A dynamic brain network decomposition method discovers effective brain hemodynamic sub-networks for Parkinson’s disease

Abstract

Objective.
Dopaminergic treatment is effective for Parkinson’s disease (PD). Nevertheless, the conventional treatment assessment mainly focuses on human-administered behavior examination while the underlying functional improvements have not been well explored. This paper aims to investigate brain functional variations of PD patients after dopaminergic therapy.
Approach. This paper proposed a dynamic brain network decomposition method and discovered brain hemodynamic sub-networks that well characterized the efficacy of dopaminergic treatment in PD. Firstly, a clinical walking procedure with functional near-infrared spectroscopy (fNIRS) was developed, and brain activations during the procedure from fifty PD patients under the OFF and ON states (without and with dopaminergic medication) were captured. Then, dynamic brain networks were constructed with sliding-window analysis of phase lag index and integrated time-varying functional networks across all patients. Afterwards, an aggregated network decomposition algorithm was formulated based on aggregated effectiveness optimization of functional networks in spanning network topology and cross-validation network variations, and utilized to unveil effective brain hemodynamic sub-networks for PD patients. Further, dynamic sub-network features were constructed to characterize the brain flexibility and dynamics according to the temporal switching and activation variations of discovered sub-networks, and their correlations with differential treatment-induced gait alterations were analyzed.
Results.
The results demonstrated that PD patients exhibited significantly enhanced flexibility after dopaminergic therapy within a sub-network related to the improvement of motor functions. Other sub-networks were significantly correlated with trunk-related axial symptoms and exhibited no significant treatment-induced dynamic interactions. Significance. The proposed method promises a quantified and objective approach for dopaminergic treatment evaluation. Moreover, the findings suggest that the gait of PD patients comprises distinct motor domains, and the corresponding neural controls are selectively responsive to dopaminergic treatment.

Aberrant flexibility of dynamic brain network in patients with autism spectrum disorder

“…Research on properties of "dynamic brain network" under such a framework are emerging, with various measures of temporal variability/stability of the brain networks proposed by different researchers (15)(16)(17)(18). Specifically, the "flexibility" (also named "switching rate" by some researchers) of a dynamic functional brain network, which estimates the temporal stability of brain network according to its changing frequencies of modularity structures, has been proved to valid (19) and widely used in recent studies (20)(21)(22)(23)(24)(25)(26)(27)(28). In these studies, the flexibility of brain network has been associated with cognition (20,21), emotion (26), aging (24), as well as many common neuropsychiatric disorders such as Parkinson's disease (27), attention-deficit/hyperactivity disorder (25), schizophrenia (22), anxiety disorder (28), and major depressive disorder (23).…”

“…Specifically, the "flexibility" (also named "switching rate" by some researchers) of a dynamic functional brain network, which estimates the temporal stability of brain network according to its changing frequencies of modularity structures, has been proved to valid (19) and widely used in recent studies (20)(21)(22)(23)(24)(25)(26)(27)(28). In these studies, the flexibility of brain network has been associated with cognition (20,21), emotion (26), aging (24), as well as many common neuropsychiatric disorders such as Parkinson's disease (27), attention-deficit/hyperactivity disorder (25), schizophrenia (22), anxiety disorder (28), and major depressive disorder (23). Notably, a published study by Harlalka et al (29) has reported that atypical flexibility of the dynamic functional brain network can quantify (positively associated with) the illness severity in ASD patients.…”