scholarly journals Hierarchical Multivariate Covariance Analysis of Metabolic Connectivity

2014 ◽  
Vol 34 (12) ◽  
pp. 1936-1943 ◽  
Author(s):  
Felix Carbonell ◽  
Arnaud Charil ◽  
Alex P Zijdenbos ◽  
Alan C Evans ◽  
Barry J Bedell ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Correlation Coefficients ◽  
Brain Network ◽  
Angular Gyrus ◽  
Connectivity Analysis ◽  
Novel Approach ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Conventional brain connectivity analysis is typically based on the assessment of interregional correlations. Given that correlation coefficients are derived from both covariance and variance, group differences in covariance may be obscured by differences in the variance terms. To facilitate a comprehensive assessment of connectivity, we propose a unified statistical framework that interrogates the individual terms of the correlation coefficient. We have evaluated the utility of this method for metabolic connectivity analysis using [18F]2-fluoro-2-deoxyglucose (FDG) positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. As an illustrative example of the utility of this approach, we examined metabolic connectivity in angular gyrus and precuneus seed regions of mild cognitive impairment (MCI) subjects with low and high β-amyloid burdens. This new multivariate method allowed us to identify alterations in the metabolic connectome, which would not have been detected using classic seed-based correlation analysis. Ultimately, this novel approach should be extensible to brain network analysis and broadly applicable to other imaging modalities, such as functional magnetic resonance imaging (MRI).

Neuroimaging in Migraine—Recent Advances and Perspectives for the Future

US Neurology ◽  
2010 ◽  
Vol 06 (01) ◽  
pp. 82
Author(s):  
Brian K Day ◽  
David W Dodick ◽  
Todd J Schwedt ◽  
◽  
◽  
...  
Keyword(s):  
White Matter ◽  
White Matter Lesions ◽  
Medical Decision ◽  
Positron Emission ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual ◽  
Sites Of Action ◽  
Functional Connectivity Mri ◽  
Neuroimaging Techniques

Migraine is a very common disorder that imposes substantial individual and societal costs. A better understanding of migraine mechanisms may lead to the development of new therapies and thus improve the management of migraine patients. Magnetic resonance imaging (MRI) techniques and positron emission tomography (PET) have revolutionized our understanding of migraine pathophysiology as a primary central nervous system (CNS) disorder, advanced the search for a central migraine generator, clarified the role of cortical spreading depression (CSD) and central sensitization in the pathogenesis of migraine, and revealed some potential sites of action of migraine medications. Structural imaging has shed light on relationships between migraine and stroke, white matter lesions, iron deposition, microstructural brain damage, and other gray and white matter aberrations. Emerging neuroimaging techniques, such as arterial spin labeling (ASL) and functional connectivity MRI (fcMRI), are beginning to provide further evidence of functional brain alterations in migraine patients. Ultimately, it is hoped that advanced neuroimaging will benefit the individual migraine patient by enhancing our diagnostic abilities, allowing for development of better treatments and serving as an important tool in medical decision-making.

scholarly journals Metabolic and haemodynamic resting-state connectivity of the human brain: a high-temporal resolution simultaneous BOLD-fMRI and FDG-fPET multimodality study

2020 ◽  
Author(s):  
Sharna D Jamadar ◽  
Phillip GD Ward ◽  
Emma Xingwen Liang ◽  
Edwina R Orchard ◽  
Zhaolin Chen ◽  
...  
Keyword(s):  
Human Brain ◽  
Temporal Resolution ◽  
Fdg Pet ◽  
Neurological Diseases ◽  
Brain Connectivity ◽  
Constant Infusion ◽  
High Temporal Resolution ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
The Brain

AbstractSimultaneous FDG-PET/fMRI ([18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging) provides the capacity to image two sources of energetic dynamics in the brain – glucose metabolism and haemodynamic response. Functional fMRI connectivity has been enormously useful for characterising interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease; but static FDG-PET cannot be used to estimate subjectlevel measures of connectivity, only across-subject covariance. Here, we applied high-temporal resolution constant infusion fPET to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterised by fronto-parietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with static FDG-PET metabolic covariance, indicating that metabolic brain connectivity is a non-ergodic process whereby individual brain connectivity cannot be inferred from group level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain, and open up the opportunity for novel fundamental studies of human brain connectivity as well as multi-modality biomarkers of neurological diseases.

scholarly journals Generalizable predictive modeling of semantic processing ability from functional brain connectivity

2021 ◽  
Author(s):  
Danting Meng ◽  
Suiping Wang ◽  
Patrick Wong ◽  
Gangyi Feng
Keyword(s):  
Individual Differences ◽  
Predictive Modeling ◽  
Semantic Processing ◽  
Brain Connectivity ◽  
Brain Network ◽  
Conceptual Information ◽  
Human Connectome Project ◽  
Unseen Data ◽  
Out Of Sample ◽  
The Individual

Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating meaningful and conceptual information. Neuroimaging studies of SP typically collapse data from many subjects, but both its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural organizations contribute to the individual differences in SP behaviors. Here we aim to identify the neural signatures underlying SP variabilities by analyzing individual functional connectivity (FC) patterns based on a large-sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two-stage predictive modeling approach to build an internally cross-validated model and to test the model's generalizability with unseen data from different HCP sub-populations and task states as well as other out-of-sample datasets that are independent of the HCP. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five semantic tasks. This cross-validated predictive model can be used to predict unseen HCP data. The model generalizability was enhanced with FCs in language tasks than resting state and other task states and was better for females than males. The model constructed from the HCP dataset can be generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out-of-sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education and improve intervention practice for patients with SP and language deficits.

scholarly journals Multimodal Imaging Brain Connectivity Analysis toolbox (MIBCA)

2014 ◽  
Author(s):  
Andre Santos Ribeiro ◽  
Luis Miguel Lacerda ◽  
Hugo A. Ferreira
Keyword(s):  
Multimodal Imaging ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Mean Diffusivity ◽  
Image Data ◽  
Structural Data ◽  
Resting State Fmri ◽  
Connectivity Analysis ◽  
Positron Emission

Aim: In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods: The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19-73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results: It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion: While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.

scholarly journals Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships

2021 ◽  
Author(s):  
Katharina Voigt ◽  
Emma Liang ◽  
Bratislav Misic ◽  
Phillip Ward ◽  
Gary Egan ◽  
...  
Keyword(s):  
Executive Functioning ◽  
Cognitive Neuroscience ◽  
Cerebral Oxygenation ◽  
Multimodality Imaging ◽  
Brain Connectivity ◽  
Neural Mechanisms ◽  
Human Cognition ◽  
Positron Emission ◽  
Neuroscience Research ◽  
Metabolic Connectivity

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18 F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterisation of the human brain. How these two modalities differ in their contribution to cognition and behaviour is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that while for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, while for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.

Modeling of Blood Flow in the Human Brain

2011 ◽  
Author(s):  
Md. Shahadat Hossain ◽  
Shriram B. Pillapakkam ◽  
Bhavin Dalal ◽  
Ian S. Fischer ◽  
Nadine Aubry ◽  
...  
Keyword(s):  
Blood Flow ◽  
Human Brain ◽  
Oxygen Deficiency ◽  
Ion Concentration ◽  
Hydrogen Ion Concentration ◽  
Arterial Blood ◽  
Positron Emission ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual ◽  
Invasive Techniques

Under normal conditions, Cerebral Blood Flow (CBF) is related to the metabolism of the cerebral tissue. Three factors that contribute significantly to the regulation of CBF include the carbon dioxide and hydrogen ion concentration, oxygen deficiency and the level of cerebral activity. These regulatory mechanisms ensure a constant CBF of 50 to 55 ml per 100g of brain per minute for mean arterial blood pressure between 60–180 mm Hg. Under severe conditions when the autoregulatory mechanism fails to compensate, sympathetic nervous system constricts the large and intermediate sized arteries and prevents very high pressure from ever reaching the smaller blood vessels, preventing the occurrence of vascular hemorrhage. Several invasive and non-invasive techniques such as pressure and thermoelectric effect sensors to Positron Emission Tomography (PET) and magnetic resonance imaging (MRI) based profusion techniques have been used to quantify CBF. However, the effects of the non-Newtonian properties of blood, i.e., shear thinning and viscoelasticity, can have a significant influence on the distribution of CBF in the human brain and are poorly understood. The aim of this work is to quantify the role played by the non-Newtonian nature of blood on CBF. We have developed mathematical models of CBF that use direct numerical simulations (DNS) for the individual capillaries along with the experimental data in a one-dimensional model to determine the flow rate and the methods for regulating CBF. The model also allows us to determine which regions of the brain would be affected more severely under these conditions.

scholarly journals Cortical patterning of abnormal morphometric similarity in psychosis is associated with brain expression of schizophrenia related genes

2018 ◽  
Author(s):  
Sarah E Morgan ◽  
Jakob Seidlitz ◽  
Kirstie Whitaker ◽  
Rafael Romero-Garcia ◽  
Nicholas E Clifton ◽  
...  
Keyword(s):  
Brain Connectivity ◽  
Interaction Network ◽  
Case Control ◽  
Brain Network ◽  
Case Control Studies ◽  
Total N ◽  
Spatially Correlated ◽  
Cortical Areas ◽  
Magnetic Resonance Imaging Mri ◽  
Structural Brain Network

