Pathophysiology of the basal ganglia in Parkinson's disease

Computational framework of neuronal-astrocytic network within the basal ganglia-thalamic circuits associated with Parkinson's disease

Parkinson's disease is the neurodegenerative disorder which involves both neurons and non-neurons, and whose symptoms are usually represented by the error index and synchronization index in the computational study. This paper combines with the classical basal ganglia-thalamic network model and tripartite synapse model to explore the internal effects of astrocytes on the Parkinson's disease. The model simulates the firing patterns of the Parkinsonian state and healthy state, verifies the feasibility of the neural-glial model. The results show that the rate of production for IP3 modulate the frequency and amplitude of slow inward current for subthalamic nucleus, globus pallidus externa and interna in two modes. Increasing the rate of production for IP3 of subthalamic nucleus and globus pallidus externa can decrease the error index and presumably alleviate the Parkinson's disease. Increasing the rate of production for IP3 of globus pallidus externa and adjusting the rate of production for IP3 of subthalamic nucleus can result in the desynchronization of network in a regular way. These obtained results emphasize the effect of neurons (especially subthalamic nucleus and globus pallidus externa), astrocytes and their interaction on the Parkinson's disease. It enriches the evidence of involvement of astrocyte in Parkinson's disease, and proposes some cognitive points to the alleviation of Parkinson's disease.

Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), respectively.The consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87-0.90, FUNC2T1: 0.67-0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinson's disease.

Multiparametric analysis based on 18F-AV133 PET/MR imaging for clinical application in Parkinson's disease

OBJECTIVE

The progressive loss of dopaminergic neurons and abnormal iron deposition in the central nervous system (CNS) are key pathogenic mechanisms of Parkinson's disease (PD). This study aimed to explore the relationship between iron deposition in specific CNS regions and striatal dysfunction using 18F-AV133 PET/MR imaging.

METHODS

Based on the Hoehn-Yahr stage, 24 patients with early-stage PD (EPD, stage ≤ 2.5), 17 patients with late-stage PD (LPD, stage ≥ 3), and 30 healthy controls (HCs) were recruited for scale evaluation. The specific uptake ratio (SUR) of striatal subregions was calculated using the occipital cortex as the reference region. Quantitative Susceptibility Mapping (QSM) values of major subcortical nuclei were derived through QSM imaging. Spearman correlation analysis was conducted to assess the relationships between SUR in striatal subregions, QSM values in nuclear groups, and PD clinical symptoms, as well as the correlation between SUR and QSM values.

RESULTS

Compared to HC, EPD and LPD patients showed significantly reduced VMAT2 distribution in the bilateral caudate nuclei and anteroposterior putamen, particularly in the contralateral posterior putamen. In PD patients, the SUR of striatal subregions and QSM values of the substantia nigra (SN), globus pallidus (GP), and external segment of the GP (GPe) were significantly correlated with disease duration, H&Y stage, UPDRS III score, and NMSS score. Moreover, SUR of striatal subregions was negatively correlated with QSM values in the SN, GP, internal segment of the GP (GPi), and GPe.

CONCLUSION

Multi-parameter analysis revealed a region-specific correlation between striatal dysfunction and iron deposition in PD, offering new avenues to elucidate the underlying mechanisms of the disease.

Resting-state functional MRI metrics to detect freezing of gait in Parkinson's disease: a machine learning approach

Among the symptoms that can occur in Parkinson's disease (PD), Freezing of Gait (FOG) is a disabling phenomenon affecting a large proportion of patients, and it remains not fully understood. Accurate classification of FOG in PD is crucial for tailoring effective interventions and is necessary for a better understanding of its underlying mechanisms. In the present work, we applied four Machine Learning (ML) classifiers (Decision Tree - DT, Random Forest - RF, Multilayer Perceptron - MLP, Logistic Regression - LOG) to different four metrics derived from resting-state functional Magnetic Resonance Imaging (rs-fMRI) data processing to assess their accuracy in automatically classifying PD patients based on the presence or absence of Freezing of Gait (FOG). To validate our approach, we applied the same methodologies to distinguish PD patients from a group of Healthy Subject (HS). The performance of the four ML algorithms was validated by repeated k-fold cross-validation on randomly selected independent training and validation subsets. The results showed that when discriminating PD from HS, the best performance was achieved using RF applied to fractional Amplitude of Low-Frequency Fluctuations (fALFF) data (AUC 96.8 ± 2 %). Similarly, when discriminating PD-FOG from PD-nFOG, the RF algorithm was again the best performer on all four metrics, with AUCs above 90 %. Finally, trying to unbox how AI system black-box choices were made, we extracted features' importance scores for the best-performing method(s) and discussed them based on the results obtained to date in rs-fMRI studies on FOG in PD and, more generally, in PD. In summary, regions that were more frequently selected when differentiating both PD from HS and PD-FOG from PD-nFOG patients were mainly relevant to the extrapyramidal system, as well as visual and default mode networks. In addition, the salience network and the supplementary motor area played an additional major role in differentiating PD-FOG from PD-nFOG patients.

Alterations of diffusion kurtosis measures in gait-related white matter in the "ON-OFF state" of Parkinson's disease

BACKGROUND

Gait impairment is closely related to quality of life in patients with Parkinson's disease (PD). This study aimed to explore alterations in brain microstructure in PD patients and healthy controls (HCs) and to identify the correlation of gait impairment in the ON and OFF states of patients with PD, respectively.

METHODS

We enrolled 24 PD patients and 29 HCs from the Movement Disorders Program at Beijing Friendship Hospital Capital Medical University between 2019 and 2020. We acquired magnetic resonance imaging (MRI) scans and processed the diffusion kurtosis imaging (DKI) images. Preprocessing of diffusion-weighted data was performed with Mrtrix3 software, using a directional distribution function to track participants' main white matter fiber bundles. Demographic and clinical characteristics were recorded. Quantitative gait and clinical scales were used to assess the status of medication ON and OFF in PD patients.

RESULTS

The axial kurtosis (AK), mean kurtosis (MK), and radial kurtosis (RK) of five specific white matter fiber tracts, the bilateral corticospinal tract, left superior longitudinal fasciculus, left anterior thalamic radiation, forceps minor, and forceps major were significantly higher in PD patients compared to HCs. Additionally, the MK values were negatively correlated with Timed Up and Go Test (TUG) scores in both the ON and OFF in PD patients. Within the PD group, higher AK, MK, and RK values, whether the patients were ON or OFF, were associated with better gait performance (i.e., higher velocity and stride length).

CONCLUSIONS

PD exhibits characteristic regional patterns of white matter microstructural degradation. Correlations between objective gait parameters and DKI values suggest that dopamine-responsive gait function depends on preserved white matter microstructure. DKI-based Tract-Based Spatial Statistics (TBSS) analysis may serve as a tool for evaluating PD-related motor impairments (e.g., gait impairment) and could yield potential neuroimaging biomarkers.

Carriers of LRRK2 pathogenic variants show a milder, anatomically distinct brain signature of Parkinson's disease

LRRK2 gene variants are a major genetic risk factor for both familial and sporadic Parkinson's disease (PD), opening an unattended window on the disease's mechanisms and potential therapies. Investigating the influence of pathogenic variants in LRRK2 gene on brain structure is a crucial step toward enabling early diagnosis and personalized treatment. Yet, despite its significance, the ways in which LRRK2 genotype affects brain structure remain largely unexplored. Work in this domain is plagued by small sample sizes and differences in cohort composition, which can obscure genuine distinctions among clinical subgroups. In this study, we overcome such important limitations by combining explicit modeling of population background variation and pattern matching. Specifically, we leveraged a large cohort of 641 participants (including 364 with a PD diagnosis) to examine MRI-detectable cortical atrophy patterns associated with the LRRK2 pathogenic variants in people with PD and non-manifesting individuals. LRRK2 PD patients exhibited milder cortical thinning compared to sporadic PD, with notable preservation in temporal and occipital regions, suggesting a distinct pattern of neurodegeneration. Non-manifesting LRRK2 carriers showed no significant cortical atrophy, indicating no structural signs of subclinical PD. We further analyzed the relationship between aggregated alpha-synuclein in cerebrospinal fluid and atrophy. We found that those with evidence of aggregated alpha-synuclein experienced pronounced neurodegeneration and increased cortical thinning, possibly defining another aggressive PD subtype. Our findings highlight avenues for distinguishing PD subtypes, which could lead to more targeted treatment approaches and a more complete understanding of Parkinson's disease progression.

