Frequency specific brain networks in Parkinson's disease and comorbid depression

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas

The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.

Stepwise Dual-Target Magnetic Resonance-Guided Focused Ultrasound in Tremor-Dominant Parkinson Disease: A Feasibility Study

BACKGROUND

Magnetic resonance-guided focused ultrasound (MRgFUS) has been applied successfully in treating refractory tremors in Parkinson disease (PD). It generates a precise thermal ablation in a specific nucleus or tract, such as ventral intermediate nucleus (VIM) or pallidothalamic tract (PTT). Despite a single lesion improving parts of the PD symptoms, the feasibility and efficacy of a stepwise dual-lesion in VIM and PTT are yet to be explored.

METHODS

Three patients with tremor-dominant PD (aged 60.7 ± 6.0 years) received dual-target MRgFUS treatment with a series of primary and secondary outcome measures. The VIM and PTT were navigated based on individual magnetic resonance imaging planning of the brain. The primary outcome measures were the off-status Clinical Rating Scale for Tremor and Unified Parkinson's Disease Rating Scale part III (UPDRS-III). The secondary outcome measures included UPDRS I, II, IV, Hohen and Yahr score, Neuropsychiatry Inventory, Quality of life in PD Rating Scale, Non-Motor Symptoms Scale, and Clinical Global Impression. The baseline data were compared with those acquired 1 day and 1 month following the treatment.

RESULTS

The severity of tremor and motor deficits represented by Clinical Rating Scale for Tremor-part B and UPDRS III were significantly improved (P < 0.05 by nonparametric Mann-Whitney U tests) after dual-target ablations. The nonmotor symptoms investigated by UPDRS II and Non-Motor Symptoms Scale also showed significant improvement at the 1-day and 1-month follow-up. There was no adverse effect except temporary procedure-related headache and dizziness during the treatment.

CONCLUSIONS

Stepwise dual-lesion targeting VIM and PTT is a safe and effective MRgFUS therapeutic strategy for patients with PD.

Brain microstructural alterations of depression in Parkinson's disease: A systematic review of diffusion tensor imaging studies

Depression, a leading cause of disability worldwide, is also the most prevalent psychiatric problem among Parkinson disease patients. Both depression and Parkinson disease are associated with microstructural anomalies in the brain. Diffusion tensor imaging techniques have been developed to characterize the abnormalities in cerebral tissue. We included 11 studies investigating brain microstructural abnormalities in depressed Parkinson's disease patients. The included studies found alterations to essential brain structural networks, including impaired network integrity for specific cortical regions, such as the temporal and frontal cortices. Additionally, findings indicate that microstructural changes in specific limbic structures, such as the prefronto-temporal regions and connecting white matter pathways, are altered in depressed Parkinson's disease compared to non-depressed Parkinson's disease and healthy controls. There remain inconsistencies between studies reporting DTI measures and depression severity in Parkinson disease participants. Additional research evaluating underlying neurobiological relationships between major depression, depressed Parkinson's disease, and non-depressed Parkinson's disease is required to disentangle further mechanisms that underlie depression and related somatic symptoms, in Parkinson disease.

Chinese Herbal Medicine in the Treatment of Depression in Parkinson’s Disease: From Molecules to Systems

Depression is one of the most common non-motor symptoms in patients with Parkinson’s disease (PD). Depression in PD (DPD) increases the disability rate and reduces the quality of life of PD patients and increases the caregiver burden. Although previous studies have explained the relationship between depression and PD through a variety of pathological mechanisms, whether depression is a precursor or an independent risk factor for PD remains unclear. Additionally, increasing evidence shows that conventional anti-PD drug therapy is not ideal for DPD. Chinese Herbal Medicine (CHM) prescriptions exhibit the characteristics of multi-target, multi-pathway, and multi-level treatment of DPD and may simultaneously improve the motor symptoms of PD patients through multiple mechanisms. However, the specific pharmacological mechanisms of these CHM prescriptions remain unelucidated. Here, we investigated the mechanisms of action of the active ingredients of single herbs predominantly used in CHM prescriptions for depression as well as the therapeutic effect of CHM prescriptions on DPD. This review may facilitate the design of new selective and effective treatment strategies for DPD.

