scholarly journals The Healthy Brain Network Serial Scanning Initiative: A resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions

2016 ◽  
Author(s):  
David O’Connor ◽  
Natan Vega Potler ◽  
Meagan Kovacs ◽  
Ting Xu ◽  
Lei Ai ◽  
...  
Keyword(s):  
Flanker Task ◽  
Brain Connectivity ◽  
Brain Network ◽  
Diffusion Kurtosis Imaging ◽  
Imaging Data ◽  
Test Bed ◽  
Technical Validation ◽  
Serial Scanning ◽  
Test Retest Reliability ◽  
Diffusion Kurtosis

AbstractBackgroundAlthough typically measured during the resting state, a growing literature is illustrating the ability to map intrinsic connectivity in task and naturalistic viewing fMRI paradigms. These paradigms are drawing excitement due to their greater tolerability in clinical and developing populations and because they enable a wider range of analyses (e.g. inter-subject correlations). To be clinically useful, the test-retest reliability of connectivity measured during these paradigms needs to be established. This resource provides data for evaluating testretest reliability for full-brain connectivity patterns detected during each of four scan conditions that differ with respect to level of engagement (rest, abstract animations, movie clips, flanker task). Data is provided for thirteen participants, each scanned in twelve sessions with 10 minutes for each scan of the four conditions. Diffusion kurtosis imaging data was also obtained at each session.FindingsTechnical validation and demonstrative reliability analyses found that variation in intrinsic functional connectivity across sessions was greater than that attributable to scan condition. Between-condition reliability was generally high, particularly for the frontoparietal and default networks. Between-session reliabilities obtained separately for the different scan conditions were comparable, though notably lower than between-condition reliabilities.ConclusionsThe described resource provides a test-bed for quantifying the reliability of connectivity indices across conditions and time. The resource can be used to compare and optimize different frameworks for measuring connectivity and data collection parameters such as scan length. Additionally, investigators can explore the unique perspectives of the brain’s functional architecture offered by each of the scan conditions.

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 Maturity of brain structures and white matter connectomes, and their relationship with psychiatric symptoms in youth

2020 ◽  
Author(s):  
Alex Luna ◽  
Joel Bernanke ◽  
Jiook Cha ◽  
Jonathan Posner
Keyword(s):  
White Matter ◽  
Child Behavior ◽  
Child Behavior Checklist ◽  
Psychiatric Symptoms ◽  
Flanker Task ◽  
Brain Network ◽  
Single Type ◽  
Age Difference ◽  
Imaging Data ◽  
Global Assessment Scale

AbstractBackgroundBrain neuromaturation can be indexed using brain predicted age difference (BrainPAD), a metric derived by the application of machine learning (ML) algorithms to neuroimaging. Previous studies in youth have been limited to a single type of imaging data, single ML approach, or specific psychiatric condition. Here, we use multimodal neuroimaging and an ensemble ML algorithm to estimate BrainPAD and examine its relationship with broad measures of symptoms and functioning in youth.MethodsWe used neuroimaging from eligible participants in the Healthy Brain Network (HBN, N = 498). Participants with a Child Behavior Checklist Total Problem T-Score < 60 were split into training (N=215) and test sets (N=48). Morphometry estimates (from structural MRI), white matter connectomes (from diffusion MRI), or both were fed to an automated ML pipeline to develop BrainPAD models. The most accurate model was applied to a held-out evaluation set (N=249), and the association with several psychometrics was estimated.ResultsModels using morphometry and connectomes together had a mean absolute error of 1.16 years, outperforming unimodal models. After dividing participants into positive, normal, and negative BrainPAD groups, negative BrainPAD values were associated with more symptoms on the Child Behavior Checklist (negative=71.6, normal 59.0, p=0.011) and lower functioning on the Children’s Global Assessment Scale (negative=49.3, normal=58.3, p=0.002). Higher scores were associated with better performance on the Flanker task (positive=62.4, normal=52.5, p=0.006).ConclusionThese findings suggest that a multimodal approach, in combination with an ensemble method, yields a robust biomarker correlated with clinically relevant measures in youth.