Differences in the movement phase condition and sensory inputs on temporal synchronization and continuation during bilateral foot-tapping tasks

In the sensorimotor synchronization (synchronized and continuous tapping) task, subjects move their limbs in synchrony with an isochronous tone presented at various tempos and continue tapping at the same pace after the tones have ceased. We investigated the ability of bilateral lower limb motor control for performing this task as a crucial metric for examining motor coordination relevant to human locomotion, such as walking. Here, sensory information such as auditory and tactile inputs is considered to improve the accuracy of sensorimotor synchronization. In this study, we explored the change in tapping variability of rhythmic motor control of the bilateral lower limb with different movement phase conditions in the presence or absence of sensory information. Thirty-three healthy volunteers performed three types of foot-tapping tasks: synchronization-continuation (SC-tap), air-tapping (A-tap), and a combination of both (SCA-tap). Participants were instructed to tap the foot-switch (or perform a similar movement in the A-tap) in synchrony with the tones presented at fixed interstimulus intervals (ISIs) between 500 and 4,800 ms. Taps were performed with either unilateral foot or, in the case of bilateral movements, with both feet, either simultaneously (in-phase) or alternately for bilateral movements (antiphase). The synchronizing tapping error and the inter-tap interval (ITI) were evaluated. The coefficient of variation (CV) of ITI was significantly smaller for the antiphase condition than for the unilateral or in-phase conditions in the SC-tap and SCA-tap tasks. In addition, considering the timing of taps on both sides, the CV was significantly lower for antiphase only in the SC-tap task. The findings indicated that the antiphase condition exhibited superior temporal stability in repetitive lower limb movements. The findings also underscored the significance of tactile feedback from the soles of the feet when stability of rhythmic limb movements unpaced by the tones in antiphase movements was taken into consideration.

Emergence of structures in neuronal network activities

Nonlinear responses of individual neurons are both experimentally established and considered fundamental for the functioning of neuronal circuitry. Consequently, one may envisage the collective dynamics of large networks of neurons exhibiting a large repertoire of nonlinear behaviors. However, an ongoing and central challenge in the modeling of neural dynamics involves the trade-off between tractability and biological realism. This is particularly important in exploring the range of possible dynamics of large networks. Our approach uses Gaussian white noise as a probe, thus capturing the full range of system responses and characteristics by using an approach inspired by the well-established Wiener - Volterra nonlinear system identification approach. We assess model behavior over a range of network architectures and noise stimulation rates and demonstrate non-monotonicity and nonlinearity as a system property. Perhaps surprisingly, our computational model suggests that recurrent systems of nonlinear neurons exhibit a range of complex behaviors that do not readily yield to linear modeling in every setting. Our results suggest that a linear interpretation of experimental data is likely to discount the critical importance of properties emerging from network architecture. The main contribution of this effort is to highlight the importance of the network's architecture operating on the nonlinear properties of individual neurons and the experimental probing approaches of the circuitry.

Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach

Parkinson's disease (PD) is a neurodegenerative disorder characterized by motor and neuropsychiatric symptoms resulting from the loss of dopamine-producing neurons in the substantia nigra pars compacta (SNc). Dopamine transporter scan (DATSCAN), based on single-photon emission computed tomography (SPECT), is commonly used to evaluate the loss of dopaminergic neurons in the striatum. This study aims to identify a biomarker from DATSCAN images and develop a machine learning (ML) algorithm for PD diagnosis. Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson's Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. To ensure interpretability, we applied the local interpretable model-agnostic explainer (LIME), identifying contralateral putamen SBR as the most predictive feature for distinguishing PD from healthy controls. By focusing on a single biomarker, our approach simplifies PD diagnosis, integrates seamlessly into clinical workflows, and provides interpretable, actionable insights. Although DATSCAN has limitations in detecting early-stage PD, our study demonstrates the potential of ML to enhance diagnostic precision, contributing to improved clinical decision-making and patient outcomes.

Functional anatomy of the subthalamic nucleus and the pathophysiology of cardinal features of Parkinson's disease unraveled by focused ultrasound ablation

The subthalamic nucleus (STN) modulates basal ganglia output and plays a fundamental role in the pathophysiology of Parkinson's disease (PD). Blockade/ablation of the STN improves motor signs in PD. We assessed the topography of focused ultrasound subthalamotomy (n = 39) by voxel-based lesion-symptom mapping to identify statistically validated brain voxels with the optimal effect against each cardinal feature and their respective cortical connectivity patterns by diffusion-weighted tractography. Bradykinesia and rigidity amelioration were associated with ablation of the rostral motor STN subregion connected to the supplementary motor and premotor cortices, whereas antitremor effect was explained by lesioning the posterolateral STN projection to the primary motor cortex. These findings were corroborated prospectively in another PD cohort (n = 12). This work concurs with recent deep brain stimulation findings that suggest different corticosubthalamic circuits underlying each PD cardinal feature. Our results provide sound evidence in humans of segregated anatomy of subthalamic-cortical connections and their distinct role in PD pathophysiology and normal motor control.

Functional efficacy of the MAO-B inhibitor safinamide in murine substantia nigra pars compacta dopaminergic neurons in vitro: A comparative study with tranylcypromine

Safinamide (SAF) is currently used to treat Parkinson's disease (PD) symptoms based on its theoretical ability to potentiate the dopamine (DA) signal, blocking monoamine oxidase (MAO) B. The present work aims to highlight the functional relevance of SAF as an enhancer of the DA signal, by evaluating its ability to prolong recovery from DA-mediated firing inhibition of DAergic neurons of the substantia nigra pars compacta (SNpc), compared to another MAO antagonist, tranylcypromine (TCP). Using multielectrode array (MEA) and single electrode extracellular recordings of spontaneous spikes from presumed SNpc DAergic cells in vitro, we show that SAF (30 μM) mildly prolongs the DA-mediated firing inhibition, as opposed to the profound effect of TCP (10 μM). In patch-clamp recordings, we found that SAF (30 μM) significantly reduced the number of spikes evoked by depolarizing currents in SNpc DAergic neurons, in a sulpiride (1 μM) independent manner. According to our results, SAF marginally potentiates the DA signal in SNpc DAergic neurons, while exerting an inhibitory effect on the postsynaptic excitability acting on membrane conductances. Thus, we propose that the therapeutic effects of SAF in PD patients partially depends on MAO inhibition, while other MAO-independent sites of action could be more relevant.

Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions

Parkinson's disease (PD) is a neurodegenerative disorder characterized by dopaminergic neuron loss, leading to motor and non-motor symptoms. Early detection before symptom onset is crucial but challenging. This study presents a framework integrating circuit modeling, non-equilibrium dynamics, and optimization to understand PD pathogenesis and enable precision interventions. Neuronal firing patterns, particularly oscillatory activity, play a critical role in PD pathology. The basal ganglia network, specifically the subthalamic nucleus-external globus pallidus (STN-GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. The framework leverages the non-equilibrium landscape and flux theory to identify key connections generating pathological activity, providing insights into disease progression and potential intervention points. The intricate STN-GPe interplay is highlighted, shedding light on compensatory mechanisms within this circuitry may initially counteract changes but later contribute to pathological alterations as disease progresses. The framework addresses the need for comprehensive evaluation methods to assess intervention outcomes. Cross-correlations between state variables provide superior early warning signals compared to traditional indicators relying on critical slowing down. By elucidating compensatory mechanisms and circuit dynamics, the framework contributes to improved management, early detection, risk assessment, and potential prevention/delay of PD development. This pioneering research paves the way for precision medicine in neurodegenerative disorders.

