Brain network topology and future development of freezing of gait in Parkinson’s disease: a longitudinal study

Li, Nannan; Lei, Du; Peng, Jiaxin; Suo, Xueling; Li, Junying; Duan, Liren; Chen, Chaolan; Gong, Qiyong; Peng, Rong

doi:10.1007/s00415-021-10817-x

Cited by 10 publications

(5 citation statements)

References 47 publications

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“…Thus, structural damage to the left gyrus rectus and MCC might impair gait directly or indirectly by affecting external and internal information integration, abstract cognition translation, and links between cognitive and motor systems. Consistently, there is sufficient evidence to demonstrate structural and functional abnormalities of the left gyrus rectus and MCC in PD-FOG (24,26,60). Taken together, our results suggested that FOG in PD is a complex network disorder.…”

Section: Discussionsupporting

confidence: 83%

“…Thus, to reduce the potential confounding effects of emotion, we added the HAMA and HAMD-24 scores as covariates in imaging analyses. Third, previous rs-fMRI studies have elucidated other altered brain regions (such as the middle frontal gyrus and cerebellum) (25,26,63) or networks (such as the dorsal attentional network and visual network) (64,65) in PD-FOG by analyzing functional networks. Such inconsistency may be due to differences in included populations, study methods, and statistical thresholds.…”

Section: Discussionmentioning

confidence: 99%

“…Two other studies have suggested that FOG in PD is also associated with sensorimotor, frontoparietal, and visual network dysfunctions (24,25). Recently, a longitudinal study demonstrated that disrupted regional topological organization might contribute to the development of FOG in PD (26). Hence, previous topological studies have deepened our understanding of the neural mechanisms underlying PD-FOG.…”

Section: Introductionmentioning

confidence: 99%

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Impaired structural and reserved functional topological organizations of brain networks in Parkinson’s disease with freezing of gait

Wang¹,

Gan²,

Sun³

et al. 2023

Quant Imaging Med Surg

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Background: Freezing of gait (FOG) is a common disabling motor disturbance in Parkinson's disease (PD).Our study aimed to probe the topological organizations of structural and functional brain networks and their coupling in FOG. Methods: In this cross-sectional retrospective study, a total of 30 PD patients with FOG (PD-FOG), 40 patients without FOG, and 25 healthy controls (HCs) underwent clinical assessments and magnetic resonance imaging (MRI) scanning. Large-scale structural and functional brain networks were constructed. Subsequently, global and nodal graph theoretical properties and functional-structural coupling were investigated. Finally, correlations between the altered brain topological properties and freezing severity were analyzed in PD-FOG. Results: For structural networks, at the global level, PD-FOG exhibited increased normalized characteristic path length (P=0.040, Bonferroni-corrected) and decreased global efficiency (P=0.005, Bonferroni-corrected) compared with controls, and showed reduced global (P=0.001, Bonferroni-corrected) and local (P=0.032, Bonferroni-corrected) efficiency relative to patients without FOG. At the nodal level, nodal efficiency of structural networks was reduced in PD-FOG compared with PD patients without FOG, located in the left supplementary motor area (SMA), gyrus rectus, and middle cingulate cortex (MCC) (all P<0.05, Bonferronicorrected). Notably, altered global and nodal properties of structural networks were significantly correlated with Freezing of Gait Questionnaire scores [all P<0.05, false discovery rate (FDR)-corrected]. However, only an increase in local efficiency (P=0.003, Bonferroni-corrected) of functional networks was identified in PD-FOG compared with those without FOG. No significant structural-functional coupling was detected among the 3 groups.Conclusions: This study demonstrates the extensively impaired structural and relatively reserved functional network topological organizations in PD-FOG. Our results also provide evidence that the pathogenesis of PD-FOG is primarily attributable to network vulnerability established by crucial structural damage, especially in the left SMA, gyrus rectus, and MCC.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Impaired structural and reserved functional topological organizations of brain networks in Parkinson’s disease with freezing of gait

Wang¹,

Gan²,

Sun³

et al. 2023

Quant Imaging Med Surg

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show abstract

“…Many previous studies have employed EEG signals to investigate the brain function of PD patients. For example, Li et al revealed that EEG BN in PD patients can be implemented to predict the severity of gait freezing [25]. Geraedts et al showed that analysis of EEG BN of PD patients can be used for effective early diagnosis of their non-motor symptoms [26].…”