STRUCTURAL BRAIN NETWORK ABNORMALITIES IN SUBJECTS WITH INTERNET ADDICTION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740031 ◽  
Author(s):  
MIN-HEE LEE ◽  
AREUM MIN ◽  
YOON HO HWANG ◽  
DONG YOUN KIM ◽  
BONG SOO HAN ◽  
...  
Keyword(s):  
Internet Addiction ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Imaging Data ◽  
Structural Brain Network ◽  
Global And Local ◽  
The Impact ◽  
Structural Brain

Although problematic overuse of internet has increased, psychopathological characteristics and neurobiological mechanisms for internet addiction (IA) remain poorly understood. Therefore, it is necessary to investigate the impact of IA on the brain. The present study included 17 subjects with IA and 20 healthy subjects. We constructed the structural brain network from diffusion tensor imaging data and investigated alteration of structural connections in subjects with IA using the network analysis on the global and local levels. The subjects with IA showed increase of regional efficiency (RE) in bilateral orbitofrontal cortex (OFC) and decrease in right middle cingulate and middle temporal gyri ([Formula: see text]), whereas the global properties did not show significant changes. Young’s internet addiction test (IAT) scores and RE in left OFC showed positive correlation, and average time spent on internet per day was positively correlated with the RE in right OFC. This is the first study examining alterations of the structural brain connectivity in IA. We found that subjects with IA showed alterations of RE in some brain regions and RE was positively associated with the severity of IA and average time spent on internet per day. Therefore, RE may be a good property for IA assessment.

scholarly journals Diffusion Kurtosis Imaging Versus DWI in the Clinical Assessment of Rectal Carcinoma: A Retrospective Study

2021 ◽  
Author(s):  
Mi Zhou ◽  
Longlin Yin ◽  
Li Lai ◽  
Ju Zeng ◽  
Shaoyu Wang
Keyword(s):  
Rectal Carcinoma ◽  
Mean Diffusivity ◽  
Diagnostic Value ◽  
Diffusion Kurtosis Imaging ◽  
Control Group ◽  
Diagnostic Efficiency ◽  
Imaging Data ◽  
Apparent Diffusion ◽  
Diagnostic Values ◽  
Diffusion Kurtosis

Abstract Background: To perform an analysis of mean diffusivity(MD)、mean kurtosis(MK) demonstrating the diagnostic value of diffusion kurtosis imaging (DKI) and diffusion weighted imaging (DWI) with respect to rectal carcinoma. Methods: A total of thirty-nine rectal carcinoma cases and thirty-nine healthy subjects (Normal control group) were enrolled in our study. All the subjects underwent multi-parameter (DWI, DKI )magnetic resonance examination. The acquired images were individually analysed by two readers. The obtained images were input into the corresponding software, then an analysis of the subjects’ apparent diffusion coefficient(ADC), MD and MK values was performed. A receiver-operating charasterictic (ROC) analysis was used to assess the diagnostic efficiency of the MK, MD and ADC parameters. The Mann-Whitney U test was used to contrast the parameters in both groups. Spearman correlation analysis was used to analyse the correlation between ADC and MD, MK. The Kappa consistency test was used to evaluate the consistency between each reader’s evaluation. Results: Reflecting their diagnostic values with respect to rectal carcinoma, the AUC for MK, MD, and ADC were 0.911, 0.888, and 0.827 (all P <0.05), respectively. Using 0.59, 2.15×10 －3 mm 2 /s, 1.35×10 －3 mm 2 /s as thresholds, the sensitivities of MK, MD, ADC were 89.50%, 78.90%, and 76.30%, respectively; meanwhile their respective specificities were 84.20%, 73.70%, and 73.70%.The ADC was directly proportional to MD ( r =0.994, P <0.05) and inversely proportional to MK ( r =－0.460, P <0.05). Analysis of the imaging data revealed consistent results from both readers,Kappa=0.737. Conclusion: The ADC、MK and MD parameters were effective in diagnosing rectal carcinoma. Moreover, the MK and MD parameters were found to provide even more valid information regarding the microenvironment with a higher diagnostic performance.