A Model with Dopamine Depletion in Basal Ganglia and Cerebellum Predicts Changes in Thalamocortical Beta Oscillations

Parkinsonism is presented as a motor syndrome characterized by rigidity, tremors, and bradykinesia, with Parkinson's disease (PD) being the predominant cause. The discovery that those motor symptoms result from the death of dopaminergic cells in the substantia nigra led to focus most of parkinsonism research on the basal ganglia (BG). However, recent findings point to an active involvement of the cerebellum in this motor syndrome. Here, we have developed a multiscale computational model of the rodent brain's BG-cerebellar network. Simulations showed that a direct effect of dopamine depletion on the cerebellum must be taken into account to reproduce the alterations of neural activity in parkinsonism, particularly the increased beta oscillations widely reported in PD patients. Moreover, dopamine depletion indirectly impacted spike-time-dependent plasticity at the parallel fiber-Purkinje cell synapses, degrading associative motor learning as observed in parkinsonism. Overall, these results suggest a relevant involvement of cerebellum in parkinsonism associative motor symptoms.

Investigating the Brain Mechanisms of Externally Cued Sit-to-Stand Movement in Parkinson's Disease

BACKGROUND

One of the more challenging daily-life actions for Parkinson's disease patients is starting to stand from a sitting position. Parkinson's disease patients are known to have difficulty with self-initiated movements and benefit from external cues. However, the brain processes underlying external cueing as an aid remain unknown. The advent of mobile electroencephalography (EEG) now enables the investigation of these processes in dynamic sit-to-stand movements.

OBJECTIVE

To identify cortical correlates of the mechanisms underlying auditory cued sit-to-stand movement in Parkinson's disease.

METHODS

Twenty-two Parkinson's disease patients and 24 healthy age-matched participants performed self-initiated and externally cued sit-to-stand movements while cortical activity was recorded through 32-channel mobile EEG.

RESULTS

Overall impaired integration of sensory and motor information can be seen in the Parkinson's disease patients exhibiting less modulation in the θ band during movement compared to healthy age-matched controls. How Parkinson's disease patients use external cueing of sit-to-stand movements can be seen in larger high β power over sensorimotor brain areas compared to healthy controls, signaling sensory integration supporting the maintenance of motor output. This appears to require changes in cognitive processing to update the motor plan, reflected in frontal θ power increases in Parkinson's disease patients when cued.

CONCLUSION

These findings provide the first neural evidence for why and how cueing improves motor function in sit-to-stand movement in Parkinson's disease. The Parkinson's disease patients' neural correlates indicate that cueing induces greater activation of motor cortical areas supporting the maintenance of a more stable motor output, but involves the use of cognitive resources to update the motor plan. © 2024 International Parkinson and Movement Disorder Society.

Involvement of KV3.4 Channel in Parkinson's Disease: A Key Player in the Control of Midbrain and Striatum Differential Vulnerability during Disease Progression?

Parkinson's disease (PD), the second most common neurodegenerative disease in the elderly, is characterized by selective loss of dopaminergic neurons and accumulation of α-synuclein (α-syn), mitochondrial dysfunction, Ca2+ dyshomeostasis, and neuroinflammation. Since current treatments for PD merely address symptoms, there is an urgent need to identify the PD pathophysiological mechanisms to develop better therapies. Increasing evidence has identified KV3.4, a ROS-sensitive KV channel carrying fast-inactivating currents, as a potential therapeutic target against neurodegeneration. In fact, it has been hypothesized that KV3.4 channels could play a role in PD etiopathogenesis, controlling astrocytic activation and detrimental pathways in A53T mice, a well-known model of familial PD. Here, we showed that the A53T midbrain, primarily involved in the initial phase of PD pathogenesis, displayed an early upregulation of the KV3.4 channel at 4 months, followed by its reduction at 12 months, compared with age-matched WT. On the other hand, in the A53T striatum, the expression of KV3.4 remained high at 12 months, decreasing thereafter, in 16-month-old mice. The proteomic profile highlighted a different detrimental phenotype in A53T brain areas. In fact, the A53T striatum and midbrain differently expressed neuroprotective/detrimental pathways, with the variation of astrocytic p27kip1, XIAP, and Smac/DIABLO expression. Of note, a switch from protective to detrimental phenotype was characterized by the upregulation of Smac/DIABLO and downregulation of p27kip1 and XIAP. This occurred earlier in the A53T midbrain, at 12 months, compared with the striatum proteomic profile. In accordance, an upregulation of Smac/DIABLO and a downregulation of p27kip1 occurred in the A53T striatum only at 16 months, showing the slowest involvement of this brain area. Of interest, HIF-1α overexpression was associated with the detrimental profile in midbrain and its major vulnerability. At the cellular level, patch-clamp recordings revealed that primary A53T striatum astrocytes showed hyperpolarized resting membrane potentials and lower firing frequency associated with KV3.4 ROS-dependent hyperactivity, whereas primary A53T midbrain astrocytes displayed a depolarized resting membrane potential accompanied by a slight increase of KV3.4 currents. Accordingly, intracellular Ca2+ homeostasis was significantly altered in A53T midbrain astrocytes, in which the ER Ca2+ level was lower than in A53T striatum astrocytes and the respective littermate controls. Collectively, these results suggest that the early KV3.4 overexpression and ROS-dependent hyperactivation in astrocytes could take part in the different vulnerabilities of midbrain and striatum, highlighting astrocytic KV3.4 as a possible new therapeutic target in PD.

Computational study of associations between the synaptic conductance of STN and GPe and the development of Parkinson's disease

There is evidence that the subthalamic nucleus (STN) and globus pallidus pars externa (GPe) involve in the development of Parkinson's disease, a neurodegenerative disorder characterized by motor and non-motor symptoms and loss of dopaminergic neurons in which the error index (EI) in firing patterns is widely used to address the related issues. Whether and how this interaction mechanism of STN and GPe affects EI in Parkinson's disease is uncertain. To account for this, we propose a kind of basal ganglia-thalamic network model associated with Parkinson's disease coupled with neurons, and investigate the effect of synaptic conductance of STN and GPe on EI in this network, as well as their internal relationship under EI as an index. The results show a relationship like a piecewise function between the error index and the slope of the state transition function of synaptic conductance from STN to GPe ( g snge ) and from GPe to STN ( g gesn ). And there is an approximate negative correlation between EI and g gesn . Increasing g snge and decreasing g gesn can improve the fidelity of thalamus information transmission and alleviate Parkinson's disease effectively. These obtained results can give some theoretical evidence that the abnormal synaptic releases of STN and GPe may be the symptoms of the development of Parkinson's disease, and further enrich the understanding of the pathogenesis and treatment mechanism of Parkinson's disease.

