Altered Functional Connectivity of the Primary Visual Cortex in Patients With Iridocyclitis and Assessment of Its Predictive Value Using Machine Learning

PurposeTo explore the intrinsic functional connectivity (FC) alteration of the primary visual cortex (V1) between individuals with iridocyclitis and healthy controls (HCs) by the resting-state functional magnetic resonance imaging (fMRI) technique, and to investigate whether FC findings be used to differentiate patients with iridocyclitis from HCs.MethodsTwenty-six patients with iridocyclitis and twenty-eight well-matched HCs were recruited in our study and underwent resting-state fMRI examinations. The fMRI data were analyzed by Statistical Parametric Mapping (SPM12), Data Processing and Analysis for Brain Imaging (DPABI), and Resting State fMRI Data Analysis Toolkit (REST) software. Differences in FC signal values of the V1 between the individuals with iridocyclitis and HCs were compared using independent two-sample t-tests. Significant differences in FC between two groups were chosen as classification features for distinguishing individuals with iridocyclitis from HCs using a support vector machine (SVM) classifier that involved machine learning. Classifier performance was evaluated using permutation test analysis.ResultsCompared with HCs, patients with iridocyclitis displayed significantly increased FC between the left V1 and left cerebellum crus1, left cerebellum 10, bilateral inferior temporal gyrus, right hippocampus, and left superior occipital gyrus. Moreover, patients with iridocyclitis displayed significantly lower FC between the left V1 and both the bilateral calcarine and bilateral postcentral gyrus. Patients with iridocyclitis also exhibited significantly higher FC values between the right V1 and left cerebellum crus1, bilateral thalamus, and left middle temporal gyrus; while they displayed significantly lower FC between the right V1 and both the bilateral calcarine and bilateral postcentral gyrus (voxel-level P<0.01, Gaussian random field correction, cluster-level P<0.05). Our results showed that 63.46% of the participants were correctly classified using the leave-one-out cross-validation technique with an SVM classifier based on the FC of the left V1; and 67.31% of the participants were correctly classified based on the FC of the right V1 (P<0.001, non-parametric permutation test).ConclusionPatients with iridocyclitis displayed significantly disturbed FC between the V1 and various brain regions, including vision-related, somatosensory, and cognition-related regions. The FC variability could distinguish patients with iridocyclitis from HCs with substantial accuracy. These findings may aid in identifying the potential neurological mechanisms of impaired visual function in individuals with iridocyclitis.

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ISOMAP and machine learning algorithms for the construction of embedded functional connectivity networks of anatomically separated brain regions from resting state fMRI data of patients with Schizophrenia

AIMS Neuroscience ◽

10.3934/neuroscience.2021016 ◽

2021 ◽

Vol 8 (2) ◽

pp. 295-321

Author(s):

Ioannis K Gallos ◽

◽

Kostakis Gkiatis ◽

George K Matsopoulos ◽

Constantinos Siettos ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Resting State ◽

Learning Algorithms ◽

Resting State Fmri ◽

Brain Regions ◽

Machine Learning Algorithms ◽

Fmri Data

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Characterizing Functional Connectivity Differences in Aging Adults using Machine Learning on Resting State fMRI Data

Frontiers in Computational Neuroscience ◽

10.3389/fncom.2013.00038 ◽

2013 ◽

Vol 7 ◽

Cited By ~ 37

Author(s):

Svyatoslav Vergun ◽

Alok S. Deshpande ◽

Timothy B. Meier ◽

Jie Song ◽

Dana L. Tudorascu ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Resting State ◽

Resting State Fmri ◽

Fmri Data ◽

Aging Adults

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Differentiation between resting-state fMRI data from ADHD and normal subjects: Based on functional connectivity and machine learning

2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012) ◽

10.1109/ifuzzy.2012.6409719 ◽

2012 ◽

Cited By ~ 3

Author(s):

Sheng-Fu Liang ◽

Tsung-Hao Hsieh ◽

Pin-Tzu Chen ◽

Ming-Long Wu ◽

Chun-Chia Kung ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Resting State ◽

Normal Subjects ◽

Resting State Fmri ◽

Fmri Data

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Aging effect on head motion: A Machine Learning study on resting state fMRI data

Journal of Neuroscience Methods ◽

10.1016/j.jneumeth.2021.109084 ◽

2021 ◽

Vol 352 ◽

pp. 109084

Author(s):

Valeria Saccà ◽

Alessia Sarica ◽

Andrea Quattrone ◽

Federico Rocca ◽

Aldo Quattrone ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Aging Effect ◽

