scholarly journals Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks

2019 ◽  
Author(s):  
Qinmu Peng ◽  
Minhui Ouyang ◽  
Jiaojian Wang ◽  
Qinlin Yu ◽  
Chenying Zhao ◽  
...  
Keyword(s):  
Markov Random Fields ◽  
Network Connectivity ◽  
Ground Truth ◽  
Brain Network ◽  
Adult Brain ◽  
Data Driven ◽  
Spatial Constraints ◽  
Normalized Cut ◽  
Brain Organization ◽  
Functional Homogeneity

AbstractBrain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Robust and homogeneous parcellation of neonate brain is more difficult with neonate rs-fMRI associated with higher level of noise and no functional atlas as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p<0.01) to noise and can delineate parcellated functional networks with significantly better (p<0.01) spatial contiguity and significantly higher (p<0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially exploratory investigations of parcellating neonate and infant brain with noisy dataset, can potentially benefit from this RNcut method.

scholarly journals Individual Variation in Brain Network Topography is Linked to Schizophrenia Symptomatology

2019 ◽  
Author(s):  
Uzma Nawaz ◽  
Ivy Lee ◽  
Adam Beermann ◽  
Shaun Eack ◽  
Matcheri Keshavan ◽  
...  
Keyword(s):  
Individual Differences ◽  
Functional Connectivity ◽  
Negative Symptom ◽  
Individual Variation ◽  
Symptom Severity ◽  
Spatial Organization ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Network ◽  
Brain Organization

AbstractBackgroundResting state fMRI (rsfMRI) demonstrates that the brain is organized into distributed networks. Numerous studies have examined links between psychiatric symptomatology and network functional connectivity. Traditional rsfMRI analyses assume that the spatial organization of networks is invariant between individuals. This dogma has recently been overturned by the demonstration that networks show significant variation between individuals. We tested the hypothesis that previously observed relationships between schizophrenia negative symptom severity and network connectivity are actually due to individual differences in network spatial organization.Methods44 participants diagnosed with schizophrenia underwent rsfMRI scans and clinical assessments. A multivariate pattern analysis determined how whole brain functional connectivity correlates with negative symptom severity at the individual voxel level.ResultsBrain connectivity to a region of the right dorso-lateral pre-frontal cortex correlates with negative symptom severity. This finding results from individual differences in the topographic distribution of two networks: the default mode network (DMN) and the task positive network (TPN). Both networks demonstrate strong (r∼0.49) and significant (p<0.001) relationships between topography and symptom severity. For individuals with low symptom severity, this critical region is part of the DMN. In highly symptomatic individuals, this region is part of the TPN.ConclusionPreviously overlooked individual variation in brain organization is tightly linked to differences in schizophrenia symptom severity. Recognizing critical links between network topography and pathological symptomology may identify key circuits that underlie cognitive and behavioral phenotypes. Individual variation in network topography likely guides different responses to clinical interventions that rely on anatomical targeting (e.g. TMS).

scholarly journals Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity

2021 ◽  
pp. 101000
Author(s):  
Moriah E. Thomason ◽  
Ava C. Palopoli ◽  
Nicki N. Jariwala ◽  
Denise M. Werchan ◽  
Alan Chen ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity ◽  
The Brain

scholarly journals Mapping Sensorimotor Cortex With Slow Cortical Potential Resting-State Networks While Awake and Under Anesthesia

Neurosurgery ◽  
2012 ◽  
Vol 71 (2) ◽  
pp. 305-316 ◽  
Author(s):  
Jonathan D. Breshears ◽  
Charles M. Gaona ◽  
Jarod L. Roland ◽  
Mohit Sharma ◽  
David T. Bundy ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Sensorimotor Cortex ◽  
Resting State ◽  
Intractable Epilepsy ◽  
Cortical Potential ◽  
Data Driven ◽  
Resting State Networks ◽  
Seed Selection ◽  
Brain Organization ◽  
Slow Cortical Potential