Deciphering social traits and pathophysiological conditions from natural behaviors in common marmosets

Nonhuman primates (NHPs) are indispensable animal models by virtue of the continuity of behavioral repertoires across primates, including humans. However, behavioral assessment at the laboratory level has so far been limited. Employing the application of three-dimensional (3D) pose estimation and the optimal integration of subsequent analytic methodologies, we demonstrate that our artificial intelligence (AI)-based approach has successfully deciphered the ethological, cognitive, and pathological traits of common marmosets from their natural behaviors. By applying multiple deep neural networks trained with large-scale datasets, we established an evaluation system that could reconstruct and estimate the 3D poses of the marmosets, a small NHP that is suitable for analyzing complex natural behaviors in laboratory setups. We further developed downstream analytic methodologies to quantify a variety of behavioral parameters beyond motion kinematics. We revealed the distinct parental roles of male and female marmosets through automated detections of food-sharing behaviors using a spatial-temporal filter on 3D poses. Employing a recurrent neural network to analyze 3D pose time series data during social interactions, we additionally discovered that marmosets adjusted their behaviors based on others' internal state, which is not directly observable but can be inferred from the sequence of others' actions. Moreover, a fully unsupervised approach enabled us to detect progressively appearing symptomatic behaviors over a year in a Parkinson's disease model. The high-throughput and versatile nature of an AI-driven approach to analyze natural behaviors will open a new avenue for neuroscience research dealing with big-data analyses of social and pathophysiological behaviors in NHPs.

Facial emotion recognition and judgment of affective scenes in Parkinson's disease

Emotional dysfunctions in Parkinson's disease (PD) remain a controversial issue. While previous investigations showed compromised recognition of expressive faces in PD, no studies evaluated potential deficits in recognizing the emotional valence of affective scenes. This study aimed to investigate both facial emotion recognition performance and the ability to judge affective scenes in PD patients. Forty PD patients (mean age ± SD: 64.50 ± 8.19 years; 27 men) and forty healthy subjects (64.95 ± 8.25 years; 27 men) were included. Exclusion criteria were previous psychiatric disorders, previous Deep Brain Stimulation, and cognitive impairment. Participants were evaluated through the Ekman 60-Faces test and the International Affective Picture System. The accuracy in recognizing the emotional valence of facial expressions and affective scenes was compared between groups using linear mixed models. Pearson's correlation was performed to test the association between accuracy measures. The groups did not differ in sex, age, education, and Mini-Mental State Examination scores. Patients showed a lower recognition accuracy of facial expressions (68.54 % ± 15.83 %) than healthy participants (78.67 % ± 12.04 %; p < 0.001). Specifically, the PD group was characterized by lower recognition of faces expressing fear, sadness, and anger than the control group (all p < 0.020). No difference was detected for faces expressing disgust, surprise, and happiness (all p ≥ 0.25). Furthermore, patients showed lower accuracy in recognizing the emotional valence of affective scenes (66.75 % ± 14.59 %) than healthy subjects (74.83 % ± 12.65 %; p = 0.010). Pearson's correlations indicated that higher accuracy in recognizing the emotional facial expressions was associated with higher accuracy in classifying the valence of affective scenes in patients (r = 0.57, p < 0.001) and control participants (r = 0.57, p < 0.001). Our study suggested maladaptive affective processing in PD, leading patients to misinterpret both facial expressions and the emotional valence of complex evocative scenes.

Cortical and subcortical functional connectivity and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We focus on canonical cortical networks linked to cognition, including the salience network (SAL), frontoparietal network (FPN), and default mode network (DMN), as well as a subcortical basal ganglia network (BGN). We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. In 82 PD patients, we found that lower MoCA scores were linked with lower intra-network connectivity of the FPN. We also found that lower MoCA scores were linked with lower inter-network connectivity between the SAL and the BGN, the SAL and the DMN, as well as the FPN and the DMN. These data elucidate the relationship of cortical and subcortical functional connectivity with cognitive impairments in PD.

Imbalance and gait impairment in Parkinson's disease: discussing postural instability and ataxia

Gait and balance difficulties pose significant clinical challenges in Parkinson's disease (PD). The impairment of physiological mechanisms responsible for maintaining natural orthostatism plays a central role in the pathophysiology of postural instability observed in PD. In addition to the well-known rigidity and abnormalities in muscles and joints, various brain regions involved in the regulation of posture, balance, and gait, such as the basal ganglia, cerebellum, and brainstem regions like the pontine peduncle nucleus, are affected in individuals with PD. The recognition of the cerebellum's role in PD has been increasingly acknowledged. Cortical areas and their connections are associated with freezing of gait, a type of frontal lobe ataxia commonly observed in PD. Furthermore, impairments in the peripheral nervous system, including those caused by levodopatherapy, can contribute to gait impairment and imbalance in PD patients. Consequently, individuals with PD may exhibit frontal ataxia, sensory ataxia, and even cerebellar ataxia as underlying causes of gait disturbances and imbalance, starting from the early stages of the disease. The complex interplay between dysfunctional brain regions, impaired cortical connections, and peripheral nervous system abnormalities contributes to the multifaceted nature of gait and balance difficulties in PD. Understanding the intricate mechanisms is crucial for the development of effective therapeutic approaches targeting these specific deficits in PD.

A dynamic computational model of the parallel circuit on the basal ganglia-cortex associated with Parkinson's disease dementia

The cognitive impairment will gradually appear over time in Parkinson's patients, which is closely related to the basal ganglia-cortex network. This network contains two parallel circuits mediated by putamen and caudate nucleus, respectively. Based on the biophysical mean-field model, we construct a dynamic computational model of the parallel circuit in the basal ganglia-cortex network associated with Parkinson's disease dementia. The simulated results show that the decrease of power ratio in the prefrontal cortex is mainly caused by dopamine depletion in the caudate nucleus and is less related to that in the putamen, which indicates Parkinson's disease dementia may be caused by a lesion of the caudate nucleus rather than putamen. Furthermore, the underlying dynamic mechanism behind the decrease of power ratio is investigated by bifurcation analysis, which demonstrates that the decrease of power ratio is due to the change of brain discharge pattern from the limit cycle mode to the point attractor mode. More importantly, the spatiotemporal course of dopamine depletion in Parkinson's disease patients is well simulated, which states that with the loss of dopaminergic neurons projecting to the striatum, motor dysfunction of Parkinson's disease is first observed, whereas cognitive impairment occurs after a period of onset of motor dysfunction. These results are helpful to understand the pathogenesis of cognitive impairment and provide insights into the treatment of Parkinson's disease dementia.

Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions

This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.

Dopaminergic neuron loss in mice due to increased levels of wild-type human α-Synuclein only takes place under conditions of accelerated aging

Understanding the intricate pathogenic mechanisms behind Parkinson's disease (PD) and its multifactorial nature presents a significant challenge in disease modeling. To address this, we explore genetic models that better capture the disease's complexity. Given that aging is the primary risk factor for PD, this study investigates the impact of aging in conjunction with overexpression of wild-type human α-synuclein (α-Syn) in the dopaminergic system. This is achieved by introducing a novel transgenic mouse strain overexpressing α-Syn under the TH-promoter within the senescence-accelerated SAMP8 (P8) genetic background. Behavioral assessments, conducted at both 10 and 16 months of age, unveil motor impairments exclusive to P8 α-SynTg mice, a phenomenon conspicuously absent in α-SynTg mice. These findings suggest a synergistic interplay between heightened α-Syn levels and the aging process, resulting in motor deficits. These motor disturbances correlate with reduced dopamine (DA) levels, increased DA turnover, synaptic terminal loss, and notably, the depletion of dopaminergic neurons in the substantia nigra and noradrenergic neurons in the locus coeruleus. Furthermore, P8 α-SynTg mice exhibit alterations in gut transit time, mirroring early PD symptoms. In summary, P8 α-SynTg mice effectively replicate parkinsonian phenotypes by combining α-Syn transgene expression with accelerated aging. This model offers valuable insights into the understanding of PD and serves as a valuable platform for further research.

