Combined multivariate pattern analysis with frequency-dependent intrinsic brain activity to identify essential tremor

Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review

Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI's performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of "low risk of bias" and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability.

Abnormalities of regional brain activity and executive function in patients with temporal lobe epilepsy: A cross-sectional and longitudinal resting-state functional MRI study

PURPOSE

We decided to track changes in regional brain activity and executive function in temporal lobe epilepsy (TLE) patients based on cross-sectional and longitudinal designs and sought potential imaging features for follow-up observation.

METHODS

Thirty-two TLE patients and thirty-three healthy controls (HCs) were recruited to detect changes in fractional amplitude of low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) and to evaluate executive function both at baseline and at two-year (23.3 ± 8.3 months) follow-up. Moreover, multivariate pattern analysis (MVPA) was used for follow-up observation.

RESULTS

TLE patients displayed lower fALFF values in the right superior frontal gyrus (SFG) and higher ReHo values in the left putamen (PUT) relative to the HCs. Longitudinal analysis revealed that TLE patients at follow-up exhibited higher fALFF values in the left postcentral gyrus (PoCG), higher ReHo values in the left PoCG and the right middle frontal gyrus (MFG), lower ReHo values in the bilateral PUT and the right fusiform gyrus (FFG) compared with these patients at baseline. The executive function was impaired in TLE patients but didn't deteriorate over time. No correlations were discovered between regional brain activity and executive function. The MVPA based on ReHo performed well in differentiating the follow-up group from the baseline group.

CONCLUSION

We revealed the abnormalities in regional brain activity and executive function as well as their longitudinal trends in TLE patients. The ReHo might be a good imaging feature for follow-up observation.

Using histogram analysis of the intrinsic brain activity mapping to identify essential tremor

Background

Essential tremor (ET) is one of the most common movement disorders. Histogram analysis based on brain intrinsic activity imaging is a promising way to identify ET patients from healthy controls (HCs) and further explore the spontaneous brain activity change mechanisms and build the potential diagnostic biomarker in ET patients.

Methods

The histogram features based on the Resting-state functional magnetic resonance imaging (Rs-fMRI) data were extracted from 133 ET patients and 135 well-matched HCs as the input features. Then, a two-sample t-test, the mutual information, and the least absolute shrinkage and selection operator methods were applied to reduce the feature dimensionality. Support vector machine (SVM), logistic regression (LR), random forest (RF), and k-nearest neighbor (KNN) were used to differentiate ET and HCs, and classification performance of the established models was evaluated by the mean area under the curve (AUC). Moreover, correlation analysis was carried out between the selected histogram features and clinical tremor characteristics.

Results

Each classifier achieved a good classification performance in training and testing sets. The mean accuracy and area under the curve (AUC) of SVM, LR, RF, and KNN in the testing set were 92.62%, 0.948; 92.01%, 0.942; 93.88%, 0.941; and 92.27%, 0.939, respectively. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways. Correlation analysis showed that there were two histogram features negatively and one positively correlated with tremor severity.

Conclusion

Our findings demonstrated that the histogram analysis of the amplitude of low-frequency fluctuation (ALFF) images with multiple machine learning algorithms could identify ET patients from HCs and help to understand the spontaneous brain activity pathogenesis mechanisms in ET patients.

Consistent functional abnormalities in patients with postpartum depression

Postpartum depression (PPD) is a common public health concern. A wide range of functional abnormalities in various brain regions have been reported in fMRI studies on PPD, however, a consistent functional changing pattern is still lacking. Herein, we obtained functional Magnetic Resonance Imaging (fMRI) data from 52 patients with PPD and 24 healthy postpartum women (HPW). Functional indexes (low-frequency fluctuation, degree centrality, and regional homogeneity) were calculated and compared among these groups to explore the functional changing patterns of PPD. Then, correlation analyses were performed to investigate the relationship between changed functional indexes and clinical measurements in the PPD. Finally, support vector machine (SVM) was performed to test whether these abnormal features can be used to distinguish PPD from HPW. As a result, we identified significantly and consistently functional changing pattern characterizing by increased functional activity in the left inferior occipital gyrus and decreased functional activity right anterior cingulate cortex in the PPD as compared to HPW. These functional values in the right anterior cingulate cortex were significantly correlated with depression symptoms in the PPD, and can be used as features to distinguish PPD from HPW. In conclusion, our results suggested that the right anterior cingulate cortex could be served as a functional neuro-imaging biomarker for PPD, which might be used as a potential target for neuro-modulation.

Combined brain network topological metrics with machine learning algorithms to identify essential tremor

BACKGROUND AND OBJECTIVE

Essential tremor (ET) is a common movement syndrome, and the pathogenesis mechanisms, especially the brain network topological changes in ET are still unclear. The combination of graph theory (GT) analysis with machine learning (ML) algorithms provides a promising way to identify ET from healthy controls (HCs) at the individual level, and further help to reveal the topological pathogenesis in ET.

METHODS

Resting-state functional magnetic resonance imaging (fMRI) data were obtained from 101 ET and 105 HCs. The topological properties were analyzed by using GT analysis, and the topological metrics under every single threshold and the area under the curve (AUC) of all thresholds were used as features. Then a Mann-Whitney U-test and least absolute shrinkage and selection operator (LASSO) were conducted to feature dimensionality reduction. Four ML algorithms were adopted to identify ET from HCs. The mean accuracy, mean balanced accuracy, mean sensitivity, mean specificity, and mean AUC were used to evaluate the classification performance. In addition, correlation analysis was carried out between selected topological features and clinical tremor characteristics.

RESULTS

All classifiers achieved good classification performance. The mean accuracy of Support vector machine (SVM), logistic regression (LR), random forest (RF), and naïve bayes (NB) was 84.65, 85.03, 84.85, and 76.31%, respectively. LR classifier achieved the best classification performance with 85.03% mean accuracy, 83.97% sensitivity, and an AUC of 0.924. Correlation analysis results showed that 2 topological features negatively and 1 positively correlated with tremor severity.

CONCLUSION

These results demonstrated that combining topological metrics with ML algorithms could not only achieve high classification accuracy for discrimination ET from HCs but also help us to reveal the potential topological pathogenesis of ET.