Identifying Methamphetamine Dependence Using Regional Homogeneity in BOLD Signals

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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Regression‐based machine‐learning approaches to predict task activation using resting‐state fMRI

Human Brain Mapping ◽

10.1002/hbm.24841 ◽

2019 ◽

Vol 41 (3) ◽

pp. 815-826 ◽

Cited By ~ 1

Author(s):

Alexander D. Cohen ◽

Ziyi Chen ◽

Oiwi Parker Jones ◽

Chen Niu ◽

Yang Wang

Keyword(s):

Machine Learning ◽

Resting State ◽

Resting State Fmri ◽

Learning Approaches ◽

Task Activation

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

Frontiers in Immunology ◽

10.3389/fimmu.2021.660554 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yan Tong ◽

Xin Huang ◽

Chen-Xing Qi ◽

Yin Shen

Keyword(s):

Machine Learning ◽

Visual Cortex ◽

Functional Connectivity ◽

Resting State ◽

Permutation Test ◽

Resting State Fmri ◽

Fmri Data ◽

Svm Classifier ◽

Postcentral Gyrus ◽

The Right

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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Improvement in prefrontal thalamic connectivity during the early course of the illness in recent-onset psychosis: a 12-month longitudinal follow-up resting-state fMRI study

Psychological Medicine ◽

10.1017/s0033291720004808 ◽

2020 ◽

pp. 1-9

Author(s):

Daniel Bergé ◽

Tyler A. Lesh ◽

Jason Smucny ◽

Cameron S. Carter

Keyword(s):

Resting State ◽

Resting State Fmri ◽

Functional Changes ◽

Recent Onset ◽

Fmri Study ◽

Clinical Meaning ◽

Mixed Pattern ◽

The Right ◽

Over Time

Abstract Background Previous research in resting-state functional magnetic resonance imaging (rs-fMRI) has shown a mixed pattern of disrupted thalamocortical connectivity in psychosis. The clinical meaning of these findings and their stability over time remains unclear. We aimed to study thalamocortical connectivity longitudinally over a 1-year period in participants with recent-onset psychosis. Methods To this purpose, 129 individuals with recent-onset psychosis and 87 controls were clinically evaluated and scanned using rs-fMRI. Among them, 43 patients and 40 controls were re-scanned and re-evaluated 12 months later. Functional connectivity between the thalamus and the rest of the brain was calculated using a seed to voxel approach, and then compared between groups and correlated with clinical features cross-sectionally and longitudinally. Results At baseline, participants with recent-onset psychosis showed increased connectivity (compared to controls) between the thalamus and somatosensory and temporal regions (k = 653, T = 5.712), as well as decreased connectivity between the thalamus and left cerebellum and right prefrontal cortex (PFC; k = 201, T = −4.700). Longitudinal analyses revealed increased connectivity over time in recent-onset psychosis (relative to controls) in the right middle frontal gyrus. Conclusions Our results support the concept of abnormal thalamic connectivity as a core feature in psychosis. In agreement with a non-degenerative model of illness in which functional changes occur early in development and do not deteriorate over time, no evidence of progressive deterioration of connectivity during early psychosis was observed. Indeed, regionally increased connectivity between thalamus and PFC was observed.

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Machine learning may predict individual hand motor activation from resting-state fMRI in patients with brain tumors in perirolandic cortex

European Radiology ◽

10.1007/s00330-021-07825-w ◽

2021 ◽

Author(s):

Chen Niu ◽

Yang Wang ◽

Alexander D. Cohen ◽

Xin Liu ◽

Hongwei Li ◽

...

Keyword(s):

Machine Learning ◽

Brain Tumors ◽

Resting State ◽

Resting State Fmri ◽

Motor Activation

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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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Abnormal regional homogeneity in Parkinson's disease: a resting state fMRI study

Clinical Radiology ◽

10.1016/j.crad.2015.10.006 ◽

2016 ◽

Vol 71 (1) ◽

pp. e28-e34 ◽

Cited By ~ 25

Author(s):

Y. Li ◽

P. Liang ◽

X. Jia ◽

K. Li

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Resting State ◽

Resting State Fmri ◽

Regional Homogeneity ◽

Fmri Study

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Distinguishing hypochondriasis and schizophrenia using regional homogeneity: a resting-state fMRI study and support vector machine analysis

Acta Neuropsychiatrica ◽

10.1017/neu.2021.9 ◽

2021 ◽

pp. 1-29

Author(s):

Kangyu Jin ◽

Zhe Shen ◽

Guoxun Feng ◽

Zhiyong Zhao ◽

Jing Lu ◽

...

Keyword(s):

Cognitive Function ◽

Resting State ◽

Parietal Lobe ◽

Brain Activity ◽

Resting State Fmri ◽

Brain Regions ◽

Regional Homogeneity ◽

Middle Temporal Gyrus ◽

Support Vector ◽

Fmri Study

Abstract Objective: A few former studies suggested there are partial overlaps in abnormal brain structure and cognitive function between Hypochondriasis (HS) and schizophrenia (SZ). But their differences in brain activity and cognitive function were unclear. Methods: 21 HS patients, 23 SZ patients, and 24 healthy controls (HC) underwent Resting-state functional magnetic resonance imaging (rs-fMRI) with the regional homogeneity analysis (ReHo), subsequently exploring the relationship between ReHo value and cognitive functions. The support vector machines (SVM) were used on effectiveness evaluation of ReHo for differentiating HS from SZ. Results: Compared with HC, HS showed significantly increased ReHo values in right middle temporal gyrus (MTG), left inferior parietal lobe (IPL) and right fusiform gyrus (FG), while SZ showed increased ReHo in left insula, decreased ReHo values in right paracentral lobule. Additionally, HS showed significantly higher ReHo values in FG, MTG and left paracentral lobule but lower in insula than SZ. The higher ReHo values in insula were associated with worse performance in MCCB in HS group. SVM analysis showed a combination of the ReHo values in insula and FG was able to satisfactorily distinguish the HS and SZ patients. Conclusion: our results suggested the altered default mode network (DMN), of which abnormal spontaneous neural activity occurs in multiple brain regions, might play a key role in the pathogenesis of HS, and the resting-state alterations of insula closely related to cognitive dysfunction in HS. Furthermore, the combination of the ReHo in FG and insula was a relatively ideal indicator to distinguish HS from SZ.

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