Susceptibility to geometrical visual illusions in Parkinson's disorder

Parkinson's disorder (PD) is a common neurodegenerative disorder affecting approximately 1-3% of the population aged 60 years and older. In addition to motor difficulties, PD is also marked by visual disturbances, including depth perception, abnormalities in basal ganglia functioning, and dopamine deficiency. Reduced ability to perceive depth has been linked to an increased risk of falling in this population. The purpose of this paper was to determine whether disturbances in PD patients' visual processing manifest through atypical performance on visual illusion (VI) tasks. This insight will advance understanding of high-level perception in PD, as well as indicate the role of dopamine deficiency and basal ganglia pathophysiology in VIs susceptibility. Groups of 28 PD patients (Mage = 63.46, SD = 7.55) and 28 neurotypical controls (Mage = 63.18, SD = 9.39) matched on age, general cognitive abilities (memory, numeracy, attention, language), and mood responded to Ebbinghaus, Ponzo, and Müller-Lyer illusions in a computer-based task. Our results revealed no reliable differences in VI susceptibility between PD and neurotypical groups. In the early- to mid-stage of PD, abnormalities of the basal ganglia and dopamine deficiency are unlikely to be involved in top-down processing or depth perception, which are both thought to be related to VI susceptibility. Furthermore, depth-related issues experienced by PD patients (e.g., increased risk for falling) may not be subserved by the same cognitive mechanisms as VIs. Further research is needed to investigate if more explicit presentations of illusory depth are affected in PD, which might help to understand the depth processing deficits in PD.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Apomorphine for Parkinson's disease: pharmacologic and clinical considerations

INTRODUCTION

In Parkinson's disease, dopamine depletion in the basal ganglia leads to symptoms including bradykinesia, gait abnormalities, and cognitive impairment. Even with treatment, the disease course leads to decreases in the amount of dopamine produced and released into the synapse. As dopamine production falls and the treatment course is insufficient to match the metabolic supply and demand, acute 'off' periods develop that cause reemergence of symptoms. Apomorphine is used to reverse these 'off' periods and restore function in patients with Parkinson's. This review will provide clinicians a concise article to read to learn more about apomorphine and its appropriate utilization.

AREAS COVERED

The research discussed is focused on the history, pharmacokinetics, and mechanism of action of Apomorphine. Its utilization as a treatment for Parkinson's Disease and its comparison to currently utilized drugs is also discussed in this review. We focused on articles published on PubMed and Google Scholar within the last 10 years, but in some instances had to go as far back as 1951 to include early articles published about apomorphine.

EXPERT OPINION

The expert opinion section focuses on the ways in which apomorphine could be administered in the future to better promote utilization and increase tolerability.

Brain Networks Involved in Sensory Perception in Parkinson's Disease: A Scoping Review

Parkinson's Disease (PD) has historically been considered a disorder of motor dysfunction. However, a growing number of studies have demonstrated sensory abnormalities in PD across the modalities of proprioceptive, tactile, visual, auditory and temporal perception. A better understanding of these may inform future drug and neuromodulation therapy. We analysed these studies using a scoping review. In total, 101 studies comprising 2853 human participants (88 studies) and 125 animals (13 studies), published between 1982 and 2022, were included. These highlighted the importance of the basal ganglia in sensory perception across all modalities, with an additional role for the integration of multiple simultaneous sensation types. Numerous studies concluded that sensory abnormalities in PD result from increased noise in the basal ganglia and increased neuronal receptive field size. There is evidence that sensory changes in PD and impaired sensorimotor integration may contribute to motor abnormalities.

Functional connectomes of akinetic-rigid and tremor within drug-naïve Parkinson's disease

AIMS

To detect functional connectomes of akinetic-rigid (AR) and tremor and compare their connection pattern.

METHODS

Resting-state functional MRI data of 78 drug-naïve PD patients were enrolled to construct connectomes of AR and tremor via connectome-based predictive modeling (CPM). The connectomes were further validated with 17 drug-naïve patients to verify their replication.

RESULTS

The connectomes related to AR and tremor were identified via CPM method and successfully validated in the independent set. Additional regional-based CPM demonstrated neither AR nor tremor could be simplified to functional changes within a single brain region. Computational lesion version of CPM revealed that parietal lobe and limbic system were the most important regions among AR-related connectome, and motor strip and cerebellum were the most important regions among tremor-related connectome. Comparing two connectomes found that the patterns of connection between them were largely distinct, with only four overlapped connections identified.

CONCLUSION

AR and tremor were found to be associated with functional changes in multiple brain regions. Distinct connection patterns of AR-related and tremor-related connectomes suggest different neural mechanisms underlying the two symptoms.

Free water imaging unravels unique patterns of longitudinal structural brain changes in Parkinson's disease subtypes

Background

Research shows that individuals with Parkinson's disease (PD) who have a postural instability and gait difficulties (PIGD) subtype have a faster disease progression compared to those with a tremor dominant (TD) subtype. Nevertheless, our understanding of the structural brain changes contributing to these clinical differences remains limited, primarily because many brain imaging techniques are only capable of detecting changes in the later stages of the disease.

Objective

Free water (FW) has emerged as a robust progression marker in several studies, showing increased values in the posterior substantia nigra that predict symptom worsening. Here, we examined longitudinal FW changes in TD and PIGD across multiple brain regions.

Methods

Participants were TD and PIGD enrolled in the Parkinson's Progression Marker Initiative (PPMI) study who underwent diffusion MRI at baseline and 2 years later. FW changes were quantified for regions of interest (ROI) within the basal ganglia, thalamus, brainstem, and cerebellum.

Results

Baseline FW in all ROIs did not differ between groups. Over 2 years, PIGD had a greater percentage increase in FW in the putamen, globus pallidus, and cerebellar lobule V. A logistic regression model incorporating percent change in motor scores and FW in these brain regions achieved 91.4% accuracy in discriminating TD and PIGD, surpassing models based solely on clinical measures (74.3%) or imaging (76.1%).

Conclusion

The results further suggest the use of FW to study disease progression in PD and provide insight into the differential course of brain changes in early-stage PD subtypes.

Salience network and cognitive impairment in Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disease with cognitive as well as motor impairments. While much is known about the brain networks leading to motor impairments in PD, less is known about the brain networks contributing to cognitive impairments. Here, we leveraged resting-state functional magnetic resonance imaging (rs-fMRI) data from the Parkinson's Progression Marker Initiative (PPMI) to examine network dysfunction in PD patients with cognitive impairment. We tested the hypothesis that cognitive impairments in PD involve altered connectivity of the salience network (SN), a key cortical network that detects and integrates responses to salient stimuli. We used the Montreal Cognitive Assessment (MoCA) as a continuous index of coarse cognitive function in PD. We report two major results. First, in 82 PD patients we found significant relationships between lower intra-network connectivity of the frontoparietal network (FPN; comprising the dorsolateral prefrontal and posterior parietal cortices bilaterally) with lower MoCA scores. Second, we found significant relationships between lower inter-network connectivity between the SN and the basal ganglia network (BGN) and the default mode network (DMN) with lower MoCA scores. These data support our hypothesis about the SN and provide new insights into the brain networks contributing to cognitive impairments in PD.

The right inferior frontal gyrus as pivotal node and effective regulator of the basal ganglia-thalamocortical response inhibition circuit

Background

The involvement of specific basal ganglia-thalamocortical circuits in response inhibition has been extensively mapped in animal models. However, the pivotal nodes and directed causal regulation within this inhibitory circuit in humans remains controversial.

Objective

The main aim of the present study was to determine the causal information flow and critical nodes in the basal ganglia-thalamocortical inhibitory circuits and also to examine whether these are modulated by biological factors (i.e. sex) and behavioral performance.

Methods

Here, we capitalize on the recent progress in robust and biologically plausible directed causal modeling (DCM-PEB) and a large response inhibition dataset (n = 250) acquired with concomitant functional magnetic resonance imaging to determine key nodes, their causal regulation and modulation via biological variables (sex) and inhibitory performance in the inhibitory circuit encompassing the right inferior frontal gyrus (rIFG), caudate nucleus (rCau), globus pallidum (rGP), and thalamus (rThal).

