Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Ding, Xiaoyu; Yang, Yihong; Stein, Elliot A.; Ross, Thomas J.

doi:10.3389/fnhum.2017.00362

Cited by 24 publications

(20 citation statements)

References 96 publications

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“…A neuroimaging study discriminated smokers and nonsmoking HC. 65 Another study using sociodemographic, clinical, and cognitive data discriminated individuals with cocaine dependence from HC. 66 Problematic internet use was discriminated from HC using clinical and sociodemographic predictors.…”

Section: Resultsmentioning

confidence: 99%

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

Pablo

Studerus

Vaquerizo‐Serrano

et al. 2020

Schizophrenia Bulletin

130

134

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Background The impact of precision psychiatry for clinical practice has not been systematically appraised. This study aims to provide a comprehensive review of validated prediction models to estimate the individual risk of being affected with a condition (diagnostic), developing outcomes (prognostic), or responding to treatments (predictive) in mental disorders. Methods PRISMA/RIGHT/CHARMS-compliant systematic review of the Web of Science, Cochrane Central Register of Reviews, and Ovid/PsycINFO databases from inception until July 21, 2019 (PROSPERO CRD42019155713) to identify diagnostic/prognostic/predictive prediction studies that reported individualized estimates in psychiatry and that were internally or externally validated or implemented. Random effect meta-regression analyses addressed the impact of several factors on the accuracy of prediction models. Findings Literature search identified 584 prediction modeling studies, of which 89 were included. 10.4% of the total studies included prediction models internally validated (n = 61), 4.6% models externally validated (n = 27), and 0.2% (n = 1) models considered for implementation. Across validated prediction modeling studies (n = 88), 18.2% were diagnostic, 68.2% prognostic, and 13.6% predictive. The most frequently investigated condition was psychosis (36.4%), and the most frequently employed predictors clinical (69.5%). Unimodal compared to multimodal models (β = .29, P = .03) and diagnostic compared to prognostic (β = .84, p < .0001) and predictive (β = .87, P = .002) models were associated with increased accuracy. Interpretation To date, several validated prediction models are available to support the diagnosis and prognosis of psychiatric conditions, in particular, psychosis, or to predict treatment response. Advancements of knowledge are limited by the lack of implementation research in real-world clinical practice. A new generation of implementation research is required to address this translational gap.

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Section: Resultsmentioning

confidence: 99%

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

Pablo

Studerus

Vaquerizo‐Serrano

et al. 2020

Schizophrenia Bulletin

130

134

View full text Add to dashboard Cite

show abstract

“…Most previous studies have extracted features based on the brain atlas, which only extracted the mean values of the metrics (such as ALFF, fractional anisotropy, mean diffusivity, regional homogeneity, functional connectivity, voxel-mirrored homotopic connectivity, etc.) in the ROI defined by the brain atlas (Dai et al, 2012 ; Cui et al, 2016 ; Ding et al, 2017 ; Tang et al, 2017 ; Sun et al, 2018 ; Zhou et al, 2020 ). In our study, we extracted not only the mean ALFF values in the predefined ROIs but also other histogram features, including the minimum, maximum, range, standard deviation, variance, median, skewness, kurtosis, 10th percentile, and the 90th percentile, which could more comprehensively reflect ALFF information in the predefined ROIs.…”

Section: Discussionmentioning

confidence: 99%

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Shi¹,

Zhang²,

Wang³

et al. 2021

Front. Aging Neurosci.

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This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson’s disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

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“…As mentioned earlier, aberrant interactions and connectivity of the DMN, ECN and SN are prominent features of nicotine addiction (Ding et al . ; Lerman et al . ; Sutherland et al .…”

Section: Discussionmentioning

confidence: 99%

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting‐state fMRI

et al. 2018

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Cigarette smoking continues to be a leading cause of preventable morbidity and mortality. Although the majority of smokers report making a quit attempt in the past year, smoking cessation rates remain modest. Thus, developing accurate, data-driven methods that can classify and characterize the neural features of nicotine use disorder (NUD) would be a powerful clinical tool that could aid in optimizing treatment development and guide treatment modifications. This investigation applied support vector machine-based classification to resting-state functional connectivity (rsFC) data from individuals diagnosed with NUD (n = 108; 63 male) and matched nonsmoking controls (n = 108; 63 male) and multi-dimensional scaling to visualize the heterogeneity of NUD in individual smokers based on rsFC measures. Machine-based learning models identified five resting-state networks that played a role in distinguishing smokers from controls: the posterior and anterior default mode networks, the sensorimotor network, the salience network and the right executive control network. The classification method constructed classifiers with an average correct classification rate of 88.1 percent and an average area under the curve of 0.93. Compared with controls, individuals with NUD had weaker functional connectivity measures within these networks (P < 0.05, false discovery rate corrected). Further, multi-dimensional scaling visualization demonstrated that controls were similar to each other whereas individuals with NUD had less similarity to controls and to other individuals with NUD. Our findings build upon previous literature demonstrating that machine learning-based approaches to classifying rsFC data offer a valuable technique to understanding network-level differences in nicotine-related neurobiology and extend previous findings by improving classification accuracy and demonstrating the heterogeneity in resting-state networks of individuals with NUD.

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Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Cited by 24 publications

References 96 publications

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson’s Disease: A Histogram Analysis

Classifying and characterizing nicotine use disorder with high accuracy using machine learning and resting‐state fMRI

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