scholarly journals Incorporating outlier information into diffusion MR tractogram filtering for robust structural brain connectivity and microstructural analyses

2021 ◽  
Author(s):  
Viljami Sairanen ◽  
Mario Ocampo-Pineda ◽  
Cristina Granziera ◽  
Simona Schiavi ◽  
Alessandro Daducci
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Drop Out ◽  
Severe Adverse Effect ◽  
Motion Artefacts ◽  
Process Data ◽  
Filtering Algorithms ◽  
Diffusion Weighted Images ◽  
Structural Brain

The white matter structures of the human brain can be represented via diffusion tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts that are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) filtering algorithm to adjust for signal drop-out artifacts due to subject motion present in diffusion-weighted images. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable to properly filter tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effect in the human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.

scholarly journals Connectivity-Informed Adaptive Regularization for Generalized Outcomes

2018 ◽  
Author(s):  
Damian Brzyski ◽  
Marta Karas ◽  
Beau Ances ◽  
Mario Dzemidzic ◽  
Joaquin Goni ◽  
...  
Keyword(s):  
Linear Regression ◽  
Brain Connectivity ◽  
Association Studies ◽  
Structural Connectivity ◽  
Laplacian Matrix ◽  
External Information ◽  
Penalty Term ◽  
Generalized Linear Regression ◽  
The Brain ◽  
Structural Brain

AbstractOne of the challenging problems in the brain imaging research is a principled incorporation of information from different imaging modalities in association studies. Frequently, data from each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization method, griPEER (generalized ridgified Partially Empirical Eigenvectors for Regression) to estimate the association between the brain structure features and a scalar outcome within the generalized linear regression framework. griPEER provides a principled approach to use external information from the structural brain connectivity to improve the regression coefficient estimation. Our proposal incorporates a penalty term, derived from the structural connectivity Laplacian matrix, in the penalized generalized linear regression. We address both theoretical and computational issues and show that our method is robust to the incomplete information about the structural brain connectivity. We also provide a significance testing procedure for performing inference on the estimated coefficients in this model. griPEER is evaluated in extensive simulation studies and it is applied in classification of the HIV+ and HIV- individuals.

scholarly journals Disorders of Consciousness Associated With COVID-19: A Prospective, Multimodal Study of Recovery and Brain Connectivity

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000013067
Author(s):  
David Fischer ◽  
Samuel B Snider ◽  
Megan E Barra ◽  
William R Sanders ◽  
Otto Rapalino ◽  
...  
Keyword(s):  
Brain Injury ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Disorders Of Consciousness ◽  
Healthy Controls ◽  
Post Discharge ◽  
Prospective Longitudinal Study ◽  
Link Type ◽  
Prospective Longitudinal ◽  
Structural Brain

Background and Objectives:In patients with severe coronavirus disease 2019 (COVID-19), disorders of consciousness (COVID-DoC) have emerged as a serious complication. The prognosis and pathophysiology of COVID-DoC remain unclear, complicating decisions about continuing life-sustaining treatment. We describe the natural history of COVID-DoC and investigate its associated brain connectivity profile.Methods:In a prospective, longitudinal study, we screened consecutive patients with COVID-19 at our institution. We enrolled critically ill adult patients with a DoC unexplained by sedation or structural brain injury, and who were planned to undergo a brain MRI. We performed resting state functional MRI and diffusion MRI to evaluate functional and structural connectivity, as compared to healthy controls and patients with DoC resulting from severe traumatic brain injury (TBI). We assessed the recovery of consciousness (command-following) and functional outcomes (Glasgow Outcome Scale Extended [GOSE] and the Disability Rating Scale [DRS]) at hospital discharge, three months post-discharge, and six months post-discharge. We also explored whether clinical variables were associated with recovery from COVID-DoC.Results:After screening 1,105 patients with COVID-19, we enrolled twelve with COVID-DoC. The median age was 63.5 years [interquartile range 55-76.3]. Excluding one who died shortly after enrollment, all of the remaining eleven patients recovered consciousness, after 0-25 days (median 7 [5-14.5]) following the cessation of continuous intravenous sedation. At discharge, all surviving patients remained dependent – median GOSE 3 [1-3], median DRS 23 [16-30]. However ultimately, except for two patients with severe polyneuropathy, all returned home with normal cognition and minimal disability – at three months, median GOSE 3 [3-3], median DRS 7 [5-13]; at six months, median GOSE 4 [4-5], median DRS 3 [3-5]. Ten patients with COVID-DoC underwent advanced neuroimaging; functional and structural brain connectivity in COVID-DoC was diminished compared to healthy controls, and structural connectivity was comparable to patients with severe TBI.Discussion:Patients who survived invariably recovered consciousness after COVID-DoC. Though disability was common following hospitalization, functional status improved over the ensuing months. While future research is necessary, these prospective findings inform the prognosis and pathophysiology of COVID-DoC.Trial Registration Information:Clinicaltrials.gov, NCT04476589, submitted 7/2020, first enrolled 7/20/2020, https://clinicaltrials.gov/ct2/show/NCT04476589

scholarly journals Conductance-Based Structural Brain Connectivity in Aging and Dementia