Results

The entire neural circuit exhibited high intrinsic connectivity and response inhibition critically increased causal projections from the rIFG to both rCau and rThal. Direct comparison further demonstrated that response inhibition induced an increasing rIFG inflow and increased the causal regulation of this region over the rCau and rThal. In addition, sex and performance influenced the functional architecture of the regulatory circuits such that women displayed increased rThal self-inhibition and decreased rThal to GP modulation, while better inhibitory performance was associated with stronger rThal to rIFG communication. Furthermore, control analyses did not reveal a similar key communication in a left lateralized model.

Conclusions

Together, these findings indicate a pivotal role of the rIFG as input and causal regulator of subcortical response inhibition nodes.

Striatal and thalamic automatic segmentation, morphology, and clinical correlates in Parkinsonism: Parkinson's disease, multiple system atrophy and progressive supranuclear palsy

Parkinson's disease (PD), multisystem atrophy (MSA), and progressive supranuclear palsy (PSP) present similarly with bradykinesia, tremor, rigidity, and cognitive impairments. Neuroimaging studies have found differential changes in the nigrostriatal pathway in these disorders, however whether the volume and shape of specific regions within this pathway can distinguish between atypical Parkinsonian disorders remains to be determined. This paper investigates striatal and thalamic volume and morphology as distinguishing biomarkers, and their relationship to neuropsychiatric symptoms. Automatic segmentation to calculate volume and shape analysis of the caudate nucleus, putamen, and thalamus were performed in 18 PD patients, 12 MSA, 15 PSP, and 20 healthy controls, then correlated with clinical measures. PSP bilateral thalami and right putamen were significantly smaller than controls, but not MSA or PD. The left caudate and putamen significantly correlated with the Neuropsychiatric Inventory total score. Bilateral thalamus, caudate, and left putamen had significantly different morphology between groups, driven by differences between PSP and healthy controls. This study demonstrated that PSP patient striatal and thalamic volume and shape are significantly different when compared with controls. Parkinsonian disorders could not be differentiated on volumetry or morphology, however there are trends for volumetric and morphological changes associated with PD, MSA, and PSP.

Substantia Nigra Pars Reticulata Projections to the Pedunculopontine Nucleus Modulate Dyskinesia

BACKGROUND

Long-term use of levodopa for Parkinson's disease (PD) treatment is often hindered by development of motor complications, including levodopa-induced dyskinesia (LID). The substantia nigra pars reticulata (SNr) and globus pallidus internal segment (GPi) are the output nuclei of the basal ganglia. Dysregulation of SNr and GPi activity contributes to PD pathophysiology and LID.

OBJECTIVE

The objective of this study was to determine whether direct modulation of SNr GABAergic neurons and SNr projections to the pedunculopontine nucleus (PPN) regulates PD symptoms and LID in a mouse model.

METHODS

We expressed Cre-recombinase activated channelrhodopsin-2 (ChR2) or halorhodopsin adeno-associated virus-2 (AAV2) vectors selectively in SNr GABAergic neurons of Vgat-IRES-Cre mice in a 6-hydroxydopamine model of PD to investigate whether direct optogenetic modulation of SNr neurons or their projections to the PPN regulates PD symptoms and LID expression. The forepaw stepping task, mouse LID rating scale, and open-field locomotion were used to assess akinesia and LID to test the effect of SNr modulation.

RESULTS

Akinesia was improved by suppressing SNr neuron activity with halorhodopsin. LID was significantly reduced by increasing SNr neuronal activity with ChR2, which did not interfere with the antiakinetic effect of levodopa. Optical stimulation of ChR2 in SNr projections to the PPN recapitulated direct SNr stimulation.

CONCLUSIONS

Modulation of SNr GABAergic neurons alters akinesia and LID expression in a manner consistent with the rate model of basal ganglia circuitry. Moreover, the projections from SNr to PPN likely mediate the antidyskinetic effect of increasing SNr neuronal activity, identifying a potential novel role for the PPN in LID. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Basal ganglia for beginners: the basic concepts you need to know and their role in movement control

The basal ganglia are a subcortical collection of interacting clusters of cell bodies, and are involved in reward, emotional, and motor circuits. Within all the brain processing necessary to carry out voluntary movement, the basal nuclei are fundamental, as they modulate the activity of the motor regions of the cortex. Despite being much studied, the motor circuit of the basal ganglia is still difficult to understand for many people at all, especially undergraduate and graduate students. This review article seeks to bring the functioning of this circuit with a simple and objective approach, exploring the functional anatomy, neurochemistry, neuronal pathways, related diseases, and interactions with other brain regions to coordinate voluntary movement.

A Pilot Study of Ex Vivo Human Prefrontal RNA Transcriptomics in Parkinson's Disease

Parkinson's disease (PD) can dramatically change cortical neurophysiology. The molecular basis for PD-related cortical changes is unclear because gene expression data are usually derived from postmortem tissue collected at the end of a complex disease and they profoundly change in the minutes after death. Here, we studied cortical changes in tissue from the prefrontal cortex of living PD patients undergoing deep-brain stimulation implantation surgery. We examined 780 genes using the NanoString nCounter platform and found that 40 genes were differentially expressed between PD (n = 12) and essential tremor (ET; n = 9) patients. One of these 40 genes, STAT1, correlated with intraoperative 4-Hz rhythms and intraoperative performance of an oddball reaction-time task. Using a pre-designed custom panel of 780 targets, we compared these intraoperative data with those from a separate cohort of fresh-frozen tissue from the same frontal region in postmortem human PD donors (n = 6) and age-matched neurotypical controls (n = 6). This cohort revealed 279 differentially expressed genes. Fifteen of the 40 intraoperative PD-specific genes overlapped with postmortem PD-specific genes, including CALB2 and FOXP2. Transcriptomic analyses identified pathway changes in PD that had not been previously observed in postmortem cases. These molecular signatures of cortical function and dysfunction may help us better understand cognitive and neuropsychiatric aspects of PD.

Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment

BACKGROUND

Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD.

METHODS

We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation.

RESULTS

We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774.

CONCLUSIONS

Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.

Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network

Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.Clinical Relevance- This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.

Parkinson's disease speech production network as determined by graph-theoretical network analysis

Parkinson's disease (PD) can affect speech as well as emotion processing. We employ whole-brain graph-theoretical network analysis to determine how the speech-processing network (SPN) changes in PD, and assess its susceptibility to emotional distraction. Functional magnetic resonance images of 14 patients (aged 59.6 ± 10.1 years, 5 female) and 23 healthy controls (aged 64.1 ± 6.5 years, 12 female) were obtained during a picture-naming task. Pictures were supraliminally primed by face pictures showing either a neutral or an emotional expression. PD network metrics were significantly decreased (mean nodal degree, p < 0.0001; mean nodal strength, p < 0.0001; global network efficiency, p < 0.002; mean clustering coefficient, p < 0.0001), indicating an impairment of network integration and segregation. There was an absence of connector hubs in PD. Controls exhibited key network hubs located in the associative cortices, of which most were insusceptible to emotional distraction. The PD SPN had more key network hubs, which were more disorganized and shifted into auditory, sensory, and motor cortices after emotional distraction. The whole-brain SPN in PD undergoes changes that result in (a) decreased network integration and segregation, (b) a modularization of information flow within the network, and (c) the inclusion of primary and secondary cortical areas after emotional distraction.

Serial deep gray nuclear DTI changes in Parkinson's disease over twelve years

Background

Deep gray nuclear pathology relates to motor deterioration in idiopathic Parkinson's disease (PD). Inconsistent deep nuclear diffusion tensor imaging (DTI) findings in cross-sectional or short-term longitudinal studies have been reported. Long-term studies in PD are clinically challenging; decade-long deep nuclear DTI data are nonexistent. We investigated serial DTI changes and clinical utility in a case-control PD cohort of 149 subjects (72 patients/77 controls) over 12 years.

