Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

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Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Background: Parkinson's disease (PD) is a highly heterogeneous disease, especially in the clinical characteristics and prognosis. The PD is divided into two subgroups: tremor-dominant phenotype and non-tremor-dominant phenotype. Previous studies reported abnormal changes between the two PD phenotypes by using the static functional connectivity analysis. However, the dynamic properties of brain networks between the two PD phenotypes are not yet clear. Therefore, we aimed to uncover the dynamic functional network connectivity (dFNC) between the two PD phenotypes at the subnetwork level, focusing on the temporal properties of dFNC and the variability of network efficiency. Methods: We investigated the resting-state functional MRI (fMRI) data from 29 tremor-dominant PD patients (PDTD), 25 non-tremor-dominant PD patients (PDNTD), and 20 healthy controls (HCs). Sliding window approach, k-means clustering, independent component analysis (ICA), and graph theory analysis were applied to analyze the dFNC. Furthermore, the relationship between alterations in the dynamic properties and clinical features was assessed. Results: The dFNC analyses identified four reoccurring states, one of them showing sparse connections (state I). PDTD patients stayed longer time in state I and showed increased FNC between BG and vSMN in state IV. Both PD phenotypes exhibited higher FNC between dSMN and FPN in state II and state III compared with the controls. PDNTD patients showed decreased FNC between BG and FPN but increased FNC in the bilateral FPN compared with both PDTD patients and controls. In addition, PDNTD patients exhibited greater variability in global network efficiency. Tremor scores were positively correlated with dwell time in state I along with increased FNC between BG and vSMN in state IV. Conclusions: This study explores the dFNC between the PDTD and PDNTD patients, which offers new evidence on the abnormal time-varying brain functional connectivity and their network destruction of the two PD phenotypes, and may help better understand the neural substrates underlying different types of PD.

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Altered Brain Functional Network in Subtypes of Parkinson's Disease: A Dynamic Perspective.

Background Depression is one of the most prevalent and disturbing non-motor symptoms in Parkinson’s disease (PD), with few dynamic functional connectivity (dFC) features measured in previous studies. Our aim was to investigate the alterations of the dynamics in de novo patients with PD with depression (dPD). Methods We performed dFC analysis on the data of resting-state functional MRI from 21 de novo dPD, 34 de novo patients with PD without depression (ndPD), and 43 healthy controls (HCs). Group independent component analysis, a sliding window approach, followed by k-means clustering were conducted to assess functional connectivity states (which represented highly structured connectivity patterns reoccurring over time) and temporal properties for comparison between groups. We further performed dynamic graph-theoretical analysis to examine the variability of topological metrics. Results Four distinct functional connectivity states were clustered via dFC analysis. Compared to patients with ndPD and HCs, patients with dPD showed increased fractional time and mean dwell time in state 2, characterized by default mode network (DMN)-dominated and cognitive executive network (CEN)-disconnected patterns. Besides, compared to HCs, patients with dPD and patients with ndPD both showed weaker dynamic connectivity within the sensorimotor network (SMN) in state 4, a regionally densely connected state. We additionally observed that patients with dPD presented less variability in the local efficiency of the network. Conclusions Our study demonstrated that altered network connection over time, mainly involving the DMN and CEN, with abnormal dynamic graph properties, may contribute to the presence of depression in patients with PD.

https://www.frontiersin.org/articles/10.3389/fnagi.2021.789785/pdf

Altered Dynamic Functional Connectivity in de novo Parkinson’s Disease Patients With Depression

Background Deep brain stimulation (DBS) improves motor and non-motor symptoms in patients with Parkinson’s disease (PD). Researchers mainly investigated the motor networks to reveal DBS mechanisms, with few studies extending to other networks. This study aimed to investigate multi-network modulation patterns using DBS in patients with PD. Methods Twenty-four patients with PD underwent 1.5 T functional MRI (fMRI) scans in both DBS-on and DBS-off states, with twenty-seven age-matched healthy controls (HCs). Default mode, sensorimotor, salience, and left and right frontoparietal networks were identified by using the independent component analysis. Power spectra and functional connectivity of these networks were calculated. In addition, multiregional connectivity was established from 15 selected regions extracted from the abovementioned networks. Comparisons were made among groups. Finally, correlation analyses were performed between the connectivity changes and symptom improvements. Results Compared with HCs, PD-off showed abnormal power spectra and functional connectivity both within and among these networks. Some of the abovementioned abnormalities could be corrected by DBS, including increasing the power spectra in the sensorimotor network and modulating the parts of the ipsilateral functional connectivity in different regions centered in the frontoparietal network. Moreover, the DBS-induced functional connectivity changes were correlated with motor and depression improvements in patients with PD. Conclusion DBS modulated the abnormalities in multi-networks. The functional connectivity alterations were associated with motor and psychiatric improvements in PD. This study lays the foundation for large-scale brain network research on multi-network DBS modulation.

https://www.frontiersin.org/articles/10.3389/fnagi.2022.794987/pdf

Deep Brain Stimulation Modulates Multiple Abnormal Resting-State Network Connectivity in Patients With Parkinson’s Disease