2020 ◽  
Author(s):  
Aina Frau-Pascual ◽  
Jean Augustinack ◽  
Divya Varadarajan ◽  
Anastasia Yendiki ◽  
David H. Salat ◽  
...  
Keyword(s):  
Healthy Aging ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Relevant Information ◽  
Regional Effects ◽  
Structural Connections ◽  
Cortical Regions ◽  
The Brain ◽  
Structural Brain ◽  
Age Prediction

AbstractBackgroundStructural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer’s disease (AD) progression.MethodsIn this work, we used our recently proposed structural connectivity quantification measure derived from diffusion MRI, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the ADNI-2 and OASIS-3 datasets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these datasets to the HCP dataset, as a reference, and eventually validated externally on two cohorts of the EDSD database.ResultsOur analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among sub-cortical regions, and cingulate and temporal cortices.DiscussionResults indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anti-correlation in structural connections.Impact statementThis work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from dMRI, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and AD.

scholarly journals eHealth intervention to manage symptoms for patients with cancer on immunotherapy (SOFIA): a study protocol for a randomised controlled external pilot trial

BMJ Open ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. e047277
Author(s):  
Christina Sauer ◽  
Jürgen Krauß ◽  
Dirk Jäger ◽  
Stefanie Zschäbitz ◽  
Georg Martin Haag ◽  
...  
Keyword(s):  
Immune Checkpoint ◽  
Pilot Trial ◽  
Drop Out ◽  
Size Estimation ◽  
Process Data ◽  
Patients With Cancer ◽  
Patient Reported ◽  
Utilisation Rate ◽  
Ehealth Intervention ◽  
Randomised Controlled

IntroductionImmune checkpoint therapy (ICT) is associated with a distinct pattern of immune-related adverse events (irAEs) caused by inadvertently redirecting immune responses to healthy tissues. IrAEs can occur at any time; however, in most cases, they arise during the first 14 weeks of the beginning of immune checkpoint blockade. In many cases, immunotherapy must be discontinued due to irAEs. Early detection of irAEs triggers the temporary withholding of ICT or initiation of short-term immunosuppressive treatment, is crucial in preventing further aggravation of irAEs and enables safe re-exposure to ICT. This prospective study aims to evaluate the feasibility of an eHealth intervention for patients under immunotherapy (managing symptoms of immunotherapy, SOFIA). The SOFIA-App consists of two components: SOFIA-Monitoring, a tool to rate patient-reported outcomes (PROs) including irAEs, and SOFIA-Coaching, which provides important information about cancer-specific and immunotherapy-specific topics and the counselling services of the National Centre for Tumour Diseases (NCT) Heidelberg.Methods and analysisWe outlined a patient-level two-arm randomised controlled pilot trial of the intervention (SOFIA) versus no-SOFIA for patients with cancer beginning an immunotherapy, aged ≥18 years, recruited from the NCT, Heidelberg. Feasibility outcomes include: recruitment rate; drop-out rate; reasons for refusal and drop-out; willingness to be randomised, utilisation rate of SOFIA-Monitoring and utilisation time of SOFIA-Coaching, physicians utilisation rate of the PROs; feasibility of the proposed outcome measures and optimal sample size estimation. The clinical outcomes are measures of quality of life, psychosocial symptoms, self-efficacy, physician-patient communication and medical process data, which are assessed at the beginning of the intervention, postintervention and at 6-month follow-up.Ethics and disseminationThis trial protocol was approved by the Ethical Committee of Heidelberg University, Germany (Reference, S-581/2018).Trial registration numberWe registered the study in the German Clinical Trial Register (Reference: DRKS00021064). Findings will be disseminated broadly via peer-reviewed empirical journals, articles and conference presentations.

scholarly journals Insights into the Connectivity of the Human Brain Using DTI

2012 ◽  
Vol 1 (1) ◽  
pp. 78-91 ◽  
Author(s):  
S Kollias
Keyword(s):  
Human Brain ◽  
Molecular Diffusion ◽  
Diffusion Tensor ◽  
Wide Spectrum ◽  
Diffusion Imaging ◽  
Structural Connectivity ◽  
Nerve Tissue ◽  
Mammalian Brain ◽  
Fibre Direction