Methods

Participating subjects underwent brain MRI at 1.5T; DTI metrics from segmented masks of caudate, putamen, globus pallidus and thalamus were extracted from three timepoints with 6-year gaps. Patients underwent clinical assessment, including Unified Parkinson Disease Rating Scale Part 3 (UPDRS-III) and Hoehn and Yahr (H&Y) staging. A multivariate linear mixed-effects regression model with adjustments for age and gender was used to assess between-group differences in DTI metrics at each timepoint. Partial Pearson correlation analysis was used to correlate clinical motor scores with DTI metrics over time.

Results

MD progressively increased over time and was higher in the putamen (p < 0.001) and globus pallidus (p = 0.002). FA increased (p < 0.05) in the thalamus at year six, and decreased in the putamen and globus pallidus at year 12. Putaminal (p = 0.0210), pallidal (p = 0.0066) and caudate MD (p < 0.0001) correlated with disease duration. Caudate MD (p < 0.05) also correlated with UPDRS-III and H&Y scores.

Conclusion

Pallido-putaminal MD showed differential neurodegeneration in PD over 12 years on longitudinal DTI; putaminal and thalamic FA changes were complex. Caudate MD could serve as a surrogate marker to track late PD progression.

Comparison of Anatomical and Diffusion MRI for detecting Parkinson's Disease using Deep Convolutional Neural Network

Parkinson's disease (PD) is a progressive neurodegenerative disease that affects over 10 million people worldwide. Brain atrophy and microstructural abnormalities tend to be more subtle in PD than in other age-related conditions such as Alzheimer's disease, so there is interest in how well machine learning methods can detect PD in radiological scans. Deep learning models based on convolutional neural networks (CNNs) can automatically distil diagnostically useful features from raw MRI scans, but most CNN-based deep learning models have only been tested on T1-weighted brain MRI. Here we examine the added value of diffusion-weighted MRI (dMRI) - a variant of MRI, sensitive to microstructural tissue properties - as an additional input in CNN-based models for PD classification. Our evaluations used data from 3 separate cohorts - from Chang Gung University, the University of Pennsylvania, and the PPMI dataset. We trained CNNs on various combinations of these cohorts to find the best predictive model. Although tests on more diverse data are warranted, deep-learned models from dMRI show promise for PD classification.

Clinical Relevance

This study supports the use of diffusion-weighted images as an alternative to anatomical images for AI-based detection of Parkinson's disease.

Sustained response in early responders to safinamide in patients with Parkinson's disease and motor fluctuations: A post hoc analysis of the SETTLE study

Safinamide is a selective, reversible, monoamine oxidase B inhibitor for the treatment of patients with Parkinson's disease (PD) and motor fluctuations. This was a post hoc analysis of the SETTLE study, in which patients with PD and motor fluctuations were randomly assigned to 24-week treatment with safinamide (50 mg/day for 2 weeks, increased to 100 mg/day if tolerated) or placebo. In the present analysis, responders were defined according to their treatment responses at Week 2 and Week 24 based on changes in ON-time without troublesome dyskinesia from baseline with cutoffs of 1 hour. It was found that 81% (103/127) of the responders at Week 2 maintained the response through Week 24 in the safinamide group. Other outcomes did not necessarily coincide with the ON-time response; however, “Early” responders who showed a treatment response at both Week 2 and Week 24 had substantial improvements from baseline in OFF-time, UPDRS Part II and III scores, and PDQ-39 summary index scores through Week 24. The safinamide group had a higher proportion of early responders than the placebo group (39% vs 20%, p &lt; 0.0001). At baseline, early responders in the safinamide group had significantly higher UPDRS Part II and III scores, shorter ON-time, and longer OFF-time than the other responder populations. In conclusion, the results of the present post hoc analysis suggest that patients with a short ON-time, severe motor symptoms, and highly compromised activities of daily living can benefit from safinamide early in treatment and over the long term.

Effect of the Metabotropic Glutamate Receptor Type 5 Negative Allosteric Modulator Dipraglurant on Motor and Non-Motor Symptoms of Parkinson's Disease

Parkinson's disease (PD) patients suffer not only from the primary motor symptoms of the disease but also from a range of non-motor symptoms (NMS) that cause disability and low quality of life. Excessive glutamate activity in the basal ganglia resulting from degeneration of the nigrostriatal dopamine pathway has been implicated in the motor symptoms, NMS and dyskinesias in PD patients. In this study, we investigated the effects of a selective mGlu5 negative allosteric modulator (NAM), dipraglurant, in a rodent motor symptoms model of PD, but also in models of anxiety, depression and obsessive-compulsive disorder, all of which are among the most prevalent NMS symptoms. Dipraglurant is rapidly absorbed after oral administration, readily crosses the blood-brain barrier, and exhibits a high correlation between plasma concentration and efficacy in behavioral models. In vivo, dipraglurant dose-dependently reduced haloperidol-induced catalepsy, increased punished licks in the Vogel conflict-drinking model, decreased immobility time in the forced swim test, decreased the number of buried marbles in the marble-burying test, but had no effect on rotarod performance or locomotor activity. These findings suggest that dipraglurant may have benefits to address some of the highly problematic comorbid non-motor symptoms of PD, in addition to its antidyskinetic effect demonstrated in PD-LID patients.

Does TRODAT-1 SPECT Uptake Correlate with Cerebrospinal Fluid α-Synuclein Levels in Mid-Stage Parkinson's Disease?

BACKGROUND

Parkinson's disease (PD) is characterized by a progressive loss of nigrostriatal dopaminergic neurons with impaired motor and non-motor symptoms. It has been suggested that motor asymmetry could be caused due to an imbalance in dopamine levels, as visualized by dopamine transporter single emission computed tomography test (DAT-SPECT), which might be related to indirect measures of neurodegeneration, evaluated by the Montreal Cognitive Assessment (MOCA) and α-synuclein levels in the cerebrospinal fluid (CSF). Therefore, this study aimed to understand the correlation between disease laterality, DAT-SPECT, cognition, and α-synuclein levels in PD.

METHODS

A total of 28 patients in the moderate-advanced stage of PD were subjected to neurological evaluation, TRODAT-1-SPECT/CT imaging, MOCA, and quantification of the levels of α-synuclein.

RESULTS

We found that α-synuclein in the CSF was correlated with global cognition (positive correlation, r² = 0.3, p = 0.05) and DAT-SPECT concentration in the putamen (positive correlation, r² = 0.4, p = 0.005), and striatum (positive correlation, r² = 0.2, p = 0.03), thus working as a neurodegenerative biomarker. No other correlations were found between DAT-SPECT, CSF α-synuclein, and cognition, thus suggesting that they may be lost with disease progression.

CONCLUSIONS

Our data highlight the importance of understanding the dysfunction of the dopaminergic system in the basal ganglia and its complex interactions in modulating cognition.

Nigral Iron Deposition Influences Disease Severity by Modulating the Effect of Parkinson's Disease on Brain Networks

BACKGROUND

In Parkinson's disease (PD), excessive iron deposition in the substantia nigra may exacerbate α-synuclein aggregation, facilitating the degeneration of dopaminergic neurons and their neural projection.

OBJECTIVE

To investigate the interaction effect between nigral iron deposition and PD status on brain networks.

METHODS

Eighty-five PD patients and 140 normal controls (NC) were included. Network function and nigral iron were measured using multi-modality magnetic resonance imaging. According to the median of nigral magnetic susceptibility of NC (0.095 ppm), PD and NC were respectively divided into high and low nigral iron group. The main and interaction effects were investigated by mixed effect analysis.

