Graph theory analysis of resting‐state functional magnetic resonance imaging in essential tremor

Chen

et al. 2022

Human Brain Mapping

Currently, machine‐learning algorithms have been considered the most promising approach to reach a clinical diagnosis at the individual level. This study aimed to investigate whether the whole‐brain resting‐state functional connectivity (RSFC) metrics combined with machine‐learning algorithms could be used to identify essential tremor (ET) patients from healthy controls (HCs) and further revealed ET‐related brain network pathogenesis to establish the potential diagnostic biomarkers. The RSFC metrics obtained from 127 ET patients and 120 HCs were used as input features, then the Mann–Whitney U test and the least absolute shrinkage and selection operator (LASSO) methods were applied to reduce feature dimensionality. Four machine‐learning algorithms were adopted to identify ET from HCs. The accuracy, sensitivity, specificity and the area under the curve (AUC) were used to evaluate the classification performances. The support vector machine, gradient boosting decision tree, random forest and Gaussian naïve Bayes algorithms could achieve good classification performances with accuracy at 82.8%, 79.4%, 78.9% and 72.4%, respectively. The most discriminative features were primarily located in the cerebello‐thalamo‐motor and non‐motor circuits. Correlation analysis showed that two RSFC features were positively correlated with tremor frequency and four RSFC features were negatively correlated with tremor severity. The present study demonstrated that combining the RSFC matrices with multiple machine‐learning algorithms could not only achieve high classification accuracy for discriminating ET patients from HCs but also help us to reveal the potential brain network pathogenesis in ET.

Section: Discussionmentioning

confidence: 99%

“…Few studies supported that the FC changes did not only restrict to the classical tremor network but also extended to other brain regions (Tuleasca et al, 2020). Using visual feedback task fMRI (Archer et al, 2018) F I G U R E 3 The receiver operating characteristic (ROC) curves on the testing set.…”

Section: Discussionmentioning

confidence: 99%

Identification of essential tremor based on resting‐state functional connectivity

Chen

et al. 2022

Human Brain Mapping

“…Previous fMRI studies have suggested that extended tremor-related brain regions are associated with the visual cortex and parietal lobule outside the conventional tremor network of the cerebello-thalamo-motor cortical pathway ( Lenka et al, 2017 ; DeSimone et al, 2019 ). Tuleasca et al (2020) found that alterations in the network interconnectivity strength in visual association areas (e.g., the retrosplenial cortex within the occipital lobe) were associated with tremor arrest before and after the thalamotomy. Moreover, the visual-related pathway was also associated with the TRS score—that is, with tremor severity ( Archer et al, 2018 ).…”

Section: Discussionmentioning

confidence: 98%

Disrupted Brain Functional Network Topology in Essential Tremor Patients With Poor Sleep Quality

Peng

Yang

et al. 2022

Front. Neurosci.

Sleep disturbances, especially poor quality of sleep (QoS), are common among essential tremor (ET) patients and may have adverse effects on their quality of life, but the etiology driving the poor QoS in these individuals remains inadequately understood. Few data are available on the neuroimaging alterations of ET with poor QoS. Thirty-eight ET patients with poor QoS (SleET), 48 ET patients with normal QoS (NorET), and 80 healthy controls (HCs) participated in this study. All subjects underwent a 3.0-T magnetic resonance imaging (MRI) scan for resting-state functional MRI data collection. Then, the whole-brain functional connectome was constructed by thresholding the partial correlation matrices of 116 brain regions. Graph theory and network-based statistical analyses were performed. We used a non-parametric permutation test for group comparisons of topological metrics. Partial correlation analyses between the topographical features and clinical characteristics were conducted. The SleET and NorET groups exhibited decreased clustering coefficients, global efficiency, and local efficiency and increased the characteristic path length. Both of these groups also showed reduced nodal degree and nodal efficiency in the left superior dorsolateral frontal gyrus, superior frontal medial gyrus (SFGmed), posterior cingulate gyrus (PCG), lingual gyrus, superior occipital gyrus, right middle occipital gyrus, and right fusiform gyrus. The SleET group additionally presented reduced nodal degrees and nodal efficiency in the right SFGmed relative to the NorET and HC groups, and nodal efficiency in the right SFGmed was negatively correlated with the Pittsburgh Sleep Quality Index score. The observed impaired topographical organizations of functional brain networks within the central executive network (CEN), default mode network (DMN), and visual network serve to further our knowledge of the complex interactions between tremor and sleep, adding to our understanding of the underlying neural mechanisms of ET with poor QoS.

“…We adopted absolute threshold method to study network topology properties. In the present study, the choice of the threshold was based on the absolute threshold method [49][50][51]. The definition of the absolute threshold method was as follows: when the correlation coefficient between nodes in the network exceeded this threshold, it indicated that there was a connection between nodes and vice versa.…”

Section: Discussionmentioning

confidence: 99%

Resting-State fMRI Whole Brain Network Function Plasticity Analysis in Attention Deficit Hyperactivity Disorder

2022

Attention deficit hyperactivity disorder (ADHD) is a common mental disorder in children, which is related to inattention and hyperactivity. These symptoms are associated with abnormal interactions of brain networks. We used resting-state functional magnetic resonance imaging (rs-fMRI) based on the graph theory to explore the topology property changes of brain networks between an ADHD group and a normal group. The more refined AAL_1024 atlas was used to construct the functional networks with high nodal resolution, for detecting more subtle changes in brain regions and differences among groups. We compared altered topology properties of brain network between the groups from multilevel, mainly including modularity at mesolevel. Specifically, we analyzed the similarities and differences of module compositions between the two groups. The results found that the ADHD group showed stronger economic small-world network property, while the clustering coefficient was significantly lower than the normal group; the frontal and occipital lobes showed smaller node degree and global efficiency between disease statuses. The modularity results also showed that the module number of the ADHD group decreased, and the ADHD group had short-range overconnectivity within module and long-range underconnectivity between modules. Moreover, modules containing long-range connections between the frontal and occipital lobes disappeared, indicating that there was lack of top-down control information between the executive control region and the visual processing region in the ADHD group. Our results suggested that these abnormal regions were related to executive control and attention deficit of ADHD patients. These findings helped to better understand how brain function correlates with the ADHD symptoms and complement the fewer modularity elaboration of ADHD research.