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform

Traditionally, functional networks in resting-state data were investigated with linear Fourier and wavelet-related methods to characterize their frequency content by relying on pre-specified frequency bands. In this study, Empirical Mode Decomposition (EMD), an adaptive time-frequency method, is used to investigate the naturally occurring frequency bands of resting-state data obtained by Group Independent Component Analysis. Specifically, energy-period profiles of Intrinsic Mode Functions (IMFs) obtained by EMD are created and compared for different resting-state networks. These profiles have a characteristic distribution for many resting-state networks and are related to the frequency content of each network. A comparison with the linear Short-Time Fourier Transform (STFT) and the Maximal Overlap Discrete Wavelet Transform (MODWT) shows that EMD provides a more frequency-adaptive representation of different types of resting-state networks. Clustering of resting-state networks based on the energy-period profiles leads to clusters of resting-state networks that have a monotone relationship with frequency and energy. This relationship is strongest with EMD, intermediate with MODWT, and weakest with STFT. The identification of these relationships suggests that EMD has significant advantages in characterizing brain networks compared to STFT and MODWT. In a clinical application to early Parkinson's disease (PD) vs. normal controls (NC), energy and period content were studied for several common resting-state networks. Compared to STFT and MODWT, EMD showed the largest differences in energy and period between PD and NC subjects. Using a support vector machine, EMD achieved the highest prediction accuracy in classifying NC and PD subjects among STFT, MODWT, and EMD.

Altered Time-Frequency Feature in Default Mode Network of Autism Based on Improved Hilbert-Huang Transform

Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder characterized by restricted interests and repetitive behaviors. Non-invasive measurements of brain activity with functional magnetic resonance imaging (fMRI) have demonstrated that the abnormality in the default mode network (DMN) is a crucial neural basis of ASD, but the time-frequency feature of the DMN has not yet been revealed. Hilbert-Huang transform (HHT) is conducive to feature extraction of biomedical signals and has recently been suggested as an effective way to explore the time-frequency feature of the brain mechanism. In this study, the resting-state fMRI dataset of 105 subjects including 59 ASD participants and 46 healthy control (HC) participants were involved in the time-frequency clustering analysis based on improved HHT and modified k-means clustering with label-replacement. Compared with HC, ASD selectively showed enhanced Hilbert weight frequency (HWF) in high frequency bands in crucial regions of the DMN, including the medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC) and anterior cingulate cortex (ACC). Time-frequency clustering analysis revealed altered DMN organization in ASD. In the posterior DMN, the PCC and bilateral precuneus were separated for HC but clustered for ASD; in the anterior DMN, the clusters of ACC, dorsal MPFC, and ventral MPFC were relatively scattered for ASD. This study paves a promising way to uncover the alteration in the DMN and identifies a potential neuroimaging biomarker of diagnostic reference for ASD.

Disentangling the Role of Working Memory in Parkinson's Disease

Working memory (WM) represents a core cognitive function with a major striatal contribution, and thus WM deficits, commonly observed in Parkinson’s disease (PD), could also relate to many other problems in PD patients. Our online study aimed to determine the subdomains of WM that are particularly affected in PD and to clarify the links between WM and everyday cognitive deficits, other executive functions, psychiatric and PD symptoms, as well as early cognitive impairment. Fifty-two mild-to-moderate PD patients and 54 healthy controls performed seven WM tasks tapping selective updating, continuous monitoring, or maintenance of currently active information. Self-ratings of everyday cognition, depression, and apathy symptoms, as well as screenings of global cognitive impairment, were also collected. The data were analyzed using structural equation modeling. Of the three WM domains, only selective updating was directly predictive of PD group membership. More widespread WM deficits were observed only in relation to global cognitive impairment in PD patients. Self-rated everyday cognition or psychiatric symptoms were not linked to WM performance but correlated with each other. Our findings suggest that WM has a rather limited role in the clinical manifestation of PD. Nevertheless, due to its elementary link to striatal function, the updating component of WM could be a candidate for a cognitive marker of PD also in patients who are otherwise cognitively well-preserved.