Diffusion tensor imaging (DTI) is a neuroimaging MR technique, which allows in vivo and non-destructive visualization of myeloarchitectonics in the neural tissue and provides quantitative estimates of WM integrity by measuring molecular diffusion. It is based on the phenomenon of diffusion anisotropy in the nerve tissue, in that water molecules diffuse faster along the neural fibre direction and slower in the fibre-transverse direction. On the basis of their topographic location, trajectory, and areas that interconnect the various fibre systems of the mammalian brain are divided into commissural, projectional and association fibre systems. DTI has opened an entirely new window on the white matter anatomy with both clinical and scientific applications. Its utility is found in both the localization and the quantitative assessment of specific neuronal pathways. The potential of this technique to address connectivity in the human brain is not without a few methodological limitations. A wide spectrum of diffusion imaging paradigms and computational tractography algorithms has been explored in recent years, which established DTI as promising new avenue, for the non-invasive in vivo mapping of structural connectivity at the macroscale level. Further improvements in the spatial resolution of DTI may allow this technique to be applied in the near future for mapping connectivity also at the mesoscale level. DOI: http://dx.doi.org/10.3126/njr.v1i1.6330 Nepalese Journal of Radiology Vol.1(1): 78-91

scholarly journals General Anesthesia and Human Brain Connectivity

2012 ◽  
Vol 2 (6) ◽  
pp. 291-302 ◽  
Author(s):  
Anthony G. Hudetz
Keyword(s):  
Human Brain ◽  
General Anesthesia ◽  
Brain Connectivity

scholarly journals Diffusion tensor-based fast marching for modeling human brain connectivity network

2011 ◽  
Vol 35 (3) ◽  
pp. 167-178 ◽  
Author(s):  
Hai Li ◽  
Zhong Xue ◽  
Kemi Cui ◽  
Stephen T.C. Wong
Keyword(s):  
Human Brain ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Fast Marching

scholarly journals Evolutionary modifications in human brain connectivity associated with schizophrenia

Brain ◽  
2019 ◽  
Vol 142 (12) ◽  
pp. 3991-4002 ◽  
Author(s):  
Martijn P van den Heuvel ◽  
Lianne H Scholtens ◽  
Siemon C de Lange ◽  
Rory Pijnenburg ◽  
Wiepke Cahn ◽  
...  
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Brain Evolution ◽  
Higher Order ◽  
Brain Functions ◽  
Human Brain Evolution ◽  
The Brain

See Vértes and Seidlitz (doi:10.1093/brain/awz353) for a scientific commentary on this article. Is schizophrenia a by-product of human brain evolution? By comparing the human and chimpanzee connectomes, van den Heuvel et al. demonstrate that connections unique to the human brain show greater involvement in schizophrenia pathology. Modifications in service of higher-order brain functions may have rendered the brain more vulnerable to dysfunction.

scholarly journals Resources available for autism research in the big data era: a systematic review

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e2880 ◽  
Author(s):  
Reem Al-jawahiri ◽  
Elizabeth Milne
Keyword(s):  
Human Brain ◽  
Brain Connectivity ◽  
Open Data ◽  
Autism Spectrum ◽  
Data Types ◽  
Participant Recruitment ◽  
Data Resource ◽  
Autism Research ◽  
Great Heterogeneity ◽  
Google Search

Recently, there has been a move encouraged by many stakeholders towards generating big, open data in many areas of research. One area where big, open data is particularly valuable is in research relating to complex heterogeneous disorders such as Autism Spectrum Disorder (ASD). The inconsistencies of findings and the great heterogeneity of ASD necessitate the use of big and open data to tackle important challenges such as understanding and defining the heterogeneity and potential subtypes of ASD. To this end, a number of initiatives have been established that aim to develop big and/or open data resources for autism research. In order to provide a useful data reference for autism researchers, a systematic search for ASD data resources was conducted using the Scopus database, the Google search engine, and the pages on ‘recommended repositories’ by key journals, and the findings were translated into a comprehensive list focused on ASD data. The aim of this review is to systematically search for all available ASD data resources providing the following data types: phenotypic, neuroimaging, human brain connectivity matrices, human brain statistical maps, biospecimens, and ASD participant recruitment. A total of 33 resources were found containing different types of data from varying numbers of participants. Description of the data available from each data resource, and links to each resource is provided. Moreover, key implications are addressed and underrepresented areas of data are identified.

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