Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience

Raamana, Pradeep Reddy; Strother, Stephen C.

doi:10.21105/joss.00380

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…graynet is dependent on the following libraries: nibabel (Brett et al, 2016), networkx (Hagberg, Schult, & Swart, 2005), numpy (Oliphant, 2007,Walt, Colbert, & Varoquaux (2011) and hiwenet (Raamana & Strother, 2017b).…”

Section: Discussionmentioning

confidence: 99%

graynet: single-subject morphometric networks for neuroscience connectivity applications

Raamana¹,

Strother²

2018

JOSS

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Section: Discussionmentioning

confidence: 99%

graynet: single-subject morphometric networks for neuroscience connectivity applications

Raamana¹,

Strother²

2018

JOSS

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“…To construct subjectwise structural networks based on GM volumetric features, we used the open-source python toolbox “graynet” ( Raamana and Strother, 2018 ). 6 Briefly, 116 cortical and subcortical regions were obtained from the Automated Anatomical Labeling (AAL) atlas ( Tzourio-Mazoyer et al, 2002 ); the modulated, voxelwise GM volumetric distribution (with “mwp1” in CAT12 output) in a given region was converted to a histogram, and the pairwise edge weight was calculated as the histogram correlation between two regions ( Tijms et al, 2012 ; Raamana and Strother, 2017 ). In this way, a symmetric 116 × 116 structural covariance matrix for each subject was constructed.…”

Section: Methodsmentioning

confidence: 99%

Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features

Kim

Sim

Yang

et al. 2023

Front. Hum. Neurosci.

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ObjectiveHeadache is among the most frequent symptoms after coronavirus disease 2019 (COVID-19), so-called long COVID syndrome. Although distinct brain changes have been reported in patients with long COVID, such reported brain changes have not been used for predictions and interpretations in a multivariate manner. In this study, we applied machine learning to assess whether individual adolescents with long COVID can be accurately distinguished from those with primary headaches.MethodsTwenty-three adolescents with long COVID headaches with the persistence of headache for at least 3 months and 23 age- and sex-matched adolescents with primary headaches (migraine, new daily persistent headache, and tension-type headache) were enrolled. Multivoxel pattern analysis (MVPA) was applied for disorder-specific predictions of headache etiology based on individual brain structural MRI. In addition, connectome-based predictive modeling (CPM) was also performed using a structural covariance network.ResultsMVPA correctly classified long COVID patients from primary headache patients, with an area under the curve of 0.73 (accuracy = 63.4%; permutation p = 0.001). The discriminating GM patterns exhibited lower classification weights for long COVID in the orbitofrontal and medial temporal lobes. The CPM using the structural covariance network achieved an area under the curve of 0.81 (accuracy = 69.5%; permutation p = 0.005). The edges that classified long COVID patients from primary headache were mainly comprising thalamic connections.ConclusionThe results suggest the potential value of structural MRI-based features for classifying long COVID headaches from primary headaches. The identified features suggest that the distinct gray matter changes in the orbitofrontal and medial temporal lobes occurring after COVID, as well as altered thalamic connectivity, are predictive of headache etiology.

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“…The core HiWeNet algorithm has been implemented in Python and is publicly available at this URL: https://github.com/raamana/hiwenet (Raamana and Strother 2017). We have also published the original Matlab code for the computation of adjacency matrices used for this study, within the hiwenet package.…”

Section: Feature Extraction Via Graynetmentioning

confidence: 99%

Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight

Raamana

Strother

2017

Preprint

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Network-level analysis based on anatomical covariance (cortical thickness) has been gaining increasing popularity recently. However, there has not been a systematic study of the impact of spatial scale and edge definitions on predictive performance. In order to obtain a clear understanding of relative performance, there is a need for systematic comparison. In this study, we present a histogram-based approach to construct subjectwise weighted networks that enable a principled comparison across different methods of network analysis. We design several weighted networks based on two large publicly available datasets and perform a robust evaluation of their predictive power under three levels of separability. One of the interesting insights include the robust predictive power resulting from lack of significant impact of changes in nodal size (spatial scale) among the three classification experiments. We also release an open source python package to enable others to implement presented network feature extraction algorithm in their research.

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Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience

Cited by 4 publications

References 12 publications

graynet: single-subject morphometric networks for neuroscience connectivity applications

graynet: single-subject morphometric networks for neuroscience connectivity applications

Multivariate prediction of long COVID headache in adolescents using gray matter structural MRI features

Does size matter? The relationship between predictive power of single-subject morphometric networks to spatial scale and edge weight

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