Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder

Díaz-Patiño, Juan Carlos; Arelio, Isaac; Alcauter, Sarael

doi:10.1101/751008

Cited by 8 publications

(13 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…TDA models the connectome as a topological space and characterizes its interaction patterns as geometric features, allowing it to simplify complex structures at different scales (Giusti et al, 2016; Santos et al, 2019; Centeno et al, 2021). In particular, TDA applied to functional connectomes is not affected by the potential biases of connectivity thresholding nor brain segmentation (Lee et al, 2012; Gracia-Tabuenca et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Development of the Functional Connectome Topology in Adolescence: evidence from Topological Data Analysis

Díaz-Patiño

Arelio

Alcauter

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Adolescence is a crucial developmental period in terms of behavior and mental health. Therefore, understanding how the brain develops during this stage is a fundamental challenge for neuroscience. Recent studies have modelled the brain as a network or connectome, mainly applying measures from graph theory, showing a change in its functional organization such as an increase in its segregation and integration. Topological Data Analysis (TDA) complements such modelling by extracting high-dimensional features across the whole range of connectivity values, instead of exploring a fixed set of connections. This study enquiries into the developmental trajectories of such properties using a longitudinal sample of typically developing participants (N = 98; 53/45 F/M; 6.7-18.1 years), applying TDA into their functional connectomes. In addition, we explore the effect of puberty on the individual developmental trajectories. Results showed that compared to random networks, the adolescent brain is more segregated at the global level, but more densely connected at the local level. Furthermore, developmental effects showed nonlinear trajectories for the integration of the whole brain and fronto-parietal networks, with an inflection point and increasing trajectories after puberty onset. These results add to the insights in the development of the functional organization of the adolescent.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of the Functional Connectome Topology in Adolescence: evidence from Topological Data Analysis

Díaz-Patiño

Arelio

Alcauter

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similar topological summary statistics have been described in analyzing brain connectomes of individuals with ADHD using 0D Vietoris-Rips complex persistent homology features 12 . Likewise, we also focus our analysis on 0D features; however, we generate our topological features using cubical complexes.…”

Section: Persistent Homology Backgroundmentioning

confidence: 80%

Persistent Homology of Tumor CT Scans is Associated with Survival In Lung Cancer

Somasundaram

Litzler

Wadhwa

et al. 2020

Preprint

View full text Add to dashboard Cite

Radiomics, the objective study of non-visual features in clinical imaging, has been useful in informing decisions in clinical oncology. However, radiomics currently lacks the ability to characterize the overall structure of the data. This field may benefit by incorporating persistent homology, a popular new algorithm that analyzes whole data structure. We hypothesized that persistent homology could be applied to lung tumor scans and predict clinical variables. We obtained computed tomography lung scans (n = 565) from the NSCLC-Radiomics and NSCLC-Radiogenomics datasets in The Cancer Imaging Archive. Segmentation data was used to create a cubical region centered on the primary tumor in each scan. For each scan, a cubical complex filtration based on Hounsfield units was generated. We created a feature curve that plotted the number of 0 dimensional topological features against each Hounsfield unit. The curve’s first moment of the distribution was utilized as a summary statistic to predict survival in a Cox proportional hazards model. The first moment of the distribution is equivalent to the area under the curve of our topological feature curves (AUC). The Kruskal-Wallis H Test and a post-hoc Dunn’s test with Bonferroni correction were used to test AUC differences among survival quartiles. After controlling for tumor image size, age, and stage, AUC, was associated with poorer survival (HR = 1.118; 95% CI = 1.026-1.218; p = 0.01). AUC was significantly higher for patients in the lowest survival quartile compared to the highest survival quartile (p < 0.001). We have shown that persistent homology can generate useful clinical correlates from tumor CT scans. Our 0-dimensional topological feature curve statistic predicts survival in lung cancer patients. This novel statistic may be used in tandem with standard radiomics variables to better inform clinical oncology decisions.

show abstract

“…We use the features Carlsson Coordinates (CC), Persistent Entropy (PE) and the kernel feature of Reininghaus et al (2015). Additionally, we investigate the 0-dimensional features Area Under the Curve (AUC) Gracia-Tabuenca et al (2019) and Integrated Persistence Feature (IPF) Kuang et al (2019a), as both have shown to provide good differentiations when applied to attention-deficit/hyperactivity disorder (ADHD) and Alzheimer's disease, respectively. We show in Table D.8 the results on the fMRI (schizophrenia) dataset and on the scalp EEG (epilepsy) data; we present in Table D.9 the results in dimension 0 for both the scalp EEG and intracranial iEEG datasets, when using the alternative approach discussed in Sec.…”

Section: D3 0-dimensional Featuresmentioning

confidence: 99%

Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

Caputi

Pidnebesna

Hlinka

2021

Preprint

View full text Add to dashboard Cite

Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two enigmatic examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; likely due to topographical, rather than topological, specificity of the differences. In seizure discrimination from scalp electroencephalography data, classification based on directed persistent homology features provided comparable results to other methods, reaching 89 percent accuracy. Specific niche for topological data analysis opens when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

show abstract

Topological Data Analysis reveals robust alterations in the whole-brain and frontal lobe functional connectomes in Attention-Deficit/Hyperactivity Disorder

Cited by 8 publications

References 54 publications

Development of the Functional Connectome Topology in Adolescence: evidence from Topological Data Analysis

Development of the Functional Connectome Topology in Adolescence: evidence from Topological Data Analysis

Persistent Homology of Tumor CT Scans is Associated with Survival In Lung Cancer

Promises and pitfalls of Topological Data Analysis for brain connectivity analysis

Contact Info

Product

Resources

About