(s):  
Signal Processing ◽  
Graph Signal Processing

scholarly journals Magnetoencephalography in the Detection and Characterization of Brain Abnormalities Associated with Traumatic Brain Injury: A Comprehensive Review

2021 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
Geoffrey W. Peitz ◽  
Elisabeth A. Wilde ◽  
Ramesh Grandhi
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Imaging Techniques ◽  
Brain Connectivity ◽  
Functional Brain Imaging ◽  
Original Research ◽  
High Temporal Resolution ◽  
Post Traumatic Stress ◽  
Clinical Role

Magnetoencephalography (MEG) is a functional brain imaging technique with high temporal resolution compared with techniques that rely on metabolic coupling. MEG has an important role in traumatic brain injury (TBI) research, especially in mild TBI, which may not have detectable features in conventional, anatomical imaging techniques. This review addresses the original research articles to date that have reported on the use of MEG in TBI. Specifically, the included studies have demonstrated the utility of MEG in the detection of TBI, characterization of brain connectivity abnormalities associated with TBI, correlation of brain signals with post-concussive symptoms, differentiation of TBI from post-traumatic stress disorder, and monitoring the response to TBI treatments. Although presently the utility of MEG is mostly limited to research in TBI, a clinical role for MEG in TBI may become evident with further investigation.

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