Comparing univalent and bivalent brain functional connectivity measures using machine learning

Chaitra, N; Vijaya, P. A.

doi:10.1109/icscn.2017.8085741

Cited by 3 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We imposed restrictions on the minimum accuracy, sensitivity, and sensibility required to consider a functional difference between the two ASD sub-classes. The cut-off point was 60%, based on values achieved by other ASD vs. non-ASD classification studies [22][23][24][51][52][53]. Thus, we discarded results with accuracy (ACC), specificity (SPC), or sensibility (SNS) less than 60%.…”

Section: Accuracy Sensitivity and Specificity Restrictions And Post-h...mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

et al. 2022

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized primarily by social impairments that manifest in different severity levels. In recent years, many studies have explored the use of machine learning (ML) and resting-state functional magnetic resonance images (rs-fMRI) to investigate the disorder. These approaches evaluate brain oxygen levels to indirectly measure brain activity and compare typical developmental subjects with ASD ones. However, none of these works have tried to classify the subjects into severity groups using ML exclusively applied to rs-fMRI data. Information on ASD severity is frequently available since some tools used to support ASD diagnosis also include a severity measurement as their outcomes. The aforesaid is the case of the Autism Diagnostic Observation Schedule (ADOS), which splits the diagnosis into three groups: ‘autism’, ‘autism spectrum’, and ‘non-ASD’. Therefore, this paper aims to use ML and fMRI to identify potential brain regions as biomarkers of ASD severity. We used the ADOS score as a severity measurement standard. The experiment used fMRI data of 202 subjects with an ASD diagnosis and their ADOS scores available at the ABIDE I consortium to determine the correct ASD sub-class for each one. Our results suggest a functional difference between the ASD sub-classes by reaching 73.8% accuracy on cingulum regions. The aforementioned shows the feasibility of classifying and characterizing ASD using rs-fMRI data, indicating potential areas that could lead to severity biomarkers in further research. However, we highlight the need for more studies to confirm our findings.

show abstract

Section: Accuracy Sensitivity and Specificity Restrictions And Post-h...mentioning

confidence: 99%

“…Since then, there has been an increase in the number of papers and improved outcomes. Many of these works used magnetic resonance imaging (MRI) and ML combined, aiming for a positive or negative ASD diagnosis by classifying subjects between ASD and TD [21], as in [18][19][20][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In this study, functional connectivity is determined using the Pearson's correlation coefficient (PCC) [3]. PCC is a measure of the linear correlation between two variables.…”

Section: B Functional Connectivitymentioning

confidence: 99%

“…The sample data from the two groups is fed as the input to 2-sample t-test that gives the tvalue. The formula for computing the 2-sample t-test is given by t= 1 − 2 (3) In equation 3, numerator indicates the difference of mean value of the first and the second group respectively and S indicates standard error of mean (SE).…”

Section: B Functional Connectivitymentioning

confidence: 99%

See 1 more Smart Citation

Identification of Alzheimer’s Disease using Functional Connectivity Measures

N*,

Shankar

et al. 2019

IJITEE

View full text Add to dashboard Cite

Alzheimer’s disease (AD) is a gradual neuro cognitive disorder caused by the damage of brain cells over a certain period of time. One non-invasive and efficient technique to investigate AD is to use functional magnetic resonance imaging (fMRI). Functional connectivity is a change in the functional connections between brain regions when an activity takes place. The correlation value gives the strength of functional connectivity. Pearson’s correlation method was used to calculate the correlation coefficient between two time series. Mutual information which denotes the information successfully transmitted through a channel was also considered. In this paper, these two measures are compared and their performance and suitability is assessed for fMRI connectivity modelling based on the classification accuracy. Machine learning techniques such as support vector machine (SVM) is employed for connectivity analysis and classification of Alzheimer’s from control population

show abstract

rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis

Santana

Carvalho

Rodrigues

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria through a lengthy and time-consuming process. Much effort is being made to identify brain imaging biomarkers and develop tools that could facilitate its diagnosis. In particular, using Machine Learning classifiers based on resting-state fMRI (rs-fMRI) data is promising, but there is an ongoing need for further research on their accuracy and reliability. Therefore, we conducted a systematic review and meta-analysis to summarize the available evidence in the literature so far. A bivariate random-effects meta-analytic model was implemented to investigate the sensitivity and specificity across the 55 studies that offered sufficient information for quantitative analysis. Our results indicated overall summary sensitivity and specificity estimates of 73.8% and 74.8%, respectively. SVM stood out as the most used classifier, presenting summary estimates above 76%. Studies with bigger samples tended to obtain worse accuracies, except in the subgroup analysis for ANN classifiers. The use of other brain imaging or phenotypic data to complement rs-fMRI information seems promising, achieving higher sensitivities when compared to rs-fMRI data alone (84.7% versus 72.8%). Finally, our analysis showed AUC values between acceptable and excellent. Still, given the many limitations indicated in our study, further well-designed studies are warranted to extend the potential use of those classification algorithms to clinical settings.

show abstract

Comparing univalent and bivalent brain functional connectivity measures using machine learning

Cited by 3 publications

References 18 publications

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

Machine Learning and rs-fMRI to Identify Potential Brain Regions Associated with Autism Severity

Identification of Alzheimer’s Disease using Functional Connectivity Measures

rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis

Contact Info

Product

Resources

About