The aim of our study was to investigate differences in whole brain connectivity at different levels between drug-naïve individuals with early Parkinson’s disease (PD) and healthy controls (HCs). Resting-state functional magnetic resonance imaging data were collected from 47 patients with early-stage, drug-naïve PD and 50 HCs. Functional brain connectivity was analyzed at the integrity, network, and edge levels; UPDRS-III, MMSE, MOCA, HAMA, and HAMD scores, reflecting the symptoms of PD, were collected for further regression analysis. Compared with age-matched HCs, reduced functional connectivity were mainly observed in the visual (VSN), somatomotor (SMN), limbic (LBN), and deep gray matter networks (DGN) at integrity level [p < 0.05, false discovery rate (FDR) corrected]. Intra-network analysis indicated decreased functional connectivity in DGN, SMN, LBN, and ventral attention networks (VAN). Inter-network analysis indicated reduced functional connectivity in nine pairs of resting-state networks. At the edge level, the LBN was the center of abnormal functional connectivity (p < 0.05, FDR corrected). MOCA score was associated with the intra-network functional connectivity strength (FC) of the DGN, and inter-network FC of the DGN-VAN. HAMA and HAMD scores were associated with the FC of the SMN and DGN, and either the LBN or VAN, respectively. We demonstrated variations in whole brain connections of drug-naïve patients with early PD. Major changes involved the SMN, DGN, LBN, and VSN, which may be relevant to symptoms of early PD. Additionally, our results support PD as a disconnection syndrome.

https://www.frontiersin.org/articles/10.3389/fnagi.2022.783634/pdf

Altered Intra- and Inter-Network Connectivity in Drug-Naïve Patients With Early Parkinson’s Disease

Background Motor symptoms in Parkinson's disease (PD) patients might be related to high-level task-control deficits. We aimed at investigating the dynamics between sensorimotor network and top-down control networks (frontal-parietal, cingulo-opercular, and cerebellar) in PD and at determining the effects of levodopa on the dynamics of these networks. Methods We investigated dynamic functional connectivity (dFC), during resting state functional magnetic resonance imaging, between sensorimotor network and top-down control networks in 36 PD patients (OFF medication, PD-OFF) and 36 healthy volunteers. We further assessed the effect of medication on dFC in18 PD patients who were also scanned ON medication. Results The dFC analyses identified three discrete states: State I (35.68%) characterized by connections between the cerebellum and sensorimotor network, State II (34.17%) with connections between the sensorimotor and frontal-parietal network, and State III (30.15%) with connection between the sensorimotor and cingulo-opercular network. PD patients have significantly fewer occurrences and overall spent less time (shorter dwell time) in State II compared to healthy controls. After levodopa intake, dwell time improved toward normal. The change in dwell time before and after taking levodopa was negatively related to the respective changes in Unified Parkinson's Disease Rating Scale, Part III. PD-OFF showed significantly decreased connectivity between sensorimotor and control networks and increased connectivity within control networks. These changes were partially improved after levodopa intake. Conclusions Dopamine depletion in PD is associated with abnormalities in temporal and spatial properties between cognitive control and sensorimotor network, possibly contributing to clinical deficits. Levodopa partially restores the network function toward the values observed in healthy volunteers. © 2021 International Parkinson and Movement Disorder Society.

Dynamics of Top-Down Control and Motor Networks in Parkinson's Disease.

Magnetic Resonance Guided Focused Ultrasound Thalamotomy, (MRgFUS) is an FDA-approved treatment for refractory Essential Tremor (ET). Previous reports have documented the development of tremor, dystonia, and myoclonus after a thalamic lesion, but these conditions have not been previously observed after MRgFUS or other lesional treatments of tremor. Here we describe a patient who developed a novel unilateral right hand tremor with dystonic posturing after MRgFUS. A 70-year-old right-handed man presented to our clinic in 2009 with a previous diagnosis and family history of ET. His tremor started at the age of 17, mainly affecting his upper extremities and voice. Neurologic exam showed postural and kinetic tremor in both upper extremities with a 1–2 cm amplitude (Video S1) and no signs of dystonia or parkinsonism. A tremor study showed a bilateral central 6 Hz action and postural tremor (R > L; Fig. 1A). MRgFUS to the left thalamus was performed in 2016 at an outside institution. Ten months after the procedure, exam showed a reduction in right hand action and postural tremor. Seventeen months after MRgFUS, he experienced a change in his symptoms. Exam revealed new dystonic posturing and a new low frequency, low amplitude tremor in his right hand during posture, action, intention, and while walking (Video S2). There were no signs of parkinsonism. A post-MRgFUS tremor study showed a 3 Hz tremor of the treated right hand and a 5 Hz tremor of the left hand during posture and with weight loading (Fig. 1B). There was no rest tremor of the upper limbs, although the accelerometers recorded a bilateral 5 Hz signal. This signal stemmed from volume transduction of a truncal tremor in the 5 Hz range, which was clinically evident during sitting. An MRI was also performed, revealing a lesion between the ventroposteriorlateral, ventroposteriomedial, ventrolateral, and mediodorsal nuclei of the thalamus (Fig. 1C–E). DTI and tractography analysis revealed projections between the lesioned area and left SMA, left primary motor cortex, and red nucleus (Fig. 1F). We attribute the new low-frequency tremor with dystonic posturing to a dystonic tremor syndrome secondary to his ultrasoundinduced thalamic lesion. To our knowledge, this is the first case of tremor and dystonia following MRgFUS. Given the latency in onset, Holmes Tremor (HT) was in the differential diagnosis but was considered unlikely given the absence during rest. The localization of the MRgFUS lesion is a region known to cause tremor and dystonia, potentially developing due to plastic changes or maladaptive rearrangement in the thalamus after lesion. These areas are also understood to project to sensory cortices, and thus the dystonic tremor may be due to disruption of sensory-motor integration at the level of the thalamus and/or the cortex. This case identifies a development of a new dystonic tremor as a potential adverse event of lesioning therapies such as MRgFUS for the treatment of tremor. Since this is a relatively new method of treating tremor syndromes, more long-term studies of delayed effects are needed. ■

Patrick Bedard

Papers

Dynamics of Top-Down Control and Motor Networks in Parkinson's Disease.

Thalamic Tremor Following Focused Ultrasound Thalamotomy for the Treatment of Essential Tremor