Diffusional kurtosis imaging of kidneys in patients with hyperuricemia: initial study

2019 ◽  
Vol 61 (6) ◽  
pp. 839-847
Author(s):  
Zhong-Yuan Cheng ◽  
You-Zhen Feng ◽  
Xiao-Ling Liu ◽  
Yao-Jiang Ye ◽  
Jun-Jiao Hu ◽  
...  
Keyword(s):  
Renal Function ◽  
Fractional Anisotropy ◽  
Renal Cortex ◽  
Gouty Arthritis ◽  
Mean Diffusivity ◽  
Renal Medulla ◽  
Diffusion Kurtosis Imaging ◽  
Imaging Data ◽  
Significant Difference ◽  
Diffusion Kurtosis

Background At present, there remains a lack of a reliable indicator for monitoring renal function in patients with hyperuricemia. Purpose This study aimed to evaluate the feasibility of diffusion kurtosis imaging in the assessment of renal function in patients with hyperuricemia. Material and Methods A total of 75 male participants, including 25 with asymptomatic hyperuricemia, 25 with gouty arthritis, and 25 age-matched male healthy controls, were enrolled in this study. Diffusion kurtosis imaging data were acquired to derive axial (Ka), radial (Kr), and mean kurtosis (MK), fractional anisotropy, axial (Da), radial (Dr), and mean diffusivity (MD) for comparisons among the three groups. They were also correlated with estimated glomerular filtration rate (eGFR). Results The MK values of the renal cortex and medulla and Kr value of the renal medulla in patients with asymptomatic hyperuricemia and gouty arthritis significantly increased compared with those in the controls ( P < 0.05). Patients with gouty arthritis showed significant higher cortical and medullary Ka values compared with the other two groups ( P < 0.05). The cortical Kr values of the asymptomatic hyperuricemia and gouty arthritis patients were significantly higher than that of the controls ( P < 0.05). The medullary fractional anisotropy value showed a significant difference between the control and gouty arthritis groups ( P < 0.05). No correlation was found between any diffusion kurtosis imaging parameters and eGFR value. Conclusion Diffusion kurtosis imaging is feasible in the assessment of the early changes of renal cortex and medulla in patients with hyperuricemia.

scholarly journals The unbalanced reorganization of weaker functional connections induces the altered brain network topology in schizophrenia

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rossana Mastrandrea ◽  
Fabrizio Piras ◽  
Andrea Gabrielli ◽  
Nerisa Banaj ◽  
Guido Caldarelli ◽  
...  
Keyword(s):  
Resting State ◽  
Functional Organization ◽  
Functional Integration ◽  
Brain Network ◽  
Modular Organization ◽  
Node Degree ◽  
Imaging Data ◽  
Brain Disorder ◽  
Functional Connections ◽  
The Brain

AbstractNetwork neuroscience shed some light on the functional and structural modifications occurring to the brain associated with the phenomenology of schizophrenia. In particular, resting-state functional networks have helped our understanding of the illness by highlighting the global and local alterations within the cerebral organization. We investigated the robustness of the brain functional architecture in 44 medicated schizophrenic patients and 40 healthy comparators through an advanced network analysis of resting-state functional magnetic resonance imaging data. The networks in patients showed more resistance to disconnection than in healthy controls, with an evident discrepancy between the two groups in the node degree distribution computed along a percolation process. Despite a substantial similarity of the basal functional organization between the two groups, the expected hierarchy of healthy brains' modular organization is crumbled in schizophrenia, showing a peculiar arrangement of the functional connections, characterized by several topologically equivalent backbones. Thus, the manifold nature of the functional organization’s basal scheme, together with its altered hierarchical modularity, may be crucial in the pathogenesis of schizophrenia. This result fits the disconnection hypothesis that describes schizophrenia as a brain disorder characterized by an abnormal functional integration among brain regions.

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