Resting State Fmri ◽

Fmri Data ◽

Head Motion ◽

Learning Study

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MALINI (Machine Learning in NeuroImaging): A MATLAB toolbox for aiding clinical diagnostics using resting-state fMRI data

Data in Brief ◽

10.1016/j.dib.2020.105213 ◽

2020 ◽

Vol 29 ◽

pp. 105213 ◽

Cited By ~ 1

Author(s):

Pradyumna Lanka ◽

D. Rangaprakash ◽

Sai Sheshan Roy Gotoor ◽

Michael N. Dretsch ◽

Jeffrey S. Katz ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Clinical Diagnostics ◽

Fmri Data ◽

Matlab Toolbox

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Altered functional connectivity of the red nucleus and substantia nigra in migraine without aura

The Journal of Headache and Pain ◽

10.1186/s10194-019-1058-0 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 5

Author(s):

Xiaobin Huang ◽

Di Zhang ◽

Yuchen Chen ◽

Peng Wang ◽

Cunnan Mao ◽

...

Keyword(s):

Functional Connectivity ◽

Substantia Nigra ◽

Resting State ◽

Migraine Without Aura ◽

Parietal Lobe ◽

Red Nucleus ◽

Correlation Method ◽

Postcentral Gyrus ◽

And Performance ◽

The Right

Abstract Background Functional connectivity (FC) has been used to investigate the pathophysiology of migraine. Accumulating evidence is pointing toward malfunctioning of brainstem structures, i.e., the red nucleus (RN) and substantia nigra (SN), as an important factor in migraine without aura (MwoA). We aimed to identify atypical FC between the RN and SN and other brain areas in patients with MwoA and to explore the association between RN and SN connectivity changes and performance on neuropsychological tests in these patients. Methods Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 30 patients with MwoA and 22 age-, sex-, and years of education-matched healthy controls (HC). The FC of the brainstem structures was analyzed using a standard seed-based whole-brain correlation method. The results of the brainstem structure FC were assessed for correlations with other clinical features. Results Patients with MwoA exhibited reduced left RN-based FC with the left middle frontal gyrus, reduced right RN-based FC with the ipsilateral superior parietal lobe, and increased FC with the ipsilateral cerebellum. Additionally, patients with MwoA demonstrated significantly decreased right SN-based FC with the right postcentral gyrus, left parietal lobule, and left superior frontal gyrus. Hypo-connectivity between the right SN and right postcentral gyrus was negatively correlated with disease duration (r = − 0.506, P = 0.004). Additionally, increased connectivity of the right RN to the ipsilateral cerebellar lobes was positively correlated with the Headache Impact Test-6 scores (r = 0.437, P = 0.016). Conclusions The present study suggested that patients with MwoA have disruption in their RN and SN resting-state networks, which are associated with specific clinical characteristics. The changes focus on the regions associated with cognitive evaluation, multisensory integration, and modulation of perception and pain, which may be associated with migraine production, feedback, and development. Taken together, these results may improve our understanding of the neuropathological mechanism of migraine.

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Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/3267949 ◽

2020 ◽

Vol 2020 ◽

pp. 1-5

Author(s):

Hufei Yu ◽

Shucai Huang ◽

Xiaojie Zhang ◽

Qiuping Huang ◽

Jun Liu ◽

...

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Regional Homogeneity ◽

Learning Approaches ◽

Control Subjects ◽

Methamphetamine Dependence ◽

Adaboost Classifier ◽

The Right ◽

Leave One Out

Methamphetamine is a highly addictive drug of abuse, which will cause a series of abnormal consequences mentally and physically. This paper is aimed at studying whether the abnormalities of regional homogeneity (ReHo) could be effective features to distinguish individuals with methamphetamine dependence (MAD) from control subjects using machine-learning methods. We made use of resting-state fMRI to measure the regional homogeneity of 41 individuals with MAD and 42 age- and sex-matched control subjects and found that compared with control subjects, individuals with MAD have lower ReHo values in the right medial superior frontal gyrus but higher ReHo values in the right temporal inferior fusiform. In addition, AdaBoost classifier, a pretty effective ensemble learning of machine learning, was employed to classify individuals with MAD from control subjects with abnormal ReHo values. By utilizing the leave-one-out cross-validation method, we got the accuracy more than 84.3%, which means we can almost distinguish individuals with MAD from the control subjects in ReHo values via machine-learning approaches. In a word, our research results suggested that the AdaBoost classifier-neuroimaging approach may be a promising way to find whether a person has been addicted to methamphetamine, and also, this paper shows that resting-state fMRI should be considered as a biomarker, a noninvasive and effective assistant tool for evaluating MAD.

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