RESULTS

The main effect of disease was observed in basal ganglia network (BGN) and visual network (VN). The interaction effect between nigral iron and PD status was observed in left inferior frontal gyrus and left insular lobe in BGN, as well as right middle occipital gyrus, right superior temporal gyrus, and bilateral cuneus in VN. Furthermore, multiple mediation analysis revealed that the functional connectivity of interaction effect clusters in BGN and medial VN partially mediated the relationship between nigral iron and Unified Parkinson's Disease Rating Scale II score.

CONCLUSION

Our study demonstrates an interaction of nigral iron deposition and PD status on brain networks, that is, nigral iron deposition is associated with the change of brain network configuration exclusively when in PD. We identified a potential causal mediation pathway for iron to affect disease severity that was mediated by both BGN dysfunction and VN hyperfunction in PD.

Patterned Stimulation of the Chrimson Opsin in Glutamatergic Motor Thalamus Neurons Improves Forelimb Akinesia in Parkinsonian Rats

Parkinson's disease (PD) is a motor disorder charactertised by altered neural activity throughout the basal ganglia-thalamocortical circuit. Electrical deep brain stimulation (DBS) is efficacious in alleviating motor symptoms, but has several notable side-effects, most likely reflecting the non-specific nature of electrical stimulation and/or the brain regions targeted. We determined whether specific optogenetic activation of glutamatergic motor thalamus (Mthal) neurons alleviated forelimb akinesia in a chronic rat model of PD. Parkinsonian rats (unilateral 6-hydroxydopamine injection) were injected with an adeno-associated viral vector (AAV5-CaMKII-Chrimson-GFP) to transduce glutamatergic Mthal neurons with the red-shifted Chrimson opsin. Optogenetic stimulation with orange light at 15 Hz tonic and a physiological pattern, previously recorded from a Mthal neuron in a control rat, significantly increased forelimb use in the reaching test (p < 0.01). Orange light theta burst stimulation, 15 Hz and control reaching patterns significantly reduced akinesia (p < 0.0001) assessed by the step test. In contrast, forelimb use in the cylinder test was unaffected by orange light stimulation with any pattern. Blue light (control) stimulation failed to alter behaviours. Activation of Chrimson using complex patterns in the Mthal may be an alternative treatment to recover movement in PD. These vector and opsin changes are important steps towards translating optogenetic stimulation to humans.

Levodopa-Induced Dyskinesia in Parkinson's Disease: Pathogenesis and Emerging Treatment Strategies

The most commonly used treatment for Parkinson's disease (PD) is levodopa, prescribed in conjunction with carbidopa. Virtually all patients with PD undergo dopamine replacement therapy using levodopa during the course of the disease's progression. However, despite the fact that levodopa is the "gold standard" in PD treatments and has the ability to significantly alleviate PD symptoms, it comes with side effects in advanced PD. Levodopa replacement therapy remains the current clinical treatment of choice for Parkinson's patients, but approximately 80% of the treated PD patients develop levodopa-induced dyskinesia (LID) in the advanced stages of the disease. A better understanding of the pathological mechanisms of LID and possible means of improvement would significantly improve the outcome of PD patients, reduce the complexity of medication use, and lower adverse effects, thus, improving the quality of life of patients and prolonging their life cycle. This review assesses the recent advancements in understanding the underlying mechanisms of LID and the therapeutic management options available after the emergence of LID in patients. We summarized the pathogenesis and the new treatments for LID-related PD and concluded that targeting pathways other than the dopaminergic pathway to treat LID has become a new possibility, and, currently, amantadine, drugs targeting 5-hydroxytryptamine receptors, and surgery for PD can target the Parkinson's symptoms caused by LID.

Altered dynamic functional network connectivity in levodopa-induced dyskinesia of Parkinson's disease

AIMS

The aim of this study was to clarify the dynamic neural activity of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD).

METHODS

Using dynamic functional network connectivity (dFNC) analysis, we evaluated 41 PD patients with LID (LID group) and 34 PD patients without LID (No-LID group). Group spatial independent component analysis and sliding-window approach were employed. Moreover, we applied a k-means clustering algorithm on windowed functional connectivity (FC) matrices to identify reoccurring FC patterns (i.e., states).

RESULTS

The optimal number of states was determined to be five, the so-called State 1, 2, 3, 4, and 5. In ON phase, compared with No-LID group, LID group occurred more frequently and dwelled longer in strongly connected State 1, characterized by strong positive connections between visual network (VIS) and sensorimotor network (SMN). When switching from OFF to ON phase, LID group occurred less frequently in State 3 and State 4. Meanwhile, LID group dwelled longer in State 2 and shorter in State 3. No-LID group occurred more frequently in State 5 and less frequently in State 3. Additionally, correlation analysis demonstrated that dyskinesia's severity was associated with frequency of occurrence and dwell time in State 2, dominated by inferior frontal cortex in cognitive executive network (CEN).

CONCLUSION

Using dFNC analysis, we found that dyskinesia may be related to the dysfunctional inhibition of CEN on motor loops and excessive excitation of VIS and SMN, which provided evidence of the changes in brain dynamics associated with the occurrence of dyskinesia.

Combination of Quantitative Susceptibility Mapping and Diffusion Kurtosis Imaging Provides Potential Biomarkers for Early-Stage Parkinson's Disease

Purpose: This study aimed to detect changes in iron deposition and neural microstructure in the substantia nigra (SN), red nucleus (RN), and basal ganglia of Parkinson's disease (PD) patients at different stages using quantitative susceptibility mapping and diffusion kurtosis imaging to identify potential indicators of early-stage PD. Methods: We enrolled 20 early-stage and 15 late-stage PD patients, as well as 20 age- and sex-matched controls. All participants underwent quantitative susceptibility mapping and diffusion kurtosis imaging to determine magnetic susceptibility (MS), fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK) in several brain regions. Results: Compared with the control group, MS and MK values in the SN were significantly increased in the early- and late-stage PD group, whereas MS values in the red nucleus (RN), globus pallidus (GP), and caudate nucleus (CN), FA value in the CN and GP, and MK value in the CN and putamen (PU) were significantly increased in the late-stage PD group. There were positive correlations between MS and MK values in the CN and MS and FA values in the GP. Furthermore, the combination of MS and MK values in the SN provided high accuracy for distinguishing early-stage PD patients from controls. Conclusions: This study identified MS and MK in the SN as potential indicators of early-stage PD.

Nigral neuropathology of Parkinson's motor subtypes coincide with circuitopathies: a scoping review

The neuropathological substrates of Parkinson's disease (PD) patients with motor subtypes tremor-dominance (TD), non-tremor dominance (nTD), postural instability and gait difficulty (PIGD), and akinetic-rigid (AR) are not completely differentiated. While extensive pathological research has been conducted on neuronal tissue of PD patients, data have not been discussed in the context of mechanistic circuitry theories differentiating motor subtypes. It is, therefore, expected that a more specific and tailored management of PD symptoms can be accomplished by understanding symptom-specific neuropathological mechanisms with the detail histology can provide. This scoping review gives an overview of the literature comparing TD and nTD PD motor subtypes by clarify observed pathology with underlying physiological circuitry theories. Studies using an array of pathological examination techniques have shown significant differences between TD and nTD PD subtypes. nTD PD patients show higher neuronal loss, gliosis, extraneuronal melanin deposits, and neuroaxonal dystrophy in multiple subregions of the substantia nigra (SN) related to the overactivity of the indirect motor loop. TD patients show more severe cell loss specifically in medial SN subdivisions, and have damage in the retrorubral field A-8 that projects to the dorsolateral striatum and ventromedial thalamus in the direct motor loop. Pathological studies are consistent with neuroimaging data and support contemporary mechanistic circuitry theories of PD motor symptom genesis. Further multimodal neuroimaging and histological studies are required to validate and expand upon these findings.