Abstract BACKGROUND: The emerging insight into resting-state cortical networks has been important in our understanding of the fundamental architecture of brain organization. These networks, which were originally identified with functional magnetic resonance imaging, are also seen in the correlation topography of the infraslow rhythms of local field potentials. Because of the fundamental nature of these networks and their independence from task-related activations, we posit that, in addition to their neuroscientific relevance, these slow cortical potential networks could play an important role in clinical brain mapping. OBJECTIVE: To assess whether these networks would be useful in identifying eloquent cortex such as sensorimotor cortex in patients both awake and under anesthesia. METHODS: This study included 9 subjects undergoing surgical treatment for intractable epilepsy. Slow cortical potentials were recorded from the cortical surface in patients while awake and under propofol anesthesia. To test brain-mapping utility, slow cortical potential networks were identified with data-driven (seed-independent) and anatomy-driven (seed-based) approaches. With electrocortical stimulation used as the gold standard for comparison, the sensitivity and specificity of these networks for identifying sensorimotor cortex were calculated. RESULTS: Networks identified with a data-driven approach in patients under anesthesia and awake were 90% and 93% sensitive and 58% and 55% specific for sensorimotor cortex, respectively. Networks identified with systematic seed selection in patients under anesthesia and awake were 78% and 83% sensitive and 67% and 60% specific, respectively. CONCLUSION: Resting-state networks may be useful for tailoring stimulation mapping and could provide a means of identifying eloquent regions in patients while under anesthesia.

scholarly journals Altered Brain Network Connectivity as a Potential Endophenotype of Schizophrenia

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Peng Li ◽  
Teng-Teng Fan ◽  
Rong-Jiang Zhao ◽  
Ying Han ◽  
Le Shi ◽  
...  
Keyword(s):  
Network Connectivity ◽  
Brain Network ◽  
Brain Network Connectivity

scholarly journals TightCap: 3D Human Shape Capture with Clothing Tightness Field

2022 ◽  
Vol 41 (1) ◽  
pp. 1-17
Author(s):  
Xin Chen ◽  
Anqi Pang ◽  
Wei Yang ◽  
Peihao Wang ◽  
Lan Xu ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Ground Truth ◽  
Data Driven ◽  
Optimization Scheme ◽  
High Quality ◽  
3D Scan ◽  
Multi Stage ◽  
Geometry Image

In this article, we present TightCap, a data-driven scheme to capture both the human shape and dressed garments accurately with only a single three-dimensional (3D) human scan, which enables numerous applications such as virtual try-on, biometrics, and body evaluation. To break the severe variations of the human poses and garments, we propose to model the clothing tightness field—the displacements from the garments to the human shape implicitly in the global UV texturing domain. To this end, we utilize an enhanced statistical human template and an effective multi-stage alignment scheme to map the 3D scan into a hybrid 2D geometry image. Based on this 2D representation, we propose a novel framework to predict clothing tightness field via a novel tightness formulation, as well as an effective optimization scheme to further reconstruct multi-layer human shape and garments under various clothing categories and human postures. We further propose a new clothing tightness dataset of human scans with a large variety of clothing styles, poses, and corresponding ground-truth human shapes to stimulate further research. Extensive experiments demonstrate the effectiveness of our TightCap to achieve the high-quality human shape and dressed garments reconstruction, as well as the further applications for clothing segmentation, retargeting, and animation.

scholarly journals Salience Network Disruption in US Army Soldiers with PTSD

2018 ◽  
Author(s):  
Chadi Abdallah ◽  
Christopher Averill ◽  
Amy Ramage ◽  
Lynnette Averill ◽  
Selin Goktas ◽  
...  
Keyword(s):  
Resting State ◽  
Right Hemisphere ◽  
Brain Connectivity ◽  
Brain Network ◽  
Data Driven ◽  
Salience Network ◽  
Symptom Provocation ◽  
Us Army ◽  
Data Driven Approach ◽  
Army Soldiers

