network neuroscience
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2021 ◽  
pp. 1-33
Author(s):  
Joe Bathelt ◽  
Hilde M. Geurts ◽  
Denny Borsboom

Abstract Network approaches that investigate the interaction between symptoms or behaviours have opened new ways of understanding psychological phenomena in health and disorder. In parallel, network approaches that characterise the interaction between brain regions have become the dominant approach in neuroimaging research. Combining these parallel approaches would enable new insights into the interaction between behaviours and their brain-level correlates. In this paper, we introduce a methodology for combining network psychometrics and network neuroscience. This approach utilises the information from the psychometric network to obtain neural correlates for each node in the psychometric network (network-based regression). We illustrate the approach by highlighting the interaction between autistic traits and their resting-state functional associations. To this end, we utilise data from 172 male autistic participants (10–21 years) from the autism brain data exchange (ABIDE, ABIDE-II). Our results indicate that the network-based regression approach can uncover both unique and shared neural correlates of behavioural measures. In addition, the methodology enables us to isolate mechanisms at the brain-level that are unique to particular behavioural variables. For instance, our example analysis indicates that the overlap between communication and social difficulties is not reflected in the overlap between their functional correlates.


2021 ◽  
Author(s):  
Chao Jiang ◽  
Richard Betzel ◽  
Ye He ◽  
Yin-Shan Wang ◽  
Xiu-Xia Xing ◽  
...  

Abstract A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in complex brain function, i.e., the functional network neuroscience (FNN). However, the variability of methodologies applied across FNN studies - with respect to node definition, edge construction, and graph measurements- makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best FNN practices by systematically comparing the measurement reliability of individual differences in FNN under different analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide reliable FNN: 1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions, 2) construct functional connectome using spontaneous brain activity in multiple slow bands, 3) optimize topological economy of networks at individual level, 4) characterise information flow with specific metrics of integration and segregation. We built an interactive online resource for reliable FNN (http://ibraindata.com/research/reliableFNN).


2021 ◽  
pp. 1-34
Author(s):  
Kirsten Hilger ◽  
Sebastian Markett

Abstract We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically-plausible model of human personality. Stable behavioral tendencies are conceived as personality ‘traits’. Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent ‘biophysical entities’ with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multi-layer structure of psychometrically-derived personality factors and the organizational properties of trait-like brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.


2021 ◽  
Vol 44 (4) ◽  
pp. 276-288
Author(s):  
Alice M. Graham ◽  
Mollie Marr ◽  
Claudia Buss ◽  
Elinor L. Sullivan ◽  
Damien A. Fair

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1733
Author(s):  
Somayeh B. Shafiei ◽  
Mohammad Durrani ◽  
Zhe Jing ◽  
Michael Mostowy ◽  
Philippa Doherty ◽  
...  

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.


2021 ◽  
Author(s):  
Eduarda Gervini Zampieri Centeno ◽  
Giulia Moreni ◽  
Chris Vriend ◽  
Linda Douw ◽  
Fernando Antônio Nóbrega Santos

AbstractThe brain is an extraordinarily complex system that facilitates the efficient integration of information from different regions to execute its functions. With the recent advances in technology, researchers can now collect enormous amounts of data from the brain using neuroimaging at different scales and from numerous modalities. With that comes the need for sophisticated tools for analysis. The field of network neuroscience has been trying to tackle these challenges, and graph theory has been one of its essential branches through the investigation of brain networks. Recently, topological data analysis has gained more attention as an alternative framework by providing a set of metrics that go beyond pair-wise connections and offer improved robustness against noise. In this hands-on tutorial, our goal is to provide the computational tools to explore neuroimaging data using these frameworks and to facilitate their accessibility, data visualisation, and comprehension for newcomers to the field. We will start by giving a concise (and by no means complete) overview of the field to introduce the two frameworks, and then explain how to compute both well-established and newer metrics on resting-state functional magnetic resonance imaging. We use an open-source language (Python) and provide an accompanying publicly available Jupyter Notebook that uses data from the 1000 Functional Connectomes Project. Moreover, we would like to highlight one part of our notebook that is solely dedicated to realistic visualisation of high order interactions in brain networks. This pipeline provides three-dimensional (3-D) plots of pair-wise and higher-order interactions projected in a brain atlas, a new feature tailor-made for network neuroscience.


2021 ◽  
Author(s):  
Andrew Cwiek ◽  
Sarah Rajtmajer ◽  
Brad Wyble ◽  
Vasant Honavar ◽  
Frank Hillary

Machine learning offers a promising set of prediction tools that have enjoyed more recent application in network neuroscience. In this NETN Perspectives, we examine the current application of predictive models, e.g., classifiers trained using machine learning (ML), within the clinical network neurosciences. Our review covers 118 studies published using ML and functional MRI (fMRI) to infer various dimensions of the human functional connectome. We identify several important methodological challenges in this literature. For example, more than half of the studies focused almost exclusively on maximizing the accuracy of classifying brain functional connectomes into one of several predetermined categories (e.g., disease versus healthy), with significantly less emphasis on reproducibility and generalizability of the findings.. . There was also a concerning lack of transparency across many of the key steps in training and evaluating predictive models using machine learning. The summary of this literature underscores the importance of external validation (i.e., lockbox or test-set data) and highlights several methodological pitfalls that can be addressed by the imaging community. We offer recommendations for the principled application of machine learning in the clinical neurosciences to advance imaging biomarkers, understand causative determinants for health risks and track the trajectory of heterogeneous patient outcomes.


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