Schizophrenia has been conceived as a disorder of brain connectivity but it is unclear how this network phenotype is related to the emerging genetics. We used morphometric similarity analysis of magnetic resonance imaging (MRI) data as a marker of inter-areal cortical connectivity in three prior case-control studies of psychosis: in total, N=185 cases and N=227 controls. Psychosis was associated with globally reduced morphometric similarity (MS) in all 3 studies. There was also a replicable pattern of case-control differences in regional MS which was significantly reduced in patients in frontal and temporal cortical areas, but increased in parietal cortex. Using prior brain-wide gene expression data, we found that the cortical map of case-control differences in MS was spatially correlated with cortical expression of a weighted combination of genes enriched for neu-robiologically relevant ontology terms and pathways. In addition, genes that were normally over-expressed in cortical areas with reduced MS were significantly up-regulated in a prior post mortem study of schizophrenia. We propose that this combination of neuroimaging and transcriptional data provides new insight into how previously implicated genes and proteins, as well as a number of unreported proteins in their vicinity on the protein interaction network, may interact to drive structural brain network changes in schizophrenia.

scholarly journals The Effective Brain Areas in Recognition of Dyslexia

2020 ◽  
Vol 7 (3) ◽  
pp. 147-152
Author(s):  
Roya Kheyrkhah Shali ◽  
Seyed Kamaledin Setarehdan
Keyword(s):  
Spectral Power ◽  
Correlation Coefficients ◽  
Svm Classifier ◽  
Brain Areas ◽  
Positron Emission ◽  
Therapeutic Exercises ◽  
Brain Activation Pattern ◽  
Magnetic Resonance Imaging Mri ◽  
Reading Activity ◽  
The Brain

Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman. Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease. Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area. Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain.

scholarly journals Multimodal Imaging Brain Connectivity Analysis toolbox (MIBCA)

2014 ◽  
Author(s):  
Andre Santos Ribeiro ◽  
Luis Miguel Lacerda ◽  
Hugo A. Ferreira
Keyword(s):  
Multimodal Imaging ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Mean Diffusivity ◽  
Image Data ◽  
Structural Data ◽  
Resting State Fmri ◽  
Connectivity Analysis ◽  
Positron Emission

Aim: In recent years, connectivity studies using neuroimaging data have increased the understanding of the organization of large-scale structural and functional brain networks. However, data analysis is time consuming as rigorous procedures must be assured, from structuring data and pre-processing to modality specific data procedures. Until now, no single toolbox was able to perform such investigations on truly multimodal image data from beginning to end, including the combination of different connectivity analyses. Thus, we have developed the Multimodal Imaging Brain Connectivity Analysis (MIBCA) toolbox with the goal of diminishing time waste in data processing and to allow an innovative and comprehensive approach to brain connectivity. Materials and Methods: The MIBCA toolbox is a fully automated all-in-one connectivity toolbox that offers pre-processing, connectivity and graph theoretical analyses of multimodal image data such as diffusion-weighted imaging, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET). It was developed in MATLAB environment and pipelines well-known neuroimaging softwares such as Freesurfer, SPM, FSL, and Diffusion Toolkit. It further implements routines for the construction of structural, functional and effective or combined connectivity matrices, as well as, routines for the extraction and calculation of imaging and graph-theory metrics, the latter using also functions from the the Brain Connectivity Toolbox. Finally, the toolbox performs group statistical analysis and enables data visualization in the form of matrices, 3D brain graphs and connectograms. In this paper the MIBCA toolbox is presented by illustrating its capabilities using multimodal image data from a group of 35 healthy subjects (19-73 years old) with volumetric T1-weighted, diffusion tensor imaging, and resting state fMRI data, and 10 subjets with 18F-Altanserin PET data also. Results: It was observed both a high inter-hemispheric symmetry and an intra-hemispheric modularity associated with structural data, whilst functional data presented lower inter-hemispheric symmetry and a high inter-hemispheric modularity. Furthermore, when testing for differences between two subgroups (<40 and >40 years old adults) we observed a significant reduction in the volume and thickness, and an increase in the mean diffusivity of most of the subcortical/cortical regions. Conclusion: While bridging the gap between the high numbers of packages and tools widely available for the neuroimaging community in one toolbox, MIBCA also offers different possibilities for combining, analysing and visualising data in novel ways, enabling a better understanding of the human brain.

Metabolic and Hemodynamic Resting-State Connectivity of the Human Brain: A High-Temporal Resolution Simultaneous BOLD-fMRI and FDG-fPET Multimodality Study

2021 ◽  
Author(s):  
Sharna D Jamadar ◽  
Phillip G D Ward ◽  
Emma X Liang ◽  
Edwina R Orchard ◽  
Zhaolin Chen ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Human Brain ◽  
Temporal Resolution ◽  
Fdg Pet ◽  
Brain Connectivity ◽  
Emission Tomography ◽  
High Temporal Resolution ◽  
Positron Emission ◽  
Metabolic Connectivity ◽  
The Brain

Abstract Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain—glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of “connectivity,” only across-subject “covariance.” Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases.

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