BACKGROUND: Better understanding of the neurobiology of posttraumatic stress disorder (PTSD) may be critical to developing novel, effective therapeutics. Here, we conducted a data-driven investigation using a well-established, graph- based topological measure of nodal strength to determine the extent of functional dysconnectivity in a cohort of active duty US Army soldiers with PTSD compared to controls. METHODS: 102 participants with (n=50) or without PTSD (n=52) completed functional magnetic resonance imaging (fMRI) at rest and during symptom provocation using subject-specific script imagery. Vertex/voxel global brain connectivity with global signal regression (GBCr), a measure of nodal strength, was calculated as the average of its functional connectivity with all other vertices/voxels in the brain gray matter. RESULTS: In contrast to during resting-state, where there were no group differences, we found a significantly higher GBCr, in PTSD participants compared to controls, in areas within the right hemisphere, including anterior insula, caudal- ventrolateral prefrontal, and rostral-ventrolateral parietal cortices. Overall, these clusters overlapped with the ventral and dorsal salience networks. Post hoc analysis showed increased GBCr in these salience clusters during symptom provocation compared to resting-state. In addition, resting-state GBCr in the salience clusters predicted GBCr during symptom provocation in PTSD participants but not in controls. CONCLUSION: In PTSD, increased connectivity within the salience network has been previously hypothesized, based primarily on seed-based connectivity findings. The current results strongly support this hypothesis using whole-brain network measure in a fully data-driven approach. It remains to be seen in future studies whether these identified salience disturbances would normalize following treatment.

scholarly journals Childhood Trauma Associated With Abnormal Resting-State Brain Network Connectivity in First-Episode Schizophrenia Patients

2020 ◽  
Author(s):  
Xiangyun Long ◽  
Jiaxin Wu ◽  
Fei Liu ◽  
Ansi Qi ◽  
Nan Huang ◽  
...  
Keyword(s):  
Childhood Trauma ◽  
Resting State ◽  
Network Connectivity ◽  
Brain Network ◽  
Traumatic Experiences ◽  
First Episode ◽  
Resting State Networks ◽  
Attention Network ◽  
Brain Network Connectivity ◽  
First Episode Schizophrenia

Abstract Childhood trauma is a central risk factor for schizophrenia. We explored the correlation between early traumatic experiences and the functional connectivity of resting-state networks. This fMRI study included 28 first-episode schizophrenia patients and 27 healthy controls. In first-episode schizophrenia patients, higher levels of childhood trauma associated with abnormal connections of resting-state networks, and these anomalies distributed among task-positive networks (i.e., ventral attention network, dorsal-ventral attention network and frontal-parietal network), and sensory networks (i.e., visual network and auditory network). These findings mentioned that childhood traumatic experiences may impact resting-state network connectivity in adulthood, mainly involving systems related to attention and execution control.

Graph Theory-Based Brain Network Connectivity Analysis and Classification of Alzheimer’s Disease

2021 ◽  
Author(s):  
A. Thushara ◽  
C. Ushadevi Amma ◽  
Ansamma John
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Graph Theory ◽  
Neurodegenerative Disorder ◽  
Brain Networks ◽  
Network Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Fiber Tracts ◽  
The Brain

Alzheimer’s Disease (AD) is basically a progressive neurodegenerative disorder associated with abnormal brain networks that affect millions of elderly people and degrades their quality of life. The abnormalities in brain networks are due to the disruption of White Matter (WM) fiber tracts that connect the brain regions. Diffusion-Weighted Imaging (DWI) captures the brain’s WM integrity. Here, the correlation betwixt the WM degeneration and also AD is investigated by utilizing graph theory as well as Machine Learning (ML) algorithms. By using the DW image obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, the brain graph of each subject is constructed. The features extracted from the brain graph form the basis to differentiate between Mild Cognitive Impairment (MCI), Control Normal (CN) and AD subjects. Performance evaluation is done using binary and multiclass classification algorithms and obtained an accuracy that outperforms the current top-notch DWI-based studies.

Sign in / Sign up

Export Citation Format

Share Document