Medical Image Analysis Using AM-FM Models and Methods

Medical image analysis methods require the use of effective representations for differentiating between lesions, diseased regions, and normal structure. Amplitude Modulation-Frequency Modulation (AM-FM) models provide effective representations through physically meaningful descriptors of complex non-stationary structures that can differentiate between the different lesions and normal structure. Based on AM-FM models, medical images are decomposed into AM-FM components where the instantaneous frequency provides a descriptor of local texture, the instantaneous amplitude captures slowly-varying brightness variations, while the instantaneous phase provides for a powerful descriptor of location, generalizing the traditionally important role of phase in the Fourier Analysis of images. Over the years, AM-FM representations have been used in a wide variety of medical image analysis applications based on a vastly reduced number of features that can be easily learned by simple classifiers. The paper provides an overview of AM-FM models and methods, followed by applications in medical image analysis. We also provide a summary of emerging trends and future directions.

Imaging behavioural complications of Parkinson's disease

In addition to motor symptoms, behavioural complications are commonly found in patients with Parkinson's disease (PD). Behavioural complications, including depression, anxiety, apathy, impulse control disorder and psychosis, together have a large impact on PD patient's quality of life. Many neuroimaging studies using PET, SPECT and MRI techniques have been conducted to study the underlying neural mechanisms of PD pathogenesis and pathophysiology in relation to its behavioural complications. This review will survey these PET, SPECT and MRI studies to describe the current understanding of the neuro-chemical, functional and structural changes associated with behavioural complications in PD patients.

Abnormal intrinsic brain activity in individuals with peripheral vision loss because of retinitis pigmentosa using amplitude of low-frequency fluctuations

The study aimed to determine alterations in intrinsic brain activity in retinitis pigmentosa (RP) individuals using the amplitude of low-frequency fluctuation (ALFF)/fractional amplitude of low-frequency fluctuation (fALFF) method. Sixteen RP individuals (10 men and six women) and 14 healthy controls (HCs) (six men and eight women) closely matched in age, sex, and education were enrolled in the study. The ALFF/fALFF method was applied to compare different intrinsic brain activities between the RP group and the HC group. The relationship between the mean ALFF/fALFF signal values of different brain regions and the visual measurements in RP group was analyzed by Pearson correlation. Compared with HCs, RP individuals had significantly lower ALFF values in the bilateral lingual gyrus (LIGG)/cerebellum posterior lobe [Brodmann area (BA) 17,18], but lower fALFF values in the bilateral LIGG/cerebellum anterior lobe (BA 17,18). Meanwhile, RP individuals had significantly higher ALFF in the bilateral precuneus cortex/middle cingulate cortex (BA 7,31), as well as higher fALFF values in the left superior/middle frontal gyrus (BA 9,10) and bilateral supplementary motor area (BA 6,8) (voxel-level P<0.01, cluster-level P<0.05). Moreover, the fALFF values of the bilateral LIGG/cerebellum anterior lobe showed positive relationships with the best-corrected visual acuity (BCVA)-oculus dexter (r=0.574, P=0.020) and BCVA-oculus sinister (r=0.570, P=0.021) in RP individuals; our results provide evidence that RP individuals may have impaired intrinsic brain activity in the primary visual area and the visuomotor coordination area that correlates with BCVA. Moreover, our findings indicate that reorganization of the dorsal visual stream and the parietoprefrontal pathway occurs in RP individuals.

Structural Brain Network Alteration and its Correlation With Structural Impairments in Patients With Depression in de novo and Drug-Naïve Parkinson's Disease

Purpose: Depression is common in Parkinson's disease (PD) and is correlated with the severity of motor deficits and quality of life. The present study aimed to investigate alterations in the structural brain network related to depression in Parkinson's disease (d-PD) and their correlations with structural impairments of white matter (WM).

Materials and Methods: Data were acquired from the Parkinson Progression Markers Initiative (PPMI) database. A total of 84 de novo and drug-naïve PD patients were screened and classified into two groups according to the 15-item Geriatric Depression Scale (GDS-15): d-PD (n = 28) and nondepression in PD (nd-PD, n = 56). Additionally, 37 healthy controls (HC) were screened. All subjects underwent DTI and 3D-T₁WI on a 3.0 T MR scanner. Individual structural brain networks were constructed and analyses were performed using graph theory and network-based statistics (NBS) at both global and local levels. Differences in global topological properties were explored among the three groups. The association models between node and edge changes and the GDS-15 were constructed to detect regions that were specifically correlated with d-PD. Tract-based spatial statistics (TBSS) was used to detect structural impairments of WM between the d-PD and nd-PD groups. The correlations between altered global topological properties and structural impairments were analyzed in the d-PD group.