Section: B4 Assessment Of Mit For Eeg Signals In the Space-time-frequ...mentioning

confidence: 99%

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Han

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Objective: Multivariate signal (MS) analysis, especially the assessment of its information transmission (for example, from the perspective of network science), is the key to our understanding of various phenomena in biology, physics and economics. Although there is a large amount of literature demonstrating that MS can be decomposed into space-timefrequency domain information, there seems to be no research confirming that multivariate information transmission (MIT) in these three domains can be quantified. Therefore, in this study, we attempted to combine dynamic mode decomposition (DMD) and parallel communication model (PCM) together to realize it. Methods: We first regarded MS as a large-scale system and then used DMD to decompose it into specific subsystems with their own intrinsic oscillatory frequencies. At the same time, the transition probability matrix (TPM) of information transmission within and between MS at two consecutive moments in each subsystem can also be calculated. Then, communication parameters (CPs) derived from each TPM were calculated in order to quantify the MIT in the space-time-frequency domain. In this study, multidimensional electroencephalogram (EEG) signals were used to illustrate our method. Results: Compared with traditional EEG brain networks, this method shows greater potential in EEG analysis to distinguish between patients and healthy controls. Conclusion:This study demonstrates the feasibility of measuring MIT in the space-time-frequency domain simultaneously. Significance: This study shows that MIT analysis in the spacetime-frequency domain is not only completely different from the MS decomposition in these three domains, but also can reveal many new phenomena behind MS that have not yet been discovered.

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“…26,27,29,33 In addition, visual-related areas also showed abnormal neural activation, and structural and functional changes in PD. [34][35][36] However, whether abnormal neural information stored in sensorimotor, visual, and cognitive-related areas was also unclear.…”

Section: Introductionmentioning

confidence: 99%

Abnormal intrinsic neural timescale and reduced grey matter volume in Parkinson’s disease

Wei

Zhang

Peng

et al. 2022

Preprint

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Background: The intrinsic neural timescale in a local brain area has been specialized, and the sensory and prefrontal areas have shorter and longer timescales, respectively. Parkinson's disease (PD) is a progressive neurological disorder that also exhibits with abnormal neural activity in sensorimotor, visual, and cognitive-related areas. However, it was unclear whether PD patients exhibited abnormal intrinsic timescales and whether PD patients exhibited different temporal feature at different stages. Methods: Here, we estimated the intrinsic timescales using the magnitude of the autocorrelation of intrinsic neural signals by resting state functional magnetic resonance imaging data in 74 PD patients, including 44 patients in the early stage (PD-ES) and 30 patients in the late stage (PD-LS), and 73 healthy controls (HC). Results: We identified that the PD group had shorter timescales in bilateral lingual and calcarine gyri, bilateral postcentral and precentral gyri, and the right middle cingulum gyrus than the HC group, which correlated with the age, but not in the HC group. Moreover, shorter timescales in the PD group also correlated with the symptom severity and reduced grey matter volume (GMV). Longer timescale in the right middle frontal gyrus was also found in the PD group than that in the HC group. Increasingly, compared to the HC group, the PD-ES group had longer timescales in the anterior cortical regions, including the left superior and inferior frontal gyri, and the right middle frontal gyrus, whereas the PD-LS group had shorter timescales in the posterior cortical regions, including bilateral calcarine and lingual gyri, which accompanied with more serious cognitive impairments and reduced GMV. Conclusions: Our findings suggest that PD patients exhibit abnormal intrinsic timescales in visual, sensorimotor, and cognitive systems, which might provide new insights for the neural substrate of PD. Distinct patterns of intrinsic timescales in cerebral cortex between the PD-LS and PD-ES groups indicate that intrinsic timescale might be a new neuroimaging biomarker across disease stage in PD.

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Brain network topology and future development of freezing of gait in Parkinson’s disease: a longitudinal study

Cited by 10 publications

References 47 publications

Impaired structural and reserved functional topological organizations of brain networks in Parkinson’s disease with freezing of gait

Impaired structural and reserved functional topological organizations of brain networks in Parkinson’s disease with freezing of gait

Assessment of Multivariate Information Transmission in Space-Time-Frequency Domain: A Case Study for EEG Signals

Abnormal intrinsic neural timescale and reduced grey matter volume in Parkinson’s disease

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