Results: The global efficiency and characteristic path length of the structural brain network were impaired in the d-PD group compared with those in the nd-PD and HC groups. Thirteen nodes and 1 subnetwork with 10 nodes and 12 edges specifically correlated with d-PD were detected. The left hippocampus, left parahippocampal, left lingual, left middle occipital, left inferior occipital, left fusiform, left middle temporal, and left inferior temporal regions were all involved in the results of node and edge analysis. No WM microstructural impairments were identified in the d-PD group.

Conclusion: Our study suggests that the integration of the structural brain network is impaired with disrupted connectivity of limbic system and visual system in the de novo and drug-naïve d-PD patients.The topological properties assessing integration of the structural brain network can serve as a potential objective neuroimaging marker for early diagnosis of d-PD.

Functional brain abnormalities in major depressive disorder using the Hilbert-Huang transform

Major depressive disorder is a common disease worldwide, which is characterized by significant and persistent depression. Non-invasive accessory diagnosis of depression can be performed by resting-state functional magnetic resonance imaging (rs-fMRI). However, the fMRI signal may not satisfy linearity and stationarity. The Hilbert-Huang transform (HHT) is an adaptive time-frequency localization analysis method suitable for nonlinear and non-stationary signals. The objective of this study was to apply the HHT to rs-fMRI to find the abnormal brain areas of patients with depression. A total of 35 patients with depression and 37 healthy controls were subjected to rs-fMRI. The HHT was performed to extract the Hilbert-weighted mean frequency of the rs-fMRI signals, and multivariate receiver operating characteristic analysis was applied to find the abnormal brain regions with high sensitivity and specificity. We observed differences in Hilbert-weighted mean frequency between the patients and healthy controls mainly in the right hippocampus, right parahippocampal gyrus, left amygdala, and left and right caudate nucleus. Subsequently, the above-mentioned regions were included in the results obtained from the compared region homogeneity and the fractional amplitude of low frequency fluctuation method. We found brain regions with differences in the Hilbert-weighted mean frequency, and examined their sensitivity and specificity, which suggested a potential neuroimaging biomarker to distinguish between patients with depression and healthy controls. We further clarified the pathophysiological abnormality of these regions for the population with major depressive disorder.

Frequency-Specific Functional Connectivity Density as an Effective Biomarker for Adolescent Generalized Anxiety Disorder

Several neuropsychiatric diseases have been found to influence the frequency-specific spontaneous functional brain organization (SFBO) in resting state, demonstrating that the abnormal brain activities of different frequency bands are associated with various physiological and psychological dysfunctions. However, little is known about the frequency specificities of SFBO in adolescent generalized anxiety disorder (GAD). Here, a novel complete ensemble empirical mode decomposition with adaptive noise method was applied to decompose the time series of each voxel across all participants (31 adolescent patients with GAD and 28 matched healthy controls; HCs) into four frequency-specific bands with distinct intrinsic oscillation. The functional connectivity density (FCD) of different scales (short-range and long-range) was calculated to quantify the SFBO changes related to GAD within each above frequency-specific band and the conventional frequency band (0.01–0.08 Hz). Support vector machine classifier was further used to examine the discriminative ability of the frequency-specific FCD values. The results showed that adolescent GAD patients exhibited abnormal alterations of both short-range and long-range FCD (S-FCD and L-FCD) in widespread brain regions across three frequency-specific bands. Positive correlation between the State Anxiety Inventory (SAI) score and increased L-FCD in the fusiform gyrus in the conventional frequency band was found in adolescents with GAD. Both S-FCD and L-FCD in the insula in the lower frequency band (0.02–0.036 Hz) had the highest classification performance compared to all other brain regions with inter-group difference. Furthermore, a satisfactory classification performance was achieved by combining the discrepant S-FCD and L-FCD values in all frequency bands, with the area under the curve (AUC) value of 0.9414 and the corresponding sensitivity, specificity, and accuracy of 87.15, 92.92, and 89.83%, respectively. This study indicates that the alterations of SFBO in adolescent GAD are frequency dependence and the frequency-specific FCD can potentially serve as a valuable biomarker in discriminating GAD patients from HCs. These findings may provide new insights into the pathophysiological mechanisms of adolescent GAD.